HMPI

Telehealth Pay Parity: The Current Debate and Insights from Business Research

Özden Engin Çakıcı, American University Kogod School of Business, and Alex F. Mills, Baruch College Zicklin School of Business

Contact: Ozden Cakici <cakici@american.edu

Abstract

What is the message? Telehealth pay-parity laws and regulations require payers to cover and reimburse certain healthcare services provided remotely to the same extent as if those services were delivered in a traditional office setting. COVID-19 has spurred additional proposed and enacted legislation aimed at ensuring telehealth pay parity. The primary argument for telehealth pay parity is that it promotes access to care and also provides better health outcomes for certain illnesses; but research shows that patients seeking acute care via telehealth may be more likely to require a duplicate visit. Several open questions remain regarding the business impact of telehealth pay-parity policies and the future of telemedicine in healthcare practices.

What is the evidence? The findings are based on a review of the literature in health policy, medicine, business operations, and recent proposed laws and regulations in the United States.

Timeline: Submitted: April 6, 2022; Accepted after review: September 21, 2022.

Cite as: Özden Engin Çakıcı, Alex F. Mills. 2022. Telehealth Pay Parity: The Current Debate and Insights from Business Research. Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 3.

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What Is Telehealth and How Effective Is It?

Telehealth or telemedicine refers to the provision of healthcare services via any remote platform, i.e., any service where the patient is not physically present with the healthcare provider. The advancement of technology has enabled many clinical services to be provided seamlessly via telehealth, while other services may be more challenging to perform in a telehealth environment. The challenges surrounding telehealth have traditionally meant that insurance coverage for telehealth services has been limited.1

The U.S. Department of Health and Human Services states that “telehealth treatment options are vast, but they vary depending on the condition and the patient’s needs and abilities”.2

To illustrate a case where telehealth can seamlessly replace or even augment an office visit, consider a patient who has diabetes, a chronic illness. This patient can measure her own blood sugar level at home using an electronic blood sugar meter, and automatically upload those measurements to her endocrinologist’s practice. In this case, telehealth may help the provider monitor this patient’s condition more frequently without the patient needing to repeatedly travel to a doctor’s office. Using a remote clinical service may improve the patient’s health outcome and, in return, may reduce overall healthcare spending by preventing patients from getting worse and requiring more expensive emergency services.

To illustrate a case where telehealth may be less effective at providing clinical services, consider a patient with a suspicious mole on her skin, who wishes to be examined by a dermatologist. Although the dermatologist can visually examine the mole via a video visit, the doctor may conclude that they cannot diagnose whether the patient’s mole is benign or malignant without a biopsy. This patient needs a subsequent office visit to receive additional clinical services for an accurate diagnosis. Therefore, choosing telehealth for this service is inefficient, because it requires two visits to the physician (one via telehealth and one in the office) where a single office visit would have sufficed.

The potential for variation in effectiveness of telehealth for different types of clinical services naturally leads to questions about how services provided by telehealth should be reimbursed, which is the focus of the remainder of this article.

What Is “Telehealth Pay Parity” and Why Does It Matter?

Telehealth has traditionally been reimbursed in a limited fashion, mainly for certain chronic illnesses and for patients in rural areas3. With the onset of COVID-19, awareness and acceptance of telehealth has increased, especially for acute care patients. Prior to the COVID-19 pandemic, fewer than 2% of clinicians provided any outpatient care via telemedicine. In the wake of COVID-19-related stay-at-home orders, providers had no choice but to switch many of their clinical services from traditional office visits to telehealth. There was a subsequent 23-fold increase in telemedicine from January to June 2020, spanning the specialties of medicine.4

As a result of this increased usage of telehealth, providers started to push more to get telehealth covered by insurance plans and to receive payment at parity with a traditional office visit. While physician offices have since largely reopened, healthcare provider organizations such as the American Medical Association and the American Hospital Association have started actively supporting the position that payers should not differentiate between telehealth and in-person services when reimbursing providers.5 Policies aimed at eliminating such differentiation are known as telehealth pay-parity policies.6 Although these policies differ by jurisdiction, they generally require that payers cover a service via telehealth if it would have been covered in the office, and provide equal payment for a clinical service, regardless of whether it was delivered via telehealth or in a physician’s office.7

Numerous telehealth pay-parity policies were introduced during the COVID-19 pandemic. State telehealth pay-parity statutes more than doubled from 2019 to 20227. The U.S. Centers for Medicare and Medicaid Services (CMS) enacted a temporary telehealth pay-parity policy for Medicare recipients in March 20208, and bipartisan legislation under consideration in the 117th U.S. Congress would make that policy permanent9.

How Does Telehealth Pay Parity Fit into the Debate about Healthcare Reimbursement in the U.S.?

In the U.S., there are two main reimbursement models for healthcare services: fee-for-service and value-based payment.10 In the fee-for-service model, each service that a provider performs is reimbursed, hence “quantity” of care is the key determinant of provider revenue.11 On the other hand, value-based payment models reward quality of care. Examples of value-based payment include capitation, bundled payment, and pay for performance. In capitation, the healthcare provider is reimbursed a fixed amount per year per patient regardless of how many services the patient receives. Bundled payment is the reimbursement of health care providers based on expected costs for episodes of care. Under pay-for-performance payment, health care providers are offered financial incentives to meet certain health outcomes, and are penalized for poor health outcomes as well as medical errors and higher-than-expected costs.12

Fee-for-service is still the main reimbursement model for physician services in the U.S. healthcare system and is the main target for telehealth pay-parity policies. This means that for conditions where telehealth may not be as effective as an office visit, patients using telehealth might use an increased amount of healthcare services (for example, a telehealth visit and an office visit, where they previously would have had their problem resolved in a single office visit). This clearly has the potential to increase costs because healthcare providers are reimbursed separately for each visit. Note that this concern is distinct from the concern about “induced demand,” i.e., that the convenience of telehealth would induce patients to use more healthcare services or to use healthcare services more frequently. Even at the same level of demand, if telehealth is not as effective as a traditional office visit, it could still result in increased healthcare usage and costs.

Under value-based payment, particularly for pay-for performance, the effectiveness of telehealth is less of a concern because inefficient care is less likely to generate additional healthcare costs. However, the types of services that are reimbursed through value-based payment in the U.S. are typically hospital services (e.g., surgeries), which are unlikely to be feasible to conduct via telehealth.

What is the Case for Telehealth Coverage and Telehealth Pay Parity?

Two primary arguments in favor of telehealth coverage and telehealth pay parity are access and fairness.13 Seema Verma, the head of the U.S. Centers for Medicare and Medicaid services from 2017-2021, stated that “telehealth serves as an additional access point for patients” and that CMS-enacted telehealth pay parity during the COVID-19 pandemic “to make these services as widely available as possible.”14  Provider groups that support pay parity echo this theme of access, and, like Verma, they emphasize the advantages of telehealth for patients who are immobile15 or in rural areas where traveling to a healthcare provider may be burdensome.5,14 Thus, covering telehealth (particularly at pay parity) makes the healthcare system more fair for patients who might otherwise be disadvantaged, by enabling them to see otherwise-inaccessible providers. At the same time, providing telehealth is not necessarily a lower-cost alternative for providers, particularly those whose operational model is optimized for in-person visits. For example, a time-based study found that telemedicine actually incurred an incremental cost to the provider compared to an in-office visit.16 This strengthens the case that telehealth pay parity is necessary to ensure access because providers might not be willing to take on these disadvantaged patients for less than full reimbursement.

It is clear that telehealth pay parity provides an incentive for providers to offer telehealth, thereby increasing the overall availability of telehealth services14. What is less clear is whether the availability of telehealth itself increases overall access to healthcare, as patients could access care through multiple other channels, such as a hospital, a traditional office visit, urgent care, or a health clinic located in a retail pharmacy. Asserting that telehealth pay parity improves access to healthcare therefore relies on the premise that the availability of telehealth improves overall access to care. One piece of evidence that telehealth may increase access to care is found in commercial claims data. An analysis of claims found that 88% – of telehealth visits represented new utilization (i.e., they did not directly replace visits to other providers), and that this increased usage resulted in an overall increase in healthcare costs17.

What Is the Case against Telehealth Pay Parity?

Telehealth pay parity faces natural opposition from libertarian and free-market political organizations like the John Locke Foundation, who argue that telehealth has been thriving even without pay-parity laws, and therefore they prefer not to impose pay parity as an additional regulation18. They further cite “overconsumption” as a potential problem induced by telehealth pay parity. In other words, by making telehealth more widely available and accessible, inappropriate usage may increase.

Other think tanks have taken a more balanced stance based on cost-benefit analysis. For example, the Commonwealth Fund recommends that telehealth payments should be limited to services for selected patient populations and health conditions, or to services from providers that are paid via alternative payment methods.10

We do not find much credence in the position that adding a regulation regarding pay parity is itself a heavy burden on payers or providers. We also do not find this “induced demand” argument to be a credible reason to oppose telehealth pay parity because the same incentives that would increase inappropriate usage (reduced frictions for things like scheduling, transportation, and so on) also would increase appropriate usage. Indeed, the study of claims data that found telehealth leading to additional utilization could not identify whether that increased usage was appropriate or inappropriate.

On the other hand, a salient argument against telehealth pay parity lies in its effectiveness. There is evidence that telehealth may not be as effective as in-person healthcare services, and consequently it may be a bad idea to pay providers equally for a less-effective alternative. A telehealth visit may be more likely to generate a subsequent in-person visit. A recent study suggests that differentially higher in-person follow-up visits after telemedicine (by phone and/or video) are consistent with pre-pandemic findings in direct-to-consumer telemedicine settings19. Also, telehealth visits are more likely to result in a routine 14-day follow-up visits20. All other things being equal, it would be better to resolve a patient’s health concern in fewer visits because in the fee-for-service environment, each additional visit represents increased healthcare costs for the payer, while in the value-based payment environment, each additional visit represents an investment of time and effort that is not separately reimbursed and thus is costly for the provider.

Several studies analyzing healthcare claims data indeed found that the rate of follow-up visits was significantly higher among patients whose first visit was via telehealth.3,21,22 For example, one study found 20% of patients receiving dermatology care required an in-person visit for reasons such as conducting a biopsy, which clearly cannot be done remotely.22 In other words, in 20% of cases, the dermatologist was paid for two visits, when one visit would have sufficed if the original visit had been in the office. These so-called “duplicate visits” represent an increase in healthcare spending without any corresponding improvement in outcomes. This increase in spending must be weighed against the consumer savings for the 80% of people who were able to avoid the time and expense of going to a doctor’s office. Hence, the costs and benefits should be analyzed for particular settings and specialties before concluding that telehealth pay parity would be the best possible reimbursement policy. When considering the effectiveness of telehealth, one may argue that traditional office visits may also have varying effectiveness but by and large, studies show that duplicate visit rates for telehealth are at least as high as that of traditional official visits, if not higher19, particularly for telehealth services that are marketed directly to consumers23. Verma, who implemented the Medicare pay-parity policy in March 2020, acknowledged the effectiveness concern, particularly around patients with acute healthcare concerns, stating that additional research was needed to assess outcomes7.

What Insights Does Business Research Offer?

While studies analyzing claims data point to an increase in access and utilization, coupled with a potential decrease in quality or effectiveness (for some specialties where duplicate visits may be more likely following telehealth than following a traditional office visit), they do not always attempt to understand the mechanism behind these impacts. Business research in the areas of operations management and decision sciences can provide insights into how patients access care and how policies such as telehealth pay parity may impact the business environment where care is delivered.

Healthcare providers act strategically when making choices about how patients should access care. In offering the decision of which access channels to offer, providers are generally motivated to obtain good health outcomes but also by financial remuneration. For instance, a healthcare provider may be interested in seamlessly delivering care across two access channels, in-person and telehealth, a practice known as omnichannel service delivery. While omnichannel service delivery is becoming more common in healthcare24, it has mostly been studied in other service contexts, such as retailing25,26 and restaurants27. But, a provider that traditionally offered all of their services in an office would have optimized their resources (e.g., personnel, supplies, equipment) for in-person care based on the demand for these services.28 Changing the delivery system to offer omnichannel healthcare by adding telehealth may lead to increased costs, coupled with the loss of revenue through shifting demand from in-person care to telehealth services, if pay parity is not enacted. The cost of bringing telehealth to the already optimized in-person care may outweigh its benefits in the short run16. So, in the short run it seems that pay parity might be a good incentive for providers to adopt telehealth.

However, payment parity may over-incentivize telehealth in the long-run after resources have been re-optimized, particularly in a fee-for-service system where the incentive is to provide as many “services” as possible. If providers are successful at driving down the unit cost of telehealth services (increasing their profitability), it might lead providers to shift too many resources to telehealth, neglecting the in-person channel. This kind of incentive-alignment problem becomes even more muddy when introducing value-based payment models, which further shift incentives. Rigorous business research that compares the operational costs and benefits of introducing telehealth under different reimbursement models is required.

Several published studies (not all of which directly study telehealth) provide some initial insight into this dilemma. Of particular interest is research showing that like providers, patients also act strategically when making choices about how to access care. Although patients are motivated to obtain good health outcomes, they are also motivated by convenience. When patients have a choice from among multiple types of providers (such as an urgent care, emergency department, or primary-care physician), providers’ managerial decisions influence patients’ subsequent decisions on where to access care29,30. Patients particularly consider trade-offs based on waiting time to get an appointment, anticipated in-clinic waiting time, time of the appointment, and preferred provider (e.g., if their primary doctor is available or not).31 These studies support the idea that by making healthcare more accessible through telehealth, patients may be more likely to seek healthcare.

For acute care, patients may arrive at a provider via scheduled appointments or without an appointment (i.e., “walk-ins”). A patient’s decision about whether to pursue an appointment or walk-in depends on the perceived convenience of each alternative, including wait times32. Incentivized by telehealth pay parity, providers may adopt telehealth by re-allocating some of the existing healthcare capacity that was previously devoted to scheduled appointments or walk-ins. Dividing existing capacity among additional channels may lead to increased wait times at the existing channels. The overall impact on patient access to acute care is therefore unclear, because most studies of telehealth neglect to consider the impact to existing channels.

Finally, a recent study shows that the adoption of telehealth for patients with chronic conditions not only results in more office visits, but it results in physicians accepting fewer new patients33. One reason for this may be that the convenience of telehealth allows providers and patients to see each other more frequently34, but more frequent visits for existing patients consume the provider’s time, which leaves less time available for new patients. As such, these studies suggest that telehealth pay parity could paradoxically decrease access to primary care by erecting barriers for access for new patients.

What is the Outlook?

Telehealth pay parity will continue to be a major policy debate through the years 2022-23. Business research suggests that policymakers should study the impact of telehealth on strategic and operational decisions of providers under different reimbursement models, which affect access. Understanding the difference in quality and convenience between a telehealth visit and a traditional office visit is also key to formulating a coherent policy, because the quality and availability of telehealth influences not only direct clinical outcomes, but also operational challenges like duplicate visits in acute care, and panel sizes in chronic care. Considering these, the business researchers should study (1) which specialties can be covered effectively via telehealth compared to a traditional office visit and (2) which reimbursement model would benefit the healthcare system if telehealth were also chosen as another access channel for healthcare delivery. The results would show a list of illness types that can be covered via telehealth and hence can be offered as an extra access channel to care. Given the mixed evidence on effectiveness of telehealth versus a traditional office visit, the overall economic impact of telehealth on providers and payers deserves further research. As telehealth becomes more widely adopted, state-level policies may be less influential as providers look to the federal government for guidance35. Policy decisions governing telehealth should therefore account for both the current state and the trajectory of health technology, patient and provider behavior, and preferences, the operational impact of changing incentives and reimbursement models.

 

References

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  35. Gaziel-Yablowitz M, Bates DW, Levine DM. Telehealth in US hospitals: State-level reimbursement policies no longer influence adoption rates. Int J Med Inf. 2021;153:104540. doi:10.1016/j.ijmedinf.2021.104540

 

Impact of State Incentives on COVID-19 Vaccination Uptake in the U.S.

Ahmed Sabit, The Johns Hopkins University, Sibbir Ahmad, Michigan State University, and Redwan Bin Abdul Baten, University of North Carolina at Charlotte

Contact: ahmed.sabit@jhu.edu

Abstract

What is the message? The authors assess how U.S. state government vaccination incentives (i.e., cash, lottery/sweepstakes, or other non-financial incentives) impacted vaccine hesitancy or reluctance.  The findings indicate that the vaccine incentive programs demonstrated modest but insignificant effects on COVID-19 vaccination efforts.

What is the evidence? An analysis of Johns Hopkins University’s state-level daily COVID-19 vaccination data, information on statewide incentives from the National Governors Association, and public datasets.

Timeline: Submitted: July 14, 2022; accepted after review: Oct 2, 2022.

Cite as: Ahmed Sabit, Sibbir Ahmad, Redwan Bin Abdul Baten. 2022. Impact of State Incentives on COVID-19 Vaccination Uptake in the U.S. Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 3.

Links: 2022-10 HMPI_Vaccine-Uptake-Supplemental-Material

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Introduction

Since its detection in Wuhan city of China in late 2019, the infectious disease COVID-19 caused by the novel coronavirus (SARS-CoV-2) has spread worldwide 1. The epidemic of one of the most contagious diseases in history took the lives of more than 6 million people and infected millions of others. As of July 7, 2022, 87 million people were infected in the United States, with a death toll of more than a million 2. In addition, it had a disastrous impact on the world economy through the closure of industries, slump in economic activities or employment opportunities 3, disruption of human capital development (both education and health) 4, and shattering of people’s economic welfare 5.

In an unprecedented effort to combat COVID-19, vaccines were rapidly developed. As the U.S. pushed towards fully reopening the economy, getting more people vaccinated had become a priority 6. Since COVID-19 has brought lots of miseries, a solid willingness to vaccinate against it might be perceived. A few studies were done before the vaccine was circulated and found that people were willing to pay for the vaccine 7-9. However, even if the COVID-19 vaccine is provided for free, vaccine hesitancy remains a harsh reality 10. Mistrust in vaccine efficacy, fear of unanticipated side effects, and preferences for natural immunity – are a few reasons for such hesitancy 11. Vaccination-pace in the U.S. slowed since mid-April 2021 when around 40% of the population got vaccinated 2,12. Although declining over time, studies 13-16. showed a high intention of vaccine uptake among U.S. adults. However, intent does not always necessarily translate into action.

The U.S. government had set a target to vaccinate 70% of adult Americans by the 4th of July 2021 17 but missed its mark due to the low vaccine uptake. Since mid-April 2021, the slow pace of vaccination has motivated many U.S. states to announce incentives to attract vaccine-hesitant people. However, contradictory opinions on policies such as offering financial incentives to reduce vaccine hesitancy have seen mixed results in different contexts. Some studies have found incentives to be a good instrument in attracting hesitant people to take vaccines against some infectious diseases 20-25. In contrast, others found it to be an ineffective tool or remained inconclusive 26,27.

Some studies have argued in favor of providing incentives to lessen COVID-19 vaccine hesitancy resulting from uncertainties about the efficacy of vaccines 28,29. A study found that financial incentives will not increase the likelihood of vaccination immediately after the vaccine becomes available as people may focus on vaccine efficacy 30. Others have recommended large compensations, claiming low incentives could backfire as small incentives might not motivate skeptical individuals 31. Chang et al. (2021) found financial incentives ineffective in an intervention of financial incentives among unvaccinated people in Contra Costa County of California 32. However, Campos-Mercade et al. (2021) did another study that found monetary incentives effective in increasing COVID-19 vaccination rates in Sweden 33. Recent studies in the U.S. suggest that a small cash incentive works better than a big lottery incentive to attract vaccine-hesitant people 34,35.

As part of their COVID-19 vaccination efforts, 26 U.S. states announced different types of incentives between April and July of 2021 36. One study conducted an early evaluation (April 15-June 9, 2021) of the impact of Ohio’s Vax-a-million lottery incentive on COVID-19 vaccination uptake. Comparing Ohio’s case with the rest of the U.S. states, they did not find any evidence of an association between incentive and vaccination rates in Ohio 34. However, some synthetic control studies found positive effects of Ohio’s lottery incentive 37-40. Other studies found mixed or no effects of state lottery incentives 41-43. As states adopt appropriate policies to ensure a smooth reopening, it is essential to generate evidence of whether these efforts were effective in combating vaccine hesitancy or not. This study aims to assess the impact of incentives on COVID-19 vaccine uptake and examine the effects of different types of incentives (financial and non-financial) on COVID-19 vaccination rates in the U.S.

Study Data and Methods

Data Sources

We used daily state-level data on the first dose of COVID-19 vaccination from April 19 through July 18, 2021, from Johns Hopkins University 44. This period was chosen as all COVID-19 vaccination-related incentives were announced by states and remained effective. However, incentives in some states were announced for a certain period which subsequently expired during the study period. From the day of expiration of incentives, such states are excluded from the analysis. The analytical sample covered all 50 states plus Washington DC.

Our study focused on the state governor-announced public incentives accessible to residents across the state. We collected information on statewide incentives from the National Governors Association 36 and reviewed all announcements issued between April 19, 2021, and July 18, 2021. For other relevant state-level reopening policies, we used public datasets 45. States differed by the type of incentives they offered, the process of signing up for the lotteries, etc. However, all states shared a common goal of improving vaccination coverage. According to the type of incentives announced, we categorized 26 states into those that announced financial incentives (17 states) and those that offered non-financial incentives (9 states). All Alabama, Idaho, and New York residents were not eligible to access the announced incentives. Therefore, along with the 24 states and Washington DC that never offered any incentives, Alabama, Idaho, and New York were part of the non-incentive providing or control group for our study, leaving 23 states to be considered in the incentive providing or treatment group. Details of the state incentives are summarized in Table 1 and the Supplement.

 Table 1: State Characteristics

State     Incentive Announced Incentives a %Vaccinated b Adoption d Enrollment process e
California 5/27/2021 Financial 55.95 Early Automatic
Colorado 5/25/2021 Financial 52.95 Early Automatic
Maine 6/16/2021 Financial 65.23 Late Registration
Massachusetts 6/15/2021 Financial 68.72 Late Registration
Michigan 7/1/2021 Financial 51.50 Late Registration
New Mexico 6/1/2021 Financial 57.87 Late Registration
New York f 5/20/2021 Financial 52.79 Early Automatic
Oregon 5/21/2021 Financial 52.02 Early Automatic
Washington 6/3/2021 Financial 57.14 Late Automatic
Illinois 6/17/2021 Financial 49.95 Early Automatic
Kentucky 6/4/2021 Financial 46.81 Late Registration
Louisiana 6/17/2021 Financial 37.51 Late Registration
Maryland 5/20/2021 Financial 49.34 Early Automatic
Nevada 6/17/2021 Financial 47.82 Late Automatic
North Carolina 6/10/2021 Financial 44.07 Late Automatic
Ohio 5/17/2021 Financial 43.16 Early Registration
West Virginia 6/1/2021 Financial 44.15 Late Registration
Connecticut 4/26/2021 Non-financial 55.19 Early Automatic
Delaware 5/25/2021 Non-financial 53.11 Early Automatic
Hawaii 06/04/2021 Non-financial 67.06 Late Automatic
Minnesota 5/28/2021 Non-financial 53.95 Early Automatic
New Jersey 5/3/2021 Non-financial 52.31 Early Automatic
Alabama f 5/7/2021 Non-financial 33.38 Early Automatic
Arkansas 5/25/2021 Non-financial 38.77 Early Automatic
Idaho f 06/16/2021 Non-financial 38.69 Late Automatic
Indiana 5/10/2021 Non-financial 38.11 Early Automatic

Notes: States that offer financial incentives, either large or small, are categorized as providing financial incentives and states that offer incentives of non-financial nature are categorized as non-financial states. b % Vaccinated on the day of announcement of incentives. c Based on Cut off vaccination coverage of 50% on the date of announcement of incentives, states are categorized into high (>50%) and low (<50%) vaccination coverage; d Based on the month of the adoption of incentives, states are categorized as early adopters if they have adopted the incentives in April or May, and late adopters if they have adopted the incentives in June or July; e Based on the enrollment characteristic of incentives, states are categorized as automatic if those vaccinated are automatically enrolled or eligible to receive incentives, or categorized as registration if the states require those vaccinated to register and become eligible for incentives. f These states provided incentives that were not accessible to all state residents and were therefore categorized as control states in all analyses.

 

Financial incentives

During the study period, 17 states had announced big or small financial incentives, ranging from lotteries with million-dollar cash prizes to small cash benefits such as coupons or gift cards, along with college scholarships and other awards. As a first state, Ohio announced a large financial incentive in the form of a lottery with a $1 million prize 46. The most considerable financial incentives were announced by California, worth $116.5 million 47. The Supplemental Material delivers a detailed description of the incentive programs for all treatment states.

Non-financial incentives

Nine states offered incentives that did not provide direct cash benefits but included various gift items which were non-financial. On April 26, 2021, Connecticut was the first state to offer non-financial incentives for COVID-19 vaccines in the form of free tickets, complimentary drinks, and food 48. Elsewhere, non-financial incentives included vacations, freebies, discounts, airline tickets, travel packages, hunting, fishing licenses, park passes, driving on racetracks, paid leave, rifles and shotguns, marijuana joints, etc.

Outcome Measurement: Daily Vaccination Rate

Following other studies 49,50, we estimated the effects of incentives (any, financial and non-financial) on the daily state-level COVID-19 vaccination rates. To get the daily vaccination rate in percentage points, we calculated the difference in the natural log of cumulative COVID-19 first dose vaccines on a given day minus the natural log of cumulative COVID-19 first dose vaccines on the previous day multiplied the difference by 100.

Statistical Analysis

To examine whether statewide incentives affected vaccination rates, we employed an event study, which allowed us to estimate the treatment effects in the context of a natural experiment. Providing state incentives can be considered a natural experiment as the states randomly announced providing such incentives, and the beneficiaries had no control over such decisions. The event study is similar to a differences-in-differences design, comparing the pre-post changes in COVID-19 vaccination rates in states with incentives versus changes in states that did not announce any such incentives. It is a critical assumption of the validity of an event study that there must be no differential pre-intervention trends among the treatment group. Our model analyzed if the pre-trend assumptions were upheld under testing or whether states issuing these incentives had differential pre-incentive trends in COVID-19 vaccination rates. In addition, we controlled for a wide range of relevant time-variant factors, including state reopening policies, such as withdrawal of mask mandates or state announced emergencies, state-level daily COVID-19 cases, and the daily number of doses shipped to the state. Time-invariant factors such as population density, education level, poverty rate, racial composition, etc., which might affect vaccination decisions, were controlled by including the state fixed effects along with month and day fixed effects.

We examined how the effects changed over five post-event periods: 1-3, 4-6, 7-9, 10-12, and 13 or more days. The reference period for estimating the incentive effects was 1-3 days before announcing the incentive. The model also tested for pre-incentive trends throughout 4-6, 7-9, 10-12, and 13 or more days before announcing the incentives. We include data from 7 days before the earliest announcement of state incentives, which was made in Connecticut on April 26. Therefore, the analytic sample included daily data on incentives and vaccination from April 19 through July 18 for all states. All models were weighted by the daily number of unvaccinated people in the state, estimated by least squares, with state-clustered and heteroscedasticity-robust standard errors.

As noted earlier, states differed by the type of announced statewide incentives. To understand the effects of heterogenous incentives (financial, non-financial) and characteristics of incentive-providing states, we assessed different model specifications and sample choices based on the following criteria: percent of the population vaccinated, the timing of adoption of incentives, and the enrollment process. Based on the state vaccination rate on the day of the announcement of the incentives, we categorized states into two groups: low vaccination coverage (<50%) and high vaccination coverage (>50%). Based on the month of the adoption of incentives, we categorize states into – early adoption (in April and May) and late adoption (in June and July). States also varied by the process through which they enrolled eligible vaccinated residents into their incentive programs, and states were grouped based on – required registration or enrolled automatically. Separate models were constructed for these specifications and robustness checks (Supplemental Material).

Results

Effects of Incentives

Table 2 shows the estimates of the effect of state incentives (any, financial and non-financial) on the daily growth rate of COVID-19 vaccination obtained from the main regression models, using state-level data from April 19 through July 18, 2021. The effects are shown throughout five periods after the announcement of the incentives, relative to the reference period of three days before the announcement. We have also demonstrated the estimated differences in daily COVID-19 vaccination growth rates between states with and without incentives for four periods before the reference period.

Table 2: Event Study Estimates of States Providing Incentives on Covid-19 Vaccination Efforts with Alternative Treatment Variable Specifications.

Any Incentives Financial Incentives Non-Financial Incentives
12 or More Days Before -0.007

(0.092)

 0.005

(0.106)

 -0.087

(0.163)
10 to 12 Days Before 0.000

(0.101)

 0.026

(0.106)

 -0.207

(0.248)
7 to 9 Days Before -0.014

(0.058)

 -0.023

(0.065)

 0.004

(0.199)
4 to 6 Days Before -0.000

(0.079)

 0.026

(0.080)

 -0.198

(0.183)
1 to 3 Days After -0.024

(0.064)

 -0.009

(0.076)

 -0.157

(0.140)
4 to 6 Days After -0.053

(0.069)

 -0.043

(0.080)

 -0.172

(0.171)
7 to 9 Days After 0.025

(0.047)

 0.065

(0.064)

 -0.209

(0.149)
10 to 12 Days After 0.032

(0.054)

 0.058

(0.063)

 -0.156

(0.131)
12 or More Days After -0.001

(0.045)

 0.004

(0.059)

 -0.083

(0.130)
N 4334 3752 2996

Notes: The table shows Incentive effects on daily vaccination rates among state residents from April 19 to July 18, 2021, using daily state-level vaccination data from Johns Hopkins University. The models adjust for state withdrawal of mask mandates or state emergencies, state-level daily COVID-19 cases, and the daily number of doses shipped to the state and include fixed effects for the day, month, and state. Standard errors are clustered by state and are reported in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

 

We found no statistically significant increase in the daily vaccination rates after the announcement of incentives. However, a slight increase in daily COVID-19 vaccination rates was observed post-announcement, with positive effects remaining on days 7-12 after announcing the incentives. Specifically, the daily vaccination rate increased by 2.5 and 3.2 percentage points within 7-9 and 10-12 days after the announcement, respectively. In contrast, the pre-incentive trends in COVID-19 vaccination growth rates were small and insignificant, which validated our research design.

Similarly, findings show a statistically insignificant moderate increase in the daily vaccination rates after the announcement of the financial incentives, and the effects are positive from day four onwards. Specifically, the daily vaccination rate increased by 6.5, 5.8, and 0.4 percentage points within 7-9, 10-12, and 13 days after the announcement, respectively. In contrast, there was a decrease in the daily vaccination rates after announcing the non-financial incentives, for which the effects remained negative and statistically insignificant in all post-intervention periods.

Treatment heterogeneity and robustness checks

For all heterogeneous treatment specifications, robustness checks evaluated whether the results were impacted by adding controls for withdrawing state face mask mandates, withdrawing the state emergencies, the state-specific daily number of COVID-19 cases, number of doses shipped to the state daily, and excluding Alabama, Idaho, and New York from the analytic sample. The detailed description and results from these robustness checks are provided in the Supplement. For all heterogeneous treatment specifications, results were robust across these checks. Effects remained negative for incentives in states with high vaccination coverage and early incentive adoption. However, states with low vaccination coverage observed slight increases in the impact of incentives. The estimates remain moderate but statistically insignificant in most checks.

Discussion

Although at the time of this study, the national vaccination coverage reached around 56 percent of the total population 2, some states such as Mississippi, Alabama, and Louisiana were still lagging far behind the coverage target 2. 4646, 45 Many states offered financial incentives while some others offered non-financial incentives to encourage more people to get vaccinated 44. This study examined the impact of such incentives on the daily growth of COVID-19 vaccination rates. To the best of our knowledge, this is the first study to evaluate the effect of state-announced incentives on COVID-19 vaccination rates, including all 50 U.S. states along with Washington DC, while considering heterogeneities within States and forms of incentives. We did not find a significant impact of incentives on vaccination rates. However, coefficients on post-intervention periods indicate that incentives have a slightly positive, though insignificant, effect on vaccination rates. Financial incentives perform better than non-financial incentives; low vaccination coverage (at the time of incentive announcement) states experience more impact on vaccination rates than high vaccination coverage states. These findings remain the same after controlling for state policies.

Vaccine hesitancy, despite millions of dollars worth of incentives, is a big concern for policymakers. A recent study shows vaccine hesitancy is higher in developed countries like the U.S. and Russia than in low- and middle-income countries 51. Studies identified that fear of uncertain side-effects of vaccines or mistrust of vaccine efficacy influenced people’s decision to take a vaccine, which indicates the risk-averse nature of the vaccine-hesitant people 11. Lotteries typically do not attract risk-averse people since the lottery outcome itself is an uncertain event 34,35,52. Our finding of better performance of financial incentives over non-financial incentives indicates that states need supplementary measures and financial incentives to boost COVID-19 vaccination coverage. Moreover, not all people who are less interested in the vaccine should be coined as vaccine-hesitant. Innovation-adoption theories reflect that adoption follows a typical pattern of making a choice slowly. So, apathy could have characterized their non-vaccination behavior as well 53.

Our finding that low-vaccination coverage states (at the time of incentive announcement) experience better daily vaccination rates than high-vaccination coverage states indicate that every state has a peak vaccine non-hesitant population. Therefore, it does not necessarily mean that announcing incentives at the stage of low coverage mitigates the vaccine hesitancy problem. It is essential to design more comprehensive strategies that address the reasons for non-commitment to vaccination. However, states were not successful in developing comprehensive marketing strategies beyond these simple incentives18. So, the failure of an effect could be the failure of specific tactics used to implement an incentive or the broader failure of a lack of a strategy to reach unvaccinated populations19.

Our study has some limitations. We cannot assess the individual-level effect of the COVID-19 vaccination efforts or measure the utilization of incentives in the community. At the time of analysis, we did not have data on county-level incentives or vaccination rates. In some control states, a few counties had announced such incentives 36. These county-level incentives did not bias the intent-to-treat estimates of state-level incentives’ effects as announced. Still, they added local-level heterogeneity not directly accounted for in the model. We could not stratify the sample by demographic characteristics such as age, gender, race, etc. This might be important as some incentives were targeted towards specific age groups, such as scholarships for young recipients. Another limitation may be that the effect of incentives on states with low base rates might reflect a recognition by states that they were in significant difficulty with vaccination. However, the signal that led to recognition by the governor could have also influenced people in the state to get vaccinated (or the initiation of other local programs within the state). Our framework could not address this possibility. Finally, we could not account for state-level hesitancy in taking COVID-19 vaccine doses, which might have impacted the vaccination rates. However, this is a comprehensive study of incentives on COVID-19 vaccination uptake that includes all U.S. states and considers the heterogeneity present across states. It contributes to the vaccine hesitancy and financial incentive literature in several aspects. We considered all states that offered financial, non-financial, or both treatment and compared those with those that never offered any incentives. Including all the states and several robustness checks increases the external validity of the results.

Conclusions

COVID-19 has impacted lives and livelihoods worldwide, and the United States is no exception. To develop herd immunity against the coronavirus, governments expected most of the population to have the vaccine. However, vaccine hesitancy is a harsh reality among people for some reason. As a result, some U.S. state governments announced different financial and non-financial incentives to encourage vaccine-hesitant populations to take the COVID-19 vaccine. Using Johns Hopkins University’s state-level daily COVID-19 vaccination data from April 19 through July 18, 2021, this study presents evidence from a natural experiment on the effects of the 26 U.S. states’ announced incentives (i.e., cash, lottery/sweepstakes, or other non-financial incentives) in 2021 to reduce vaccine hesitancy or reluctance. The research design is an event study assessing the changes in the daily state-level COVID-19 vaccination rates between April 19 and July 18. Any (financial and non-financial) incentives are associated with an increase in the daily COVID-19 vaccination rates by 2.5 and 3.2 percentage points in 7-9 and 10-12 days after the governors announced statewide incentives, but the effects were not statistically significant. Estimates suggest that state daily vaccination rates increased marginally due to the announcement of financial incentives but not for non-financial incentives. The findings of this impact evaluation study of the vaccine incentive program suggest modest but insignificant effects of statewide incentives on COVID-19 vaccination efforts.

 

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Rethinking the Rural Hospital: A Rural Health Alliance

Stephanie Dodgen, Joseph Spear, and Cholan Wijekumar, Baylor University, Hankamer School of Business

Contact: stephanie_dodgen2@baylor.edu

This article is based on the winning presentation in the 2022 Business School Alliance of Healthcare Management (BAHM) Case Competition. Students representing schools that are members of BAHM, the publisher of HMPI, compete annually and propose new models to help innovate health care. 

Abstract

What is the message? To increase access, provide quality care, and reduce healthcare costs for the Eastern Oklahoma Native American population, the authors propose establishing a novel rural health alliance, called the East Oklahoma Allied Health, comprised of existing clinics and critical access hospitals, a university partnership to train and recruit Native American healthcare professionals in the area, and a hospital partnership to provide access to specialty care.

What is the evidence? The authors investigated the need for healthcare change in the Eastern Oklahoma Native American population by researching current clinics, critical access hospitals, current physician specialties and social determinants of health. As seen in rural hospital data, without change, critical access hospitals will continue to close and further restrict access to these vulnerable populations. Along with this information, the authors researched financial data of the six facilities, created an in-depth financial analysis based on reimbursement rates, and found supporting evidence based on other successful healthcare alliances and academic partnerships across rural America to show that a rural health alliance is sustainable and will provide quality care through the Eastern Oklahoma Native American population.

Submitted: July 14, 2022; accepted after review: October 11, 2022.

Cite as: Stephanie Dodgen, Joseph Spear, and Cholan Wijekumar. 2022. Scenario Planning Tools For Organizations Struggling With Healthcare Reform Uncertainty – The Case Of Oscar Health Insurance. Health Management, Policy and Innovation, Volume 7, Issue 3.

Links: Executive Summary and Appendices 

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Introduction

The 2022 BAHM Case Competition prompt asked: Can the Hospital of the Future Solve the Growing Challenges of Rural Hospital Care?

Due to a major population shift from rural to urban areas, rural communities have faced a major healthcare crisis due to limited access to sustainable health services. The rural hospital closure rate has increased in the last two years, leaving underserved populations without adequate primary or specialty care (20). Even with several federal programs targeting rural hospitals, such as critical access status, negative margins are ultimately reducing the number of rural hospitals (19). The purpose of the 2022 BAHM Case Competition was to identify a key population in a rural or remote area and to design a “hospital of the future” that increases access to quality care. We hope our proposed solution can serve as a blueprint for addressing access issues in rural areas.

Our team, comprised of three students from Baylor University’s Robbins Healthcare MBA program, focused on the Eastern Oklahoma Native American region that includes three major tribes: Osage Tribe, Cherokee Nation, and Creek Nations. These populations struggle with poor social determinants of health, including mental and behavioral health issues, structural inequality, limited food availability and food insecurity, and historical trauma (1). On average, these tribes have a lifespan that is 4.4 years shorter than that of other populations, are twice as likely to be diagnosed with diabetes, and are more likely to be obese and have high blood pressure.(1) Many of these issues lead to depression, alcoholism, and behavioral health problems. (1) In addition, individuals in these tribes lack support and sufficient internet access, which limits use of telehealth resources. Like many other rural communities, these nations struggle with a medical personnel shortage.(1, 2, 3) The Osage Nation comprises around 20,000 people, the Creek Nation over 80,000, and Cherokee Nation over 141,000 people living within their borders. (4, 5)

Currently, there are six clinics and critical access hospitals within this region. The six rural facilities are the Wah-Zha-Zhi Health Center, WW Hastings, Okmulgee Hospital, Vinita Hospital, Okemah Hospital, and Coweta Hospital. (5, 6, 7, 8, 9) Within the Osage nation, the Wah-Zha-Zhi Health Center has the most limited access, with only one primary care physician. In the Cherokee Nation, there are 37 practicing physicians across six specialties, and in the Creek Nation, there are 17 practicing physicians. (5, 8, 9) The standard doctor-to-patient ratio is less than one doctor per 1,000, well below the national average of 2.6 per 1,000. (5) The current breakdown of primary and specialty services provided at each of the rural hospitals and clinics can be seen in Appendix 1.

The payer mix of the population is 45% Medicaid, 25% Medicare, 20% private insurance such as the Creek Nation Native Blue Cross PPO, and 10% uninsured.(13)

Even with significant government funding through Indian Health Services (IHS), Medicare, and Medicaid, the net present service revenue is -40%, with margins ranging from -23% to -56%.(1, 3) The hospitals of the East Oklahoma Native American tribes are at risk of closing, further restricting healthcare access to this vulnerable population.

Solution

Due to these unsustainable financial circumstances and limited access to care for Eastern Oklahoma Native American tribes, we proposed a rural health alliance comprised of the following: a newly formed East Oklahoma Allied Health (EOAH) combining six existing clinics and critical access hospitals, as well as partnerships with the Saint Francis Health System and Oklahoma State University. Our solution also recommends practice consolidations and home-health and telehealth initiatives. Our rural health alliance can provide a sustainable rural hospital system that adequately serves the community and provides quality primary and specialty care while respecting Native American Nation values.

East Oklahoma Allied Health

The six rural facilities – Wah-Zha-Zhi Health Center, WW Hastings, Okmulgee Hospital, Vinita Hospital, Okemah Hospital, and Coweta Hospital – will form a mutually beneficial alliance to help increase access and reduce costs. The first way this alliance will reduce costs is by pairing down existing physician roles (Appendix 1) and management roles. These six facilities will primarily focus on providing primary and emergency care, while some of the more central locations will offer specialties such as optometry and behavioral health to combat the diabetes and mental health endemic in East Oklahoma. Through the alliance, every resident in the area will have access to each of the specialties through a telehealth and mobile health initiative (addressed below). By increasing the primary care focus, each of the hospitals and clinics can expand their care delivery. Most of the patients across the region have limited access to primary care and therefore, opportunities to prevent disease. With increased focus on primary care and prevention, the hospitals can provide better continuity of care and manage patients in a value-based environment. By also regionalizing specialty services, the alliance helps to retain critical physician specialties. It also allows the alliance to provide competitive salaries and increase overall access to specialty care. The areas we picked for each specialty (Appendix 1) are based on centralized hospitals and population density. However, every patient will have equal access to these physicians through the mobile and telehealth initiatives.

The second way the EOAH will reduce costs is by centralizing and standardizing contracts such as laundry, labs, billing, and electronic medical records (EMR). That will reduce the repetition of services, and centralized hospitals such as Coweta and Okmulgee can provide the services directly. The third way to reduce costs is to reduce the number of administrators across the hospital system and create one consolidated management team. One CEO, CFO and CMO will be in charge of the six facilities. Each facility will have their own VP of Finance, Operations and CNO to ensure operations run smoothly. These positions will first be filled by the current administration at each of the rural hospitals or clinics. Any open position will be filled by individuals who hold a stake in the community. Physicians, nurses, administrators, and other healthcare personnel will be retained through competitive salaries and in line with the mission of the East Oklahoma Allied Health: to serve the community, to provide quality care, and to establish a sustainable rural health system. Quality leadership is also crucial to a sustainable alliance. We propose that these six facilities seek servant leaders who are dedicated to gaining understanding, to serving the community, and to obtaining buy-in from all parties. These leaders must be collaborative, believe in accountability, be technically competent, think longer term, and emphasize the vision, values and motivation of the alliance.(10, 11, 12)

It is important to note that although we recommend a strategic alliance, there may be weaknesses. By centralizing contracts and reducing costs across each of the hospitals, there is a greater focus on the whole system instead of individual facilities. This may lead to one system obtaining better results while another hospital or clinic suffers. Additionally, alliances may fail due to lack of cooperation from other hospitals within the system and there may not be much added value from each hospital.(25) However, strategic alliances have also done significantly better than individual entities because of their ability to work together, consolidate similar services, expand other services, increase overall revenue, reduce costs, and provide increased access to physicians.(26) Cerner provides several examples of rural partnerships that were successful.(27) One of the most challenging aspects of a strategic alliance is starting the partnership. However, once the alliance is set up, it becomes easier to establish a continuity of care for the community. Rural hospitals have opportunities and a history of enhancing the patient experience through these strategic alliances, further strengthening community connections and building on their local governance to provide quality care. Without alliances, rural hospitals and critical access hospitals will continue to close. We believe that this proposal will be successful since several other rural healthcare systems have established a similar solution. The majority of critical access hospitals have been declining over the last 25 years due to limited financial benefits. However, a network membership (similar to what we are proposing) has become significantly more common among these critical access hospitals.(28) Additionally, tightly integrated physician organizations are more common in these settings since they provide greater access to patient populations in rural communities, ultimately increasing the financial economies of scale.(28)

Saint Francis Partnership

The Saint Francis Partnership provides EOAH with specialists that are not currently integrated into the rural health system. We chose St. Francis because of its range of specialists that patients could access through telehealth and mobile health initiatives. Some examples of specialty care physicians are psychiatrists, endocrinologists, dieticians, pharmacists, and surgeons. This partnership business model would be based on an agreement to have dedicated specialists rotate through the EOAH van once a week: mental health specialists on Monday, dietitians on Tuesday, and so on, until Friday. Considering the current staffing climate, it would be difficult for specialists to be available for longer periods. However, as EOAH vans will be equipped for different specialty services, the van would be used every workday. EOAH will pay specialists one fifth of  their salary plus their portion of benefits. In addition, if a specialty is not offered by one of the six facilities, EOAH would agree to refer patients to their hospital to help drive referrals and volume.

OSU Academic Partnership

A partnership with the Oklahoma State University  (OSU) Academic center is essential for the EOAH to promote and retain talented healthcare professionals. The facilities will partner with OSU to create a Rural Nurse Recruitment Program that provides nursing pathways for Native American residents to earn their Advanced Practitioner Nursing (APN) license. Individuals in the community are provided with financial aid to attend the program and then to serve in the community as APNs through the mobile health initiative. Previous research has shown that minority community patients are much more comfortable with providers from similar backgrounds.(18)

OSU is committed to rural health and already has in place some pathways to place current healthcare staff in rural communities. EOAH would capitalize on these established programs and offer a full tuition reimbursement for undergraduate nursing programs as well as various advanced practitioner pathways (up to a limit). The EOAH model falls in line with the institution’s mission and vision, so there should be no issues affecting the participation of students from these tribes.

Home Health Initiative

In addition to EOAH’s Saint Francis and OSU partnerships, we propose a Home Health Initiative to increase access to quality medical care. One year after forming EOAH, Advanced Practitioner Nurses (APNs) will begin visiting patients at their homes. These APNs will travel with a laptop connecting patients to specialty physicians through Cerner’s telehealth technologies.(21) Each provider will have access to an electronic health record that provides a digital platform for telehealth appointments through an app. Telehealth access will be mediated by Starlink satellite internet, which addresses the limited signal and internet in these rural areas.(22) The APNs will use this technology to connect the patient to a provider after completing physical exams. They will travel Monday through Friday for two years until the cost savings from the consolidation enables EOAH to outfit a mobile health vehicle.

By year three, the mobile clinic will be operational and operated by APNs. The mobile health vehicle will be outfitted with common medical equipment including a scale, blood pressure cuff, thermometers, ophthalmoscope, tuning forks, a small fridge, as well as a telehealth connection point. The vehicle will have 24/7 high-speed internet through Starlink satellite internet.

The Home Health Initiative will allow patients to see culturally competent providers in or near their home instead of having to travel up to two hours to receive care. The Home Health Initiative will also provide EOAH an additional revenue stream to further the mission of improving access to quality care for members of the Osage, Cherokee and Muskogee tribes.

A framework for evaluating public health proposals such EOAH’s Home Health Initiative is the six-factor alignment: structure, financing, accountability, public policy, consumers, and technology.(16) The structure of the Home Health Initiative is relevant to the current environment because it is an additional service and revenue stream to the conventional rural hospital. Additionally, the home health service will help alleviate capacity issues at the rural hospitals. The plan will be financed through the common mechanisms of Indian Health Services, Medicare, Medicaid, and private insurance. The cost savings from the EOAH consolidation will provide the seed money. The initiative will be held accountable by open-source accounting, quality metrics, as well as a demonstrated increase in access to care. The innovation is perfectly aligned with public policy including care initiatives spurred by the COVID-19 pandemic that support telehealth services. Consumers will welcome the proposal because of the increased access to specialty physicians, providing a long-term and much broader continuum of care that these patients currently do not receive. Additionally, the home health service will alleviate transportation and disability/mobility issues for those patients who have trouble leaving their houses. Lastly, no new technologies are presented that could not be easily upgraded or replaced. However, there is an estimated upfront cost of $45,000 to set up internet (Appendix 4), which is further explained in the Financials section of the paper.

Other important considerations are environmental, social, and governance concerns. The alliance and Home Health Initiative will eliminate unnecessary waste and redundant in-office healthcare visits. Socially, the proposal addresses the need for culturally competent physicians through the OSU partnership. These new practitioners will be encouraged and incentivized to return to their communities to provide care. Lastly, the Initiative will rely on Indian Health Services and local community governance to make it a reality.

Implementation

Timeline

The implementation timeline (Appendix 3) will begin when the six hospitals agree to form EOAH and complete the consolidation efforts. In establishing the alliance, EOAH will purchase Cerner as their EHR (explained further in the Financials section). This purchase falls under year zero with the consolidated contract services along with one lab contract, one laundry contract, and one billing system. The partnerships with Oklahoma State University and St. Francis Hospital will also be worked out in year zero. After these partnerships have been established, the first set of APNs begin in year one, as does a big push to begin financing and outfitting the van to provide home health and telehealth services. While the van is being outfitted, home health and specialty telehealth services begin. In year two, the mobile clinic will begin to be marketed to the community for its introduction by the start of year three. By year three, the van will be run by the APNs. At this time, quality metrics and greater access to care will be presented to the hospital board as well as to the public through tribal councils. In year five, another mobile health clinic will be operational.

Engaging the Community

The most important piece of this proposal is community engagement and empowerment. As it stands, the hospitals are all struggling with access and financial issues even with extensive government support. However, together, the community has the necessary resources and expertise to be successful. This proposal simply gives them a framework for maximum success. To achieve it, the financial, access, and quality benefits will need to be presented to the many stakeholders, including tribal councils and Indian Health Services. Before these presentations, it will be important to find EOAH allies who are influential members of these organizations. Because the rural health alliance is to provide quality care for and by the community, we need these champions to advocate and help push the proposal through the necessary steps. These leaders will be responsible for the governance of the hospitals and buy-in from the community. We believe that this alliance needs to be community led because of the many cultural considerations. With community governance, the tribes will feel empowered to change their own health and trajectory. Another key issue is the consolidation of contracts. Due to the history of the United States and these tribes, it is important to be clear about the purpose of consolidation, explaining the financial benefits without giving the impression that the tribes will lose resources. Lastly, there may be differences among the tribes. Community leaders championing the proposal sets an example for the rest of the Native American community.

Marketing

Marketing the rural health alliance begins in year zero and will consist of a massive rebranding to include community fliers, a user-friendly website, and an application (app). The slogan will be “Together We Thrive.” The mobile health initiative and academic partnership will also be a large part of the marketing plan. To promote widespread awareness, marketing efforts will take place at tribal councils and community events.

Key Performance Indicators

A number of key performance indicators will be used to provide objective feedback and ensure goals are being met. These indicators can be further subdivided into structural, process, and outcome measures at both macro and micro levels as outlined by the Donabedian Model.(17) Structural measures will assess the systems-level inputs to ensure the alliance is providing benefits at a macro level. Process and outcome measures will be used to determine success at the patient level and provide a more specific framework for improvement at the micro level.

The structural measures are focused on more convenient accessibility for both patients and providers. The proposal calls for implementing a universal electronic medical record throughout the alliance. This is critical to the success of the EOAH, considering the Department of Indian Health Services does not use a standardized EMR systemwide (Appendix 3 under consolidated contract services). A commercial version of Cerner would be more cost effective than using alternatives such as Epic or Meditech (explained further in the Financials section). Other structural measures include increasing the provider-to-patient ratio and percentage of referrals in-network.

The process and outcome metrics will measure specific clinical outcomes of the patient population. The Native American population has higher rates of hypertension, obesity, diabetes and mental health disorders than the general U.S. population. Many of these can be addressed with preventative visits, so the first goal here is process oriented: for over 75% of current patients to undergo annual preventative visits. For diabetic patients, the goal is over 80% of the patient population with controlled A1C levels. Finally, given the prevalence of mental health issues, the alliance calls for over 80% of the at-risk population to be screened, diagnosed, and treated with medication or to receive counseling. Outcome measures will focus on a standard array of inpatient metrics related to patient safety and outcomes, such as hospital-acquired infections, surgical mortality, surgical complications and readmission rates, as well as emergency room use (i.e., left without being seen and time until provider is seen after check-in).

It is important to note that the EOAH aims to address key social determinants of health but may not be able to address them all. These social determinants are a core focus of the alliance but given that the community is rural and the population is spread out, the six facilities may not be able to positively impact every population group. It is still imperative, however, to set and pursue realistic goals as the focal point of the EOAH mission. Additionally, it is difficult to monitor the impact of efforts to address the social determinants of health due to the extended period that individuals are affected by them. However, we believe the first step to addressing these issues is to recruit individuals that value change and want to serve their community. By providing opportunities for a better education and a greater commitment to health continuity, this population will have greater representation in their own healthcare experiences and in their future.

Financials

East Oklahoma Allied Health Financials

The current financial situation for the hospitals and clinics is dire. They currently have net patient service revenue margins that range from -23% to -56%, with a system-wide average of about -40%. (13) Compared to other hospitals in the region and country, this represents a significant lag lag that is not viable for long-term operation. To make up for poor reimbursement and volume constraints that cause significantly negative revenues, the federal government allots funds for these hospitals to continue operating. Even with these subsidies, the hospitals have a negative overall margin. Consolidating and merging the hospitals would allow them to save on administrative and operational costs. Similar consolidations in other regions achieved between 15% and 30% operational cost savings.(14, 15) Considering our proposal allots some funds for scholarships, mobile health, and other projects, we estimate a 10% reduction in total costs. With sustained levels of government aid, this would result in a positive profit margin of approximately 5% for the system. One source of government aid is from the Indian Health Services, which receives a budget appropriation from the federal government to help run the hospitals and facilities under their domain. In 2020, this amount was approximately $6 billion.(23) This money can be allocated to each facility directly or held in a reserve until it is needed. In addition, grants for various initiatives (diabetes, mental health, water sanitation) are awarded.(24) On average, less is spent on an IHS patient than the average patient in the United States ($4078 compared to $9726, respectively). Taking this into consideration, a sensitivity analysis was conducted to estimate the range of potential outcomes. The worst-case scenario would only achieve a reduction of costs of about 3%, whereas the best-case scenario achieved a 20% cost reduction (Appendix 4). This proposal would improve the financial outlook for the alliance and allow for continued operations.

Van Financials

The estimated startup funds for the van and mobile health initiative would be approximately $400,000 to obtain, equip, and brand a van. The funds would be generated from the cost savings of the consolidation. The van would be in operation by the third year, though there would be a ramp-up period to facilitate patient adoption of this form of healthcare. It is estimated that by the fourth year, the van would be in full operation and by the fifth year, there would be a second van. With these estimates and a 10% cost of capital, there is a five-year net present value of $23,750 and an internal rate of return of about 12%. It is important to note that the vans will continue to operate and generate profit for many years beyond the initial five-year project period, making it quite sustainable with potential to subsidize other areas.

Conclusion

This proposal aims to address the issues of the Eastern Oklahoma Native American population by establishing a rural health alliance comprised of East Oklahoma Allied Health (an alliance made up of six existing clinics and critical access hospitals in the region), the Oklahoma State University Academic Partnership, which will recruit natives of the area to become an APN, and the Saint Francis Partnership, which provides access to more specialty care. Through the alliance and a unified health system, this rural population will take their health into their own hands while costs will be reduced. The cost savings will allow EOAH to fund a telehealth and mobile health initiative. Each element of this proposal furthers the ability of these rural hospitals to be sustainable, to provide quality care, and to increase access to healthcare throughout the Osage, Creek and Cherokee Nations.

 

References

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Word from the Editors

This is an exciting issue of HMPI, with features relating to drug and technology development, design of health plans and service payment models, and updates from the 2022 University of Miami health care conference.

Our lead article looks at the intersection of real-world evidence with our drug development and reimbursement paradigms. The authors argue how we can use data from clinical practice to make more informed decisions about emerging technology in the field of precision healthcare. Later in this issue, we return to drug discovery and development with a pair of papers looking at bench-to-bedside translation from the perspective of a scientist, as well as from the perspective of the institutional, business, and geographic ecosystem required to support translation.

Several articles examine the design of health plans and service payment models. The first examines the issue of whether and how patients can be informed consumers of healthcare services, a proposition underlying a lot of health plan designs. The second article is a timely look at staffing shortages, asking whether deskilling and top-of-the-license strategies can offer a solution. A provocative paper asks the question of whether auction theory can lead to better prices than the current administered pricing approach. And finally, the University of Miami healthcare conference brought together leaders to examine critical issues that are likely to be challenges in the coming year.

In the wake of the pandemic, national attention has moved from healthcare to other pressing national issues. However, underlying trends in the field are likely to change this dynamic. While consolidation is continuing across provider organizations, cost pressures and labor shortages will drive hospital to use their leverage in price negotiations with commercial health plans. The result will be a return to aggressive price increases in commercial health insurance premiums. Required price transparency on the part of hospitals and health plans will bring increased scrutiny to the private market with the release of health plan data this summer.

In the meantime, the growth of Medicare spending has slowed as a result of the impact of the COVID pandemic on life expectancy in the United States. CMS must make major decisions this coming year about the future of telemedicine and other technologies in the fee-for-service program, decisions that could shape the delivery system in profound ways.

Kevin Schulman, MD, MBA
Acting Editor in Chief, Health Management, Policy and Innovation (HMPI)
President, Business School Alliance for Health Management (BAHM)
Professor of Medicine, Stanford University

The Nursing Workforce Shortage – What’s the Solution?

Ernest Grant, President, American Nurses Association

Contact: Ernest.Grant@ana.org

Abstract

What is the message? The COVID-19 pandemic exacerbated an already growing nursing shortage, and reversing that shortage demands addressing issues such as burnout and dissatisfaction. Doing so requires revamping existing systems and work environments and increasing opportunity, education and compensation – solutions crafted with input from nurses and active listening by the C-Suite.

What is the evidence? The author examined results from The Current Pulse of the Nation’s Nurses COVID-19 Survey series (June-July 2022), from American Nurses Association (ANA) listening sessions and issues being addressed by a newly formed National Nurse Staffing Task Force

Timeline: Submitted: August 29, 2022; accepted after review: September 15, 2022.

Cite as:  Ernest Grant. 2022. The Nursing Workforce Shortage – What’s the Solution? Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 3.

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Introduction

When COVID-19 hit the shores of the United States, there were plenty of indications that the already existing nursing workforce shortage would balloon to crisis proportions. Prior to the start of the pandemic, the nursing profession was (and had been) experiencing multiple shortages due to factors such as: economic downturns; the retirement of older nurses; and younger nurses leaving the bedside to become travelers or Advanced Practice Nurses [APRNs i.e., Nurse Practitioners or Nurse Anesthetists]. The increase in workplace demands as healthcare trends moved from the acute care setting to the broader community, also contributed to the workforce shortage as more nurses were needed to staff these newly created positions. Recent studies have documented an estimated predicted workforce shortage of half a million to 1 million Registered Nurse (RN) jobs in the U.S. by 2030.1  One report also noted that close to half of the RNs practicing today are 50 years of age or older. 2 Once the pandemic hit, these factors and others contributed to the worst nursing shortage ever seen in the U.S. Whereas the nursing shortage has been experienced in all practice areas, this article addresses the experience felt in acute care settings. The shortage is so significant, that in September of 2021, I wrote a letter to Health and Human Services Secretary Xavier Bocerra, asking him to declare the shortage a crisis and to bring all stakeholders together to develop short- and long-term solutions to address this issue.3 Although many issues contribute to the current workforce shortage, I shall address three areas that I see as a concern.

The Drivers Behind the Shortage

First, in addition to factors listed above, there were several other drivers accelerating the current nursing shortage. When the COVID-19 pandemic hit, very little was known about the mode of transmission. One obvious factor was that older adults seemed to be the most vulnerable. As hospitals began to fill up with the COVID-19 population, older nurses began to realize that they were just as vulnerable, especially given the lack of sufficient personal protective equipment (PPE). Many older nurses made the decision to retire early to protect their health and the health of their family members. Thus, we had the dilemma of nurses choosing to retire outpacing the number of new nurses entering the nursing field; this not only created a huge workforce drain, but also a huge knowledge, or brain, drain as well. The brain drain is also thought to contribute to younger nurses leaving the bedside because there is no one to precept them in their new work environment.

Second, as hospitals continued to fill up with COVID-19 patients, nurses and other members of the healthcare team were asked (and in some cases mandated) to work overtime, leading to excessive physical and mental burnout across all age groups. The American Nurses Foundation (ANA) conducted a fourth “Pulse of the Nation’s Nurses Survey series on Mental Health and Wellness” at the two-year mark of the pandemic.4  Over 9,000 nurses participated in the survey, which highlighted such factors as:

  • 34% of nurses are either not or not at all emotionally healthy (of note, 51% of those respondents were between the ages of 25 and 34 years)
  • 50% of nurses indicated they intend to stay in their position in the next six months, with 21% saying they intend to leave and 29% saying they may leave.

When asked why, nurses named the negative effect work has on their health and well-being (48%), staffing shortages, and lack of support from their employer (41%).

  • Resiliency was mixed among nurses; nurses rated their ability to recover with an average score of 6.64 on a scale of 0-10.

Because a state of emergency had been declared across the U.S., employers could impose mandatory overtime to meet staffing demands. The downside to this and other such measures was the unforeseen consequences that, if left unchecked, increased fatigue (whether mental or physical) and the likelihood of performance errors (poor judgments, increased medication errors and complications, increased mortality, etc.). As the health of the individual continues to become diminished (physical ailments, poor diet, poor sleep patterns, detachment, depression, and irritability, etc.), there is the real potential of burnout. Burnout may be linked t0 absenteeism, increased job vacancies, substance abuse, or suicidal ideation.

A third reported reason for the nursing workforce shortage is nurses’ dissatisfaction with their work environment. Nurses were viewed as heroes at the onset of the pandemic. As the pandemic wore on and burnout among all healthcare professionals increased, nurses began to feel they were not valued or appreciated by their employer or institutions. They registered complaints about feeling unsafe or that their workplace was not considered a safe environment. There were documented reports of nurses being bullied or assaulted by patients and their family members, or even their own colleagues. In some cases, nurses did not feel supported by their employer when such incidents were reported. In the latest survey of The Current Pulse of the Nation’s Nurses COVID-19 Survey series (June-July 2022), nurses reported that the incidences of bullying, incivility, and violence are increasing, with most incidents involving patients and their families.5 This creates an environment in which nurses would rather walk away than remain in a challenging setting. Additionally, some nurses chose to move to a different role due to the insufficient number of nurses available to staff a unit. This staffing shortage may increase the desire of even more nurses to leave their employer due to the increased workload created by the departure of other nurses.

In late spring of 2022, the American Nurses Association (ANA) conducted a series of listening sessions with its constituent state nurses associations to discuss the nursing staffing shortages. More than 20 states were represented at these sessions and each state reported some level of nurse shortages in various care settings. Additionally, the ANA, American Association of Critical Care Nurses (AACN), the American Organization of Nursing Leadership (AONL), the Institute for Healthcare Improvement (IHI), and the Healthcare Financial Management Association (HFMA) launched the National Nurse Staffing Task Force. The Task Force has worked to develop short-term actionable strategies to address the nurse staffing crisis. Some strategies or solutions were set up as “low-hanging fruit” opportunities and the priority areas identified were healthy work environments, innovative care delivery models, work flexibility and scheduling, total compensation, stress injury continuum, and Diversity, Equity and Inclusion.

The Proposed Solutions

So, what is the answer to help address and alleviate the nursing workforce shortage? Many solutions have been proposed, such as mandated staffing ratios, increased rate of pay or hiring additional staff or travel nurses, and increased enrollment in nursing programs. All of these solutions may have their benefits, but the heart of the matter remains that there is not a one-size-fits-all solution. There are many different causes for the workforce shortage in different areas of the country, such as rural versus urban issues, critical access facilities versus academic medical centers, etc. Personally, I think a vital question asked during the June-July survey is a good starting point. The survey pointed out that 31% of the respondents selected the item “Genuinely listen to my voice and respond to my needs” as one of the top three ways to improve work satisfaction. Since the start of the pandemic, I have been encouraging chief nursing officers and other members of the C-Suite to host town hall meetings with their employees and to actively  listen to what they are saying. Attempting to fix the problem without consulting those who are directly affected by that fix will ensure that the crisis continues. By hosting town halls, C-suite executives can gather data (much like what we do when applying the nursing process) to begin to solve the problem. One important factor to consider while gathering that data is that there may be a variety of solutions offered. What one nurse may view as a solution (i.e., higher wages) may not be what appeals to another nurse (i.e., safe work environment). It is crucial to ask each nurse what it will take for them to feel valued and an important, vital member of the healthcare team. It is critically important that senior nurses are listened to and are asked questions such as, “what keeps you here; what can we put in place to continue to keep you here and attract others?”

Nursing salaries should be taken into consideration when addressing the workforce shortage.  Many hospitals began to pay attention to this when nurses began to leave their facilities in droves to work as travelers (sometimes working in the same facility they left) and receive pay that was five  to 10 times their previous hourly rate. A Nursing Solutions Inc. (NSI); report found that the average cost of turnover for a nurse at the bedside is over $40,000. That same report noted that in 2020, the turnover rate for staff RNs was 18.7 percentage points higher than in 2019.6  In an effort to stem the mass exodus, and as travel funds began to eat into budgets, many hospitals have set up “intracare” travel agencies within their institutions and hospital systems, as well as offering bonus pay programs. This is a temporary stop-gap measure but it still does not address the root of the problem. Using the results of the National Nursing Staffing Task Force listed above is a good starting point as well.

Another solution proposed to alleviate the workforce shortage is to increase the number of nursing programs or to have existing programs admit more students per class or semester. Petitions have been made to Congress and state government leaders to increase funding for such programs, faculty, or building space. Whereas such measures may increase the number of graduates, the reality is that a gap remains in the number of new graduates per year (approximately 200,000) and the number of available jobs. Because of the documented shortage of faculty and educational and clinical space, it will be difficult to meet the continued rising demands on the profession as healthcare expands beyond the acute care setting. We cannot depend on the yearly graduate numbers to backfill our way out of this shortage. Most graduates of nursing programs are of the millennial or Gen Z generations; these are individuals who seek more flexibility in their chosen careers and often seek out opportunities to try different things.

Perhaps further exploration of a redesign of nursing care delivery with a focus on staff well-being may be in order, one that improves and incorporates nursing competency and empowerment and results in better patient outcomes. This can be achieved without deskilling in areas where nursing knowledge, intuition, and critical thinking skills reign supreme. Some acute care facilities have returned to the concept of the “Team Nursing” model in which a group of RNs, LP/VNs and nursing assistants work together as a team to address all aspect of care for a group of patients. This model may help to address the nurse-patient ratio conflicts and embrace technology and innovation  to assist with care and promote well-being and satisfaction among the staff.  Because of the number of individuals involved in the care, and the high level of efficiency this model promotes, nurses may feel that they are better able to provide and meet more of the patient/family care needs.

Finally, it is vital that employers put in place a system that provides for a better work environment, scheduling, appropriate compensation packages, and prioritization of patient safety to attract and retain nurses today and in the future. It is also imperative that the type of nurses and their required competencies to meet patient care demands and requirements are taken into consideration, and that appropriate compensation and other efforts such as continuing education offerings are considered. Nurses deserve appropriate compensation in the form of salaries, wages, incentives, and payment models commensurate with their knowledge and education and with the safe, quality care that they provide to their patients and communities.

There is no one-size-fits-all solution to solving the current nursing crisis, the result of years of neglect, cover-up, and patchy fixes. If the nursing profession and the future of healthcare is to survive, all parties must come to the table with an open mind and a desire to address short- and long-term measures that will eventually lead to a resolution.

References

  1. Zhang, Xiaoming, Tai, Daniel, Pforisch, Hugh, and Lin, Vernon W.; United States Registered Nurse Workforce Report Card and Shortage Forecast: A Revisit; American Journal of Medical Quality 2018, Vol. 33(3) 229–236.
  2. Impact of the COVID-19 pandemic on the hospital and outpatient clinician workforce: challenges and policy responses (Issue Brief No. HP-2022-13). Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. May 2022.
  3. ANA President’s Letter to HHS Secretary Bocerra – accessed Nursingworld.org, August 23, 2022
  4. “Pulse of the Nation’s Nurses Survey series on Mental Health and Wellness” at the two-year mark of the pandemic. American Nurses Foundation. Accessed via Nursingworld.org
  5. “Pulse of the Nation’s Nurses Survey series on Mental Health and Wellness” June-July 2022. American Nurses Foundation. Accessed via Nursingworld.org
  6. Nursing Solutions Inc. (NSI); National Health Care Retention & RN Staffing Report Published by: NSI Nursing Solutions, Inc. nsinursingsolutions.com. March 2022

 

 

 

 

The Pandemic as a Stimulus for Innovation in Pediatric Cancer Care

Kerstin Lynam, The Pablove Foundation; Howard Smith, Milgard School of Business, University of Washington Tacoma; Sarah M. Wolff,  University of Nevada Las Vegas; Neill F. Piland, Idaho State University

Contact: smithhl@uw.edu 

Abstract

What is the message? The paper serves as a case study of COVID-19-related innovation in pediatric cancer care services. The pandemic promoted the Pablove Foundation to revamp and expand its Pablove Shutterbugs Photography Program, an artistic outlet for pediatric cancer patients.

What is the evidence? The authors draw on their experience with Pablove Foundation and other relevant organizations.

Timeline: Submitted: May 20, 2021; accepted after review: November 6, 2021.

Cite as: Kerstin Lynam, Howard Smith, Sarah M. Wolff, Neill F. Piland. 2022. The Pandemic as a Stimulus for Innovation in Pediatric Cancer Care. Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 3.

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The Pandemic Can Stimulate Innovation in Pediatric Cancer Care

The harsh reality of COVID- 19’s withering impact on society is well-documented and justifiably feared. Like many complex problems, diverse perspectives exist regarding how to approach the virus and minimize its adverse effects. This paper examines the perhaps unsettling perspective of the pandemic’s upside for healthcare delivery — opportunities for innovation. It is possible to reframe society’s struggle with the virus and explore COVID-19 as a stimulus for disruptive innovation.

COVID-19’s propensity to stimulate innovation surfaced in The Pablove Foundation (hereafter the Foundation ), an organization working closely with medical groups and healthcare providers treating pediatric cancer patients. Headquartered in Los Angeles, with six additional program sites across the United States, the Foundation grappled with COVID-19’s sweeping impact. The virus not only caused a seismic shift in the Foundation’s patient care and research programs, but it also underscored the importance of maintaining a closely knit, and scalable, clinical network. In such a network, information can be rapidly shared, innovations nurtured, new protocols vetted for pilot testing, continuous evaluation undertaken, and agility fostered in both the organization and among its constituents (i.e., managers, physicians, scientists, volunteers, staff and patients). Most importantly, the Foundation and its partners remained agile in an ambiguous care delivery environment focused on highly vulnerable children with cancer.

Passages of Innovation

In the United States, approximately 11,050 children under the age of 15 are estimated by the American Cancer Society to be diagnosed with cancer.1 This represents 11,050 families that will bear the burden of navigating cancer’s potential lasting physical, cognitive, and psychological effects on their children.2 Pediatric cancer is the second leading cause of death in children under the age of 15. In addition to these medical realities, families face the economic burden of an estimated expense of $833,000 per child, including medical costs and lost parental wages.3 These facts paint a vivid picture of the disheartening medical and economic toll that pediatric cancer continues to take on U.S. families.

The Foundation was established through a broad base of private donations and focuses on two objectives: 1) improving the quality of life for pediatric cancer patients through art, while physicians, allied clinicians, and partnering medical groups try to work their clinical magic, and 2) investing in high-pay-off pediatric cancer research focused on eradicating cancer and providing better treatment options. This paper focuses on the quality-of-life objective uniquely embraced by the Foundation.

Pablo’s Story

The Foundation was founded 2008 in reaction to the horrifying words, “Your child has cancer”, and the story of a young boy, his family, and his community. Pablo’s parents endured a 13-month roller coaster when their five-year-old son was diagnosed with bilateral Wilms Tumor, a rare childhood cancer. Despite an aggressive treatment regime and intensive collective efforts by the medical community, Pablo lost his battle with cancer only six days after his sixth birthday. In the following weeks, his parents continued to find photos Pablo had taken with their smart phones and other equipment.

Pablo’s parents reached an important realization after his passing. Photography had enabled Pablo to chronicle and document inspiring moments of his life, as well as to record his love of his family through an abundance of family portraits. Looking back over the exhausting experience, his parents began to wonder whether other children and parents could benefit from a similar artistic respite.

The Pablove Shutterbugs Photography Program

Inspired by the retrospective understanding of Pablo’s experience, the Pablove Shutterbugs Photography Program (PSPP) was created within the Foundation in 2011. This program offers children living with cancer an opportunity to “develop their creative voice through the art of photography” while interacting with a master teaching artist and a community of other children. In this respect, PSPP programming is uniquely designed to foster participatory arts. Children living with cancer have the opportunity to re-identify with a subject beyond the context of cancer. They seek respite, joy, self-esteem, courage, creativity, identity, voice, learning and achievement beyond their cancer. By living largely beyond the looming stigma as cancer victims, they can envision a new normality where they are not solely defined by the disease, where a kid can be a kid again.

Interest from other U.S. communities led to a second innovation: a national expansion to accommodate the PSPP. The Foundation expanded from Los Angeles, home of its administrative headquarters, to new program sites in Houston, New York, Seattle, New Orleans, Boston, and San Francisco. The PSPP’s impressive quality-of-life dimensions became accessible to other children, while at the same time galvanizing a national community.

COVID-19 as a Catalyst for Disruptive Innovation

With the many restrictions imposed by COVID-19, the Foundation experienced a decreased ability to assist physicians, their medical groups, and pediatric cancer patients. Nothing about the COVID-19 experience suggested that the Foundation would see anything other than detrimental impacts on its clinical care delivery programs and research. The virus loomed as a potential constraint that the Foundation could not overcome in its efforts to help children and their families cope with cancer during a pandemic.

Twenty-five years ago, Bower and Christensen (1995) captured the essence of innovations that entirely transform products and services via the term “disruptive innovation.”4 Originally, disruptive innovations were defined as rudimentary or simple improvements that replaced more sophisticated or technologically complex products/services. Disruptive innovations gained fame as low-cost, but efficacious, substitutes. Eventually, disruptive innovation broadly opened what previously had been relatively narrow markets. Innovative low-cost products/services substantially broadened market share and gradually became dominant leaders. Illustrative instances of disruptive innovation include retail medical clinics, personal computers, and digital photography, among many others.

Although COVID-19 is neither a product nor service, it is a distinct causal factor that overturns traditional thinking about organizing and delivering medical products and services. Revisions in medical practice protocols, care delivery guidelines, and ancillary/supporting services due to COVID-19 are, in some cases, game-changing. Innovation is a necessary adaptation that allows traditional care practice and delivery to continue. Most adaptations begin as a conscientious effort to fine-tune and adapt an existing way of doing things. However, clinical providers and their organizations quickly realize that altogether more creative protocols and practices are essential in responding to the disrupter – for example, the COVID-19 virus and its variants – and thus grander innovation is spurred.

Disruptive Innovation in the Pablove Program Fueled by COVID-19

The public health challenges of the COVID-19 pandemic prompted the Pablove Shutterbug Photography Program to grind to a halt. The provider team paused to consider the best path forward given that every child participating in the PSPP program was at very high risk for infection. This hiatus led to lengthy discussions by staff, advisory board members, volunteers, clinicians, and participants (and their families). They debated whether an overabundance of caution should be used in scheduling further programming. In some respects, this appeared to be little more than stalling, hopefully waiting for the COVID-19 outbreak to be resolved so that PSPP programming could resume.

Limitations of Waiting as a Patina of Prudent Deliberation

Over time, it became increasingly difficult to square inaction with the general ethos of the Foundation: respite, hope, joy, and development of the creative voice of kids living with cancer. These cornerstones were believed more necessary during the pandemic than ever before. How to assemble a new and functional PSPP, despite the constraints, became the challenge. Additionally, the successive iterations of “new normal” definitions for life during the COVID-19 pandemic signaled that it was truly time to thoroughly re-engineer PSPP or otherwise admit defeat.

Ultimately a brainstorming session by the leadership team made an exponential leap in envisioning an entirely new hybrid PSPP that would be: 1) on par with its ambitious mission; 2) consistent with the constraints of highly vulnerable kids; and 3) lofty enough to transform the PSPP into an altogether next-generation version. This open mindset encouraged the team to think out of the box while pursuing alignment within the Pablove community of patients, providers, and staff.

In effect, COVID-19 gave the Foundation license to be more aggressive in rebuilding what had been an elegant and safe way to provide services for highly vulnerable children. The traditional face-to-face model was sidelined as a virtual model arose as a replacement. This sort of fresh thinking has become prevalent practice across many venues. COVID-19 became the disruptive causal factor for change. A unique twist for the Foundation was realizing that a missing subset of inpatients – children with cancer who must remain within institutional care could now be easily integrated, as could children and teens living outside the reach of the established program sites.

Initial evidence indicates that a virtual format in 2020, 2021, and 2022 (normalized to account for partial years) resulted in average annual participation enrollment of 237 children as compared to average annual participant enrollment of 201 children in 2017, 2018, and 2019 with in-person delivery. In part, higher enrollment in 2020, 2021, and 2022 is explained by the greater safety of virtual delivery to immune-compromised children as well as the ability of all seven Pablove sites to offer virtual delivery. Higher enrollment driven by virtual class delivery is also expected to increase total overall enrollment when in-person classes are reinstituted.

The Foundation had been talking for years about a virtual program, but rather than moving from intention to action, there was no pressing need to upset a functioning system of programming. Further, at full capacity, “being busy” stood in the way of disruptive innovation. COVID-19 nudged the Foundation forward, and as leaders came on board, fresh perspectives emerged on how the virtual model could overcome the at times cumbersome structure of face-to-face instruction. Perhaps more importantly, a vision of enhanced scalability underscored this innovation. Going virtual would allow multiples of children to be reached and cared for while maintaining an equally intimate relationship with master teaching artists that characterized the traditional PSPP delivery.

Reflecting on the dynamic shift in perspective and implementation of a broadly redefined pedagogy, the Foundation compared its progress with that of other medical providers who were unable to rise above the stable, but now insufficient, models they had always followed. Many medical and healthcare providers and their organizations inadvertently avoid change — usually with the wish and a prayer that things would once again return to normal. As the nation struggled with the distribution of vaccines, increased access to testing, and the development of new therapeutics, the Foundation realized that unless it moved toward a new model, its very existence was precarious.

Looking Forward

Observed through the evolution of the Foundation, disruption led to an emergent need, which stimulated the integration of innovation. What started as a good idea has been transformed into an ever-evolving search to improve the quality of life for children with cancer, while sparking innovation in research that is imbued with high-payoff potential. In and of themselves, the Foundation’s passages of innovation are significant and noteworthy for changing the landscape for medicine and healthcare organizations delivering pediatric cancer care. COVID-19 ushered in a new constraint that validated the search for ingenuity, while affirming an ever-deepening recognition that it is always possible to achieve new levels of innovation.

There are very robust implications for the healthcare community and patients due to the Foundation’s adaptations to COVID-19. As novel infectious diseases emerge or historical ones reemerge, there is great risk that some clinicians, healthcare leaders, and their organizations might reduce their service scope and lose market share, or even possibly eliminate those services. Consider the vast shocks that have happened to the airline industry, long-term care, education, entertainment, hospitality, and so many other economic sectors.

Health and medical leaders are encouraged to remain vigilant about possibilities for disruptive innovations in how their organizations address likely future debilitating viral events or catastrophic forces that may arise in the future. This can be very difficult when teams of providers and ancillary staff are exhausted from day-to-day battles with an unrelenting enemy. But those expectations about cultivating a climate of optimism and nurturing opportunities to instill ingenious responses are all part-and-parcel of courageous and inspired leadership. COVID-19 will not be the last threatening challenge that we face. Thus, now is a perfect time to create a milieu of imaginative thinking and bold execution of clever strategies designed to move forward. This is an opportunity to infuse all care delivery organizations with a unique culture that is capable and confident in addressing the toughest problems.

 

References

  1. American Cancer Society, 2020: https://www.cancer.org/cancer/cancer-in-children/key-statistics.html.
  2. National Cancer Institute, 2020: https://cac2.org/interest-groups/awareness/childhood-cancer-fact-library.
  3. United States Environmental Protection Agency 2017: https://www.epa.gov/sites/production/files/2017-10/documents/niehs_epa_childrens_centers_impact_report_2017_0.pdf.
  4. Joseph L. Bower & Clayton M. Christensen. “Disruptive Technologies: Catching the Wave.” Harvard Business Review, January/February 1995.

Shopping for Healthcare: Can We Be Good Consumers?

Barak Richman, Katherine T. Bartlett Professor of Law and Professor of Business Administration, Duke University

Contact: richman@law.duke.edu

Abstract

What is the message: Efforts to make healthcare markets work efficiently are laudable but often suffer from ideological blinders, a failure to assess the nuances of empirical research, and an inadequate approach to the morality of the marketplace. Critics of market approaches often exhibit the same shortcomings.

What is the evidence: This article assesses the empirical literature on the successes of market transparency and healthcare consumerism, offers some tempered enthusiasm for certain market-based efforts, and identifies the underemphasized value of agency as a guidepost for healthcare reform.

Timeline: April 21, 2022; accepted after review: April 22, 2022.

Cite as: Barak Richman. 2022. Shopping for Healthcare: Can We Be Good Consumers? Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 2.

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This article is adapted from the 2019 Nordenberg Lecture at the University of Pittsburgh. I thank Sydney Engle and Jennifer Behrens for outstanding research assistance, and I thank Alan Meisel, Mark Nordenberg, the University of Pittsburgh Health Law Faculty for their invitation and hospitality during that visit.

I additionally want to recognize Mark Nordenberg and the late Thomas Detre for creating the Nordenberg Lecture. Their collaboration offers a model that this article aspires to follow: providing good healthcare requires a consultation with many values, and improving the health sector requires contributions from multiple disciplines and perspectives. The collaboration between Thomas Detre and Mark Nordenberg offers a model for the rest of the academic community. Thomas Detre was the chancellor of the health system. Mark Nordenberg was the chancellor of the university. Dr. Detre was a refugee from Budapest and a survivor of the Holocaust from Budapest. Professor Nordenberg grew up in the upper Midwest and spent his entire career in the heartland of America. They came to their jobs and to their careers with vastly different outlooks and life histories, but they collaborated to bring the resources of the university together to improve healthcare for their communities and for America. That is a model for moving forward.

Introduction: A Picture Is Worth 1,000 Words

On June 27, 2019, President Trump issued Executive Order #13877, “Improving Price and Quality Transparency in American Healthcare To Put Patients First,” to require disclosure in one of the least transparent and most important parts of our healthcare system: what insurers and payers are paying for healthcare services.[1]  Seema Verma, then the administrator of Centers for Medicare and Medicaid Services, said that the Executive Order would be a first step toward consumerism.[2] If everyone knows what the prices are, then everyone can act as intelligent and effective consumers.

To show its support, the U.S. Senate Committee on Health, Education, Labor, and Pensions (HELP) distributed a T-shirt with the truism: “American Health Care  Danish Cement.”[3]

The T-shirt, not the truism, is worth the proverbial thousand words. The committee, like the Executive Order, was operating under the common presumption that markets can function properly only when consumers can compare prices, and thus healthcare markets can work when prices for physician and hospital services are disclosed. But then some Danish researchers threw a wrench into that neat theory.  Government-Assisted Oligopoly Coordination? A Concrete Case revealed after the Danish government publicly published prices for concrete, concrete prices increased by 15% to 20%.[4]  Though the article cannot determine whether prices went up because of the price disclosures, its findings certainly challenge the syllogism that more information leads to better competition and lower prices.

Source: Margot Sanger Katz (@sangerkatz). August 20, 2019. https://twitter.com/sangerkatz/status/1163830456963555335

The T-shirt offers two lessons. First, the Senate HELP Committee’s shirt reveals more than just an underlying debate about the effect of price transparency on healthcare prices. It shows that this debate veers toward the ideological and away from the empirical. By posting the truism that concrete is not the same as healthcare, and that Denmark is not the same as the United States, the committee is trying to marginalize, rather than learn from, an important and relevant article.

And second, more fundamentally, the T-shirt reveals that we lack the most basic understandings of how American healthcare markets operate, including the degree to which market information is beneficial.  There is a comforting logic to thinking that healthcare markets conform to the theories taught in an economics undergraduate classroom, where markets operate smoothly and rationally. But it is equally comforting to think that American healthcare markets are exceptional and operate in ways that are antithetical to the laws of economics.  Policymakers tend to occupy these polar extremes and participate in a dichotomous debate over whether healthcare markets work (or not). Both of these extreme positions, with their ideological parsimony, fit neatly onto T-shirts (did the HELP Committee say how the two markets are different? There wasn’t enough room on the shirt to say. But would they have bothered?). But those of us who think the answer is somewhere in the middle – that sometimes, the disclosure of information helps healthcare consumers shop intelligently, and sometimes it does not – need more than a T-shirt to explain.[5] This article tries to do that.

The Problem with Non-Disclosure

The prices that payers and providers negotiate have long been claimed to be trade secrets, and industry leaders have fought aggressively to keep them secret. It is hard to suggest that markets can work with this degree of opacity, just as it is hard to defend a regime that defends keeping prices unknown to the public. This is probably true in every market. In Flash Boys, Michael Lewis tells a story about a bunch of renegades on Wall Street who try to bring transparency to electronic high-frequency trading, a particularly impenetrable sector that Lewis argues has been exploiting consumers. Brad Katsuyama, one of the leaders of this upstart, said, “The fact that it is such an opaque industry should be alarming. The fact that the people who make the most money want the least clarity possible—that should be alarming, too.”

Katsuyama’s comment maps neatly onto how industry players reacted to the Administration’s proposal for greater transparency.  Following the Trump Administration’s Executive Order, the American Hospital Association, the Federal of American Hospitals, the Rural Hospital Coalition, Association of American Medical Colleges, and the Association of Health Insurance Plans voiced strong opposition to the Administration’s transparency effort.[6] They succeeded in part when the Trump Administration shelved its transparency proposal.[7] However, the administration promised to revisit the rule, and it did.[8] During the last few months of the administration, the U.S. Department of Labor and the U.S. Department of Health and Human Services finalized a rule requiring plans and insurers to disclose cost-sharing estimates to consumers ondemand.[9]

But even if opacity benefits the insiders that enjoy information advantages, it does not mean that disclosure of market information always benefits consumers. The question of whether more price transparency leads to better consumer behavior, and thus to lower prices and more competitive markets, is the question provoked by the Danish cement study, and it the question that might have answers in a careful assessment of the effects of information disclosure and dissemination on healthcare behaviors.

So, would transparency lead to more effective markets and more intelligent consumer behavior? Several states have already instituted price disclosure rules, forcing hospitals and payers to disclose prices. A good place to start is to evaluate the information that those laws disclosed and their effect on prices, consumers, and markets.

Pricing Transparency and Its Effect on Consumer Behavior

The Ugly: Posting Prices and Little More

California was among the first states to force pricing transparency[10] when it developed the Office of Statewide Health Planning and Development (OSHPOD).[11] Among the OSHPOD’s first initiatives was to construct a website that would collect and disclose the prices that hospitals charge.

Sample price data from OSHPOD

The result, however, can only be described as a Kafkaesque. Price data is located behind tabs that are ambiguously named “Data & Reports” and “Topics.” The disclosed price data includes ten years of complex hospital chargemasters, which are lists of off-the-street prices for various hospital services. The consumer must download a large file to view each chargemaster, and they then are confronted with procedure codes that would even confound the physicians that perform those services. If this is what transparency looks like, then it is worth rethinking the entire strategy. Gathering the data might be useful for researchers, but it’s not useful for patients, and there is reason to conclude that industry is revealing itself to be hostile to the entire enterprise.

Massachusetts offers another venture into state-led transparency. To its credit, the state has invested significant legislative effort and political resources into gathering health price data, and the recently created Center for Health Information and Analysis (CHIA) and home to what is probably the most comprehensive All-Payer Claims Database in the nation.[12] But, even though CHIA offers more helpful information than the OSPHOD website, it, too, has produced disappointing results. In 2019, the Massachusetts Attorney General issued a comprehensive assessment of the state’s transparency efforts that concluded that those efforts have not controlled healthcare spending.[13] While remarking that “price transparency for consumers is essential,” the Attorney General called for “real solutions to control escalating costs.”[14] Evidently, disclosure alone did not constrain consistent price inflation, and there seems to be a disconnect between the transparency that states impose and the market effects they want to achieve.

These transparency failures, at the very least, should give caution to transparency enthusiasts. To be sure, they do not suggest that all transparency initiatives are ill-conceived – indeed, the theoretical underpinnings of transparency are compelling; markets simply cannot work if prices are entirely hidden. But a good theoretical argument should not fuel policy that is uninformed by empirical realities.

For the late Uwe Reinhardt, who many call his generation’s top health economist, and who I call perhaps the top economist-moralist, the failure of transparency initiatives to help patients caused enormous cause for alarm. He worried, consistent with discussions surrounding the HELP T-shirt ploy, that the transparency debate had devolved into an ideological fight, and that there was a growing danger that transparency proponents will be unswayed by facts.  Reinhardt warned:

Consumer-directed health care so far has led the newly minted consumers of US health care (formerly patients) blindfolded into the bewildering US health care marketplace, without accurate information on the prices likely to be charged by competing organizations or individuals that provide healthcare or on the quality of these services. Consequently, the much ballyhooed consumer-directed health care strategy so far has been more a cruel hoax than a smart and ethically defensible health policy.[15]

It is worth repeating that Professor Reinhardt is an economist, and as such he believed that markets can work—that is, consumers acting on their own best interests can, as a collective, bring prices down if meaningful price information were genuinely available and intuitively accessible. But markets cannot work otherwise. And Professor Reinhardt warned that it is simply cruel to expose patients to a dysfunctional market.  Perhaps this reflects the notion that market failures in healthcare are more devastating than market failures in other markets. Patients are vulnerable and easily exploited, and it is a moral failure to expect patients to survive market failures.

Some Modest Progress: “Shoppable” Services

Some states are finding more success. For example, early evidence shows that New Hampshire’s effort to make prices more transparent through its NH Healthcost website[16] have lowered some prices.[17] This is at least better than the Danish cement story.

In particular, one study revealed that after NH Healthcost disclosed prices for MRIs, prices decreased by 1% to 2% and overall spending for medical imaging (MRIs plus CT scans and x-rays) went down 3%.[18] This suggests that consumers needing MRIs shopped between available prices and gravitated toward lower prices, and that providers responded to consumer shopping by lowering prices. However, the study did not find any price decreases for other services.[19] To quote one of the authors of the study, “We don’t have evidence that this is a magic bullet . . . It seems to lead to some modest savings. The effects aren’t huge.”[20]

Nonetheless, the results indicate that something is moving in the right direction, that some services are indeed “shoppable.” The New Hampshire experience suggests that price transparency can make certain markets more competitive and can generate benefits for consumers, but that only some markets respond to transparency. Perhaps transparency efforts should focus specifically on services that people can shop for, such as MRIs, that are largely commodities and where alternative providers are easy to locate. Unfortunately, this is unlikely to meaningfully bend the cost curve, as relatively little will be gained if medical imaging ($94.7 billion in 2020, 2% of healthcare US expenditures[21]) are offered in competitive markets but hospital services ($1.27 trillion in 2020, 31% of US healthcare expenditures[22]) are not.

Perhaps consumer responsiveness to “shoppable” services justifies using copayments to encourage economizing behavior. Studies dating back to the Rand Health Insurance Experiment establish that consumers are sensitive to copayments, although this did not necessarily make them good shoppers, as higher copayments deterred individuals from seeking both appropriate and inappropriate care.[23]  If insurance plans are constructed wisely, then even if patients are unlikely to learn of and respond to hospital prices, perhaps consumers will economize based on copayments and thereby seek appropriate care. One particular expression of this strategy—one that has had success reducing healthcare costs and stimulating price competition—is reference pricing. Reference pricing refers to health insurance products that offer standard coverage if a patient chooses cost-effective providers but require considerable cost sharing if more expensive alternatives are selected. Its advocates have described it as a change to the “choice architecture” and have reported that its short-term impact has been to shift patient volumes from hospital-based to freestanding surgical, diagnostic, imaging, and laboratory facilities.[24]

Reference pricing—and any strategy that employs cost-sharing to encourage economizing behavior—rests on the heavy assumption that insurance products will be designed with economizing behavior in mind. It is a response to the less refined argument for price disclosure since it pays primary attention to the prices (i.e. copayments) that consumers confront and can readily see. To work on a population scale, these strategies rely on insurers to be efficient intermediaries. Perhaps insurers would be better intermediaries if hospitals were required to disclose prices, though it is more likely that insurers prefer prices to remain hidden.  Reference pricing therefore is more likely to emerge as an alternative if consumers—not insurers—demand it.

Another justification for pursuing price transparency is that it will encourage the development of shopping tools that will make more services “shoppable.” In other words, even if consumers cannot obtain and understand hospital prices, they can understand information gathered and presented by search devices. Some websites, such as New Choice Health[25] and Castlight, gather price data from the OSHPOD and similar, normally impenetrable sites and then give consumers and insurance enrollees their expected copay for certain services at different locations. Thus, they synthesize price data related to alternative points of service and present them in an intuitive format. This converts the abstruseness of the chargemaster into a format that is more akin to how Google Maps displays prices at gas stations.

Studies that have examined the effects of these price transparency tools suggest that they also are not a silver bullet. A Health Affairs study from 2017 examined consumer use of a transparency tool offered by Castlight and its market effects.[26] It found that some sizable portion of participants used the price tool, but the vast majority of those who used it did so only once, and only a small population of participants used the price tool regularly.[27] Accordingly, the overall savings from Castlight were minimal.[28] This is consistent with other studies, which have found that even when shopping tools or other consumer-oriented intermediaries enable patients to shop for services, most prefer to follow their doctors’ recommendations.[29] It seems that even the combination of shopping tools and price transparency—circumstances where shopping is as simple as possible for consumers—does little to induce shopping behavior.

 

Effects of a Transparency Tool (Desai, et al., 2017)

In a similar 2014 study, researchers evaluated the effects of a mobile app price tool offered by Castlight Health.[30] Like the 2017 study of the Castlight website, this study looked at whether individuals used the app and whether obtaining information about higher quality and lower cost services affected consumer behavior. The reported results, which examined effects on laboratory tests, imaging services, and clinical office visits, were modest, but they nonetheless offer some promise for shopping that is consistent with other research: first, the Castlight Health price tool reduced prices for laboratory tests down about 10% to 15%, or by about $3; second, the tool reduced on prices for advanced imaging services, such as MRIs also by about 10% to 15%, or by about $75 to $100; and third, the tool had a minimal effect on prices for the clinical office visits.[31]  In sum, consistent with findings from New Hampshire’s Healthcost initiative, the price tool apparently stimulates some price competition in certain markets, but the effect is small and only was evident in markets for laboratory or imaging services.

Effects of Price Transparency on Diagnostics vs Clinical Visits (Whaley, et al., 2014)

More recent research, however, suggests that price transparency tools might be counterproductive; instead of inducing consumers to shop for lower cost services, they might induce providers to increases prices.  A study published in 2021 examining the impact of a price transparency tool sponsored by New York State, found that prices increased for certain imaging services, which are always insured and rarely elective, while decreased for psychology and chiropractic services, which are less often insured and more elective.[32] Moreover, the pricing tool’s upward influence on prices outweighed the tool’s comparatively weaker effect on consumer price shopping.[33] Thus, it seems that the Danish cement experience does translate, at least in some circumstances, to American health markets. The authors of the 2021 study, like the authors of the Danish cement research, could not determine why transparency led to higher prices, but they speculated about two possible mechanisms: (1) transparency could enable providers to tacitly collude, or (2) transparency informs low-price providers that their prices are below market averages, prompting them to increase their rates to match their competitors’.[34]

The collection of studies offers mostly uninspiring results. Some healthcare services, like lab tests and imaging services, are shoppable because they are commoditized, rudimentary, readily available at multiple locations. But even for these services, transparency reduces prices only modestly, and they don’t represent the existential cost problems in the United States.  For services that occupy a greater portion of our national spend, such as clinician and hospital services,[35] transparency efforts appear to have little impact, even when navigation tools are available.[36]

Even though price transparency efforts have yielded unsatisfying results, it cannot mean that lack of transparency is better. Instead, as professor Dr. Sherry Glied recognized, price transparency “is best understood as an intermediate stage in the policy process.”[37]  The challenge begins, not ends, with arming consumers with information. There is much remaining work, after transparency, in structuring meaningful choice within workable markets.

Another Kind of Transparency: Shaming

Maybe there are other ways to use transparency to force markets to respond. One example is a story that started in 2000, when the Institute of Medicine released a publication called To Err is Human, reporting that “as many as 98,000 people die in any given year from [conditions contracted because of] medical errors that occur in hospitals.”[38]  Just to be clear, these deaths are consequences of mistakes or hospital-acquired conditions (HACs). The Institute of Medicine concluded that “[i]t would be irresponsible to expect anything less than a 50 percent reduction in errors over five years.”[39]

The publication was widely influential in academic circles but unfortunately triggered few improvements in care delivery, and thus hospital-acquired conditions continued at around the same rate for the next decade.  But improvements were triggered in 2011 when the Affordable Care Act (ACA) implemented its Partnership for Patients.[40] The program has three main elements.  First, hospitals could be financially penalized for HACs.[41] The government could decline to reimburse, partially reimburse, or fine a hospital whose patient gets an HAC and requires treatment.[42] Second, hospitals were given technical assistance to improve their quality management and quality assurance.[43]  And third, hospital-level HACs were publicly reported, so hospital administrators could see how they compared to their peers.[44] It became known how many preventable deaths fell to each hospital.

Source: www.innovation.cms.gov

 

Reductions in HACs after Partnership for Patients program (Source: AHRQ)

It seems that the Partnership for Patients program has produced results, and it illustrates another transparency strategy that could work. One might call it shaming. Rather than telling consumers where they could save a dollar, these data instead show where people died. The success of this experience, in conjunction with the disappointing results from other transparency experiments, might suggest that patients are more responsive to death risks than to opportunities to save money; or it might suggest that physicians are more motivated by threats to their reputation than patients are to opportunities to economize, and thus transparency efforts should target physician esteem rather than consumer budgets. Regardless, if policymakers are ever to harness the power of markets, there needs to be good science on how information affects behavior. Perhaps these assorted results can help.

Other Opportunities to Shop: Consider Selecting Health Insurance

There are other ways that consumers might be able to shop. For example, most Americans get their health insurance through their employers, and most of those employees select among different health insurance offerings every year. In fact, this annual selection of health insurance is usually a robust exercise in choice: different insurance products are presented in a framework that is both intuitive and substantive for employees. The plans’ differences in price and coverage are explained carefully, and employees generally have a meaningful opportunity to act as an informed consumer.

A typical example is illustrated in the graph below, in which an employer’s publication during open enrollment presents four alternative insurance plans.[45] The employee handbook spells out the details of each plan, including a basic set of prices and the monthly employee contributions.[46] Indeed, one could imagine similar tables for other services—MRIs, x-rays, and other screening services—to enable meaningful patient shopping and to facilitate greater price competition in other markets.

 

Source: Duke Human Resources

However, the accompanying table that includes the monthly premiums for each plan also illustrates how insurance plan selection actually prevents genuine price competition from taking place.[47] Note that from the employee’s perspective, the annual premiums for the most expensive and least expensive plans are less than $1,800 apart: annual premiums for the least expensive plan is $432 ($36×12) while the most expensive is $2,195 ($183×12). But focusing only on what the employee faces hides the full cost of the insurance. The true cost difference between the two plans, based on the COBRA premiums, is $7,576.56 ($1,059.78×12 – $428.40×12).

The discrepancy between perceived price and actual price emerges because employers do not advertise that the employee’s direct contribution is only part—usually around one quarter—of the total cost of health insurance.[48]  Critically, the employee also pays for the employer’s contribution to health insurance, albeit indirectly through reduced take-home wages. So, in the above example, the employee is likely to think she is paying only $2,195 for the most generous insurance plan, whereas she really is paying the full $12,717.36.[49]

If she knew she were paying for the full amount, would she be more vocal in asking for less expensive options, and would she shop more aggressively? If she knew that she were spending nearly $13,000, would she prefer to spend those dollars in other ways?

Professor Regina Herzlinger, who is often called the godmother of consumer-driven healthcare,[50] has frequently argued for using the annual purchase of health insurance to expose individuals to a broad menu of options. In work with coauthors,[51] she suggests giving employees control over their full contributions to health insurance; after they purchase a qualifying insurance plan, they may use any remaining funds for any other purpose, including converting the remainder into taxable take-home pay.[52]  Not only might this allow individual employees to spend their limited resources in ways that best address their many financial needs, but it also exposes the market directly to price-sensitive employees. The market will be rewarded if it responds to consumer demands for affordable options, and consumer shopping opportunities will generate benefits throughout the economy.

This idea made its way through the Trump administration. In 2019, the administration finalized a rule permitting certain employees to use employer-funded health reimbursement accounts (HRAs) to purchase qualifying insurance on the individual market.[53] HRAs are designed both to allow employees to economize on health insurance dollars and to use any remaining dollars in other ways, and it has real promise for injecting useful competition into healthcare markets.

This approach also might offer broader lessons on how consumers can fruitfully shop.  Although it is difficult to shop for individual knee replacements, it is not so difficult to shop for insurance plans. Consumers do that annually and are familiar with the available options. It would be wise to structure insurance options within a useful information context: perhaps requiring disclosure of the plans’ actuarial value and an insured’s total expected costs with each; or requiring plan comparisons (beyond merely premiums) or providing tools for consumers to evaluate the merits of each plan at the time they purchase insurance. Additional research is needed to confirm whether consumers make good choices for themselves and for the market when they shop for insurance, and what choice architectures encourage good decisions. But because consumers routinely purchase insurance within these frameworks, and because this is a purchase that is not made under duress or after an illness has already emerged, there is little reason to think that shopping will lead to the harms feared by Professor Reinhardt. Instead, it would be wise to inform consumers and to harness these market energies.

Preliminary Conclusions: Creating Opportunities to Shop

Can we be good consumers? The collective evidence offers a nuanced answer: sometimes.

One lesson is that consumers cannot do it on their own, and that a set of rigorous market settings is necessary. Markets only work if they offer adequate choices. They only work if consumers have useful information. They only work if they rest atop an intuitive framework and a familiar setting within which those choices and that information are presented. And, even with all these prerequisites, they still might require navigators or other intermediaries to walk consumers through the process and make comparisons easy, and they might require baking in some forgiveness as consumers inevitably make mistakes.

So, is healthcare like Danish cement? No. But it is not so dissimilar from it either. Markets work, but they cannot work on their own. We should not embrace laissez-faire economics as an ideological or theoretical matter, but if we help consumers walk through the complexities, then maybe for some services we can bring some competitive energies and some real social value.

The Opposite of Shopping

What is the opposite of shopping? This might be an even more important question about healthcare.

The opposite of shopping is best epitomized by what we now call “surprise bills.”  Surprise bills occur when a patient receives healthcare and later is issued a bill that exceeds the prevailing market price. For example, a patient goes to an emergency room complaining of a headache, the ER staff then administers an MRI, and later the hospital sends the patient a bill for $5,000. The ER staff did not inform the patient of the price before the diagnostic was administered, and the charged price – $5,000 – is far more than any insurer pays.

Surprise billing has become a common strategy by providers, mostly hospitals, to raise revenue and to force patients and insurers into their network. It is often justified as a mechanism to compensate for low reimbursements or to flex market power, but the unavoidable tragedy is that it targets and exploits the vulnerable. As Professor Reinhardt noted, consumers are vulnerable when they cannot know prices in advance. Perhaps more important, consumers are uniquely vulnerable when they enter a healthcare setting as patients.

Why are surprise bills the opposite of shopping? Shopping implies agency:  autonomy, deliberateness, and an awareness of the surrounding market.

The lack of shopping is not the lack of agency since deciding not to shop can be consistent with having agency – one can rationally decide to forego the expenses and benefits of searching for better prices. But surprise bills deny agency precisely because they deny patients the opportunity to learn from and respond to the environment around them. They exploit a lack of information and deny any opportunity to act autonomously.

With the passage of the No Surprises Act, policymakers have collectively condemned surprise bills and have taken measures to bring them to an end. But the moral criminality – and I use that language advisedly – of the practice still has not been adequately articulated. In a series of articles with Kevin Schulman and Mark Hall, I have tried to describe why agency is central to delivering healthcare and why surprise bills are a foundational transgression. In 2012, we introduced the term “informed financial consent” to convey the importance of making sure patients were aware of the financial burdens they were incurring in seeking care.[54]  We observed the unfortunate irony that the healthcare sector places a premium on informed consent but very little on informed financial consent:[55] providers assiduously seek a patient’s assent before the performing a procedure, but if the patient asks how much the procedure costs, providers usually say that they do not know, and that their ethical obligation is only to inform patients of the health risks but not the financial consequences. In later articles, we expressed the hope that the No Surprises Act would not just protect patients from exploitive billing practices but also advanced their autonomy, dignity, and agency,[56] and that the canon of medical ethics would recognize both financial informed consent and the realities of financial toxicity as central to the practice of medicine.

Agency and Health

The centrality of agency in healthcare extends beyond the sector’s financial practices—not just whether shopping is possible, prices are hidden, and patients can act as informed consumers—but to most every aspect of healthcare. The notion of agency is relevant every time patients interact with the health sector.

First, scholars have identified the importance of agency in health. Literal control over aspects of one’s environment improves health outcomes, such as asserting control over daily routines and physical spaces, and assorted studies confirm that giving patients—especially the elderly—even rudimentary elements of control can improve health outcomes.[57] Researchers have also found that patients exhibit worse health outcomes when they lacked privacy, heard outside noises, and could not control the television in their hospital rooms.[58]

More broadly, it is known that additional years of education improve health outcomes, even when controlling for income, job status, insurance status.[59] Many think that one reason is that more education leads to greater agency: greater education offers status and autonomy and therefore more control our lives. Status and autonomy also contribute more specifically to beneficial engagements with healthcare providers.

Could shopping—the mere opportunity to exercise agency and shop—improve health outcomes on its own? And could the opposite of shopping worsen health outcomes? We must take seriously the notion of shopping, not just through the economic lens of consumers and prices, but also through the potential enhancement of agency and autonomy in the health care sector. This notion could offer an enormously powerful tool, but the broader concept remains largely underappreciated and unexplored.

Conclusion:  A(nother) Picture is Worth 1,000 Words

The moral imperative and the substantial health benefits of expanding agency in medicine does not mean that doing so will be easy. There are certain situations where patients are reasonably told to forego agency. There are also many doctor-patient interactions where it is unreasonably difficult for patients to assert agency.

One such example occurred to me recently, and this offers another opportunity to share a picture: this is me, in May 2019, shortly after I was just discharged from the hospital after undergoing heart surgery. I am not the first person to have undergone a medical experience to then write about it, nor am I the first health policy scholar to have had a medical encounter affect policy ideas or a research agenda. But my experience was illustrative in the challenges of achieving meaningful agency in healthcare.

Despite being an expert in contracts law and health policy, I was struck by the enormity of my informational disadvantage when I was admitted for my surgery. The admissions contract I signed read, “You are here for mitral valve surgery, replacement/repair.” Because the hospital staff could not tell me in advance whether a repair was possible or a replacement was necessary, it could not be more specific. The second line said, “I agree to everything that is deemed to be medically necessary or appropriate to fulfill this service.” It said nothing about any details that involve significant surgical decisions, and I was not consulted about any of them. The contract was nearly as vague as a blank cheque.  When I rent a car for two days at AVIS, I have a four-page contract. This was one page for open heart surgery and five days in the hospital.

I do not know if there is a better contract out there or if more contract specificity is advisable. I do not know if the doctor should have asked me in advance if I would prefer the heart bypass machine go through the chest or the groin, or if I would have preferred a reinforcement ring to go entirely or just mostly around the valve. I suppose the surgeon could have intelligently presented all these options to me, allowed me to research those options, and then let me participate in shared decision-making. But that is not the way our health system works, and I am not entirely sure that it should.

Can we shop for MRI services? Yes, though it will require a market that is committed to flexibility, transparency, and consumer engagement. Is that going to bring healthcare costs down? If at all, probably minimally. Can we shop for insurance?  Yes, and perhaps there can be genuine savings, though again it would require a market structure that provides intuitive and meaningful consumer choice.  Can we shop for heart surgeries, or other complex inpatient care?  Very unlikely, even though inpatient care constitutes the largest spend.

But can we more meaningfully incorporate agency into healthcare?  It’ll be hard, but it’s essential to engage in these larger and often intractable questions.  Enabling consumers to shop is not just a means to reduce healthcare costs, it also allows patients to have agency when they engage in the health sector. That brings material benefits and is a moral imperative.  And it highlights that, as with so much else in healthcare, science and markets are inseparable from deep ethical challenges.

 

References

[1] Exec. Order No. 13877, 84 Fed. Reg. 30,849 (June 27, 2019).

[2]  Jennifer Bresnick, Verma: Price Transparency Rule a “First Step” for Consumerism, Health Payer Intelligence (Jan. 11, 2019), https://healthpayerintelligence.com/news/verma-price-transparency-rule-a-first-step-for-consumerism; see also Seema Verma, Administrator, Centers for Medicare & Medicaid Services, Remarks at the America’s Health Insurance Plan’s 2019 National Conference on Medicare (Sept. 24, 2019), https://www.cms.gov/newsroom/press-releases/remarks-administrator-seema-verma-americas-health-insurance-plans-ahip-2019-national-conference (“We believe in a healthcare system in which patients – not the government – are empowered with choice and control, price and quality transparency . . . . When consumers have choices and are empowered with information, businesses deliver value to attract customers to succeed.”)

[3] See, e.g., Bipartisan House and Senate Committee Leaders Announce Agreement on Legislation to Lower Health Care Costs, U.S. Senate Committee on Health, Education, Labor & Pensions (Dec. 8, 2019), https://www.help.senate.gov/chair/newsroom/press/bipartisan-house-and-senate-committee-leaders-announce-agreement-on-legislation-to-lower-health-care-costs- (noting bipartisan agreement on legislation to require greater price transparency).

[4] Svend Albæk, Peter Møllgaard, & Per B. Overgaard, Government-Assisted Oligopoly Coordination? A Concrete Case, 45 J. Indus. Econ. 429–433 (1997).

[5] When The New York Times addressed the issue, it said, “It makes intuitive sense—publish prices negotiated within the health care industry, and consumers will benefit.. . . [G]ive patients more information about what health care will cost before they get it.” It then quoted one of the authors of the Danish concrete study who succinctly said, “I’m sure there are some similarities between pricing of various health care services and ready-made concrete in Denmark in the early 1990s . . . but I’m also sure there might be huge differences.” Margot Sanger-Katz, Why Transparency on Medical Prices Could Actually Make Them Go Higher, N.Y. Times: Upshot (June 24, 2019), https://www.nytimes.com/2019/06/24/upshot/transparency-medical-prices-could-backfire.html

[6] Kelly Gooch, Hospitals Slam CMS Proposal to Disclose Negotiated Rates, Becker’s Hospital Review: Becker’s Hospital CFO Report (Sept. 30, 2019), https://www.beckershospitalreview.com/finance/hospitals-slam-cms-proposal-to-disclose-negotiated-rates.html.

[7] Bob Herman, Trump Administration Punts Hospital Price Transparency Rule, AXIOS (Nov. 1, 2019), https://www.axios.com/trump-punts-hospital-price-transparency-rule-c90b7ab5-a865-470f-b954-42927022eb2e.html.

[8] Id.

[9] Katie Keith, Verma: Trump Administration Finalizes Transparency Rule for Health Insurers, Health Affairs (Nov. 1, 2020), https://www.healthaffairs.org/do/10.1377/hblog20201101.662872/full/.

[10] See April Dembosky, California Governor Signs Law To Make Drug Pricing More Transparent, NPR: Shots Health News From NPR (Oct. 10, 2017), https://www.npr.org/sections/health-shots/2017/10/10/556896668/california-governor-signs-law-to-make-drug-pricing-more-transparent (noting that in October 2017, “California Gov. Jerry Brown . . . sign[ed] the most comprehensive drug price transparency bill in the nation that will force drug makers to publicly justify big price hikes”).

[11] Office of Statewide Health Planning and Development, https://oshpd.ca.gov/ (last visited July 09, 2021).

[12] Center for Health Information and Analysis, https://www.chiamass.gov/ (last visited July 10, 2021).

[13] https://www.mass.gov/doc/examination-of-health-care-cost-trends-and-cost-drivers-2019 (“The new report finds online pricing tools that allow patients to compare the cost of certain health care services may provide patients with useful information, but they fail to control health care spending. According to the report, these websites are used by only a tiny fraction of residents and are not influencing consumer decision-making in a meaningful way.”)

[14] Press Release, Office of Attorney General Maura Healey, AG Healey: Online Pricing Tools & Alt. Payment Arrangements are Not Enough to Contain Health Care Costs (Oct. 17, 2019), https://www.mass.gov/news/ag-healey-online-pricing-tools-and-alternative-payment-arrangements-are-not-enough-to-contain

[15] Uwe E. Reinhardt, Health Care Price Transparency and Economic Theory, 312 JAMA 1642–43 (2014).

[16] New Hampshire Insurance Department, Compare Health Costs & Quality of Care, NH HealthCost, https://nhhealthcost.nh.gov/ (last visited July 9, 2021).

[17] See Zach Y. Brown, Equilibrium Effects of Health Care Price Information, 101 Rev. Econ. & Stats. 699 (2019); Melanie Evans, One State’s Effort to Publicize Hospital Prices Brings Mixed Results, Wall St. J. (June 26, 2019), https://www.wsj.com/articles/one-states-effort-to-publicize-hospital-prices-brings-mixed-results-11561555562 (noting that the price data “has helped lower prices somewhat” but that “few people have used New Hampshire’s site, which researchers say has reduced its impact on prices and costs”).

[18] Id.

[19] Id.

[20] Id.

[21] https://www.grandviewresearch.com/industry-analysis/us-imaging-services-market

[22] National Health Expenditure Accounts, Centers for Medicare and Medicaid Services, https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical

[23] Robert Brook, et al., The Effect of Coinsurance on the Health of Adults Results from the RAND Health Insurance Experiment, Santa Monica, CA: RAND Corporation, 1984. https://www.rand.org/pubs/reports/R3055.html

[24] https://www.healthaffairs.org/doi/pdf/10.1377/hlthaff.2016.1256

[25] New Choice Health, https://www.newchoicehealth.com/ (last visited July 9, 2021).

[26] Sunita Desai et al., Offering a Price Transparency Tool Did Not Reduce Overall Spending Among California Public Employees and Retirees, 36 Health Affairs 1401–1407 (2017).

[27] See id. at 1404 fig.1 (noting on a graph that by month 12, over 12% of participants had used the price tool at least once, about 4% of participants had used the tool twice at least thirty days apart, and about 2% had used it three times on different days).

[28] See id. at 1405 fig.2 (noting on a graph that the control price is consistently and slightly higher).

[29] Sherry Glied, Price Transparency—Promise and Peril, 325 JAMA 1496–97 (2021).

[30] Christopher Whaley et al., Association Between Availability of Health Service Prices and Payments for These Services, 312 JAMA 1670–76 (2014).

[31] Id. at fig.1.

[32] Hunt Allcott et al., The Impact of Price (Charge) Transparency in Outpatient Provider Markets, E-Health Conference 1–46 (Apr. 4, 2021), https://www.ehealthecon.org/pdfs/Glied.pdf.

[33] Id. at 5.

[34] Id.

[35] See NHE Fact Sheet, CMS.gov, https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet (last updated Dec. 16, 2020) (noting, for example, that of the $3.8 trillion spend on health care in 2019, $772.1 billion was spent on physician and clinical services).

[36] Sherry Glied, supra note 26.

[37] Sherry Glied, supra note 26.

[38] Institute of Medicine Committee on Quality of Healthcare in America, To Err is Human: Building a Safer Health System (Linda T. Kohn et al. 2000).

[39] Id.

[40] Centers for Medicare and Medicaid Services, Partnership for Patients, CMS.gov, https://innovation.cms.gov/innovation-models/partnership-for-patients (last updated June 24, 2021).

[41] Center for Medicare and Medicaid Innovation, Project Evaluation Activity in Support of Partnership for Patients: Task 2 Evaluation Progress Report, CMS.gov 17 (July 10, 2014), https://innovation.cms.gov/files/reports/pfpevalprogrpt.pdf (noting that “Section 3008 of the ACA . . . provides for payment penalty based on high rates of hospital-acquired conditions, beginning in fiscal year (FY) 2015”).

[42] Id.; Patient Protection and Affordable Care Act of 2010, Pub. L. No. 111–148, 124 Stat. 119 (2010).

[43] Centers for Medicare and Medicaid Services, supra note 41.

[44] Id.  See also AHRQ National Scorecard, at https://www.ahrq.gov/hai/pfp/index.html

[45] See 2021 Medical Care Plans Comparison Chart, Duke Human Resources, https://hr.duke.edu/benefits/medical/medical-insurance/plan-comparison (last visited July 10, 2021) (noting five plans available for Duke employees).

[46] Id.

[47] See Monthly Medical Premiums (2021), Duke Human Resources, https://hr.duke.edu/benefits/medical/medical-insurance/premiums (last visited July 10, 2021) (stating that $295 is the lowest 2021 monthly premium for employee/children and $446 is the highest, which is an annual difference of $1,812).

[48] Regina E. Herzlinger, Barak D. Richman, & Richard J. Boxer, How Health Care Hurts Your Paycheck, N.Y. Times (Nov. 2, 2016), https://www.nytimes.com/2016/11/02/opinion/how-health-care-hurts-your-paycheck.html.

[49] Id.

[50] “Herzlinger: The Godmother of Consumer-Driven Health Care,” https://www.youtube.com/watch?v=-TXDyr0mQnY

[51] HBR, Working paper.

[52] Herzlinger et al., supra note 47.

[53] Kathy Hempstead, The HRA Rule Could be a Game Changer for Health Insurance, STAT (Oct. 22, 2019), https://www.statnews.com/2019/10/22/hra-rule-health-insurance-individual-market/.

[54] Barak D. Richman et al., Overbilling and Informed Financial Consent—A Contractual Solution, 367 New Eng. J. Med. 396–97 (2012).

[55] Id. at 396.

[56] Barak D. Richman et al., The No Surprises Act and Informed Financial Consent, 385 New Eng. J. Med. 1348–51 (2021).

[57] Barak D. Richman, Behavioral Economics and Health Policy: Understanding Medicaid’s Failure, 90 Cornell L. Rev. 705, 741-43 (2005). See also, e.g., Roger S. Ulrich, supra note 60; Roger S. Ulrich, Effects of Interior Design on Wellness: Theory and Recent Scientific Research, 3 J. Health Care Interior Design 97–109 (1991); Douglas Raybeck, Proxemics and Privacy: Managing the Problems of Life in Confined Environments, in From Antarctica to Outer Space: Life in Confined Environments 317–30 (1987).

[58] Ann Sloan Devlin & Allison B. Arneill, Health Care Environments and Patient Outcomes: A Review of the Literature, 35 Env’t & Behav. 665–94, 672 (2003) (citing Roger S. Ulrich, How Design Impacts Wellness, 35 Healthcare F. J. 20–25 (1992)).

[59] Les Picker, The Effects of Education on Health, Nat’l Bureau of Econ. Rsch.: Digest (Mar. 2007), https://www.nber.org/digest/mar07/effects-education-health.

Workforce Woes: Tackling Labor and Productivity Challenges in Healthcare

Alan Z. Yang, Kushal T. Kadakia, Harvard Medical School, Adam M. Licurse, Brigham and Women’s Advanced Primary Care Associates

Contact: alan_yang@hms.harvard.edu

Abstract

What is the message? Traditional staffing models in healthcare delivery result in labor shortages, financial strains, and vulnerabilities to fluctuating demand for services. The productivity imperative, however, is not a problem for healthcare only. We investigated strategies for workforce transformation in other industries and identified three key lessons for how healthcare can optimize team sizes, better allocate skill sets, and create flexible labor models to meet episodic demand.

What is the evidence? For cross-industry learnings, we used case studies from manufacturing, banking, and customer service organizations. For healthcare management, we drew from the literature on time-driven activity-based costing studies and published outcomes from initiatives pursued at individual healthcare institutions.

Timeline: Submitted: March 29, 2022; accepted after review: April 4, 2022.

Cite as: Alan Yang, Kushal Kadakia, Adam M. Licurse. 2022. Workforce Woes: Tackling Labor and Productivity Challenges in Healthcare? Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 2.

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Healthcare is fundamentally a “people” business, where delivery of high-standard care is built on a labor-intensive foundation. However, the pandemic has called into question the sustainability of this workforce model, with COVID-19 inducing substantial financial and workforce pressures1. Since the start of the pandemic, nearly 100,000 hospital staff have left their jobs. As hospitals struggle to retain and backfill staff, the cost of clinical labor per patient-day has increased by 8% since 2020, creating further strain on the system2. The mismatch between workforce supply and demand is especially severe for nursing, with hospitals increasingly relying on travel nursing firms whose pandemic-era rate hikes have elicited concern from both health system leaders and members of Congress3–5.

While COVID-19 presents an extreme shock to the system, the structural failings of the medical labor model were well-documented prior to the pandemic, with the healthcare industry’s turnover rates exceeding those of other industries6. Indeed, substantial research has highlighted how excessive administrative burdens, underutilized clinical capacity, and the inefficient use of information technology all contribute to inefficiencies in care delivery7–9. These inefficiencies increase the cost of healthcare by sub-optimally assigning labor resources10. Researchers have used time-driven activity-based costing (TDABC) – an accounting methodology that quantifies the cost of business per unit of time – to expose how the conventional healthcare labor model used in specialties ranging from hospital medicine to ophthalmology results in significant opportunities to improve efficiency and reduce labor costs11–15. Beyond increasing health care costs, productivity inefficiencies also add to providers’ workloads and compromise clinicians’ time with patients, creating the conditions for burnout16.

Of course, the productivity imperative is not unique to healthcare. Leading companies in many other industries have sought to redesign their workforce in response to increasing competition for talent, heightened consumer expectations for efficiency and quality, and evolving customer experiences in the digital era. For example, hospital managers could learn from the experience of the LEGO Company, which successfully reduced heterogeneity and bloat in its production lines to save itself from bankruptcy. Likewise, clinicians could apply lessons from the banking industry – which has sought to digitize front-end services and reskill customer service representatives – to close the skills gap between a provider’s training and the health services that they render17. Furthermore, emergency departments could look to the evolution of call centers, which have designed flexible shift work models to address fluctuating demand, to optimize staffing for variable demand for health services18,19.

While “healthcare is different” is a common refrain in response to cross-industry management learnings, the reality remains that hospitals and health systems are unprepared for the looming post-pandemic reckoning for productivity and labor. In this article, we use the literature on TDABC and productivity research in other industries to identify lessons for rethinking healthcare’s labor model, with a focus on optimizing the “size” of care, digitizing and deskilling front-end delivery, and managing episodic demand.

Right-Sizing the Healthcare Workforce

TDABC research highlights a key issue: there is too much variability in how care is delivered. Given the high-cost nature of clinical labor, inefficient deployment of clinical capacity results in excess labor costs for health systems. For example, a case study at a large academic medical center found that treating low-severity, acute-onset conditions like UTIs demanded different amounts of provider time in different settings (e.g., 17 minutes of an MD or physician assistant’s time at the telehealth primary care clinic vs. 32 minutes with a resident and an attending physician in the emergency room), leading to different costs ($63.48 at the telehealth clinic vs. $210.86 in the emergency room)11. Furthermore, studies have also shown that team sizes vary even when performing the same task under similar conditions, such as a total knee arthroplasty, with personnel costs varying up to 1.9-fold even after controlling for salary rates13. These studies highlight the need to optimize and standardize teams for delivering care for common conditions with well-established treatment protocols. Such changes could not only improve patient care – as consistency is the cornerstone of quality – but also potentially reduce healthcare spending.

The LEGO Company also encountered the costs of variability. During the 1990s, the company responded to stagnating sales by launching new products, doubling the number of unique parts between 1997 and 200420–22. This added complexity ended up disrupting their supply chain and inventory. As retailers and end-consumers grew frustrated, the company lumbered close to bankruptcy. Eventually, a new CEO helped turn the company around by focusing their business on a smaller number of core products and alleviating the supply chain issues wrought by the increased complexity.

Like the LEGO Company, healthcare delivery has too many building blocks, each with different shapes and sizes. Given both service complexity and labor costs, healthcare teams should be designed for specific purposes and consist of only those providers needed for those purposes.

To operationalize the lesson from the LEGO Company, health systems could adopt a “care pathways” approach23, which maps out the ideal intervention and clinical team at each stage of treatment for a given condition. For instance, the Cleveland Clinic Neurological Institute has developed disease-specific Care Paths in which providers use digital tools and evidence-based algorithms that are integrated into the electronic medical record to send patients to different teams of providers managing different conditions, such as concussion, ischemic stroke, and low back pain24. By making sure the patients are seen by the right team and the providers are applying their expertise most efficiently, this design reduces heterogeneity in care and optimizes workflows25.

Surgical procedures are particularly amenable to this kind of labor optimization. Consider the example of cataract surgery, a procedure performed by a multidisciplinary team at high volumes each year in the United States. TDABC studies have shown that the costs of cataract surgery vary widely between sites in the US and between the US and other countries, with a substantial portion of the spending differential attributed to excess labor costs26. While the percentage of clinical time for attending physicians was consistent across all sites, US teams used excess nursing staff to perform various pre- and post-operative activities that in other countries are delegated to mid-level providers. Using TDABC to carefully understand the care processes can thus lead to the de-skilling of the care team with a more defined skills mix that could help optimize skills matching and reduce costs for cataract surgery.

Optimizing Skills Allocation in Healthcare

In addition to right-sizing care teams, an important part of improving productivity is optimizing skills allocation. TDABC studies have shown that there is variation across sites not only in the size of teams, but the kinds of providers hired to perform similar tasks. For example, a recent study illustrated how variation in the costs of managing low-acuity conditions such as migraines or ankle sprains was attributed not to the services rendered, but rather to where care was delivered (e.g., virtual clinic versus emergency department) and who provided the services (e.g., medical assistant versus nurse versus physician)11. This mismatch between the skills of providers and the clinical tasks they actually perform contributes to inefficiency and higher costs of care. The nursing staffing crisis during COVID-19 — and the resulting strain on hospital finances — is a salient contemporary example of the skills misalignment in care delivery, and it illustrates the need for managers to reevaluate staffing models across all stages of the patient care journey.

To improve “skills-matching”27 or “talent-matching”28 in care delivery, healthcare leaders may benefit from learning from the workforce innovations deployed in banking. Consumer banking, like healthcare, has traditionally been a brick-and-mortar service experience. Clients typically use the local branch of a bank that is the most conveniently located to them in their community, have a mix of annual (e.g., deposits, taxes) and time-sensitive (e.g., loans) interactions, and may interact with a range of personnel from receptionists to branch managers. However, banks today face a significant challenge: the number of bank-tellers is declining, the reliance on local branches is decreasing as populations become more mobile, and consumers expect an increasingly digital experience with on-demand access. Consequently, banks needed to pivot to optimize productivity. To this end, banks focused on digitizing and de-skilling traditional front-end banking functions while boosting the customer service workforce17. For example, many banks now allow for all interactions to be conducted online or via a mobile application. With digitization rendering the position of “bank-teller” obsolete, banks focused on retraining these personnel to deliver a wider array of financial advisory functions.

These two strategies offer valuable lessons for healthcare. On the digital front, with patients exhibiting increased comfort with virtual platforms following the COVID-19 pandemic, providers and plans could consider adopting digital tools as a first-line approach to triaging low-acuity concerns. The advent of so-called “virtual-first” primary care plans and the implementation of processes such as electronic consults (eConsults) can streamline patient access, optimize the use of clinician time, and balance caseloads between different primary care sites, especially for same-day care29,30. With regards to skills-matching, health systems could consider how the use of non-physician providers can optimize labor allocations. For instance, the Cleveland Clinic implemented a program to delegate documentation tasks at a family medicine practice to non-physician staff to reserve the time of high-cost physicians for evaluating more patients31. Increased efficiency at primary care clinics through greater integration of medical assistants and nurse practitioners has also been reported in the literature32–36.

Creating Flexible Labor Models to Meet Episodic Demand

A common challenge in many industries is creating teams and supply chains that are elastic enough to adapt in response to episodic surges in demand. For example, many industries have seasonal components (e.g., holiday shopping) that require rapid upscaling of capacity to meet temporarily heightened demand. Episodic demand also exists in healthcare, from the one-off experience of surges and nadirs in cases during COVID-19, to the more common experience of variation in service utilization according to time of day (e.g., evenings and emergency departments) or year (e.g., flu season). However, as the experience of healthcare systems during COVID-19 illustrates, care teams are not built to have excess capacity, and the cost of acquiring clinical back-up (e.g., travel nurses) on a regular basis is very high.

Consequently, there is an imperative to build flexibility into care models19. Other industries have employed a range of strategies to address this challenge (e.g., short-term hiring of seasonal workers). Consider, for example, call centers. Call centers, like emergency departments, have a basal level of volume throughout the day; however, there are peak hours when demand spikes. To optimize capacity, call centers start with the numbers, using historical data and conventional and machine learning tools to forecast the times when demand is most likely to spike. Based on these trends, managers can use workforce management tools (e.g., flexible shifts) to adjust staffing schedules and build in flexibility for potential demand spikes.

In healthcare, managers in the status quo recognize that staffing has to be adjusted in response to demand; this is why many hospitals increase hiring of temporary workers during flu season.37 However, as COVID-19 has shown, demand matching remains an imperfect science for health systems, leaving hospitals susceptible to price gouging.38 This is due to two issues. First, health systems lack tools for forecasting demand and identifying excess staffing supply. Second, even when health systems increase staffing, demand for services can still be limited by the availability of fixed assets like bed space.

The call center analogy is applicable for both of these challenges. Just as call centers have begun using forecasting models, health systems may benefit from investing in in-house analytics or partnering with third-party vendors. Likewise, just as call centers have transitioned from a static staffing model (which leaves 40% of agent time unoccupied)18 to a dynamic approach (with flexible shifts), health systems may benefit from recalibrating staffing levels to a lower base patient census. New start-up companies have also emerged to facilitate provider-to-health system matching to reduce the friction associated with workforce matching.

Call centers are also useful references for thinking through the physical capacity constraints that also restrict swells in staffing. The value of the call center has always been its decentralized nature; when demand spikes, the only limiting factor is the number of agents, not the number of offices. In medicine, however, even if health systems can procure additional locum tenens, they cannot magically conjure up additional beds. Consequently, to achieve staffing flexibility, hospital managers must also consider how they can create added capacity in a decentralized fashion. One example of a decentralized approach is the “Availabist” model which New York Presbyterian (NYP) has for emergency care39,40. At NYP, patients that arrive in the emergency department are managed using a hybrid approach, with a “virtual ED” activated to triage low-acuity concerns to enable prioritization of more time-sensitive clinical cases. In this way, the system has built-in clinical flexibility that enables NYP to tune staffing levels to demand without being capped by physical capacity constraints.

Looking Forward

The COVID-19 pandemic has exacerbated long-standing challenges with labor shortage and costs in healthcare. In response to workforce attrition and added financial and logistical pressures, health systems need to develop strategies for optimizing and standardizing the labor inputs to care delivery to build a more robust system. Data from previous studies and lessons learned from other industries suggest that optimizing team sizes, skills allocation, and responses to episodic demand, as appropriate, are effective interventions. The key challenge is implementing these changes.

From a clinical perspective, professional societies could develop recommendations for best practices on team size and team member function. From a financial perspective, value-based payment models can refocus physician time around optimizing care for the patient as opposed to increasing service volume. Regulatory changes could also help facilitate some of these models for capacity building, such as more expansive medical licensure provisions similar to the flexibilities introduced during the height of the COVID-19 pandemic41. But individual institutions can move the needle, too, by carefully defining care teams, matching workflows to the right personnel, and investing in alternative modes of care delivery.

 

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Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 1: Overcoming Barriers to Commercialization of Original Research

Kevin Ho, Graduate School of Business, Stanford University

Contact: kevho@stanford.edu

Abstract

What is the message? Developing a life science product is extremely complex and difficult: there are many steps along the way when a would-be biotech entrepreneur can be derailed. This is especially true of academic scientists seeking to bring their discoveries from bench to bedside. Translational science, fundraising, and commercialization require a wide array of skills and a lot of luck.  Focusing on Stanford University, this paper details some common pitfalls experienced by scientists seeking to commercialize their original research, as well as some attributes common to many success stories. It suggests means of mitigating common sources of failure to enable more scientific discoveries to be developed into lifesaving products.

What is the evidence? Interviews with several individuals with experience across parts of the life sciences research, development, and commercialization value chain. Emphasis on university-based principal investigators with experience translating basic science from their laboratories into for-profit life sciences firms. Analysis and interpretation of publicly available data from multiple sources.

Timeline: Submitted: December 14, 2021; accepted after review: March 31, 2022.

Cite as: Kevin Ho. 2022. Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 2: What Makes Life Sciences Innovation Ecosystems Tick. Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 2.

To read the companion paper,  Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 1: Overcoming Barriers to Commercialization of Original Research, click here.   

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Introduction

Patients benefit from investment in basic science research through the translation of science into novel medical products and services. Yet, despite the demand for innovation from patients and providers, it is incredibly challenging to translate discoveries made in academic laboratories into products that benefit patients. Since the beginning of large-scale government funding of university research in the 1940s, the process has worked as follows: (1) government funds academic scientists to do research; (2) scientists make discoveries and publish their findings; (3) eventually (often after years or decades), industry identifies one idea or synthesizes multiple findings into a technology, which can then be developed into a product.[i]

The Bayh-Dole Act of 1980 was enacted to encourage universities to commercialize government-funded research. The act clarified ownership of intellectual property resulting from government-funded studies, formally transferring ownership and a responsibility for translation and licensure to universities. It enabled a new pathway whereby scientific discoveries are shepherded directly from the laboratory into startups, then into industry, which then have an opportunity to develop products.[ii]

However, the post-1980 pathway from academic science to commercialization remains challenging. There are a handful of locations in the world where translation is most efficient in the life sciences, the most notable being the Bay Area and the Boston / Cambridge region.[iii] This paper represents the collective experience and wisdom of several participants in the scientific discovery and commercialization process (largely based in the Bay Area) and seeks to understand the process of commercializing discoveries and challenges involved in bringing scientific ideas to patients. The perspective is largely one of examining the business risk of drug development; it does not address the scientific risk (i.e., around the validity of the underlying scientific hypotheses). It concludes by discussing implications of this work and potential ideas for overcoming barriers to commercializing research.

Context: How the Process Works From Bench To Newco

The pathway by which life science discoveries in the lab translate into commercialized products for patients proceeds roughly as follows:

  • Scientists (principal investigators, postdoctoral students, and graduate students) conduct laboratory-based research. They discover new biological targets, tool molecules, or develop potential platform technologies, and demonstrate initial proof of concept.
  • Principal investigators work with university technology licensing offices to file relevant patent applications on discoveries.
  • Scientists publish their initial findings in academic journals.
  • Scientists continue to conduct translational experiments (e.g., target validation, additional in vitro studies, animal studies for efficacy and safety) to further prepare for translation into clinical development.
  • Scientists apply for non-dilutive funding from the Small Business Innovation Research (SBIR) and/or raise private angel funding. They form a startup company and start recruiting a management team while doing additional translational science. University personnel may move into the startup or may serve as advisors.
  • The startup, equipped with a preliminary management team, raises venture capital investment.
  • The startup works with the university technology licensing office to in-license patents related to the discovery. In the meantime, the VC(s) helps recruit additional management personnel. The management team makes important strategic decisions (e.g., which indications to pursue). The startup is now in the pre-clinical development stage.
  • The startup continues to develop technology while raising money. Management works towards successfully hitting developmental milestones agreed upon with investors. Preclinical studies are completed and an Investigational New Drug (IND) application is filed with the Food and Drug Administration (FDA).
  • The startup moves into the clinical development stage with human clinical trials (Phases 1, 2 and 3). Clinical development requires significant investment, which requires further venture rounds or partnership/sale to larger pharmaceutical firms. For the fortunate few with successful clinical trials, the startup receives approval of their new drug application (NDA) by the FDA. The startup becomes a commercial stage company.

Of note, while this somewhat formulaic playbook works for therapeutics, it does not apply neatly to diagnostics or scientific tools. That said, it remains useful for illustrative purposes.

Common Challenges

Scientists face several challenges throughout the process of commercializing their research, something that they typically have not trained for and do not know how to do. The most commonly raised challenges are detailed below.

Short timeline and bandwidth constraints

Researchers typically have only two years and six months after filing for a provisional patent application to find a licensee (often his/her own venture-backed startup) for that patent application.[1] Over this short time, several additional activities need to occur:

  • The basic research must be published in an academic journal.
  • Additional translational research must be conducted to confirm initial findings and de-risk sufficiently to encourage venture capitalists to finance the idea. This step can be extensive and onerous – there is typically a large gap between the state of research immediately post-publication and the studies that must be done to raise investment, as scientists race to publish their research as quickly as possible in a competitive environment.
  • A team to manage the startup must be recruited.
  • Capital must be raised to fund the startup.

In addition to these activities, researchers must continue to do their day jobs at the University. Further, once the company is established, the University might place restrictions on the inventors/faculty given the potential conflict of interest between their University and company roles. Given these short timelines and intense demands, scientists face difficulty juggling competing responsibilities – there are simply not enough hours in a day nor days in a year to do it all. If a given scientist has not yet achieved tenure, it can be risky to spend so much time on translational research that will not support his/her case for tenure.[2]

To compound these challenges, commercializing science requires bridging the gap between academia and industry, two sets of institutions with misaligned incentives and a large cultural gap. While academia rewards basic scientific research, which is more likely to be published in prestigious journals, industry seeks to maximize profit and prioritizes translational research. Academics sometimes view industry-based drug development as intellectually unsophisticated, while industry scientists view academic work as unreliable and not replicable.[iv]

Lack of know-how and experience

Developing a drug requires a long series of complex, difficult, and nonintuitive steps. It requires varied skill sets and knowledge. From pitching the right investors for a specific disease area to good manufacturing practice (GMP), from recruiting and managing a team to working with the FDA, the sheer number of intermediate steps required to commercialize a drug increases the opacity of the entire process. Missing any of these could be a fatal roadblock.

Academics who have never undergone the process of raising money and developing a drug often do not realize just how much time, money, and effort is required. After all, the biotechnology industry operates under an apprenticeship model, where most people who have learned to translate their research and start companies do so by watching others with more experience. Even knowing everything in the playbook is an inadequate substitute for executing; as with surgery, a textbook cannot teach everything. For scientists who have no experience in commercial activity and lack ready access to people who have successfully navigated this process, translation can become an unachievable objective.

Lack of experience can also increase business risk. It can lead to unrealistically high expectations regarding scientific founder ownership stake and influence on a startup’s day to day operations. Without full understanding of all the steps and skill sets required to bring a drug to market, scientific founders may underestimate the value brought by investors, management, and employees. This can cause friction within new startups that cripples their chances of success.

Recruiting talent

Given the complexity and risks involved in the process, good management is critical for success in life sciences commercialization. Drug development requires a wide variety of specific skill sets and experiences, and these requirements change over time as a company progresses from pre-clinical to clinical to commercial stages. For a startup, cash flows are the primary metric, with management and investors carefully monitoring burn rates required to achieve milestones at each development stage. Mistakes or delays can leave a startup short of cash at critical periods. Experienced leadership allows a biotech startup to (hopefully) avoid mistakes.

Investors understand that many great technologies fail due to poor management, and often care more about the management team than even the technology of a potential portfolio company. Many are not interested in funding a company unless a strong team is already in place.

Unfortunately, good, experienced management is in high demand and difficult to recruit to early stage companies. CEO talent is especially make or break, and especially rare: very few people can lead a ten-person company, grow it to a 50-person company, and eventually take it public. Furthermore, team dynamics are critical – many programs have failed due to intra-team dysfunction. Finally, retention of key staff can be nearly as difficult as recruitment – departure of a key scientist for greener pastures can tank an early-development program. These personnel challenges – recruiting and retaining a talented management team that works will together – will only intensify as more biotech companies receive venture funding.

Raising money

Biotechnology startups must constantly raise money to remain solvent while developing products that take years to reach the market. Raising capital, especially early on, requires the ability to do great science and be able to sell that science to investors. This combination is rare in academic scientists, who also often do not understand the market for their discoveries and pathways for translation.

It is even harder to raise money for truly novel ideas. Venture capitalists often prefer to invest in technologies with which they are familiar, rather than ones that present additional scientific risk. Even some of Stanford’s faculty who are most successful in translation have had difficulty finding investment for their most groundbreaking ideas. Jennifer Cochran, for example, has found investors more receptive to antibody-based therapeutics than therapeutics based on engineered cysteine knot peptides (“knottins”). Carolyn Bertozzi, despite her academic accomplishments and commercial track record, has faced relative difficulty raising money for glycoscience-based startups (i.e., Palleon Pharmaceuticals and InterVenn Biosciences) compared with her easier success for startups developing more widely understood technologies (e.g., Lycia, which is developing a protein degradation technology).

Technology licensing resource constraints

Given the substantial resources required to file patent applications, pay internal staff and external consultants, and hire legal support, technology licensing offices must be selective about the commercial potential of the science they support through the patenting and licensing process. Increased selectivity can have a positive effect – the high bar encourages researchers to think big (i.e., develop technologies applicable to large markets). On the other hand, bandwidth constraints force tech licensing offices to make tradeoffs based on limited information. For scientists navigating this process without full knowledge of the process and constraints, technology licensing offices can appear to be another roadblock to translation.

Developing valuable products that are not rewarded by the market

Translating scientific discoveries through venture-backed startups only works when these startups have the potential to be worth billions of dollars and generate positive returns for venture capital firms. This method does not work for products with more limited markets or for products without novel intellectual property. Despite years of work to raise venture and philanthropic funding for pediatric cancers, Crystal Mackall has had difficulty upending the physics of the market and building a “business case” for pediatric therapeutics that do not also work in adult cancers. The same issue – lack of market incentive – applies to next-generation antibiotics to fight the coming antibiotic resistance crisis.[v] Public policy measures, like the Orphan Drug Act or modifications of payment models are needed to incentivize development in these valuable but financially unattractive markets.[vi] [3]

Where Success Comes from on an Individual Level

Scientists typically attribute their own success in translating ideas from laboratory to startup to a mixture of skill, experience, and luck. A closer glance suggests that these advantages break down into several specific success factors.

Mentorship

Biotech is commonly described as an apprenticeship-based industry. To learn the ropes, it is important to see how things are done well, and useful to have someone you can ask questions of. Mentorship is thus invaluable – it enables scientists without any commercial experience to skip unnecessary steps and sidestep risks, thereby increasing speed and chance of success.

Many scientists who have seen success in commercializing their ideas worked with mentors who had prior experience with the biotech industry. A 15-minute conversation with Paul Yock yielded Tom Soh connections that saved him three months of cold outreach. On a longer time horizon, Ravi Majeti benefited from Irv Weisman’s mentorship in developing his discovery of CD47 into a clinical stage monoclonal antibody housed within Forty Seven, a biotech startup ultimately acquired by Gilead Sciences. Garry Nolan gained early exposure to industry as a PhD student when Leonard Herzenberg invited him to observe contract negotiations with pharmaceutical firms.

Ability to sell

Academic scientists have universally honed their grant writing abilities over years of practice. However, raising capital from private investors and writing grants require different skills, and can be difficult for scientists. Specifically, raising money requires the following:

  • Strong communication ability. Raising startup financing requires scientists to develop pitch decks that tell simple, compelling stories; they must then deliver sales pitches repeatedly with confidence and clarity. Scientists sometimes dive deeply into the details of their science without pausing to discuss higher level implications, thereby causing potential investors’ eyes to glaze over.
  • An ability to empathize with investors. Crafting a compelling story requires tailoring messages for specific audiences. Different types of investors – government, disease-specific foundations, venture capitalists – have different assumptions and goals. A good salesperson understands what the buyer wants and messages appropriately.
  • The ability to paint a big vision with conviction, while remaining realistic about potential challenges and risks. Venture capitalists seeking home run returns will not invest simply based on a reasonable two-year development plan – they need to be convinced of the potential for a billion-dollar exit. Many scientists, trained for years not to make claims they cannot back up, find it difficult to truly believe and sell the story of a big exit.

Network

Having access to a dense network facilitates fundraising and recruitment of a management team, the most critical bottlenecks in the early stage of commercialization. Given the number of pitches that VCs see every day, personal connections can be a prerequisite for an opportunity to pitch the right investors. Relationships also increase the chance of raising multiple funding rounds, even in the face of development setbacks, and facilitate recruitment of talented management teams.

Track record and experience

Any kind of experience, especially past success, helps tremendously in subsequent attempts to commercialize scientific discoveries. Experience builds wisdom and understanding – of what errors to avoid, what VCs are seeking to fund, and how to be self-critical about subpar ideas. Experience in commercializing products also facilitates rapid network building; multiple successes over time can build a reputation for scientific excellence and startup acumen. For this reason, venture capitalists proactively reach out to some investigators (e.g., Irv Weissman, Stephen Quake) who have progressed multiple ideas successfully from bench to bedside.

Entrepreneurial mindset

Given the significant differences in incentives and culture between academia and the industry, successful translation of basic research requires a willingness to work in the chaos, frustration, and uncertainty of a biotech startup. Scientists seeking to play a role in commercializing their research must be comfortable with ambiguity, willing to start a company and operate in a structure very different from that of an academic lab.

Naivete can also be very helpful in lowering the activation energy required for starting a company. Sometimes, scientists stumble into entrepreneurship without knowing the negative implications. Tom Soh started CytomX after applying for an SBIR grant to fund his research and realizing that he had to house his newly funded work in a commercial entity to make use of the funding.

Luck

As with any entrepreneurial activity, a substantial amount of success in translating research into commercialized products comes down to luck. This is especially salient in the life sciences, a field characterized by unknown unknowns. Development of Rituxan, the first and perhaps most famous monoclonal antibody treatment, would have died an early death had Ron Levy not met David Ebersle through a chance encounter at Stanford. Their conversation over lunch convinced Ebersle to push for Genentech to fund continued development of Rituxan just as IDEC, its parent company, was running out of money.[vii] Rituxan went on to become the world’s top selling oncology drug for nearly a decade, with sales eventually peaking at nearly $9 billion annually.[viii] Many other drugs on the market experienced similar near-death situations, only to be serendipitously rescued on the path to commercialization.

Addressing the Challenges

Given the daunting path from bench to bedside, universities, other organizations, and individuals have developed novel programs to address the challenges outlined in this paper. Several solutions have been designed at Stanford University and in the Bay Area at large to accelerate translation of discovery to the market.

Wraparound drug development support

Stanford’s SPARK program in translational research provides invaluable guidance for faculty, postdocs, and graduate students through the step-by-step process of translational research and commercialization of lab discoveries. It convenes industry mentors who, along with faculty and staff, provide high-touch support for translation. The program also provides $50,000 per team per year to fund the translational work that may be necessary to test preclinical hypotheses.[4]

The university developed the Innovative Medicines Accelerator (IMA) to support faculty in one key aspect of drug development: medicinal chemistry. While the IMA is brand new (founded in 2019), it has been able to recruit medicinal chemists to facilitate development of small molecule drugs.

Stanford’s Chemistry, Engineering & Medicine for Human Health (ChEM-H), an institute designed to bring multiple drug development disciplines under one roof, has moved to support its researchers by hiring a full-time clinical coordinator to handle clinical operations (e.g., trial design, biostatistics, patient recruitment, collaboration with contract research organizations, institutional review board interactions).

The Center for Definitive and Curative Medicine (CDCM) was developed with an understanding of commercialization needs: it supports clinical development of cell and gene therapies through proof-of-concept trials.[ix]

Reduced Barriers to Gaining Know-How and Experience

Today, researchers gain valuable introductions, mentorship, and experience through ad hoc conversations following presentations, at convenings, and through informal relationships. Despite these venues for knowledge-sharing, many scientists still feel lost when it comes to what is required to commercialize a life science product. At Stanford, several programs have been developed to introduce scientists to basic business concepts and provide a baseline level of mentorship and guidance.

Ignite, a 4–8-week program offered through the Graduate School of Business (GSB), teaches business fundamentals to graduate students and postdocs interested in commercializing their ideas.[x]  It is built around turning a scientific idea into a “venture project.”

The Accel Leadership Program, a 6-month program, prepares technically minded students (both undergraduate and graduate) to start and lead startups through workshops, projects, and exposure to industry leaders.[xi]

The Faculty Entrepreneurship Leadership Program, started by Jennifer Cochran based on her own experience commercializing technology, offers a two-quarter bootcamp for STEM faculty to gain knowledge and skills to commercialize their laboratory research.[xii]  It exposes Stanford faculty to industry leaders and introduces scientists to fundraising, negotiation, intellectual property, management, and industry collaboration.

Finally, frequent events (e.g., conferences, like Stanford Drug Discovery Symposium; networking events; classes with a slate of guest speakers from industry), expose researchers to stories of success, help answer basic questions, and enable introductions to people with complementary skill sets for commercializing discoveries in the lab.[xiii]

Network of future biotech management talent

Difficulty recruiting experienced management talent is one of the most common causes of failure for biotech startups. The apprenticeship model prevalent in the biotech industry has resulted in a shortage of management talent, as the recent explosion in company formation and funding has outpaced the rate at which talent has “graduated” from older startups. Additionally, many scientists with great ideas do not have networks dense enough to rapidly recruit teams to manage their startups.

Venture capital firms provide a stopgap solution to the shortage of management talent by temporarily filling empty leadership positions with their own staff and serving as part time executive recruiters. However, they do little to train the next generation of management talent. Furthermore, a startup is usually limited to the networks of its own investors.

In the Bay Area, informal networks help to train and nurture talent. For example, one chat on GroupMe has grown from a small group of young business development and strategy managers seeking peers to socialize with during the JP Morgan Healthcare Conference into to a hundreds-strong support group. Today, members use the group chat to get information, seek advice, and post job opportunities.

Conclusion

Translating scientific discovery to clinical products that benefit patients remains a significant challenge. Based on interviews, many of these challenges have been identified. One Bay Area institution has developed several overlapping efforts to address these challenges, but the effort required to impact patient care remains significant. A companion paper examines these challenges from an ecosystem perspective.

 

Acknowledgements

I would like to thank the Stanford University faculty and researchers who provided important information and perspectives for this paper:

  • Carolyn Bertozzi, PhD, Director of Stanford ChEM-H, Professor in the School of Humanities and Sciences, Stanford
  • Matthew Bogyo, PhD, Professor of Pathology, of Microbiology, and of Immunology, Stanford
  • Scott Boyd, MD, PhD, Associate Professor of Pathology, Stanford
  • Jennifer Cochran, PhD, Chair of the Department of Bioengineering, Professor of Bioengineering, Stanford
  • Scott Dixon, PhD, Associate Professor of Biology, Stanford
  • Edgar Engleman, MD, Professor of Pathology and of Medicine, Stanford
  • Linda Grais, MD, JD, former CEO, Ocera Therapeutics, former Partner at InterWest Partners
  • Kevin Grimes, MD, MBA, Professor of Chemical and Systems Biology, Stanford
  • Stephen Johnson, JD, Lecturer, Stanford Graduate School of Business, former Partner, Kirkland & Ellis
  • Perry Karsen, MIM, Chairman, Graphite Bio
  • Robert Langer, ScD, Institute Professor, MIT
  • Josh Lehrer, MD, CEO, Graphite Bio
  • Ron Levy, MD, Professor in the School of Medicine, Stanford
  • Crystal Mackall, MD, Professor of Pediatrics and Medicine, Stanford
  • Ravindra Majeti, MD, PhD, Professor of Medicine, Chief of the Division of Hematology, Stanford
  • Garry Nolan, PhD, Professor of Pathology, Stanford
  • Matthew Porteus, MD, PhD, Professor of Pediatrics, Stem Cell Transplantation, Stanford
  • Stephen Quake, PhD, Professor of Bioengineering and Professor of Applied Physics, Stanford; President, Chan Zuckerberg Biohub
  • Michael Snyder, PhD, Professor of Genetics, Stanford
  • Tom Soh, PhD, Professor of Radiology, Electrical Engineering, Stanford
  • Ansuman Satpathy, MD, PhD, Assistant Professor of Pathology, Stanford
  • Mona Wan, MBA, Associate Director of Licensing, Office of Technology Licensing, Stanford
  • Joseph Wu, MD, Director of Stanford Cardiovascular Institute, Professor of Radiology, Stanford

References

[1] At the end of this two-and-a-half-year period, university technology licensing offices must file for the patent in question (~$85,000 to cover North America, Europe, Japan, and China).  Universities are typically unwilling to pay for expensive patent applications without reasonable certainty that a commercial entity will license the resulting intellectual property. Researchers often receive help from university technology licensing offices and other supporting groups in finding licensees.

[2] Translational research also often requires funding separate from basic science research, which must be applied for separately. This is also a time-consuming process.

[3] Carolyn Bertozzi’s Thios Pharma developed a treatment for a rare disease indication prior to the Orphan Drug Act. It boasted great science, great preclinical data with an IND-ready asset, and a good management team, but shut down after it was unable to secure Series B funding. Years later, after the passage of the Orphan Drug act, another company pursuing the same indication successfully raised funding and commercialized its product.

[4] $50,000 is often not enough to fund all the translational work required to de-risk a drug. SPARK teams sometimes seek additional funding.

———

[i] Leyden DP and Menter M. The legacy and promise of Vannevar Bush: rethinking the model of innovation and the role of public policy. Economics of Innovation and New Technology. 2018; 27(3): 225-242. https://doi.org/10.1080/10438599.2017.1329189

[ii] Mowery DC, et al. The growth of patenting and licensing by U.S. universities: an assessment of the effects of the Bayh–Dole act of 1980. Research Policy. 2001 (30):99-119. doi.org/10.1016/S0048-7333(99)00100-6

[iii] Owen-Smith J, Powell WW. Accounting for Emergence and Novelty in Boston and Bay Area Biotechnology. Research Gate. 2006. DOI: 10.1093/acprof:oso/9780199207183.003.0004

[iv] Begley C G and Ellis L M. Nature (2012): 483. Raise standards for preclinical cancer research. https://doi.org/10.1038/483531a

[v] Plackett B. Why big pharma has abandoned antibiotics. Nature Outlook. 2020 Oct; 586:S50-S52. https://doi.org/10.1038/d41586-020-02884-3

[vi] Gandhi N, Schulman KA. New Medicare Technology Add-On Payment Could Be Used As A Market Support Mechanism To Accelerate Antibiotic Innovation. Health Aff (Millwood). 2021 Dec;40(12):1926-1934.

[vii] Colin C. The Lunch. Genentech: A Member of the Roche Group. 7 Jul 2016. https://www.gene.com/stories/the-lunch

[viii] Pierpont, T M, et al. Past, Present, and Future of Rituximab—The World’s First Oncology Monoclonal Antibody Therapy. Frontiers in Oncology. 4 Jun 2018. https://doi.org/10.3389/fonc.2018.00163

[ix] Stanford Medicine.  Center for Definitive and Curative Medicine.  https://med.stanford.edu/cdcm

[x] Stanford Graduate School of Business. Stanford Ignite. https://www.gsb.stanford.edu/exec-ed/programs/stanford-ignite

[xi] Stanford Technology Ventures Program. Faculty Entrepreneurship Leadership Program. https://stvp.stanford.edu/alp

[xii] Stanford Technology Ventures Program. Faculty Entrepreneurship Leadership Program. https://stvp.stanford.edu/felp

[xiii] Stanford Medicine. Stanford Drug Discovery Symposium 2022. https://med.stanford.edu/cvi/events/2022-drug-discovery-conference.html

Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 2: What Makes Life Sciences Innovation Ecosystems Tick

Kevin Ho, Graduate School of Business, Stanford University

Contact: kevho@stanford.edu

Abstract

What is the message? The benefits of economic clusters have been well observed: proximity of firms and other institutions associated with a given industry enables increased productivity and innovation. In the life sciences, the San Francisco Bay Area and Greater Boston area are the world’s preeminent biotechnology hubs. What present-day institutions enable such robust engines of innovation? What historical occurrences or decisions led to the formation of these economic clusters in the first place?  This paper highlights the critical components of the system – research universities, academic hospitals, biotechnology firms (large and small), and venture capital – and the roles that they play in turning science into lifesaving products.  It further draws lessons for other regions of the world attempting to build their own biotech innovation hubs.

What is the evidence? Interviews with several individuals with experience across parts of the life sciences research, development, and commercialization value chain.  Emphasis on university-based principal investigators with experience translating basic science from their laboratories into for-profit life sciences firms.  Analysis and interpretation of publicly available data from multiple sources.

Timeline: Submitted: December 14, 2021;accepted after review: March 31, 2022.

Cite as: Kevin Ho. 2022. Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 2: What Makes Life Sciences Innovation Ecosystems Tick. Health Management, Policy and Innovation (www.HMPI.org), Volume 7, Issue 2.

To read the companion paper,  Commercializing Science: Turning Life Science Discoveries Into Lifesaving Products – Part 1: Overcoming Barriers to Commercialization of Original Research, click here.

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Introduction

In 1976, Herb Boyer and Bob Swanson founded Genentech, Inc., and along with it, the biotechnology industry. Genentech was founded based on technology developed by Boyer, a professor at UCSF, and Stanley Cohen, a professor at Stanford University. Boyer and Cohen’s recombinant DNA technology was patented with the help of Stanford’s Office of Technology Licensing, then licensed non-exclusively to Genentech and other companies. Genentech, based in South San Francisco and seeded with an investment from Kleiner Perkins, successfully developed the first synthetic human growth hormone and human insulin, thereby proving the scientific and commercial viability of biotechnology.[1] Its success inspired a generation of biotech startups in the San Francisco Bay Area and beyond.

While the birth of biotechnology was serendipitous, it is not entirely an accident that it occurred in the San Francisco Bay Area. Genentech’s founding built on a legacy of innovation, from Hewlett Packard to Fairchild Semiconductor, from Intel to Atari. It developed in the fertile ecosystem of world-class university research and talent, risk-seeking financial capital, and a culture of entrepreneurship and labor mobility.

Today, the Bay Area and Greater Boston area are the world’s preeminent biotechnology hubs. These two metro areas and economic clusters are uniquely effective at taking scientific discoveries, often made locally, and developing them into commercial products that help patients. More than half of all life sciences venture capital funding in the US consistently goes towards these two geographies.[2] While other regions of the United States and globally have attempted to create their own Silicon Valleys and Kendall Squares, no other geography has yet to achieve comparable bench-to-bedside throughput.[3] [4]

This paper reviews the elements – the institutions and intangibles – that have made these regions so successful at taking basic science and developing commercialized products. Academic research has described the important role of geography in the innovation process: the power of economic clusters has been well documented.[5] [6] [7] [8] This paper provides additional context, details specific Bay Area (especially Stanford University-based) and Greater Boston institutions and their histories, and offers considerations for other regions seeking to develop life sciences innovation ecosystems.

Major Institutions

Universities, hospitals, industry, and financing form the foundation of innovation ecosystems. When these complementary resources and capabilities cluster together, they provide both the raw materials and the developmental capacity to turn scientific discoveries into products. This is especially valuable in biotech, where a wide array of specialized functions must come together to turn an idea for a drug into something that helps patients.

World-class research universities amenable to entrepreneurship

Research universities are the foundational anchors of any innovation ecosystem. They employ scientists and engineers who generate and refine new ideas; they provide institutional support (e.g., funding, job security, facilities) for research; they train new talent. Furthermore, research universities have staying power: they are unlikely to move or go out of business. As such, research universities are the anchor tenants around which other parts of the innovation ecosystem are built.

One way to think of great research universities is as pools of talent. Great universities attract star scientists, who then attract other star scientists. Bob Langer, one of twelve Institute Professors at MIT, cites the example of Arthur Kornberg, a Nobel Prize-winning biochemist, as being important to the rise of biological sciences at Stanford. When he became Head of the Biochemistry Department in 1959, he attracted other talented scientists (e.g., Joshua Lederberg) to the university. Talented scientists like Langer, Kornberg, and Lederberg develop great ideas that serve as the raw material for scientific innovation. Importantly, they also provide mentorship and serve as role models for other scientists.

While great science is required for commercialized innovation, it is not sufficient. Academic culture is critically important. Universities that anchor innovation ecosystems have also proven to be supportive of entrepreneurship and amenable to relationships with commercial entities. Here, UCSF and Stanford provide contrasting examples. Typical of most university faculty until the last few decades, J. Michael Bishop, Chancellor of UCSF, viewed commercial activity with skepticism and believed that potential conflicts of interest could contaminate academic integrity.[9] He preferred for UCSF scientists to focus on basic science rather than applied research.

In contrast, Stanford University was created to “qualify its students for personal success, and direct usefulness in life.”[10] At Stanford, education and research were meant to be practical, and the institution was, from its inception, amenable to collaboration with industry. Frederick Terman, Dean of Stanford’s School of Engineering and eventual Provost, further promoted this relationship with industry: he established Stanford Industrial Park and encouraged technology companies to move in, created a program through which industry engineers could study part time at Stanford, and encouraged Bill Hewlett and David Packard to start a business rather than stay in academia.[11] Niels Reimers, founder and former Director of Stanford’s Office of Technology Licensing (OTL), formalized the means by which Stanford supported its scientists in patenting technology by allowing them to benefit financially from patent licenses. His revenue-sharing model encouraged university-based researchers to disclose their ideas and inventions, thereby increasing their rate of commercial success and netting proceeds for both individual inventors and the university.[12] The model paid off immediately in the form of the Boyer-Cohen patent; this success bred confidence in this model of university-supported commercialization of science.

Medical schools and hospitals

In the life sciences, scientists often develop technology to prevent, treat, or cure disease. Doing so often requires clinical development to gain approval by the Food and Drug Administration (FDA) and other regulatory agencies. It requires an understanding of how therapeutics, diagnostics, medical devices, and tools may be used in the clinic.

Clinicians understand clinical medicine intimately, in a way that pure bench scientists often do not. Enabling interaction between clinicians and scientists can thus be useful for developing biotechnology for medical applications. Working with physicians helps scientists identify tangible problems, brainstorm technological solutions, and circumvent bottlenecks. Hospitals also have access to patients and samples (e.g., tumor biopsies) and are sometimes equipped to run clinical trials. These resources can also be highly valuable for laboratory research.

Stanford’s world-class medical school and hospitals represent an important advantage for its life science researchers. Housing both academic medicine and research facilities under one administrative roof enables seamless collaboration and increases the rate at which people with different skill sets serendipitously meet each other at retreats and seminars.

MD/PhD students further strengthen the connective tissue between bench and bedside at Stanford. People who understand both molecular and human biology can, for example, avoid selecting an animal model that is not representative of human biology prior to the initiation of clinical trials. Understanding how drugs work in the clinic can help avoid early mistakes around administration, formulation, and safety profile.

The value of medical schools and hospitals to life sciences innovation will only become more important over time. While small molecule blockbuster drugs required mass manufacturing and huge clinical trials (neither is conducive for a university-based clinical setting), newer, curative modalities (i.e., cell and gene therapies) require artisanal manufacturing and small clinical trials (given their high therapeutic indices), which make them ideal for an academic setting. Institutes at Stanford, like the Center for Definitive and Curative Medicine (CDCM) and Chemistry, Engineering, & Medicine for Human Health (ChEM-H), were developed with an understanding of these circumstances.[13] [14]ChEM-H recently hired a full-time clinical operations coordinator to support testing in human subjects and is actively seeking to recruit MD/PhDs.

Companies

Biotechnology companies, large and small, serve several functions in an innovation ecosystem:

  • They introduce academics to industry. Scientists who have been trained through academia typically lack prior exposure to the industry dynamics and the various functions required for industrial biotech R&D. Nearby biotech companies often hire university-based scientists in a consulting capacity and provide valuable exposure in the process. Edgar Engleman, for example, experienced his first exposure to biotech through consulting for Bay Area firms seeking their scientific expertise.
  • They provide additional training for scientific and management talent. Graduate students and postdocs that do not plan to continue in academia can join biotechnology companies and continue their scientific training. Larger companies, especially, serve as excellent training grounds for future managers of biotech firms. Genentech, which has had outstanding corporate and scientific leadership, “incubated” many biotech management teams, and is the foremost example of this in the Bay Area.[15]
  • They serve as a reservoir for talent. In addition to providing training, biotech companies give students a reason to remain in a geographic location even after they are no longer associated with their university. A corporate ecosystem also makes it easier for someone to move his or her family to a given area to work at a risky startup – even if that startup fails, he/she will likely be able to find employment nearby without having to uproot family again. Biopharma companies absorb talent and retain it in an area, especially during times of turnover.

It is important to have companies of different sizes for a true innovation ecosystem to function well. New York, New Jersey, and Philadelphia are home to large pharmaceutical firms (e.g., Pfizer, Merck, Bristol Myers Squibb) but a relative dearth of small, innovative firms. The Bay Area and Boston, in contrast, have both large firms (e.g., Genentech and Gilead in the Bay Area, Pfizer, Novartis, and Biogen in Boston) and a host of smaller biotechs. The large companies soak up talent and provide basic scientific and management training, while the small biotechs develop new ideas and encourage movement of talent between organizations.

Venture capital

Venture capital firms provide the investment capital that is the commercial lifeblood of R&D-oriented startups. Biotech companies, like other R&D-oriented firms, require substantial upfront funding for many years before profits can be made. Biotech VCs evaluate these firms on technology and management talent and determine how to allocate capital to the most promising ideas. This is not easy – it requires substantial technical expertise and a willingness to commit large sums of money to risky projects. Notably, biotechnology investment is the domain of specialized firms, generally with a focus on specific stages of technology.[16]Beyond capital, VCs, armed with experience serving on boards across multiple firms, provide mentorship and support in recognizing and responding to the challenges.

Finally, established venture capital firms have large networks of management talent. They help new startups quickly build the talented management teams required to move fast and utilize funding efficiently. Once a company gets started, VCs dig into their rolodexes to facilitate business development and partnering deals and raise later stages of financing. Overall, they serve as the connective tissue across biotech innovation ecosystems.

The Bay Area’s VCs provide a critical advantage for the region’s ability to commercialize scientific discoveries. Without Kleiner Perkins, Genentech may never have had the capital to run its first experiments.[17] While Sand Hill Road’s technology VCs receive most of the national media limelight, the region’s life sciences VCs generate stellar returns, as well. The high density of these technically savvy, founder-friendly investors means that scientists and management teams in the area can find money and mentorship when their ideas are promising.

Supporting institutions

While major institutions are required for any innovation ecosystem to function, supporting institutions improve the efficiency with which ideas are developed into commercialized products. Stanford boasts a plethora of supporting institutions that facilitate development of promising ideas into products for patients. These range from education and mentorship programs designed to familiarize students, postdocs, and faculty with industry dynamics and basic business skills to incubation programs that guide innovators through critical steps on the path to commercialization and offer mentorship from industry experts.

A non-comprehensive list of these supporting institutions is provided in a companion paper.

Additional Factors

Beyond local institutions, additional factors play a role in a region’s ability to turn commercialize science. Many of these – history and culture, for example – are intangible. Others, like proximity, may be at least a partial function of geography. They are often difficult to influence, but critically important.

History

Innovation ecosystems cannot be built overnight. It takes time for major institutions to mature, then develop trust and productive cross-institution working relationships. At an interpersonal level, it takes time to develop strong, informal networks of experienced executives and scientists who understand the biotech industry. Hardware can be built quickly, but software takes time to develop.

Here, the Bay Area and Boston benefit from a decades-long head start on the rest of the country. California and Massachusetts were home to elite universities and recipients of significant federal government spending on science and engineering starting in the 1940s, both in the form of grants to top research universities and contracts awarded to defense contractors. This federal funding attracted technical talent and seeded a culture of engineering. Venture capital followed government funding to take advantage of this non-dilutive seed financing and nascent talent pool. Over decades, a dense infrastructure connecting academia, industry, and venture capital developed. Important institutional support – bankers and lawyers with business models tailored for startups – sprang up to support local ideas and businesses.[18]

Culture

As institutions take root and grow, they shape the norms, business practices, discussion, and people in an area. In other words, they shape culture, and this culture, in turn, shapes institutions.[19]

The Bay Area has historically been where revolutionary things happen. In Silicon Valley, innovators have, for decades, left comfortable jobs behind, questioned authority, and created entirely new industries.[20] These practices eventually created a culture where commercializing new ideas is neither strange nor especially scary. This strong entrepreneurial culture then self-selects for entrepreneurial people and converts prior non-entrepreneurs who “catch the bug.” In contrast, until recently, East Coast universities and companies were seen as more hierarchical and conservative than their West Coast counterparts. Broadly, academics were less willing to step out of their ivory towers to associate with industry. Aspiring entrepreneurs were less willing (and able, due to enforceable noncompete agreements) to leave large employers and set out on their own.

At Stanford, a cultural bent toward industry and entrepreneurship has important practical implications. Startup culture is the norm, and a part of typical interpersonal interactions. When seemingly everyone is willing to start a company, the social barrier to doing so diminishes. For professors, pursuing entrepreneurial ideas takes time, but does not detract from tenure decisions. The acceptance and promotion of entrepreneurship at Stanford fosters selection bias: on balance, comparatively industry-oriented, entrepreneurial students and faculty elect to study and work at Stanford over other universities.

This cultural dynamic creates a virtuous cycle in the life sciences. Commercially minded professors can maintain tenure while exploring entrepreneurial ideas. Entrepreneurial students actively seek to work with these commercially minded professors. With guidance from their commercially minded professors, entrepreneurial students spin companies out upon graduation, thereby enabling those professors to remain in academia while remaining involved with the startups that arise from their labs. These commercially minded professors continue running academic labs and mentoring the next generation of entrepreneurial students. Over time, such a dynamic creates a self-perpetuating, critical mass of people with great ideas and enthusiasm for collaboration with VC and industry.

Proximity of ecosystem players

In a world where remote work is commonplace, important elements of life sciences innovation – ideas, talent, and capital – can interact across distances. However, geographic proximity remains critical; while working remotely can be productive, laboratory research requires working in person.

Geographic proximity fosters personal connections, which are critical in a biotechnology industry that operates under an apprenticeship model. Being physically close together enables learning and collaboration: novices can ask uncomfortable questions; experienced scientists and managers can provide better mentorship over coffee than through a screen; venture capitalists can better diligence new companies and support portfolio companies that are just a short drive away.

Physical proximity also increases the chance of serendipitous interaction. Stanford and other university campuses are designed to house a high density of talented people who may meet serendipitously and produce innovative work. Word-of-mouth success stories can be inspiring for building a culture that facilitates commercialization of science.

In short, geographic proximity between academia, medicine, industry, and financing can create a superstructure akin to a protein complex that enables extremely efficient enzyme function. As in other areas of knowledge economy, agglomeration effects in the life sciences are significant. For this reason, companies founded elsewhere often move to the Bay Area or Boston to gain proximity to talent, capital, and culture.

Government support

Beyond the financial support for basic research provided in the form of grants from the National Institutes of Health (NIH) and National Science Foundation (NSF), the government provides additional non-dilutive funding for biotech startups. The federal government’s Small Business Innovation Research (SBIR) grants enable biotech startups to grow early on, before seeking dilutive VC funding. These grants are often the first funding that a biotech startup receives. State governments provide additional financing. The California Institute for Regenerative Medicine (CIRM), for example, has received $8.5 billion in taxpayer funding since its founding 2004 to support stem cell research.[21] The founders of many Bay Area biotech companies, including Forty Seven and Graphite Bio, received CIRM funding to develop their technologies within an academic setting before incorporating and taking on private investment.[22] [23] In Texas, the Cancer Prevention and Research Institute of Texas (CPRIT) plays a similar role.[24]

More importantly, government can shape the physical and human environment in a way that encourages innovation. The example of Cambridge’s Kendall Square illustrates the impact that government initiatives can have on stimulating an academic and industrial renaissance.

In the early 2000s, the Boston area was already home to a few established biotech firms (e.g., Biogen, Genzyme, Vertex Pharmaceuticals). At the time, however, Boston lagged behind the Bay Area in terms of biotech revenue, jobs, and research and development funding.[25] A 2003 report on Massachusetts’ competitive positioning in life sciences developed by Michael Porter found that Boston fell behind the Bay Area in life sciences employment, wage growth, and patent output.[26] Porter identified the Boston area’s world-class universities and hospitals, as well as its existing biotech industry and high density, as critical advantages. Despite the availability of raw material for a dynamic biotech ecosystem, Boston remained in second place and in danger of falling further behind.

Around this time, the Massachusetts state government initiated a deliberate effort to stimulate the local biotech economy by retaining existing firms and expanding the footprint of the industry. A 2003 economic development bill included tax credits for life sciences companies that promised to create jobs in the state. Over the next few years, Bristol Myers Squibb, encouraged by $67 million in tax breaks and other incentives, built a $750 million R&D facility in Cambridge.[27] Around this time, Novartis invested $250 million to move its worldwide R&D headquarters to Cambridge (after reportedly turning down offers of state-sponsored assistance).[28] [29] Merck and Pfizer, among other companies, also established R&D operations in the area. Meanwhile, venture capital firms and incubators sprang up; tech companies moved in.[30] As a result of this work, Ranch Kimball, Massachusetts’ Secretary of Economic Development from 2004 to 2007, was named “Outstanding State Executive” nationally by the Biotechnology Innovation Organization (BIO).[31] Thomas Finneran, president of the Massachusetts Biotechnology Council, called Mitt Romney “the best life scientist governor of the U.S.”[32]

The Massachusetts Life Sciences Initiative, enacted in 2008 under Governor Deval Patrick, built on the success of the early 2000s success by committing $1 billion in life sciences investment and tax incentives over ten years.[33] Meanwhile, the Kendall Square area, heart of Cambridge’s biotech ecosystem, developed from a “Nowhere Square” of post-industrial parking lots to a re-zoned, revitalized, mix-used hub of activity with apartments, hip restaurants and bars, and retail alongside offices and labs.[34] Today, Kendall Square can genuinely lay claim to being the “the most innovative square mile on the planet.”

Implications for Existing and Developing Innovation Ecosystems

It takes a village to take an idea from the lab and develop it into a commercialized product. In this case, some villages function more effectively than others. Below, I lay out suggestions for continued progress, both for regions that are working to build biotech ecosystems and innovation hubs that are already the envy of the rest of the world.

Higher density increases innovation

Stanford is the only top-five engineering school that is also home to a top medical school and world-class hospital.[35] In contrast, scientists at UC Berkeley who want to work with clinicians at UCSF must drive up to an hour across the Bay Bridge and enter a different institution to collaborate. This contrast highlights a significant advantage that Stanford enjoys: having the major academic components of a biomedical innovation ecosystem physically on the same campus and organizationally under the same administration, enables more frequently serendipitous meetings and seamless collaboration.

Similarly, Cambridge benefits from higher density than the Bay Area. Kendall Square represents a tight geographic radius, where biotech firms, VCs, and university labs often occupy the same buildings. An MIT student can graduate, start a new job, then move to a different company without changing his or her daily commute. Boston-area institutions have built on this physical density by removing administrative barriers and increasing “organizational” density. For example, the Broad Institute, established in 2004, enables seamless collaboration between Harvard and MIT scientists.[36] Collaboration between academia and industry in the Bay Area, on the other hand, requires commuting long distances. No formalized institution akin to the Broad exists to encourage collaboration between Stanford, UCSF, or UC Berkeley.

Given the importance of density, it may be instructive to compare the relative sizes of various life sciences innovation clusters.[37]

 

Innovation ecosystem “Corners” of triangle Area (square miles)
Boston / Cambridge Harvard, Mass General, Brigham and Women’s (MIT / Kendall Square fall in the middle) 3.3
Research Triangle Duke, North Carolina Chapel Hill, Raleigh 102
Bay Area Stanford, UCSF, UC Berkeley 186
Loxbridge Triangle Oxford, Cambridge, London 1,251
Texas Triangle Austin, Houston, Dallas/Fort Worth 13,392

 

The Boston / Cambridge area is two orders of magnitude more compact than Research Triangle or the Bay Area, which are both an order of magnitude smaller than Loxbridge Triangle. The “Texas Triangle,” comprised of three Texas cities that are combining resources in the hope of creating a new biotech hub, is so large that it barely qualifies as a single “cluster” compared to the other geographies.[38] The sheer size of this Texas Triangle suggests that significant efforts to increase organizational density must be made for the region to function as a cohesive innovation ecosystem; investment may be more effective if concentrated on one corner of the triangle rather than dispersed across the entire 13,000-square-mile region.

Institution-building takes time

Major institutions are not typically built overnight. The Bay Area’s success in commercializing scientific discoveries are built on the culture and infrastructure of Silicon Valley, the venture capital of Sand Hill Road, and the flow of ideas from Stanford, UCSF, and UC Berkeley. The Boston area’s biotech renaissance started in the early 2000s and gathered steam quickly over the next decade, but relied on critical institutions – world-class universities and hospitals and a handful of well-respected biotech firms – that were already in place. Given the extended time and effort required to create an ecosystem capable of turning laboratory discoveries into commercial products, regions seeking to develop biotech clusters should understand their weaknesses and build on their existing strengths.

Public policy is critical for developing innovation clusters

The rise of Kendall Square since the early 2000s, with a heavy assist from the Massachusetts state government, demonstrates the substantial role that public policy can play in enabling the development of life sciences innovation ecosystems. Life sciences research has been heavily subsidized by government funding since the creation of the NIH; more recently, it has become clear that government can also play a large role in shepherding ideas through the process of development and commercialization.

The Chinese government has taken this lesson to heart and poured billions of dollars of investment into developing a homegrown biotechnology industry. The last decade has witnessed an explosion in the amount of venture capital available to Chinese biotech firms and a growth of biotech startups, many of which are developing novel therapeutics for export to Western markets. The Chinese biotech industry is concentrated in three clusters: Beijing-Tianjin-Hebei, Shanghai, and the Pearl River Delta (i.e., Guangzhou, Shenzhen, and Hong Kong); local governments in each of these regions have supported industrial development with government funding and supportive policies. While the Chinese biotech industry is still in its relative infancy, it has made tremendous progress in a few short years, thanks in large part to enormous government investment.[39]

Thanks to its decades-long head start, the United States still leads the world in life sciences innovation. However, in recent years, the federal government has invested less in life sciences research – between 2002 and 2015, NIH funding declined in inflation-adjusted 2022 dollars and, as of 2021, has still yet to reach 2002 levels.[40] Here, the federal government would do well to look to the example set by state and local governments that have supported the translation of new ideas to commercial products.

There remains room for improvement

From the 1950s through the 1970s, the epicenter of American technology rested around Route 128 in Massachusetts, not Silicon Valley in California. Digital Equipment Corporation, Raytheon, and Lotus Development Corporation were all founded and headquartered along “America’s Technology Highway,” and contributed to Massachusetts’ economic dynamism through the 1980s. The rise of Silicon Valley, however, shifted the worldwide epicenter of technology westward.[41] Today, Route 128 is something of a footnote in the collective 21st century understanding of information technology.

The Bay Area is the birthplace of biotech – it showed the world that scientists collaborating across institutions (Stanford and UCSF) could come up with a groundbreaking idea, then turn it into a world changing technology and company with the help of venture capital financing. However, the Boston/Cambridge area has since surpassed the Bay Area as the world capital of biotech. In the meantime, neither the California state government nor local governments have made concerted efforts to support the growth and development of the life sciences industry (with the exception of stem cell research). Without additional attention, it is not inconceivable that the Bay Area biotech ecosystem could further lose its preeminence in the coming decades. The COVID-19 crisis and the rise of remote work environments will certainly put the existing model to the test.

For Stanford and the Bay Area as a whole, the major pieces of the puzzle are clearly established, but this is a dynamic environment. Stanford, for its part, continues to blend academia and industry under Marc Tessier-Lavigne, President of the University and former Chief Scientific Officer at Genentech, and Lloyd Minor, Dean of the School of Medicine and promoter of Stanford’s role in enabling precision biomedicine through translational research.[42] [43] Ongoing efforts to deepen networks and increase the likelihood of serendipity will continue to make the whole even greater than the sum of its parts.

Acknowledgements

I would like to thank the Stanford University faculty and researchers who provided important information and perspectives for this paper:

  • Carolyn Bertozzi, PhD, Director of Stanford ChEM-H, Professor in the School of Humanities and Sciences, Stanford
  • Matthew Bogyo, PhD, Professor of Pathology, of Microbiology, and of Immunology, Stanford
  • Scott Boyd, MD, PhD, Associate Professor of Pathology, Stanford
  • Jennifer Cochran, PhD, Chair of the Department of Bioengineering, Professor of Bioengineering, Stanford
  • Scott Dixon, PhD, Associate Professor of Biology, Stanford
  • Edgar Engleman, MD, Professor of Pathology and of Medicine, Stanford
  • Linda Grais, MD, JD, former CEO, Ocera Therapeutics, former Partner at InterWest Partners
  • Kevin Grimes, MD, MBA, Professor of Chemical and Systems Biology, Stanford
  • Stephen Johnson, JD, Lecturer, Stanford Graduate School of Business, former Partner, Kirkland & Ellis
  • Perry Karsen, MIM, Chairman, Graphite Bio
  • Robert Langer, ScD, Institute Professor, MIT
  • Josh Lehrer, MD, CEO, Graphite Bio
  • Ron Levy, MD, Professor in the School of Medicine, Stanford
  • Crystal Mackall, MD, Professor of Pediatrics and Medicine, Stanford
  • Ravindra Majeti, MD, PhD, Professor of Medicine, Chief of the Division of Hematology, Stanford
  • Garry Nolan, PhD, Professor of Pathology, Stanford
  • Matthew Porteus, MD, PhD, Professor of Pediatrics, Stem Cell Transplantation, Stanford
  • Stephen Quake, PhD, Professor of Bioengineering and Professor of Applied Physics, Stanford; President, Chan Zuckerberg Biohub
  • Michael Snyder, PhD, Professor of Genetics, Stanford
  • Tom Soh, PhD, Professor of Radiology, Electrical Engineering, Stanford
  • Ansuman Satpathy, MD, PhD, Assistant Professor of Pathology, Stanford
  • Mona Wan, MBA, Associate Director of Licensing, Office of Technology Licensing, Stanford
  • Joseph Wu, MD, Director of Stanford Cardiovascular Institute, Professor of Radiology, Stanford

 

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