HMPI

Word from the HMPI Editor

As described in this issue, we are undergoing two major transitions in healthcare: a demographic transition and a technology transition. On the demographic side, aging populations will put enormous strain on health are systems by increasing demand at a time when we might become supply constrained in terms of personnel. One major healthcare system in the United States is touting its ability to maintain its provider level despite the rapid pace of retirements as a competitive advantage.

It’s no secret that technology is moving forward at an amazing pace. One of my colleagues at Stanford, Fei-Fei Li, has just written a memoir about the evolution of AI (The Worlds I See). She describes Geoffrey Hinton’s first successful use of a neural network for computer vision research in 2012, little more than a decade ago. It’s important to reflect on how early we are in the use of this technology. One great metaphor I have heard is that we’re back at the dawn of electricity – we’ve invented the copper wire, but not yet the light bulb, and so we’re constantly getting shocked as we explore use cases and applications. The withdrawal of Cruise from San Francisco and the recall of Tesla Autopilot are examples of this process of discovery in real time. Thus, the appropriate concern about safety in healthcare applications – applications that are, frankly, being developed with teams that have far fewer resources than these well-funded efforts.

But AI is a technology. In the Clayton Christensen world, to have true cost and quality improvements in a market, we need technology innovation and business model innovation. For all of the excitement of AI, we’re ignoring the business model and business process challenges. Building from the work of Gerry Anderson and Ge Bai at Johns Hopkins, my coauthors and I recently described how dysfunctional the healthcare market has become in terms of complexity (https://jamanetwork.com/journals/jama/fullarticle/2812255).

We are going to have to wake up to this unfortunate reality if we are going to realize the full potential of this technology as a solution to our demographic and work force challenges. Dissecting and solving the business process challenge is a great role to play for all of us studying healthcare management and healthcare systems.

Kevin Schulman, MD, MBA
BAHM President & HMPI Editor-in-Chief
Professor of Medicine, Stanford University

 

Preparing for Future Emergencies: Insights from Costa Rica and the Dominican Republic in Maintaining Essential Health Services During COVID-19

Andrea M. Prado, Andy A. Pearson and Claudio A. Mora-García, INCAE Business School, and Magdalena Rathe, Plenitud Foundation

Contact: andrea.prado@incae.edu

Abstract

What is the message? When public health emergencies occur, health systems must not only manage the outbreak, they must also maintain their essential health services. The authors provide recommendations on how to better prepare for future pandemics: 1. coordinating actions and distributing resources across levels of care and geographic regions, including access to real-time resource data, and 2. developing crisis management plans that define administrative procedures, health services priorities and delivery, and financing.

What is the evidence? A series of recommendations for health systems’ preparedness based on on the analysis of efforts by Costa Rica and the Dominican Republic to maintain EHS during COVID-19, The two countries were selected among six countries worldwide for their exemplary handling of the pandemic, compared to similar nations, based on certain quantitative outcomes.

Timeline: Submitted: September 21, 2023; accepted after review December 16, 2023.

Cite as: Andrea Prado, Andy Pearson, Claudio Mora-García. 2023. Preparing for Future Emergencies: Insights from Costa Rica and the Dominican Republic in Maintaining Essential Health Services during COVID-19. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 3.

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Introduction

COVID-19 represented a significant challenge for health systems worldwide, both in the approach health institutions took to the new disease and in maintaining active essential health services (EHS) for the general population. Data from the WHO pulse surveys reveal that 90% of countries reporteddisruptions to EHS during the COVID-19 pandemic (1). In the face of the unprecedented challenges brought about by the pandemic, the importance of crisis management and preparedness in the health sector became exceptionally crucial (2,3). This phenomenon renewed the interest of consulting companies and scholars in managing crises (4–7). Overall, the cumulative experiences derived from crisis-driven reorganization and expert leadership underscore the importance of preparedness in navigating the inherent complexities of crises.

In this paper, we provide insights for future pandemic preparedness based on the responses of two healthcare systems in Latin America that undertook measures to maintain their EHS during the COVID-19 pandemic. Costa Rica’s and the Dominican Republic’s healthcare systems have significantly different organizational structures for providing mandatory healthcare insurance. Healthcare provision in Costa Rica—at all levels of care—is under the responsibility of a single public provider. In the Dominican Republic, multiple healthcare providers—public and private—coexist at various levels of care. As in previous literature, we refer to the former as an “integrated” healthcare system and to the latter as “fragmented” (8).

Both these countries have a universal health insurance mandate, but they differ in terms of healthcare providers, how funding is distributed among them, and who collects the insurance contributions. Funding mechanisms for both systems include 1) a contributory regime financed by payroll taxes that covers formalized workers and 2) a subsidized regime financed by general taxes that covers low-income and unemployed people.

In Costa Rica, the Costa Rican Social Security Fund (CCSS) is the single institution responsible for providing the healthcare services of the mandatory health insurance known as Sick and Maternity Insurance (SEM). SEM provides access to the same health package of services to all users. The CCSS provides healthcare through a network of 626 primary-level clinics, 20 regional hospitals, 17 second-level clinics, three third-level national hospitals, six specialized hospitals, and six specialized centers that cover over 97% of the population (9,10).

Healthcare provision in the Dominican Republic is offered by public and private providers, organized under regulated competition in a compulsory health insurance system (11,12). In 2001, the Dominican Republic created a universal health insurance scheme called Family Health Insurance (SFS) that provides all users the same basic package of services. in the contributory regime is provided by multiple insurers (i.e., public and private, for-profit and not-for-profit), known as Administradoras de Riesgos de Salud (ARS). These ARS receive the same per-capita payment from the Social Security Treasury each month to provide a single package of services that the National Council of Social Security defines. They contract services with a network of public or private providers organized by levels of care, known as Prestadoras de Servicios de Salud (PSS). The most important ARS in terms of insured population is public, the Seguro Nacional de Salud (SeNaSa), which is the only one authorized to affiliate the population in the subsidized regime. Although it originally contracted mainly public providers at the Servicio Nacional de Salud (the general provider’s network), if it cannot find quality services within the network, it can pay private providers for these instead. By 2019, 78% of the population had mandatory health insurance. In 2020, due to the pandemic, the government increased the insurance coverage to 95%.

Costa Rica and the Dominican Republic differ in who collects the money and how it is distributed between healthcare providers. In Costa Rica, the CCSS is responsible for collecting SEM contributions. The money goes to a shared pool managed by the CCSS, which distributes it on a capitation basis and according to historical transfers of the different clinics and hospitals. In the Dominican Republic, SFS contributions also go to a shared pool managed by the Social Security Treasury, which distributes it among insurance agencies on a capitation basis, and insurance agencies pay healthcare providers through a service fee.

Table 1 compares selected indicators measuring Costa Rica’s and the Dominican Republic’s health system performance in 2019.

Table 1: Selected indicators measuring Costa Rica and Dominican Republic health system’s performance in 2019 (or the latest year available)

 

Indicator Costa Rica Dominican Republic
HEALTH OUTCOMES
Population (in million) 5.1 10.8
Life expectancy at birth (in years) 80.1 73.2
Median age (in years) 33.4 28.0
Universal Health Care

effective coverage index (0-100)

 79.0 52.0
DTP3 coverage in children (%) 97% 75%
Age-standardized smoking prevalence (%) Women: 7.2%
Men: 15.3%		 Women: 9.3%
Men: 13.7%
Average body mass index 27.5 27.5
All-age diabetes prevalence (%) 8.1% 4.1%
All-age cardiovascular diabetes

prevalence (%)

 5.8% 5.8%
All-age cancer prevalence (%) 6.5% 4.8%
Mean PM2.5 air pollution 15.4 13.3
RESOURCES
Physicians (per 1,000 people) 2.89 1.45
Nurses and midwives (per 1,000 people) 3.41 1.46
Hospital beds (per 1,000 people) 1.10 1.56*
HEALTH EXPENDITURE AND FINANCING
GDP per capita (current international $ PPP) $22,608 $18,942
Current health expenditure (per capita) $1,623 $803
Current health expenditure (% GDP) 7.22% 4.24%
Out-of-pocket health expenditure (per capita) $201 $180
Out-of-pocket health expenditure (% of total health expenditure) 18.1% 36.8%

Notes: (*) refers to data from 2017. Sources: own elaboration based on analysis from (14–16).

Methods

We based this paper on a multi-country effort led by the Exemplars in Global Health (EGH) initiative from the Gates Ventures Foundation to understand the COVID-19 response from six countries and how it affected or contributed to maintaining Essential Health Services (EHS). The individual country results are available on the EGH website (14). The six countries were chosen for their exemplary handling of the pandemic compared to similar nations, based on key COVID-19 metrics (like age-adjusted death rates, per capita case numbers, and test positivity rates) and EHS metrics, such as the impact on vital immunizations. The selection process for the EGH report also included a review of relevant literature and policies, interviews with regional experts, and the potential applicability of their strategies.

Local research teams in each of the six countries then conducted comprehensive research, using mixed methods to uncover effective strategies for managing COVID-19 and sustaining EHS from April 2021 to September 2022. This included extensive desk research, interviews with key stakeholders, and quantitative and qualitative data analyses. The teams produced final reports summarizing national efforts to preserve EHS amid the pandemic and highlighting effective policies and practices for handling COVID-19. In this paper, we examined the country case reports for Costa Rica and the Dominican Republic in the EGH website and compared the findings from the two countries to uncover actionable lessons and insights to enhance other health systems’ resilience and improve preparedness for future pandemic responses.

Recommendations for strengthening preparedness for future pandemics

Based on Costa Rica’s and the Dominican Republic’s responses to maintaining EHS during COVID-19 (for more detail on these countries’ responses and outcomes, see (14,17,18)), we provide the following recommendations. First, governments must aim to coordinate the response throughout the health system. Second, health systems must define ex-ante a crisis management plan. While these recommendations were undoubtedly useful for Costa Rica and the Dominican Republic, they can be considered by other countries when preparing for emergencies.

Recommendation 1: Coordinate the response throughout the healthcare system.

Health systems are organized to provide multiple levels of care, with hospitals and clinics distributed in various geographic regions throughout the country. The provision of health services to the population are often shared by public and private institutions, whose prominence will depend on how the country’s mandatory health insurance operates. During an outbreak, physical and human resources, as well as medical equipment, become insufficient. Thus, managing limited resources and avoiding saturation of facilities is essential to maintaining EHS. Coordination among facilities and levels of attention within a single institution like in the CCSS or among public and private health providers like in the Dominican Republic contributes towards this goal. Such coordination efforts are enhanced if there is access to data that allows decisionmakers to allocate resources efficiently. To illustrate how health systems could implement this coordination, we describe what these two countries did in terms of the transfer of patients and access to data for decision-making.

a. Transfer patients throughout the health system. CCSS’s vertically and horizontally integrated system allowed for the implementation of new strategies for transferring COVID-19 and non-COVID-19 patients among hospitals in the network and among various levels of care. The CCSS noticed that the pandemic affected regions differently, so COVID-19 patients were distributed across different areas of the country. Aiming to make efficient use of the available resources, the institution also created a communication infrastructure specialized in emergency transfers of COVID-19 patients. The communication infrastructure included a team of healthcare workers (known as PRIME, an acronym in Spanish for First Specialized Medical Intervention) and a communications center (the COV-19 team) that centrally coordinated transfers based on real-time data about network availability and patients’ health records.

PRIME transferred patients located throughout hospitals to the CEACO, Costa Rica’s COVID-19 specialized center, and vice versa. This transfer of patients was important given that some clinics and hospitals in rural areas were not equipped to handle large numbers of seriously ill COVID-19 patients. The COV-19 team focused on finding available beds in the system for COVID-19 patients, hence contributing to maintaining EHS by allowing the system to avoid hospital overcrowding and the collapse of hospital care.

In the case of the Dominican Republic, the coordination efforts and transfer of patients were not within a single institution but instead among public and private health providers in the system. Strong leadership at the highest level of the Health Cabinet—above the Ministry of Health—was crucial to achieving a public-private partnership that allowed for the transfer of patients among providers. The Health Cabinet provided a coordinated response through multisectoral and public-private partnerships and effective coordination between the government and the private sector. The Health Cabinet coordinated this partnership, which helped ensure big hospitals did not reach maximum capacity and that ambulances were available in selected hospitals to help transfer patients among facilities.

Organizing a coordinated response within a single institution, such as the CCSS, is likely to need fewer stakeholders sitting in the same room than in a health system with multiple public and private providers. The former involves reaching agreements and redistributing resources (e.g., financial or personnel) internally; regarding the latter, the different logic and incentives of public and private providers are likely to increase the complexity of the coordination efforts. Thus, strong leadership is even more essential when communicating among stakeholders from different organizations. Finally, one challenge to providing a coordinated response has to do with budgets. Transferring patients and healthcare workers between and within health facilities challenges the directors of hospitals and clinics that run these facilities under tight budgets.

b. Share real-time data for decision-making. Health facilities throughout the system should establish a mechanism to share data in real time during an outbreak. Information on the availability and location of health resources (e.g., beds, ICUs, surgery rooms, ambulances, doctors) is essential to support the coordination efforts in the system, both for coping with the pandemic and for maintaining EHS. This data can help decisionmakers allocate patients (from the pandemic and EHS) across the system and effectively deploy the beds, intensive care units (ICUs), laboratories, and healthcare workers. Ideally, this information would be made available in a centralized information system.

In Costa Rica, the existence of a single digital medical health record that is accessible from the “cloud” helped the CCSS maintain EHS. The Single Digital Health Record (EDUS for its acronym in Spanish) works as a unified and integrated technological tool for individual patient health records. Healthcare workers can access EDUS at the point of care and can record notes about patients, which are accessible by colleagues. EDUS also records information on the provision side, including, for example, production indicators from the healthcare workers and bed occupancy rates. This digital record helps to monitor the system’s production indicators through reports and real-time dashboards. EDUS allowed for real-time data access on the number of available beds throughout the system and for sharing patients’ medical health records among doctors in different health facilities. In addition, this technology was deployed by a decision-making top-management team that analyzed the information generated by this platform and used it to reassign COVID-19 and non-COVID-19 patients and resources throughout the network.

In the Dominican Republic, in view of the need for a centralized information system, the Ministry of Defense developed a digital platform that centralized data from public and private hospitals, clinics, laboratories, pharmacies, and insurance agencies. The Command, Control, Communications, Computers Cybersecurity and Intelligence Center (C5i) of the Armed Forces and the Ministry of Health (MOH) coordinated this platform that provided real-time data on the number of beds available and in use in a hospital, ICUs, ventilators, and ambulances. Private sector providers volunteered their data to feed the system, strengthening the public-private partnership mentioned above and facilitating the transfer of patients among networks. The platform was most useful in ensuring that large hospitals did not reach maximum capacity. With the hospital occupancy data, the SNS transferred patients who were not in need of exclusive treatment in specific hospitals to less-occupied health centers, depending on the complexity and severity of their cases.

In addition to these platforms that allowed the sharing of resources’ inventory data, and in the case of Costa Rica, also patients’ medical records, both countries had special committees or artificial intelligence systems tracking the evolution of the pandemic and making projections to predict the behavior of the virus and act accordingly.

Previous research on crisis management has highlighted the vital role of systematically gathering and using precise information, emphasizing its direct impact on possible outcomes (19,20). Studies in this field outline key elements like timely information distribution, optimal data for decision-making, and smooth information flow as essential to effectively manage crises (19). In addition, they assert the need for leaders to embrace innovative methods (e.g., big data analytics) to optimize crisis response strategies (20).

Recommendation 2: Define ex-ante crisis management structures for decision-making.

A crisis management plan needs to begin with early identification and intervention to mitigate potential issues (21). This procedure involves anticipating problems, allocating resources, establishing information systems, and formulating action plans. Afterward, it is cruical that the organizations implement a prevention plan that include continual monitoring, audits, a crisis scenario assessment, and comprehensive planning to be ready for a potential crisis.

Decision-making, data sharing, and coordination efforts during an emergency are likely to require additional structure, like special committees and cross-disciplinary and inter- or intra-institutional teams. Our second recommendation advises decisionmakers to develop an ex-ante crisis management plan that establishes a basic governance structure and procedures to support EHS during a pandemic so that it becomes the first “go-to” structure that helps sustain EHS services and addresses the emergency. Of course, such a governance structure does not have to operate all the time, but health systems should establish a fundamental “go-to” operation mode that activates as soon as authorities declare a public health crisis.

The crisis management plan should include at least four elements:

a. Determine exemption rules. 

Once a pandemic or public health emergency starts, it is difficult to process permissions to change managerial procedures or make exemptions. Market conditions might require expedited procurement practices, which need to be authorized by other institutions. Given the high levels of urgency during crises, these exemptions should be authorized in “normal” times before the next crisis. The exemptions can also include flexible job descriptions for HCWs once an emergency is declared along with task shifts, i.e., a rational redistribution of tasks among health workforce teams to help address shortages of HCWs (22). The objective is to activate a set of exceptions under certain conditions, decreasing improvisation or negotiating permissions under pressure.

Local legislation in Costa Rica foresees some procurement and managerial exceptions, and other countries can review their legislation to confirm the existence of similar opportunities in the public sector. More flexible bureaucratic procedures during an emergency are fundamental to facing a pandemic, as it is likely that there would be scarcity, price speculation, and competition for health supplies and equipment in international markets. In the Dominican Republic, once an emergency is declared, public sector entities can make more flexible internal and external procurement decisions. Their experience using this administrative mechanism builds on previous public health emergencies (e.g., Zika) and natural disasters (e.g., hurricanes). However, even with irregular fast-track procurement, it is important to keep minimum quality controls active, even during emergencies. While eased purchasing procedures in Costa Rica were undoubtedly important during the pandemic, they have also been accompanied by criticisms from the local press and institutional audits related to questionable procurement processes.1

b. Establish a list of Essential Health Services

Authorities must establish a list of which are to be considered EHS in case of an outbreak. The services listed must continue to be provided while the health systems battle the pandemic. During an emergency, the authorities can revise the list of EHS and prioritize according to the pandemic’s context. Neither the CCSS in Costa Rica nor the Dominican Republic had such a list. However, in the case of Costa Rica, a mandate by CCSS headquarters early in the pandemic to reduce in-person medical visits indirectly prioritized some specialties. Similarly, the Dominican Republic implicitly defined what was essential, given the allocation of resources among non-COVID-19 patients.  Nevertheless, establishing ex-ante what the EHS are, would include the country’s epidemiological profile, priorities, and costs of interrupting services for specific patients.

c. Define which services should be delivered in person or through alternative modalities.

The experience with alternative modalities of care during COVID-19 can inform decision-making, not only in terms of the choice of the services to deliver virtually, but also which technology or platforms appropriate are most appropriate. For online services, authorities should consider how to provide auxiliary services (e.g., x-rays, lab tests, medications) for healthcare workers to deliver effective services to patients, having the appropriate inputs and data for recommendations, diagnoses, or prescriptions.

In Costa Rica, the pandemic increased the use of alternative modalities of care that were already being adopted on a small scale; these modalities included remote patient monitoring, call centers for COVID-19 patients, and partnerships to expand service delivery. Supporting alternative modalities of care allowed the CCSS to maintain outpatient visits, to follow up remotely on non-COVID-19 patients, and to protect healthcare workers and patients from infection. Remote patient monitoring (RPM) enabled healthcare workers to conduct remote consultations via telephone or video calls. RPM proved valuable for certain specialties that did not require physical examinations. Additionally, RPM facilitated post-diagnostic follow-up once lab results were ready in the electronic medical record, EDUS, further reducing non-urgent in-person visits.

In the Dominican Republic, the Critical Care Telemedicine Project aimed to connect healthcare workers of provincial hospitals with high-complexity base centers specializing in critical care. The project improved the referral system and flow of coronavirus patients to higher-level care centers, improved the efficiency and quality of health services in critical care, and promoted collaborative work between local teams of healthcare workers. Additionally, some private ARS, such as ARS Humano, introduced telemedicine services with the company TELEMED, providing audio or videoconference services with a network of doctors who offer information and guidance, interpretation of test results and diagnosis, and electronic prescriptions. Another one of the largest private ARS, called Mapfre, offered advice and medical support via telephone and the Internet through its Audio Doctor platform. ARS Universal has its Universal En-Línea app, in which it offers guidance on coverage for the Covid-19 test, laboratories, and other services with its affiliates.

The pandemic provided an opportunity for health systems to scale up what, at the time, were pilot telemedicine projects and to learn about the challenges these modalities pose in terms of equity and quality of healthcare.  While useful, RPM also raised concerns among key stakeholders around the quality of care and equitable access to these alternative modalities of care. Challenges included healthcare providers’ limited experience with these tools, insufficient quality control, and barriers to technology access for low-income patients. Patient associations noted feelings of neglect in the absence of in-person doctor visits despite the benefits of telemedicine. Key stakeholders also reported during the interviews that patients with a scheduled appointment did not answer the phone or did not have a data plan to access the video call; other patients required help, given technological gaps in the use of smartphones and video call applications. RPM highlighted challenges related to healthcare equity and quality, particularly for services that necessitated in-person attention, such as surgeries.

d. Define how to finance the response through contingency funds.

Public health emergencies are likely to demand that the health system acquire additional medical equipment and supplies, human resources (new hires or extra hours), technologies, and medications. Thus, it is essential to have a contingency fund when an emergency is declared. In Costa Rica, the existence of sufficient financial resources was one of the main reasons CCSS was able to maintain EHS during the pandemic. The CCSS used its contingency fund to build infrastructure, purchase equipment, and hire additional HCWs. Created in 2016, the fund provides monetary resources to the CCSS in case of disasters such as earthquakes, floods, or fires affecting the CCSS health services. During the pandemic, the fund received additional investments from the Costa Rican government and loans from international finance institutions. This fund financed most of the $101 million adaptations the CCSS underwent through the pandemic (23). In addition, the CCSS was able to tap $40 million from different MoH programs (like AIDS and Vector-borne diseases budgets) and a $52 million Emergency Fund from the National Emergency Commission.

In the Dominican Republic, the government assigned financial resources from an international loan that was previously approved for a specific project. Despite having a contingency fund, authorities were not able to draw resources from it, as it had no available funds available, and for years the responsible organizations did not allocate the corresponding resources.

Conclusion

Neither Costa Rica nor the Dominican Republic are wealthy countries. Yet, they have achieved better health outcomes than many high-income countries, while spending significantly less on health (15). In the case of Costa Rica, sustained investments in the welfare state have enabled a functioning healthcare system that works even in the event of unexpected public health  emergencies. In 1995, the CCSS adopted a primary healthcare reform recognized as a role model worldwide (24). With its tradition of innovation, by 2018, the CCSS had implemented a unique digital medical record throughout its integrated network of health facilities, allowing the institution to have real-time data for decision-making. This timely innovation enhanced CCSS’s response to the COVID-19 pandemic, while also enabling Costa Rica to maintain EHS.

In contrast to Costa Rice, both public and private insurers are providers of mandatory health insurance in the Dominican Republic. Nevertheless, through strong leadership at the highest level (e.g., Health Cabinet) and relying on previous crisis management experience, the country managed the pandemic effectively.

Insights from these two health systems suggest that it is extremely helpful for a country to be able both to: 1) pool scarce healthcare resources to maximize their effectiveness; 2) have a centralized system of data collection that is available to all key healthcare decision-makers; and 3) have an institutional flexibility to change procedures, and quickly, when necessary.

References

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Regi’s “Innovating in Health Care” Case Corner

Case: Pear Therapeutics’ Failure: Paying the Trailblazer Tax (Case: SM-369; date: 08/22/23; length: 19 pages)

Authors: James Tai, Ethan Goh, Margaret Wenzlau, Shikha Avancha, and Professor Kevin Schulman, MD, MBA, Stanford Graduate School of Business

Background

Digital therapeutics (DTX) offers the promise of using digital technology to benefit patients. The concept is to build from existing science and clinical strategies to create algorithms to support improve patient outcomes. In contrast with consumer applications, DTX algorithms are meant to carry a clinical label, and be subject to FDA review as medical devices (potentially including clinical trials to support the labeled indications).

One of the most interesting companies developing the DTx concept was Pear Therapeutics. At one time Pear was a digital health unicorn valued at over $1 billion. It pushed for the first mover advantage in the DTx space by aggressively expanding its R&D portfolio. Unfortunately, the reimbursement for the DTx market was slow to develop, and Pear’s aggressive investment in research and marketing outmatched market acceptance of their products, leading to Pear’s bankruptcy in 2023. This case asks the question of whether this is the end of the DTx concept, or just the “Trailblazer’s Tax” leaving interest in the space for new entrants.

The case reviews the development of the DTx Concept, and the regulatory and reimbursement challenges for this class of technologies.

Download the case. For inquiries, contact Kevin Schulman kevin.schulman@stanford.edu

 

Aging in the Americas: A Multi-Country Discussion

Steven G Ullmann, University of Miami Herbert Business School, and Francisco Palao Reines, Purpose Alliance

Contact: sullmann@bus.miami.edu

Abstract

What is the message? Aging in the Americas is putting increasing pressure on Latin American nations, their public and private sectors, as well as on families, caregivers, and the aged themselves. This is a function of not only the growing number of elderly, but the significantly increasing longevity of this population and the associated quality of life.

What is the evidence? High-level roundtable participants in six countries, Argentina. Brazil, Chile, Colombia, Costa Rica, and Mexico, convened in person in their countries and then gathered virtually to discuss and develop initiatives associated with increased longevity and quality of life.

Timeline: Submitted: November 3, 2023; accepted after review December 5, 2023.

Cite as: Steven Ullmann, Francisco Reines. 2023. Aging in the Americas: A Multi-Country Discussion. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 2.

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Introduction

Aging in the Americas is becoming a critical issue impacting healthcare systems, indeed impacting entire economies in the region. To explore this challenge in depth, and in a multi-country format, the University of Miami Center for Health Management and Policy hosted simultaneous roundtables in six Latin American countries, Argentina, Brazil, Chile, Columbia, Costa Rica, and Mexico, bringing together more than fifty leaders from various sectors of these healthcare ecosystems. The purpose was to promote collaboration and the exchange of ideas, best practices, and experiences to help co-create a viable future for healthcare systems. A key area of focus was to find ways to adjust and adapt these systems to prepare for a future in which life expectancy continues to increase.

Prior to the event, all participants received online training on Purpose Launchpad [1], an open framework that leverages state-of-the-art innovation methodologies to support the generation of new initiatives, such as startups and new products, and to evolve existing organizations to make a positive impact.

Setting the stage for the roundtable discussions were introductory remarks by Mauricio Ortiz, president of Boston Scientific for Latin America and sponsor of the program, and keynote addresses by Julio Frenk, MD, PhD, President of the University of Miami and former Minister of Health of Mexico; Alex Azar, former United States Secretary of Health and Human Services; Josè Cordeiro, coauthor of “The Death of Death,” [2] and founder of the not-for-profit organization Purpose Alliance; and Francisco Palao author of the book “Positive Impact” [3].

Each country’s roundtable discussions led to a proposed initiative associated with a specific objective to help create “the healthcare system of the future in Latin America, thereby improving the quality of life and promoting greater longevity in its entire population by the year 2030.” After the individual country roundtables discussed their respective initiatives, they presented these to the entire group during a virtual meeting.

Argentina: A Virtual Health Assistant

The roundtable discussion revolved around different stakeholder communities, including patients with diabetes, individuals with mental health issues, and elderly populations, with a special focus on people living alone and lacking social support systems. Participants addressed the provision of healthcare through both public and private healthcare systems, NGOs, patients and their families, healthcare professionals, and payers.

With the objective of improving the patient experience, reducing costs, improving access, and promoting the prevention of illness and injury, the roundtable participants proposed the development of a technological tool that uses a gaming platform to encourage patient compliance. With an integrated multilingual medical record, universal access to prescriptions, and the use of big data and data interoperability, they identified the opportunity to develop specific and unique patient profiles.

Brazil: Educating for a Better Life

The roundtable discussion in Brazil incorporated the experiences of the 2016 Olympics in Rio de Janeiro, allowing for the focus on the hospitable nature of the Brazilian people and an emphasis on well-being and quality of life through sports. By focusing on a five-year timeframe, roundtable participants agreed to prioritize vulnerable communities and the three million low-income families lacking access to healthcare. To address the issue, participants emphasized the need for a collaboration among university medical schools, healthcare researchers, health facilities, health education organizations, the medical equipment sector, and the pharmaceutical industry.

The group agreed to develop a wellness ecosystem to provide community education, highlighting prevention, to achieve advanced longevity and quality of life. The methodology incorporates the broad spectrum of health system stakeholders including the government and its regulatory agencies, the department of health, patient-focused agencies, health-focused academic institutions, the health insurance industry, medical equipment companies, and the pharmaceutical industry. The initiative involves the development of an integrated primary healthcare core structure that includes both physical care elements and mental health considerations. The system would utilize big data to identify areas of need that require strengthening to improve the quality of life with resultant health prevention and promotion campaigns.

Chile: Conscious Health

The Chilean expert roundtable identified multiple communities with significant medical and health needs associated with a lack of access to information – from the elderly and financially disadvantaged to the youngest and unborn, a function of future parents lacking knowledge about health and the importance of prevention. Given this wide focus, the team chose to concentrate on the young and financially disadvantaged and individuals living in remote regions of the country. They determined that stakeholders who could deliver solutions include healthcare providers, medical suppliers, payers, public systems, and healthcare startups.

The roundtable participants came up with a health interoperability methodology to create a prevention mindset by connecting stakeholders such as providers, suppliers, private and public payers, and health technology startups. The anticipated result is to use individuals’ personal data and personal experiences to create incentive programs to motivate the adoption of healthy living habits. The technology is there, but patients’ privacy is a concern. A younger population segment should be the focus as they are generally unaware of prevention and the impact on healthy aging.

Colombia: Caregiver’s Caregiver

The Colombian team reflected on needs of the elderly that go beyond medical requirements. Specifically, they focused on often neglected services such as social therapies and exercise facilities and discussed whether there was a contradiction between the desire to live longer and the quality of a longer life. The consensus was that a longer life comes with a greater need for, and expanded concept of, the “caregiver.” Family members and other caregivers require a need for knowledge, education, and training on managing longevity. Responsibility for developing this culture of awareness and care must involve academia, healthcare and scientific organizations, healthcare and provider organizations, social services, insurance providers, trainers, the young, and overlooked segments of potential support such as entrepreneurs and urban planners.

The initiative that arose from these discussions was the creation of an ecosystem of social support services for caregivers providing care to older adults. This has been an area of significant and often ignored social need, both in terms of the physical health and the mental and emotional health of the caregiver. Self-care for the elderly is an area of focus. New technologies, big data analytics, information, scientific research, and technical knowledge are all elements to make this effort scalable. Five areas were explored:

  • Education: Customized to each patient and caregiver, education promotes the adoption of new technical and emotional skills, attitudes, and methods; an important component is peer-to-peer interaction to allow for experiential learning.
  • Solution Center: A focus on social services specific to each case.
  • Supportive Community for Caregivers: Peer support for caregivers to help alleviate pressure.
  • Elderly Roadmap: Allowing the elderly to sustain themselves through planning, monitoring the evolutionary progress, and offering feedback to allow for greater control of the individual’s length and quality of life.
  • Chat GPT for the Caregiver: Rapid and up-to-date information to support the ability of the caregiver to perform support services. The roundtable team emphasized the need for widespread and universal access to these resources.

Costa Rica: Platform for Decentralizing Health Data

The Costa Rican team discussed whether to focus on the responsibilities of the public sector or on strengthening the private sector, including entrepreneurship and the private provision of services. Roundtable participants agreed on a continued significant role for the public system, including national, regional, and local government sectors, as well as for the social security system, which collects and maintains much of the critical population health data. Participants also supported the involvement of private sector stakeholders, including academia, the scientific research sector, private insurers, and healthcare-released startups. Placing the primary focus on vulnerable populations was a recurring theme among the various country roundtable discussions.

Participants identified as an important need the development of a “Platform for Decentralizing Health Data” to create algorithms and dashboards to guide prevention and early detection of disease processes, allowing for prioritizing and focusing on population needs. Focused on enhanced longevity and quality of life, important data would be accessible through centralized platforms. Data would be accessed through Social Security and public and private health systems and would include medical imaging, biopsies, and other test results, as well as other forms of important medical data, using encryption to ensure patient privacy. The data would be made available to the scientific community, academia, and healthcare startup companies to identify potential opportunities and risks at the local community level and, in turn, to create individualized health solutions. The platform would allow for early detection, concentrating on conditions such as hypertension, overweight, and clinical obesity, as well as other diseases or precursors to future disease processes, utilizing algorithms and allowing for early interventions. The objective is to establish public policies, education, and prevention campaigns to improve the quality of life for millions of people in Latin America by 2030.

Mexico: Artificial Intelligence to Create Healthy Habits

The roundtable participants – a heterogenous group including multispecialty physicians, biomedical engineers, medical insurers, academicians, government officials, health system CEOs – were focused on building consensus. The discussion revolved around whether to focus on three specific diseases: diabetes, heart disease, and cancer. Social security and financial security were also discussed as areas impacted by longevity and quality of life. In addition, participants indicated that chronic degenerative diseases in the lower end of the socioeconomic spectrum should be the focus. Support should come from households, educational institutions beginning as early as preschool, the food industry, clinics focused on aging and its aesthetic, the government sector, clinical laboratories, and professional areas including psychiatry, sociology, geriatrics, biomedical engineering, and the bench sciences. Participants agreed that data collection and analytics are critical elements of successful policymaking, that the government cannot be indifferent to health policy issues, and, as such, that the interrelated roles of public and private health care entities are of importance.

Artificial intelligence (AI) was identified as a methodology to provide guidelines specific to each individual patient. One priority is to focus on individuals with a risk of a disease, including younger individuals with chronic degenerative diseases, and individuals from marginalized socio-demographic and financially challenged groups. The government is a significant stakeholder. The roundtable members cited as a potential target a hypothetical individual, Enriqueta, a 22-year-old indigenous woman from the Sierra of Oaxaca. Given that she is from a vulnerable group, her access to social media and the internet occurs in the public square. The intent would be to improve Enriqueta’s quality of life through continuous monitoring aimed at improving health. A digital AI platform would personalize the promotion of a healthier lifestyle. The platform as indicated here would also be able to detect health and psychosocial risks.

Reflections

Common themes emerged in individual country roundtable initiatives: a focus on patients, not only current patients but also potential future patients and caregivers; longevity and quality of life among the elderly; and a consensus that to achieve health, one must start at a significantly younger age. Further, the participating healthcare leaders emphasized the additional focus on vulnerable socio-demographic and financially challenged populations. Prevention and education are key. Public and private partnerships are critical. Government, private insurers, healthcare providers, academia, scientific think tanks, and start-up companies are all critical stakeholders. The use of big data, data analytics, and AI are all important to develop algorithms and to develop individualized prevention protocols, educational tools, and when needed, treatment protocols all the while being aware of the need to protect patient privacy. Ultimately, the healthcare leaders agreed on the importance of developing individualized and population-focused improvements in the length of life and quality of life in Latin America.

 

Disclosure: This project was funded by Boston Scientific, the IDEAction Project.

 

References

  1. Purpose Launchpad open framework: purposealliance.org/purpose-launchpad
  2. Cordeiro J, & Wood The Death of Death: The Scientific Possibility of Physical Immortality and its Moral Defense. 1st ed. Springer Nature Switzerland; 2023.
  3. Francisco Palao, “Positive Impact: the mindset and the framework Purpose Launchpad to improve your startup, your organization, and the world”. Deusto/Planeta Editorial, 2022.

 

 

 

 

 

 

Association of Hospital System Affiliation with COVID-19 Capacity Burden

Zachary Levin, University of Minnesota School of Public Health; Pinar Karaca-Mandic, University of Minnesota Carlson School of Management, Richard J. Boxer, David Geffen School of Medicine, University of California, Los Angeles, Regina E. Herzlinger, Harvard Business School

Contact: rherzlinger@hbs.edu

Abstract

What is the message? The COVID-19 pandemic exposed the highly variable and uncoordinated responses by hospitals. The authors found that while the non-top ten system affiliated hospitals had a larger COVID-19 share index relative to independent hospitals, top-ten system hospitals did not. Nonprofit status was also associated with higher COVID-19 patient share relative to the share of hospital beds, and hospitals in counties with a higher percentage of poverty and Black populations had a lower share of COVID-19 patients when hospital markets had low market concentration (more competitive hospital markets). The results suggest that proactive planning could help spread the COVID-19 burden on hospitals through resource allocations.

What is the evidence? Regression analysis of data from the Department of Health and Human Services (HHS) and the American Hospital Association’s 2020 Annual Survey.

Timeline: Submitted: July 31, 2023; accepted after September 21, 2023.

Cite as: Zachary Levin, Pinar Karaca-Mandic, Richard J. Boxer, Regina E. Herzlinger. 2023. Association of Hospital System Affiliation with COVID-19 Capacity Burden. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 3.

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Introduction

Hospital capacity, particularly in the ICU, during the height of the COVID-19 surges was predictive of patient mortality.1,2 Although there were opportunities for greater resource sharing, hospital responses were highly variable and uncoordinated.3,4  We investigated the association of hospital characteristics and their share of regional COVID-19 patients.

Data and Methods

We used data on 7-day averaged hospitalizations for COVID-19 from the Department of Health and Human Services (HHS) during the four weeks between 12/18/2020-1/14/2021, the first substantial peak nationally for COVID-19 hospitalizations.  We matched each hospital to its Hospital Referral Region (HRR) based on zip code. For each hospital, we linked hospital characteristics (rural, available pediatric ICU unit, trauma center, ICU availability, for-profit/non-profit/government ownership, system affiliation) from the American Hospital Association’s 2020 Annual Survey. We accounted for heterogeneity in population by linking county demographics (income, poverty, race/ethnicity, and population over 65), and 2-week lag of COVID-19 infections.

Our outcome variable for each hospital-week was the COVID-19 share index at the HRR level: the ratio of the hospital’s share of patients hospitalized for COVID-19 in the HRR divided by that hospital’s share of total licensed beds in the HRR. We estimated multivariate regression models, stratifying hospitals by their market concentration using the Herfindahl–Hirschman Index (HHI) as low (HHI < 1,500) and moderate/high concentration (HHI > 1,500) – with these cutoffs being consistent with how the U.S. Department of Justice characterizes market competition. Additional analyses accounted for the interaction between ownership and system affiliation. All models included week and state indicators.

Results 

Over the four-week period, 2,901 unique hospitals reported an average of 45.7 COVID-19 patients each day (Table 1).  Several types of hospitals that are not represented in these data from the HHS include  the U.S. Department of Veterans Affairs (VA) hospitals, Defense Health Agency (DHA), Indian Health Services (IHS) hospitals and psychiatric and rehabilitation hospitals. In addition, hospitals in the data are identified by their contract number with Medicare (CCN number), and some hospitals may not have a CCN number, while some individual hospital facilities may share the same CCN number. Finally, cell suppression is applied by HHS, which means that a hospital-week observation with fewer than 5 COVID-19 patients are redacted.

Affiliation with a top ten largest hospital system nationally (relative to independent status) was not associated with the COVID-19 patient share index (p < 0.001) after adjusting for all hospital and geographic characteristics described above (Table 2).  Affiliation with other systems (relative to independent status), and nonprofit status (relative to for-profit), was associated with higher COVID-19 patient share relative to their share of hospital beds (p < 0.001). In a sensitivity analysis that included interaction of system affiliation with ownership type, the association between system affiliation and COVID-19 share index did not vary significantly at p-value of less than 0.05 by ownership type.

Presence of pediatric and trauma units was associated with lower share of COVID-19 hospitalizations in moderate/high concentration markets. Hospitals in counties with higher percentage of poverty and Black population had a lower share of COVID-19 patients in low concentration markets. On the other hand, higher percentage of Hispanic county population was associated with higher COVID-19 hospitalization share independent of hospital concentration.

Discussion

Health system size has been linked to higher prices5, with no significant improvement in quality on average compared with non-system physicians and hospitals.6 We hypothesized that large systems could share COVID-19 hospital burdens due to  the potential  for greater integration. Our study examined the first major peak during the COVID-19 pandemic, one of the most capacity constrained periods for hospitals. We found that while the non-top ten system affiliated hospitals had larger COVID-19 share index relative to independent hospitals, top-ten system hospitals did not.

Our predicted outcome variable, hospital COVID-19 share index in low market concentration markets was 1.14 for non-top ten system affiliated hospitals, 1.03 for independent hospitals and for top ten system hospitals (based on estimates reported in Table 2, adjusting for all other hospital characteristics). An average non-top ten system hospital had 5.47% of licensed beds in its HRR (denominator of the COVID-19 share index). Our estimates suggest that its share of COVID-19 patients in the HRR (numerator of the COVID-19 share index) was 6.23% (multiplying predicted outcome variable 1.14 with 5.47%). At an average HRR with 1,014 COVID-19 patients in a week, this corresponds to 63 COVID-19 patients cared for by an average non-top ten system hospital. In contrast, our results imply that an average independent hospital’s share of COVID-19 patients was 5.04% while their share of licensed beds was 4.90% in their HRR. Similar to independent hospitals, an average top ten system hospital’s share of CCOVID-19 patients was 5.01% and their share of licensed beds in the HRR was 4.84%.

Our study has several limitations. Although our data did not allow their identification, nonprofit and non-top ten system affiliated hospitals were more likely to be designated as COVID-19 specialized hospitals, leveraging possible economies of scale. The geographic market, HRR, is commonly used in the hospital market literature, but our findings could differ by alternative definitions of geographic areas.

It is alarming that Black Americans, who have the highest rate of death due to Covid-196, access to hospitals was predictive of mortality,1,2 and our data showed that hospitals in counties with higher percentage of poverty and Black population had a lower share of COVID-19 patients in low hospital concentration markets.

Conclusion

These results suggest that proactive planning could help spread the COVID-19 burden among hospitals through resource allocations.

 

Acknowledgements: This research uses publicly available data from the Department of Health & Human Services, accessed August 29, 2022 at: https://healthdata.gov/dataset/covid-19-reported-patient-impact-and-hospital-capacity-facility

We also acknowledge access to proprietary data from the American Hospital Association Survey through the National Bureau of Economic Research (NBER).

Funding: No funding organization funded this study.

 

Endnotes

  1. Karaca-Mandic,P, Sen, S, Georgiou, A, Zhu, Y, Basu, A. Association of COVID-19-Related Hospital Use and Overall COVID-19 Mortality in the USA, Journal of General Internal Medicine, August 19, 2020, https://doi.org/10.1007/s11606-020-06084-7.
  2. French, G, Hulse, M, Nguyen, D, Sobotka, K, Webster, K, Corman, K, et al. Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic — United States, July 2020–July 2021, Morbidity and Mortality Weekly Report 70, no. 46 (November 19, 2021): 1613–16, https://doi.org/10.15585/mmwr.mm7046a5.
  3. Kerlin, MP, Costa, DK, Davis, BS, Admon, AJ, Vranas, KC, Kahn, JM, et al. Actions Taken by US Hospitals to Prepare for Increased Demand for Intensive Care During the First Wave of COVID-19, Chest 160, no. 2 (August 2021): 519–28, https://doi.org/10.1016/j.chest.2021.03.005.
  4. Herzlinger R, Boxer R. Transparency As A Solution For COVID-19-Related Hospital Capacity Issues, Health Affairs, February 18, 2022, https://doi.org/10.1377/forefront.20220216.997771
  5. Fisher, E, Shortell, SM, O’Malley, AJ, et al Financial Integration’s Impact On Care Delivery And Payment Reforms: A Survey of Hospitals And Physician Practices,”Health Affairs, August ,2020 ,
    https://doi.org/10.1377/hlthaff.2019.01813
  6. Vasquez Reyes, M. The Disproportional Impact of COVID-19 on African Americans. Health Hum Rights. 2020 Dec;22(2):299-307. PMID: 33390715; PMCID: PMC7762908.

 

 

Table 1: Characteristics of the Hospital Sample

All Low Market Concentration Medium/High Market Concentration
  (HHI < 1,500) (HHI >= 1,500)
Mean HHI 1,768 836 2,877
Has ICU (%) 89.1% 91.3% 86.6%
Rural Hospital (%) 14.1% 10.5% 18.3%
Pediatric Unit (%) 39.2% 35.1% 44.1%
Obstetric Unit (%) 62.0% 61.7% 62.4%
Trauma Hospital (%) 39.5% 37.1% 42.4%
Non-Profit (%) 67.7% 67.0% 68.6%
Government Run (%) 13.5% 12.7% 14.3%
Top Ten System Affiliated (%) 21.6% 22.9% 20.1%
Non-top Ten System Affiliated (%) 54.8% 55.4% 54.2%
Median Household Income ($1,000s) 64.5 68.5 59.7
% Poverty in county 13.3% 12.5% 14.2%
% Hispanic in county 15.7% 16.6% 14.7%
% Black in county 13.3% 14.7% 11.6%
% Aged 65+ in county 17.0% 16.4% 17.8%
Lagged 2 Week COVID Cases (1,000s) 4.8 7.6 1.5
Average daily hospitalized COVID-19 patients 45.7 51.1 39.0
Average daily hospitalized COVID-19 ICU patients 11.8 12.8 10.5
Unique hospitals 2,901 1,582 1,319
       Independent and non-profit 365 188 177
       Non-top ten system and non-profit 1296 694 602
       Top-ten system and non-profit 298 174 124
       Independent and for-profit 73 40 33
       Non-top ten system and for-profit 152 96 56
       Top-ten system and for-profit 325 189 136
       Independent and government 248 117 131
       Non-top ten system and government 138 82 56
       Top-ten system and government 6 2 4

Notes:

HHI: Herfindahl–Hirschman Index in the Hospital-Referral Region

Top 10 largest systems in the United States, were identified based on  (https://www.hospitalmanagement.net/analysis/top-ten-largest-health-systems-in-the-us-by-number-of-hospitals-affiliated/): Advent, Prime, LifePoint, Ascension, Common Spirit, CHS, HCA, Providence, Trinity, Tenet.

Data Sources:

  1. Department of Health and Human Services. COVID-19 Reported Patient Impact and Hospital Capacity by State. https://healthdata.gov/dataset/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/6xf2-c3ie. Accessed August 29, 2022.
  2. Agency for Healthcare Research and Quality. Area Health Resource File 2020-2021. https://data.hrsa.gov/data/download?data=AHRF#AHRF. Access August 29, 2022.
  3. Johns Hopkins Center for Systems Science and Engineering. COVID-19 Daily Reports. https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports. Accessed August 29, 2022.

 

Table 2: Association of hospital, organizational and county characteristics with hospital COVID-19 share index

Low Market Concentration Moderate/High Market Concentration
Hospital Characteristics    
Rural Hospital 0.001 -0.032
(-0.078 – 0.080) (-0.108 – 0.045)
Pediatric Unit -0.017 -0.126***
(-0.073 – 0.039) (-0.189 – -0.063)
Obstetric Unit -0.056* -0.025
(-0.117 – 0.006) (-0.093 – 0.043)
Trauma Unit -0.042 -0.102***
(-0.098 – 0.014) (-0.166 – -0.037)
ICU Unit 0.037 0.019
(-0.100 – 0.175) (-0.099 – 0.137)
Organizational Characteristics
Non-Profit (Relative to For-Profit) 0.101*** 0.124***
(0.032 – 0.170) (0.040 – 0.207)
Government (Relative to For-Profit) 0.034 0.031
(-0.061 – 0.129) (-0.082 – 0.144)
Top-Ten System (Relative to Independent) 0.004 -0.050
(-0.075 – 0.083) (-0.142 – 0.043)
Non-top Ten System (Relative to Independent) 0.094*** 0.076**
(0.032 – 0.156) (0.004 – 0.148)
County Characteristics
Median Household Income ($1,000s) -0.001 0.002
(-0.005 – 0.003) (-0.003 – 0.007)
% Poverty -0.014** 0.004
(-0.026 – -0.002) (-0.010 – 0.018)
% Hispanic 0.005** 0.006**
(0.001 – 0.009) (0.000 – 0.011)
% Black -0.004*** -0.002
(-0.007 – -0.001) (-0.007 – 0.002)
% 65+ 0.003 -0.010*
(-0.005 – 0.012) (-0.021 – 0.002)
Lagged 2 Week COVID Cases (1000s) -0.001 -0.006
(-0.004 – 0.002) (-0.020 – 0.009)
Observations 5,070 4,141
R-squared 0.114 0.180
FE State Week HRR State Week HRR
Outcome variable  (Mean) 1.093 1.090

Notes:

*** p<0.01, ** p<0.05, * p<0.1

The outcome variable hospital COVID-19 share index is measured at the hospital-week level as the ratio of patients hospitalized for COVID-19 divided by that hospital’s share of total beds in the HRR, separately for total licensed beds and ICU beds.

Price Variability of Heart Transplant and Ventricular Assist Procedures Across the United States

Ishan Paranjpe, Stanford University School of Medicine; Chen Wei, Stanford University School of Medicine; Pranav Sharma, Drexel University College of Medicine; Roy H. Lan, Stanford University School of Medicine; Maitreyi Sahu, University of Washington; Joseph Dieleman, University of Washington; Kevin Schulman, Clinical Excellence Research Center, Stanford University; Alexander Sandhu, Stanford University

Contact: kevin.schulman@stanford.edu

Abstract

What is the message? This study characterizes and identifies factors contributing to price variations in orthotopic heart transplantation and ventricular assist devices both within and across United States hospitals.

What is the evidence? An analysis of reported price data from 6,378 hospitals aggregated in the Turquoise Health database.

Timeline: Submitted: November 30, 2023; accepted after review December 13, 2023.

Cite as: Ishan Paranjpe, Chen Wei, Pranav Sharma, Roy H. Lan, Maitreyi Sahu, Joseph Dieleman, Kevin Schulman, Alexander Sandhu. 2023. Price Variability of Heart Transplant and Ventricular Assist Procedures across the United States. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 3.

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Introduction

The prevalence of heart failure (HF) in the United States is expected to increase from 6.9 million in 2020 to 8.5 million in 2030.(1) According to the American College of Cardiology/American Heart Association (ACC/AHA) guidelines, treatment of end-stage HF with orthotopic heart transplantation (OHT) and ventricular assist devices (VAD) are both Class I recommendations.(2) Both OHT and VAD are resource-intensive interventions with high healthcare costs and market prices (3). Prior studies suggest that the financial toxicity of cardiovascular care can negatively impact quality of life and worsen clinical outcomes.(4–6) For advanced HF, little is known about the actual price of LVAD implantation and how this varies across hospitals.

In order to promote price transparency, the 2021 Hospital Price Transparency Final Rule mandated hospitals publicly disclose insurer-negotiated and self-pay rates for all medical services(7). This study sought to characterize and identify factors contributing to variations in OHT and VAD prices both within and across United States hospitals using self-reported hospital prices compiled with a commercial database.

Methods

Data Sources

Following passage of the federal Hospital Price Transparency Rule in 2021, all US hospitals were mandated to publicly release pricing data. We used the Turquoise Health database, an aggregation of these reported price data from 6,378 hospitals. Each hospital disclosed several prices, including the gross chargemaster price, discounted self-pay price, negotiated commercial insurance prices, and Medicare prices (hospitals were required to post these prices, not any information on the cost of providing the service). Given the significant variability in compliance with this federal mandate, many hospitals reported prices for only certain of these categories.

We restricted our analysis to all cardiac surgery hospitals approved for adult heart transplants by the Organ Procurement and Transplantation Network. We included hospitals that reported prices for Medicare Severity Diagnosis Related Code (MS-DRG) 001 and 002 or common procedural terminology (CPT) codes 33982, 33983, 33979, and 33980 which encode services related to heart transplant and ventricular assist devices. The payments for the procedural CPT codes represent the amount to the proceduralist for the individual procedure while the MS-DRG represents the hospital payment for the overall care during the hospitalization, including the procedure. To account for regional differences in labor cost, prices for each procedural code were normalized to Medicare Fee Schedule using Resource-Based Relative Value Scale (RVU) and Geographic Practice Cost Indices (GPCI) adjustment factors as previously done(8). Adjusted price = Commercial price / (RVUwork*GPCIwork + RVUpractive expense*GPCIpractice expense + RVUmalpractice*GPCImalpractice).

For each hospital in the Turquoise Health dataset, we obtained hospital characteristics using several publicly available data sources(9–18). All data sources were linked to the Turquoise dataset using each hospital’s Medicare provider ID. We extracted the overall hospital ranking, HF-specific mortality, and HF-specific readmission rate from the Centers for Medicare and Medicaid Services (CMS) Hospital Care Compare dataset. From the American Hospital Association Survey (AHA), we identified teaching hospitals, AHA region codes, and hospitals with cardiac surgery facilities. For teaching hospital status, major teaching hospitals were defined as members of the Council of Teaching Hospitals (COTH). Hospital margin data was obtained from the Healthcare Cost Report Information System.

We constructed a measure of hospital market concentration as previously described(19). Market concentration was defined for each hospital using the Herfindahl–Hirschman index (HHI). We defined HHI as the sum of squared market share defined by inpatient bed capacity. The resulting HHI metrics ranges from 0 to 10,000. 0 represents perfect competition and 10,000 represents a complete monopoly.

Statistical analysis

To limit the effect of outliers, we excluded hospitals with reported commercial prices below the bottom 5st percentile above the top 5th percentile (visual inspection of these values suggested that the data were not plausible). Similar to prior work(20,21), to measure the variation in commercial insurance plan pricing within a hospital, we defined the within-hospital-ratio as the ratio of the 90th to 10th percentile payor-negotiated rate for a given billing code. To measure the pricing variation between hospitals, we defined the across-hospital-ratio as the ratio of the 90th percentile to 10th percentile median negotiated price across all hospitals for a given billing code.

We compared prices across payor type using a Kruskal-Wallis test with Dunn post-hoc analyses. We also compared the median commercial rates across hospital-specific characteristics using Kruskal-Wallis tests.

Results 

From the 6,378 hospitals in the Turquoise Health dataset, we limited our analysis to the adult heart transplant hospitals which reported prices for at least one heart transplant MS-DRG or CPT code. After removing outliers, we included 61 hospitals in our analysis. Hospitalization prices varied significantly by payor class. For MS-DRG 001, the median Medicare rate of $211,993 (IQR: $185,198 – $240,679) was significantly lower than commercial ($410,076, IQR: $338,451 –$501,594), chargemaster ($966,894, IQR: $733,065 – $1,174,278) and self-pay rates ($447,025, IQR: $350,684- $683,687) (Figure 1A, Table 1, P <0.05).  Similar price differences were observed for MS-DRG 002 rates.

We observed significant variability across negotiated commercial contracts within the same hospitals. In terms of overall hospitalization prices, represented by MS-DRG codes, (Table 2), the median within center ratio (90th:10th percentile commercial rate) was 2.1 (IQR: 1.7 – 2.8) for MS-DRG 001 and 2.1 (IQR: 1.6 – 2.6) for MS-DRG 002. Intra-hospital payor variation was lower for individual procedures (Table 2). For individual surgical procedures (Table 2), CPT 33944 (backbench preparation of donor heart) had the greatest median within center ratio of 3.4 (IQR: 1.2– 8.3).  Price variability across hospitals, as captured by the across-center ratio (90th:10th percentile median negotiated price), was 2.3 for MS-DRG 001 and to 2.1 for MS-DRG 002.

We then compared the median payor-negotiated commercial rate across hospital characteristics. In bivariate analyses (Table 3), we did not find statistically significant variation in price by geographic region. Commercial rates at major teaching hospitals were significantly higher than those at non-teaching hospitals (Table 3) for both MS-DRG 001 ($438,731 vs. $381,136, P = 0.02) and MS-DRG 002 ($258,066 vs. $200,658, P0.02).  Hospital margin, total revenue, and total bed capacity were not significantly associated with hospitalization prices (Table 3). In a multivariate model adjusted for hospital margin, total revenue, total bed capacity, and geographic region, commercial rates for both MS-DRG 001 (P = 0.18) and MS-DRG 002 (P = 0.05) were not significantly associated with teaching hospital affiliation.

We then correlated quality metrics with reported commercial hospitalization prices.  The median hospitalization price of MS-DRG 001 was not significantly associated (Table 3) with CMS heart failure specific mortality (P = 0.45) heart failure specific readmission rate (P = 0.87), or overall star rating (P = 0.96). Findings for quality metrics were similar for MS-DRG 002.

We hypothesized that market dynamics may influence hospitalization prices. We associated a previously reported measure of hospital market concentration (HHI) with median commercial hospitalization prices (Figure 2). HHI was not significantly associated with commercial price (Figure 2A) or the ratio of commercial price to Medicare price (Figure 2B) for MS-DRG 001 and MS-DRG 002.

Discussion

To combat rising healthcare costs, the 2021 Federal Hospital Price Transparency Rule mandated hospitals release price information for procedures and medications provided at their respective centers. Analysis of these data can offer insight into the magnitude of price variability in the commercial health plan market: there is substantial price variation both across hospitals and between commercial plans at each hospital, and median commercial prices were significantly higher than Medicare prices, but this variation in price does not seem to be related to care quality as there was no association between the price of care and HF-specific hospitalization outcome metrics.

Similar to prior reports of non-cardiovascular procedures(22,23), Medicare prices were substantially lower than commercial and self-pay rates. Although this was not an unexpected result, we also found variability in commercial prices within a single institution. For example, we found a 3-fold difference between 90th and 10th percentile commercial payor prices within individual hospitals for transplant/LVAD hospitalization costs.  The substantial variation observed within hospitals suggest commercial insurance plans may have differential negotiating ability. One recent work found that insurers in the market with the fewest number of insurers (least competitive) pay 15% less to hospitals compared to insurers in the most competitive insurance markets(24), highlighting the role of market concentration in controlling price.

We hypothesized that specific hospital characteristics drive price differences. Notably, we found that teaching hospitals commanded significantly higher commercial rates compared to non-teaching hospitals. This association was not significant in multivariate analysis likely due to low statistical power. Prior work has reported mixed findings in terms of healthcare costs at teaching hospitals. One study of hospitalization costs for 21 common conditions among Medicare patients found that index hospitalizations were more expensive but readmissions were less expensive at teaching hospitals as compared to nonteaching hospitals(25). The exact reason for this association remains unclear. However, one potential explanation is that this price variability reflects uncontrolled differences in patient complexity (beyond case-mix adjustment). Teaching hospitals may care for more complex, higher risk transplant patients than non-teaching hospitals, although given the nature of the procedures we evaluated it is hard to understand how selection could be so systematic. These prices differences may also be explained by differences in negotiating power. Notably, other hospital characteristics, such as revenue, margin, bed capacity, and CMS quality metrics were not associated with price.

In addition to hospital-specific characteristics, we also studied the impact of hospital market concentration. Hospitalization prices were not statistically significantly associated with market concentration, although there was a trend to higher prices in more concentrated markets in relationship to Medicare prices. Previous studies have shown that more competitive markets tend to have lower prices(24,26). Our analysis was limited to a rare service performed by a small set of hospitals and thus may not generalize to prices of other services where market dynamics may play a larger role.

Price was also not associated with CMS heart failure specific readmission, mortality rates, or overall star rating. Unlike other industries in which higher price signals greater quality of goods and services, price and quality are frequently disassociated in healthcare. Previous work has also found that patients are unable to objectively evaluate healthcare quality(27). Our data support these findings and suggest that for heart transplants, price negotiations are likely not driven by objective quality metrics. Again, since our analysis was limited to heart transplant centers this finding may not be generalizable.

Conclusion

Here, we report several insights into the magnitude and sources of price variability for heart transplant and VAD hospitalizations. We report both significant inter- and intra-hospital price variation for commercially insured patients. These data raise several questions about the determinants of heart transplant price and whether the effect of hospital and market characteristics on price is generalizable across clinical service lines.

 

Figure 1: Variability in hospitalization price of heart transplant and VAD implantation by payor class. P values were computed using a Kruskal-Wallis test.

 

Figure 2: Market concentration and commercial price. Market concentration HHI was computed for a 30-mile region near each hospital. HHI was associated with A) median commercial hospitalization price and B) to the ratio of commercial price to Medicare price by fitting linear regression models.

Table 1: Median heart transplant hospitalization prices by payor class

MS-DRG Payor class Number of hospitals Median price
1 Chargemaster 20 $966,894 ($733,065 – $1,174,278)
Commercial 57 $410,076 ($338,451 – $501,594)
Medicare 49 $211,993 ($185,108 – $240,679)
Self-pay 24 $447,025 ($350,684 – $683,687)
2 Chargemaster 8 $528,451 ($428,881 – $641,850)
Commercial 42 $224,850 ($196,725 – $292,881)
Medicare 37 $114,661 ($101,810 – $129,210)
Self-pay 14 $178,130 ($152,722 – $335,487)

Table 2: Summary of commercial insurance negotiated hospitalization prices for heart transplant. The within center ratio is defined as the ratio of the 90th to 10th percentile commercial rate at each hospital. The across center ratio is defined as the ratio of the 90th percentile median commercial rate to the 10th percentile median commercial rate across hospitals.

Code Code description Number of centers Commercial median price, $ (IQR) Within center ratio (IQR) Across center ratio
MS-DRG 001 Heart transplant or implant of heart assist system with MCC 57 410,076 (338,451 – 501,594) 2.1 (1.7 – 2.8) 2.3
MS-DRG 002 Heart transplant or implant of heart assist system without MCC 42 224,850 (196,725 – 292,881) 2.1 (1.6 – 2.6) 2.1
CPT 33945 Heart transplant, with or without recipient cardiectomy 14 5,950 (4,347 – 7,718) 1.9 (1.6 – 3.3) 3.5
CPT 33944 Backbench standard preparation of cadaver donor heart allograft prior to transplantation, including dissection of allograft from surrounding soft tissues to prepare aorta, superior vena cava, inferior vena cava, pulmonary artery, and left atrium for implantation 12 3,391 (1,428 – 4,243) 3.4 (1.2 – 8.3) 6.1
CPT 33940 Donor cardiectomy (including cold preservation) 9 6,171 (3,391 – 26,679) 1.4 (1 – 2.5) 52
CPT 33982 Replacement of ventricular assist device pump(s); implantable intracorporeal, single ventricle, without cardiopulmonary bypass  11 3,468 (2,872 Р6,794) 1.6 (1.2 Р2.8) 9.4
CPT 33983 Replacement of ventricular assist device pump(s); implantable intracorporeal, single ventricle, with cardiopulmonary bypass  11 3,468 (2,935 Р4,353) 1.6 (1.2 Р2.3) 3.5
CPT 33979 Insertion of ventricular assist device, implantable, intracorporeal, single ventricle  15 3,377 (2,428 Р4,770) 2 (1.2 Р3.5) 10
CPT 33980 Removal of ventricular assist device, implantable, intracorporeal, single ventricle  14 3,179 (2,219 Р5,469) 2 (1.3 Р3.1) 8.9

Table 3: Factors associated with price variability for negotiated commercial rates by MS-DRG billing code

MS-DRG 001 MS-DRG 002
Number of hospitals Median Commercial Price, $ (IQR) P Value Number of hospitals Median Commercial Price, $ (IQR) P Value
AHA Region
New England (CT, MA, ME, NH, RI, VT) 2 469,830 (453,948 – 485,712) 0.17 1 330,341 (330,341 – 330,341) 0.1
Middle Atlantic (NJ, NY, PA) 11 381,452 (251,890 – 413,587) 8 221,086 (208,616 – 311,039)
South Atlantic (DC, DE, MD, VA, WV, NC, SC, GA, FL) 12 498,002 (383,826 – 529,380) 7 275,211 (249,148 – 313,696)
East North Central (OH, IN, IL, MI, WI) 13 484,080 (388,134 – 534,958) 9 258,066 (213,475 – 272,224)
East South Central (KY, TN, AL, MS) 1 364,261 (364,261 – 364,261) 2 152,380 (132,161 – 172,600)
West North Central (MN, IA, MO, ND, SD, NE, KS) 5 393,423 (297,719 – 412,117) 4 215,232 (182,028 – 270,389)
West South Central (AR, LA, OK, TX) 11 365,330 (286,164 – 415,600) 9 200,658 (183,858 – 207,873)
Mountain (MT, ID, WY, CO, NM, AZ, UT, NV) 1 352,800 (352,800 – 352,800) 1 352,800 (352,800 – 352,800)
Pacific (WA, OR, CA, AK, HI) 1 549,960 (549,960 – 549,960) 1 302,482 (302,482 – 302,482)
Teaching status
Major Teaching 19 381,136 (289,092 – 401,749) 0.021 15 200,658 (175,842 – 239,961) 0.02
Non-teaching 38 438,731 (366,966 – 532,194) 27 258,066 (213,505 – 301,365)
Number of hospitals Correlation Coefficient P value Number of hospitals Correlation Coefficient P value
Margin 56 -0.0021 0.99 41 -0.1 0.52
Total Revenue ($ USD) 56 0.13 0.33 41 0.33 0.038
Total Beds 57 0.11 0.43 42 0.11 0.49
CMS Overall Star Rating 55 0.0069 0.96 40 0.037 0.82
CMS 30-day Heart Failure Specific Readmission Score 55 -0.021 0.87 40 -0.11 0.49
CMS 30-day Heart Failure Specific Mortality Score 55 -0.099 0.45 40 0.034 0.84

 

References

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Promoting Cultural Inclusivity in Healthcare Artificial Intelligence: A Framework for Ensuring Diversity

Nivisha Parag, Rowen Govender, and Saadiya Bibi Ally, Regent Business School, South Africa

Contact: Nivisha.parag@regent.ac.za

Abstract

What is the message? Artificial intelligence (AI) is proving to be a transformative force in healthcare, promising improved diagnosis, treatment, and patient care. However, the application of AI in healthcare is not without challenges, especially in the context of cultural diversity. The authors propose a framework for AI adoption in culturally diverse settings, emphasizing the importance of ethical considerations, transparency, and cultural competence.

What is the evidence? The authors describe cases illustrating the complexities of AI in healthcare within culturally diverse contexts, and how their framework could be applied in culturally diverse regions of the world.

Timeline: Submitted: September 15, 2023; accepted after review November 26, 2023.

Cite as: Nivisha Parag, Rowen Govender, Saadiya Bibi Ally. 2023. Promoting Cultural Inclusivity in Healthcare Artificial Intelligence: A Framework for Ensuring Diversity. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 3.

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Introduction

Artificial intelligence (AI) is rapidly evolving into a powerful tool that is reshaping various industries, and healthcare is no exception. The integration of AI technologies in healthcare promises to revolutionize medical diagnosis, treatment, and patient care, with the aim of enhancing overall health outcomes. However, the successful implementation of AI in healthcare requires a nuanced understanding of the diverse cultural landscapes that influence healthcare practices and patient experiences worldwide.

Cultural diversity plays a pivotal role in healthcare, impacting patients’ beliefs, behaviors, and perceptions of health and illness. Cultural diversity in healthcare refers to the presence of individuals from different cultural, ethnic, linguistic, and socioeconomic backgrounds seeking healthcare services. These cultural factors significantly influence how individuals perceive health, interact with healthcare providers, and make healthcare decisions.1 AI and digital health technologies must adapt to these diverse cultural contexts to be effective and equitable.

This article explores the multifaceted role of AI in healthcare through the lens of cultural diversity. We examine the opportunities AI presents, such as personalized medicine and efficient healthcare delivery, alongside important ethical and cultural considerations. We will also assess the benefits and potential pitfalls of AI in healthcare within culturally diverse settings, emphasizing the need for responsible and inclusive AI implementation. The importance of acknowledging and including cultural diversity into AI implementation in healthcare is known, but guidance on its adoption in a manner that respects cultural diversity is limited. As such, we have developed a proposed framework for AI adoption in culturally diverse settings.

The following cases illustrate the complexities of AI in healthcare within culturally diverse contexts:

a. Diabetes Management in Indigenous Populations

Diabetes is a global health concern, affecting diverse populations. Indigenous communities, in particular, face unique challenges related to cultural practices, dietary habits, and access to healthcare.2 An AI-driven mobile application was developed to assist indigenous populations in managing diabetes.

The application incorporated culturally relevant dietary recommendations and incorporated traditional healing practices. AI algorithms analyzed blood glucose levels, physical activity, and dietary choices to provide personalized advice. The results were promising, with improved diabetes management and better adherence to treatment plans among indigenous individuals.

However, challenges emerged concerning data privacy and trust. Indigenous communities expressed concerns about the collection and use of their health data, requiring additional efforts to build trust and ensure data protection aligned with their cultural values.

b. Machine Translation in Multilingual Healthcare Settings

In culturally diverse healthcare settings with patients who speak multiple languages, communication barriers can hinder accurate diagnosis and treatment. An AI-driven machine translation system was implemented in a hospital to bridge these language gaps and provide quality care to patients.

The system allowed healthcare providers to communicate with patients in their preferred language, improving the patient-provider relationship and reducing misunderstandings. However, the accuracy of machine translation, especially for medical terminology, posed challenges. Misinterpretations of symptoms or treatment instructions occurred, highlighting the importance of human oversight and cultural competence.3

This article aims to review the role of AI in healthcare through the lens of cultural diversity.

AI in Healthcare: Cultural considerations and potential pitfalls

Diagnostic Advancements

AI has demonstrated remarkable capabilities in medical image analysis, aiding in the early and accurate diagnosis of various diseases. Machine learning algorithms excel at recognizing patterns and anomalies in medical images such as X-ray, MRI, and CT scans.4 These advancements have the potential to benefit individuals across diverse cultural backgrounds, ensuring timely and precise diagnoses. For instance, deep learning models have shown exceptional accuracy in detecting diabetic retinopathy, a condition that can lead to vision loss. This technology is vital for early intervention, especially in regions with limited access to specialized ophthalmologists. However, to harness the full potential of AI-driven diagnostics, it is imperative to ensure that the underlying datasets are representative of diverse populations to prevent biases that may disproportionately affect certain cultural groups. AI plays a crucial role in diagnostic and treatment planning, but its effectiveness can vary across different cultural groups. For example, diagnostic algorithms may need to consider variations in disease prevalence, symptom presentation, and genetic factors among different ethnicities. Culturally tailored diagnostic models can help ensure that AI-powered systems provide accurate and relevant recommendations to individuals from diverse backgrounds.

Personalized Medicine

One of the most promising applications of AI in healthcare is personalized medicine. By analyzing an individual’s genetic makeup, medical history, and lifestyle, AI can tailor treatment plans to the specific needs of the patient.5 This approach not only enhances treatment efficacy but also respects the cultural beliefs and preferences of patients.

For example, pharmacogenomics, which uses AI to predict an individual’s response to medication based on their genetic profile, can reduce adverse drug reactions and optimize treatment outcomes. In a culturally diverse healthcare setting, personalized medicine can accommodate variations in drug metabolism and efficacy among different populations.

However, the implementation of personalized medicine must be culturally sensitive. It is essential to consider cultural factors that may influence patients’ choices and adherence to treatment plans. Patient engagement and consent should be informed by an understanding of cultural norms and values.

Healthcare Delivery and Access

AI has the potential to revolutionize healthcare delivery by improving efficiency and accessibility. Telemedicine, powered by AI-driven chatbots and virtual health assistants, can bridge gaps in healthcare access, especially in remote or underserved areas. These technologies can provide medical advice, monitor patients’ conditions, and offer health education in multiple languages, accommodating cultural diversity.

Furthermore, AI can optimize hospital operations, from appointment scheduling to resource allocation. Predictive analytics can help healthcare providers anticipate patient needs and allocate resources effectively,6 ensuring that culturally diverse patient populations receive equitable care. In addition, equitable allocation of resources require diverse, representative data sets to build predictive algorithms.

Ethical Considerations in AI-Enabled Healthcare

Bias and Fairness

One of the central ethical challenges in AI-driven healthcare is addressing bias and ensuring fairness in AI algorithms. Biased algorithms can perpetuate healthcare disparities, particularly in culturally diverse populations. These biases can arise from biased training data or algorithmic design flaws, leading to inaccurate diagnoses or treatment recommendations.7

To mitigate bias, healthcare AI developers must prioritize diverse and representative datasets, which encompass various ethnic, cultural, and socioeconomic backgrounds. Additionally, continuous monitoring and auditing of AI systems are essential to identify and rectify bias that may emerge over time.7

Informed Consent

Informed consent is a fundamental ethical principle in healthcare, and its importance extends to AI applications. Patients must be adequately informed about how their data will be used in AI-driven healthcare solutions and should have the right to opt out if they choose.8 This is particularly crucial when dealing with culturally diverse populations, as cultural norms and expectations regarding data sharing may differ. Data security and privacy concerns are universal, but the cultural perspective on data sharing can vary significantly. In some cultures, there may be strong reservations about sharing health-related data, especially with technology companies or healthcare providers. AI-driven health systems should provide clear information about data usage and obtain culturally informed consent to address these concerns.

Cultural competence among healthcare providers and AI developers is essential to ensure that informed consent processes are culturally sensitive and respectful of patients’ beliefs and preferences. Patients from diverse backgrounds should feel comfortable participating in AI-enabled healthcare without fear of discrimination or exploitation. Safeguarding patient information is crucial, especially when dealing with sensitive cultural or health-related data. Striking a balance between data accessibility and security is an ongoing challenge.

Transparency and Accountability

Transparency in AI algorithms is critical for building trust among patients and healthcare providers. Patients have a right to understand how AI is being used in their healthcare, from diagnosis to treatment recommendations. Furthermore, establishing accountability mechanisms is essential to address any adverse outcomes or errors caused by AI.9

In culturally diverse healthcare settings, transparency is even more critical because patients may have different expectations and understandings of AI’s role in their care. Clear communication and education about AI’s capabilities and limitations can help foster trust and facilitate culturally competent care.

A framework for AI adoption in culturally diverse settings

Realizing the full potential of AI and digital health solutions in culturally diverse healthcare settings necessitates a multifaceted approach that prioritizes cultural competence, fairness, and sensitivity throughout the entire lifecycle of these technologies, from design and deployment to evaluation and ongoing refinement. A proposed framework for the adoption of AI in culturally diverse settings would give consideration to each item shown in the following diagram:

Figure 1: A Framework for AI Adoption in Culturally Diverse Settings

The next section explores each item in the framework in greater detail:

1. Cultural Competence in Design:

Cultural competence begins at the design phase of AI and digital health solutions. Developers and designers must ensure that their technologies are inclusive and respectful of diverse cultural backgrounds. This involves:

Cultural Research: Conduct extensive research into the target cultural populations to understand their health beliefs, practices, and preferences. This knowledge informs the design choices, such as the use of culturally appropriate imagery, language, and symbols.

User-Centered Design: Apply user-centered design principles to create interfaces and user experiences that are intuitive and engaging for individuals from various cultural backgrounds. This may involve conducting usability testing with diverse user groups to identify and address cultural usability issues. Culturally competent design fosters trust and enhances user engagement.

Customization: Allow for customization or personalization of the user experience, where individuals can tailor the interface or content to align with their cultural preferences. This fosters a sense of ownership and comfort among users.

Cultural Competency Training: Offer training to healthcare professionals and AI system operators on cultural competency and sensitivity. This ensures that they can effectively use and support these technologies in diverse healthcare settings.

Consideration must be given to the cultural beliefs, norms, and practices of the target population when designing algorithms and user interfaces. Collaboration with healthcare professionals from diverse backgrounds and the involvement of cultural advisors can facilitate this process.

2. Fairness in Data and Algorithms:

Ensuring fairness in AI-driven healthcare solutions is critical to avoid perpetuating biases and health disparities. Key considerations include:

Data Collection: Ensure that the training datasets used for AI algorithms are diverse and representative of the population. Biased datasets can lead to biased algorithms that may not work equally well for all cultural groups. Datasets should encompass various ethnic, cultural, and socioeconomic groups to ensure that AI-driven healthcare solutions are equitable and accurate for all patients. It is essential to continuously monitor and assess AI systems for potential bias, as these technologies can inadvertently exacerbate disparities if not carefully designed and tested for fairness across diverse populations.

Bias Mitigation: Implement bias mitigation techniques, such as re-sampling underrepresented groups or adjusting algorithmic weights, to ensure equitable performance across diverse populations. Studies have revealed that AI algorithms trained predominantly on male datasets may perform alarmingly poorly when diagnosing medical conditions in women. This discrepancy can result in gender-based disparities in healthcare outcomes, with some research indicating that certain algorithms exhibit an error rate of up to 47.3% in identifying heart disease in women compared to just 3.9% in men.10 Furthermore, biased data can perpetuate racial or ethnic disparities in disease diagnosis and treatment recommendations, with some investigations showcasing that certain AI systems exhibit significantly lower accuracy rates when diagnosing skin conditions in darker-skinned individuals compared to lighter-skinned individuals, with an error rate disparity as high as 12.3%.10

Unintended bias in algorithms is another concern, with potential to worsen social and healthcare disparities. The decisions made by AI systems reflect the input data they receive, so it’s crucial that this data accurately represents patient demographics. Additionally, gathering data from minority communities can sometimes result in medical discrimination. For instance, HIV prevalence in minority communities can lead to the discriminatory use of HIV status against patients.11

Moreover, variations in clinical systems used to collect data can introduce biases. For example, radiographic systems, including their outcomes and resolution, differ among providers. Additionally, clinician practices, such as patient positioning for radiography, can significantly influence the data, making comparisons challenging.12

However, these biases can be mitigated through careful implementation and a systematic approach to collecting representative data.

Transparency: Provide transparency in algorithmic decision-making, so users and healthcare providers can understand how AI-generated recommendations are made. This transparency helps build trust.

3. Cultural Sensitivity in User Engagement:

Effective user engagement is essential for the adoption and success of AI and digital health solutions. To engage diverse cultural groups the following needs consideration:

Multilingual Support: Offer multilingual support for user interfaces and communication. This ensures that individuals who speak languages other than the dominant language in the healthcare system can access and use the technology.

Cultural Tailoring: Customize content and recommendations based on cultural preferences and health beliefs. For instance, dietary recommendations or health education materials can be tailored to align with cultural norms.

Cultural Advisors: Collaborate with cultural advisors or community leaders to ensure that the technology aligns with cultural values and practices. Their insights can guide content and engagement strategies.

4. Ethical Considerations and Informed Consent:

Respecting cultural diversity also means addressing ethical considerations related to data privacy and informed consent:

Informed Consent: Ensure that the informed consent process is culturally sensitive. This may involve providing information in multiple languages, using culturally appropriate communication channels, and respecting cultural norms regarding decision-making.

Data Ownership: Clearly define data ownership and usage rights, considering cultural perspectives on data sharing and control. This fosters trust and empowers individuals to make informed decisions about their data.

Guidelines and accountability: Regulatory bodies and healthcare organizations should establish clear ethical guidelines for the development and deployment of AI in healthcare. These guidelines should address issues related to bias, transparency, informed consent, and accountability. Oversight mechanisms should be put in place to monitor AI systems and ensure compliance with ethical standards.

5. Community Engagement and Policymaking:

Engaging with culturally diverse communities and involving them in policymaking and decision-making processes is vital:

Community Feedback: Actively seek feedback from diverse user groups to continuously improve and refine AI and digital health solutions. Communities can provide valuable insights and identify issues that may not be apparent to developers.

Policy Frameworks: Develop and implement policies and regulations that promote cultural competence, fairness, and sensitivity in healthcare technologies. These policies can set standards for inclusivity and equity in digital health.

Ongoing Evaluation and Adaptation:

The journey does not end with deployment; continuous evaluation and adaptation are essential:

Monitoring for Bias: Continuously monitor AI systems for bias and disparities, with a particular focus on how they affect different cultural groups. Addressing biases promptly is crucial.

User Feedback: Solicit and act on user feedback to refine and adapt the technology over time. Users’ needs and cultural contexts may evolve.

Sample framework deployment

In each of the scenarios described below, the proposed framework can ensure that AI-driven healthcare solutions are culturally competent, sensitive, and fair, addressing the unique cultural and healthcare challenges of the regions while promoting inclusivity and equity:

South Africa, as just one example, boasts a rich diversity of cultural and ethnic groups, with eleven official languages (twelve when sign language is included), and each with their own unique traditional healing systems. These systems are deeply rooted in indigenous knowledge, spirituality, and the interconnectedness of individuals with their environment. All of these elements require that AI technologies respect and integrate these cultural aspects as being essential for the successful adoption, acceptance, and effectiveness of such solutions. AI-powered language translation tools are invaluable in overcoming language barriers in healthcare settings with diverse patient populations. These tools can help healthcare providers communicate effectively with patients who speak different languages, ensuring that patients understand their diagnoses and treatment options. However, the accuracy of these tools and their ability to handle medical terminology in various languages must be continuously improved to avoid miscommunication.

By deploying the proposed framework to the South African landscape, the following important considerations would be addressed: developers of AI-powered language translation tools can conduct cultural research to understand the linguistic diversity and health beliefs of different cultural groups. This knowledge can inform the design of user interfaces and content, incorporating culturally appropriate imagery and multilingual support. Customization options should be offered, allowing users to tailor their experience based on their cultural preferences. It is also essential to ensure that AI algorithms are trained on diverse and representative datasets that encompass the various cultural and linguistic groups. Bias mitigation techniques should be in place to address disparities in performance across diverse populations. Cultural competency training for healthcare professionals and AI system operators should be implemented to ensure that they can effectively use and support these technologies in diverse healthcare settings. Collaboration with traditional healers and community leaders would ensure that the technology aligns with cultural values and practices. Community feedback would be actively sought to refine AI and digital health solutions.

Similarly, in Japan, a country renowned for its rapidly aging population, healthcare systems face the unique challenge of providing culturally sensitive care to elderly individuals. AI technologies are being thoughtfully adapted to address these concerns, ranging from AI-driven robotic companions that cater to specific cultural norms and preferences to the development of AI-powered monitoring systems that respect the dignity and privacy of elderly patients.

The framework would ensure that user-centered design principles are applied, with usability testing involving diverse user groups. This ensures that interfaces are intuitive and engaging for individuals from these various cultural backgrounds. Content and recommendations can be culturally tailored to align with the health beliefs and preferences of the aging population.

In the United States, a nation characterized by its rich cultural diversity, healthcare disparities among immigrant populations are a pressing concern. AI applications are emerging as crucial tools in reducing these disparities by facilitating better communication and care delivery in multilingual and multicultural healthcare settings. These applications include AI-driven language translation tools, culturally tailored health education platforms, and AI-assisted diagnostic tools that account for the diverse backgrounds and perspectives of patients.

Applying elements of the framework in this setting can address bias mitigation techniques to ensure that AI-driven language translation tools and diagnostic tools do not perpetuate healthcare disparities among immigrant populations. Continuous monitoring for bias and disparities in AI systems, with a focus on how they affect different cultural groups, should be carried out in all scenarios. User feedback should be solicited and acted upon to refine and adapt the technology over time, considering evolving user needs and cultural contexts.

In all case scenarios, the informed consent process should be culturally sensitive, providing information in multiple languages and respecting cultural norms regarding decision-making.

By applying the framework elements to the described case scenarios, AI and digital health solutions can become more culturally competent, respectful, and inclusive, ultimately improving healthcare outcomes for diverse populations in South Africa, Japan, the United States and many other countries. This framework provides a robust, holistic approach to AI adoption in culturally diverse healthcare settings.

Conclusion

Artificial intelligence holds immense promise in revolutionizing healthcare by enhancing diagnosis, treatment, and healthcare delivery. However, in the context of cultural diversity, it presents both opportunities and challenges. Understanding and respecting cultural norms and values is essential to harness the full potential of AI in healthcare while avoiding biases and disparities.

This article has explored the multifaceted role of AI in healthcare within culturally diverse settings, emphasizing the importance of ethical considerations, transparency, and cultural competence. Empirical case studies have illustrated how AI can benefit diverse populations while highlighting the need for ongoing monitoring and adaptation to ensure culturally sensitive care.

As AI continues to advance in healthcare, stakeholders must prioritize inclusivity and cultural diversity to ensure that these transformative technologies benefit all individuals, regardless of their cultural background. The successful integration of AI and digital health in culturally diverse settings necessitates a nuanced understanding of the complex interplay between technology and cultural diversity. Achieving the full potential of AI and digital health in culturally diverse contexts requires a holistic approach that integrates cultural competence, fairness, and sensitivity into every aspect of technology development and deployment. By prioritizing these principles, developers, healthcare organizations, and policymakers can create healthcare technologies that are truly inclusive, equitable, and effective for all individuals, regardless of their cultural backgrounds.

 

References

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The TEACH Framework for Introducing Faculty, Students, and Other Business School Stakeholders to Diversity Topics: Pronouns as an Exemplar

Kevin D. Frick, Johns Hopkins Carey Business School

Contact: kfrick@jhu.edu

Abstract

What is the message? Business schools with healthcare management programs are likely to teach an increasingly diverse group of students to manage increasingly diverse workers and make decisions impacting increasingly diverse patients. The author developed a framework to help faculty and other stakeholders effectively address and support diversity in educational settings.

What is the evidence? The author’s TEACH framework guided the production of a video on the Business of Pronouns at the Johns Hopkins Carey Business School.

Timeline: Submitted: December 2, 2023; accepted after review December 5, 2023.

Cite as: Kevin Frick. 2023. The TEACH Framework for Introducing Faculty, Students, and Other Business School Stakeholders to Diversity Topics: Pronouns as an Exemplar. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 3.

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In the future, business schools with healthcare management programs are likely to teach an increasingly diverse group of students to manage increasingly diverse workers and make decisions impacting increasingly diverse patients. The Johns Hopkins Carey Business School’s focus on diversity flows from efforts to encourage diversity, equity, inclusion, and belonging (DEIB) as a manifestation of one of the school’s four core values, “unwavering humanity.” Placing belonging alongside diversity facilitates students, faculty, staff, and alumni being their authentic selves at the school and gives students an approach to take with them when they graduate. Diverse individuals being their authentic selves allows a focus on work and learning rather than on how they are perceived.

Teaching and developing tools to teach about diversity benefits from having a framework to build on. For my diversity-related work, I developed the TEACH framework: the Tangibility of both the diversity exemplar and the steps to improve DEIB; the instructors’ and students’ needs to demonstrate Empathy in an Authentic manner; and the instructors’ and students’ needs to be both Curious and Humble about how they seek information about a marginalized population.

In this this commentary, I describe how the TEACH framework guided the production of a video on the Business of Pronouns at the Johns Hopkins Carey Business School.

The Video

One part of DEIB is gender identity, which has been in the news quite a bit in the past year:  laws have been passed and court decisions handed down on whether individuals should be treated as their gender assigned at birth or as the gender with which they identity and how they present. The Johns Hopkins Carey Business School has taken steps to ensure individuals of all gender identities and presentations receive respect and feel they belong in the classroom and workplace.

One aspect of facilitating belonging for individuals of all gender identities is the use of pronouns reflecting individuals’ gender identities. For many, pronouns have not been considered a “choice.” Most individuals accept labels given by society, simply using “he” or “she” and related pronouns. Some individuals realize their gender identity is not what society assigns at birth; they may choose to use the other binary pronoun (e.g., from she to he) or choose to use a non-binary pronoun (e.g., they). Some choose to respond to and be described using any pronoun. Some individuals choose to use “neo-pronouns” such as “ze,”  “xe,” and “per” (https://intercultural.uncg.edu/wp-content/uploads/Neopronouns-Explained-UNCG-Intercultural-Engagement.pdf). All these are acceptable choices to express gender identity.

Students at the Carey Business School can choose to indicate pronouns they would like to be called (traditional or otherwise) in both the learning management system and the directory. Not all community members are familiar with issues regarding pronouns and gender identity. In response  to one student’s non-positive experiences, a Carey Business School team was formed to create an educational video. Participants included a broad group of the school’s stakeholders: the dean, an associate dean, and a student co-leader of the Pride Business Association provided introductory remarks; a panel including myself (while still making mistakes and being corrected), a school staff member ally to the trans community, a university staff member from an office called Gender & Sexuality Resources, an alumnus who uses all pronouns, a student who had experienced misgendering (i.e., someone using an inappropriate pronoun), and one of that student’s classmates conducted a panel discussion on relevant issues; and, other participants in the video included one alumnus who suggested ways of dealing with misgendering and two business communication faculty who reflected on communication with and about pronouns.

The video included useful information for those unaccustomed to using anything other than pronouns based on gender assigned at birth. Faculty were reminded of the students’ choice to indicate pronouns in the learning management system. Faculty are expected to be aware of and use students’ pronouns. If a faculty member misgenders a student, the mistake may be pointed out by the student or by one of the student’s classmates, or the faculty member may realize it themselves. When corrected, the most appropriate response is “Thank you,” whether it is a first instance or a reminder. If the faculty member recognizes it themselves, a quick acknowledgment of the mistake is appropriate. Regardless, the faculty member should also note that they will try harder in the future to use the correct pronoun.

The students and alum also provided valuable lessons in the video. The student who had been misgendered described constantly correcting others as “exhausting”. An alum made suggestions for those who are misgendered: (1) having a “pronoun buddy” who can politely point out the mistake; (2) taking the person who misgendered someone aside to explain the issues; and (3) dealing with it directly in the moment. A student will notice if the misuse of pronouns behavior continues for an extended period, although change will inevitably take time.

The video is almost fifty minutes long, with distinct breaks in conversation to allow viewers to watch shorter segments. Even so, fifty minutes  is not sufficient to provide a detailed and nuanced discussion of all possible issues. DEIB leadership hopes individuals will have conversations about these issues. One model of learning is for an employer, particularly one focused on education, to provide opportunities for individuals to have discussions after watching the video so that they can process and continue to learn.

Application of the TEACH Framework

The TEACH framework was applied in the process of creating this video. Prior to producing the video, it was noted that a leader may have a grand vision for increasing diversity, the perception of equity or inclusiveness, or the feeling of belonging by individuals within an organization. The lofty vision does not help stakeholders to understand the process of reaching the point to which the organization is headed. Spelling out exactly what element of diversity is being focused on and the first few steps in the process of reaching the final goal creates a sense of tangibility that can  help individuals to feel that they understand what they are being asked to do and why in the short run. Each individual involved in the video production knew the level of effort expected, the duration of time required, and the short-term outcome being sought. When the video was announced to faculty, other contributions to tangibility included making the time required to watch the video clear and the involvement of leadership.

Stakeholders naturally  demonstrate varying levels of empathy as part of their personalities, and multiple types of empathy have been identified ( https://acuityinsights.app/2020/06/empathy-1/#:~:text=Renowned%20psychologists%20Daniel%20Goleman%20and,%3A%20Cognitive%2C%20Emotional%20and%20Compassionate). One type of empathy is “compassionate,” in which one individual appreciates another’s story but is able to maintain sufficient psychological distance. In addition, that person is sufficiently dispassionate to be analytical and to offer a set of ideas and solutions to an issue by taking a step back and separating out emotion and therefore is, perhaps, more rational (https://www.crslearn.org/publication/the-power-of-empathy/compassionate-empathy-the-greater-good-and-social-justice/#:~:text=Compassionate%20empathy%20is%20considered%20the,others%20(Batson%2C%202014).  The video is intended to evoke compassionate empathy for individuals of all gender identities.

The video was developed to promote use of appropriate pronouns in the classroom to signal to all students that authenticity of all gender identities is welcomed and encouraged. The instructor signals an understanding of related issues and demonstrates norms. Students can feel comfortable that their non-binary or transgender status will not be judged and they, as their authentic selves, will be treated like all other students. The instructor also demonstrates their own authenticity by recognizing their ability to use appropriate pronouns and acknowledging when they do not.

Instructors need to continue to be curious throughout their careers to keep themselves up to date in their fields. This is particularly true in healthcare management, where treatments, state and national policies, organizational structures, and incentives for and provided by healthcare organizations change rapidly. Curiosity to learning about how individuals of all gender identities express themselves is an important skill for instructors and for future managers. The curiosity needs to be realized with humility. Part of being a good ally is to be able to listen and to advocate for those in the community one is allied to. Individuals who do not have a lived experience can try to understand and grasp a lived experience but cannot claim to have had the experience and can only appreciate to a certain degree certain aspects of the experience. Individuals in marginalized groups often wish for allies to gain information by doing some “homework” on their own rather than always asking for explanations from members of the allied community. The video is one step in providing reliable information for members of the Carey Business School community to gain information about pronouns before asking for additional detail from a non-binary or transgender individual.

Conclusion

The TEACH framework was effectively applied to the issue of pronouns in business. The video production experience can serve as an example of applying the framework so others can plan to apply the framework to producing educational materials about other areas of diversity.

Word from the HMPI Editors

We’re excited to publish this special issue of HMPI focusing on the topic of pharmaceutical costs and benefits. The Inflation Reduction Act of 2022 provides a new pathway for direct negotiation of drug prices by the Medicare program. This is a change from the Medicare Modernization Act of 2003, which prohibited direct negotiation of drug prices by the government. Despite the rhetoric surrounding this law, the program itself is modest, with a small number of products subject to negotiations in 2024, with no changes to actual prices for these prices until 2026. The program will continue to add new products to the negotiation program each year.

There are significant questions about the program – will it really impact prices for consumers or is the benefit most likely to accrue to taxpayers (who pay 75% of the costs of Medicare Parts B and D)? Will the program impact innovation in terms of investment in new products or product categories? The industry appears to respond to financial incentives. For example, the portfolio of products under development leans heavily towards oncology, in part due to price inelasticity in this part of the market (and in part due to the advances in medical science in this field). Finally, will this effort have any carry-over to the private market? One can imagine that it might force prices upwards as manufacturers attempt to recoup lost revenue that was promised to investors. As Mark Pauly, our guest editor says, we’ll have to wait and see.

One interesting question not addressed in this issue is the mechanics of price negotiation. How should we negotiate these prices. First, we have to ask the question of whether price negotiation is based on gross price, Wholesale Acquisition Cost, or net price, or the amount actually received by the manufacturer. For Part D drugs, the difference between gross and net is explained by rebates and other potential transactions between pharmaceutical firms and Pharmacy Benefit Managers. For Part B drugs, the issue is the required price discounts of the 340B program, now impacting over 54,000 covered entity sites. Once this is determined, how could the government justify it’s price: economic analysis (like cost-effectiveness analysis), reference pricing to prices in other markets, competitive bidding, or maybe just a required discount based on number of years the product has been in the market. It will be a real challenge to develop an approach that can withstand public scrutiny and potential further court challenges.

We’re grateful to Mark Pauly and the authors of the papers in this special issue for helping to bring their perspectives to this fascinating topic.

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

Drug Pricing in the United States: Theory and Evidence

Mark V. Pauly, University of Pennsylvania, The Wharton School, William S. Comanor, Department of Health Policy and Management, UCLA Fielding School of Public Health, and H.E. Frech, III, University of California, Santa Barbara

Concern about high and rising medical care spending is increasingly focused on prescription drug spending, especially spending on patent-protected brand-name drugs. Reducing either the prices or the quantities of such products would obviously reduce costs for public and private insurers, and legislative action has already been undertaken in the United Sates to compel lower Medicare prices in the future. However, drugs also are known to provide substantial marginal health benefits—which implies a policy tradeoff: lower current prices may reduce current cost and increase use of today’s effective drugs but lower prices will also discourage investment in R&D for future effective drugs.

This special issue deals with pharmaceutical innovation — and also current pricing of patent-protected drugs in international markets. In U.S.- branded markets, final prices are often set through negotiations between drug companies as sellers and insurance companies as buyers. In this setting, both sides have strong positions as clearly indicated by the resulting outcomes. As recently reported, net branded pharmaceutical prices on average fall much below the list price set originally by drug companies.

While drug companies, like all sellers, seek the profit maximizing prices for their products, the obvious question is what motivates insurance companies to pay more than production costs. The answer to this query surely rests on their judgment as to the value of the health benefits resulting from a drug. Our earlier research, published in the Journal of Benefit Cost Analysis, documents an empirical connection, on average, between value and price (Frech et al, 2022). Prices furnish the links among payments, value, and innovation; and this special issue is, therefore, devoted to papers that clarify those links.

Better understanding of the links will permit improvements in public policy and private insurance purchasing that can enhance welfare and avoid unintended adverse side effects. The goal is to assemble papers that help to fill in some of the blanks in the complex relationship between profit-motivated drug firms and buyers who are collective and individual and who are public and private. Papers focus on the situation in the United States, the largest single-country drug market but also consider causes and consequences from policies in the rest of the world. The resulting papers are listed below.

One set of papers considers the persistent and resistant mystery of drug pricing for patent-protected drug sellers. While prices, on average, definitely increase with health value, it is the upward deviations from this average, and the process that determines how much an increment in health will cost, that remains a mystery. Why are some drugs so expensive?  The theoretical paper by Pauly, Comanor, Frech, and Martinez (this issue) helps to explain why. The paper shows that even in a simple model in which drug firms charge the simple monopoly price given insurance coverage, and insurers and their customers choose their ideal insurance, given drug prices, equilibrium may not exist, or may be associated with prices so inefficiently high as to make no insurance and no new drugs a better alternative. It is the simple form of insurance, demanded for protection against big bills, that itself can make those bills higher than they would otherwise—and most especially when patients and even sophisticated insurance and benefits managers have to deal with drug company that has a good product and a bulletproof patent. While drug companies with patents have government protection for their monopoly power, and can set any price they want, they cannot sell any quantity they want. The profit-maximizing price takes account of this limitation. However, insurance attenuates the usual discipline that requires enough value for the price by insulating patients from the price at the point of use, only to have the high cost brought home as insurance premiums rise.

The paper by Ippolito and Levy (this issue) illustrates that one approach to making sense in insurance coverage—using evidence on a drug’s clinical benefit relative to the price drug sellers charge—at present yields puzzling results. When a drug’s price is low because it is generic, buyers do use more of it. However, insurers and patients do not seem to respond to the cost effectiveness of branded drugs; those priced low relative to their value are not demanded in larger amounts. Private insurers are less aggressive in using evidence on overpricing of branded drugs relative to their benefits in selecting which drugs to favor than they are in encouraging the use of generics.

Finally, the paper by Hernandez, Gabriel, Guo, Sepassi, Gellad, and Dickson (this issue), a provides some useful evidence of regularity on one of the more confusing features of US drug pricing and insurance coverage, the offering of discounts (or variation in net price received) by drug sellers—mandatory government discounts for new therapeutic classes are growing faster than discounts to private sector buyers, and bigger insurance plans do better than smaller ones.

The other set of papers in this issue deals with a second puzzle in drug economics—the link between the excess of price or revenue over the marginal cost of making drugs and investment in the R&D needed to discover and bring to market new effective drugs. The paper by Dunn, Fernando, and Liebman (this issue) explores how much of US spending growth is due to these new drugs and other treatments. The authors use a novel method of measuring innovation in health care and show that such innovation makes up a significant share of spending growth. This provides context for how much high-cost new drugs and treatments contribute to spending growth.

The link between profit incentives and innovation is taken into account in discussions of drug pricing and potential drug price caps in the US, but in the rest of the world (ROW) it is largely ignored. Does this “free riding “ lead to harmful effects on the global supply of new drugs? In Chen, Comanor, Frech, and Pauly (this issue) the authors find that ROW did make a positive (if lower than US) contribution to drug firm profits in the case of new drugs approved by the FDA.

Finally, Salant (this issue) attributes the small number of online personal imports of branded prescription drugs from other high-income countries with lower prices to groundless consumer fears that such imports are unsafe, a fear stoked by pharmaceutical industry spending. Unlike personal imports for own use, commercial imports for resale are strictly banned (unless the importer is a drug manufacturer). Salant theorizes that removing this ban would prompt manufacturers to narrow the price differentials in order to maintain import deterrence, benefiting Americans through lower drug prices.

Drug pricing and drug innovation are even more in the news these days because of recent legislation that attempts to have one part of government (Medicare) bargain with drug sellers in order to undo the consequences (monopoly prices) from what another part of government (the US Patent Office and the Food and Drug Administration) have done. The papers in this issue will help in untangling the resulting confusion— even if much of it will still remain. They emphasize that some drugs provide substantial health benefits, benefits which by any standard of money value are worth more than their cost. But not every pill in every use provides value for money; some imprecision is inevitable because medicine is imprecise, but poorly designed insurance and poorly functioning insurance markets may allow value to leak out of many transactions.

Drug firms charge different prices for the same drug, sometimes through varying discounts in the U.S. market and sometimes as lower prices in other countries. The incentive to patients to seek out these lower prices constrains the ability of firms to charge high prices to the rest of the U.S. market, so such price discrimination can be helpful in holding down overall prices. If Medicare is able to negotiate even lower prices for its insureds, according to this pattern that should lead to lower prices in other markets in the U.S. as well.

As suggested, at present U.S. public policy on drug spending has moved to begin a brute force approach: just make the high prices and high profits associated with (government-enforced) patents illegal, and see what happens. There will be some side effects: changes in volume of use, reduction in the flow of new drugs, but the hope is that the benefit to the government’s budget and possibly some increase in use of high-value, now more affordable drugs that will more than offset these effects. The financial benefits, if they do arise, will show up right away, and the adverse consequences on innovation are going to take longer to emerge if they do happen, so evaluating this experiment will require time and effort.

The papers in this volume do show that recent new drugs have substantial value, and that contributions toward drug firm profits, whether from higher prices in the U.S. or from some other countries in the rest of the world, do potentially make a difference in pushing drug investor evaluations of potentially promising new ideas over the financial hump. But they also show that blockbusters—with both high sales and high net health value (even at high prices) are much harder to predict. Did the drugs that eventually became standards of care and best sellers have golden prospects from the beginning, or were there surprises (good for these drugs, bad for other drugs that many were betting on)? We know that in the history of many recent high-benefit innovations there were episodes when the idea and project was near death, at least in the telling of the eventual winners.

More generally, to provide evidence on the effects of lower drug prices requires, among other things, a much clearer understanding than we now have about the process of initiating or curtailing drug development efforts, and the link between those efforts and the emergence of highly effective treatments and their prices. In addition, we need to know how usage of drugs is affected by the prices sellers charge and how insurers manage care (and not just by patient cost sharing). These papers contribute to that discussion but the final verdict on whether efforts to change prices will on balance do more good than harm, has yet to be rendered.

Abstract

Timeline: Submitted June 10, 2023; accepted after review Sept. 1, 2023.

Cite as: Special Issue on Drug Pricing in The United States: Theory and Evidence. 2023. W. Health Management, Policy and Innovation (www.HMPI.org), Volume 8, Issue 1.

Contact: pauly@wharton.upenn.edu

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References

Chen, Angela, William S. Comanor, H.E. Frech, III, and Mark V. Pauly. The Global Distribution of New Drug R&D Cost:  Does the Rest of the World Free Ride? (this issue).

Frech, H.E III, Mark V. Pauly, William S. Comanor, and Joseph R. Martinez, Jr..  2022.   Costs and Benefits of Branded Drugs: Insights from Cost-Effectiveness Research.  Journal of Benefit-Cost Analysis 13(2):  166-181.

Dunn, Abe, Lasanthi Fernando, and Eli Liebman. A Direct Measure of Medical Innovation on Health Care Spending: A Condition-Specific Approach. (this issue).

Hernandez, Immaculada, Nico Gabriel, Jingchuan Guo, Aryana Sepassi, Walid F. Gellad, and Sean Dickson. Decomposition of Pharmaceutical Manufacturer Discounts into Voluntary and Mandatory Discounts for Glucagon-like peptide-1 Receptor Agonists. (this issue).

Ippolito, Benedict J., and Joseph F. Levy. Drug Pricing Decisions and Insurance Coverage: Evidence from Medicare Part D. Healthcare (this issue).

Pauly, Mark V., William Comanor, H.E. Frech, III and Joseph R. Martinez, Jr.  Efficiency, Consumer Welfare, and Market Equilibrium in Private Insurance Coverage of Patented Drugs.  (this issue).

Salant, Stephen W.,  Arbitrage Deterrence: A Theory of International Drug Pricing, (this issue)