HMPI

Insights from the 10th Annual Business of Health Care Conference at the University of Miami

Karoline Mortensen, Steven G. Ullmann, and Richard Westlund, Miami Herbert Business School, University of Miami

Contact: sullmann@bus.miami.edu

Abstract

What will you learn? The Miami Herbert Business School of the University of Miami held its tenth annual Business of Health Care Conference. This year’s designated theme was, “Policy, Politics, and the Pandemic- U.S., and Beyond.” The lead panel discussion focused on Policy and Patient Care: A View from Health Care Leaders. Key topics included vaccine hesitancy, value-based care models, telehealth services, stress and burnout, and Biden administration initiatives.

What is the evidence? The authors summarize the discussion at the panel.

Timeline: Submitted: May 28, 2021; Accepted after review: November 5, 2021

Cite as: Karoline Mortensen, Steven G. Ullmann, and Richard Westlund. Insights from the Tenth Annual Business of Health Care Conference at the University of Miami. 2021. Health Management Policy and Innovation (hmpi.org), Volume 6, Issue 2.

The University of Miami recently held its tenth annual Business of Health Care Conference. This year’s designated theme was, “Policy, Politics, and the Pandemic- U.S., and Beyond.” With a national and global audience of more than 1,200 attendees the conference sessions, focusing on such areas as global response, economic impact, and policy issues as seen by former secretaries of the U.S. Department of Health and Human Services, provided significant in-depth insights.

One of the important aspects of this annual conference, organized by the Miami Herbert Business School, is the convening of a panel of high-level health care leaders. This conference is the only venue in which the heads of the major health care organizations come together in one place at one time to discuss issues affecting the health care sector and reflect upon the impact of the health care industry on the economy and society at large. This year there has been even greater impact due to the COVID-19 pandemic.

Panelists for the session, “Policy and Patient Care: A View from Health Care Leaders,” were Susan Bailey, MD, president of the American Medical Association; Matt Eyles, president and CEO of America’s Health Insurance Plans; Joseph Fifer, president and CEO of the Healthcare Financial Management Association; Halee Fischer-Wright, MD, president and CEO of the Medical Group Management Association; Ernest Grant, president of the American Nurses Association; and Lisa Kidder Hrobsky, group vice president, federal relations of the American Hospital Association. The panel was moderated by Patrick J. Geraghty, president and CEO of Florida Blue and its parent company, Guidewell.

The Biden Administration’s Response

Kicking off the discussion, the panelists emphasized the success of the Biden administration’s vaccine rollout. With nearly 200 million doses distributed by mid-April, and now, with 50 percent of the population fully vaccinated, the administration had doubled its initial target for May 1, said Eyles. “We still need to focus on vaccine equity, reaching the most socially vulnerable communities across the country, and making care more affordable.”

Another important step was passage of the $1.9 trillion American Rescue Plan (ARP) stimulus package. “We can’t overestimate the importance of the financial support for our hospitals and health systems,” said Hrobsky. Noting that elective surgeries and routine care visits were canceled or postponed, she said the nation’s hospitals were projected to have lost $320 billion in 2020 alone. She added, “Relief from the federal government has been a lifeline for providers across the country.”

The Biden administration also increased access to coverage through the Affordable Care Act (ACA) with lower premiums and an extended enrollment period. “Making premiums more affordable for a broad swath of the population will help transform the market,” said Eyles. Geraghty noted that in Florida, for example, there are now 320,000 more residents eligible for a subsidy as compared to last year, and those subsidies are larger than before.

Eyles added that there is also increasing recognition among federal and state policy makers that the ACA is here to stay, although there is still some uncertainty with regard to a pending U.S. Supreme Court ruling. Hrobsky added, “Now we need to make those ACA provisions permanent as part of the social infrastructure.”

Fifer, Grant and Bailey agreed that insurance coverage improves individual and family health, which in turn, contributes to a better economic climate. “People live sicker and die younger without insurance,” said Bailey. “We believe everyone should have affordable and accessible insurance and the American Rescue Plan has taken a big step forward in that direction.”

Stress And Burnout

One of the concerns discussed by the panelists was burnout and emotional stress experienced by physicians and nurses. The American Nurses Association launched a “nurses’ resilience” site and is seeking increased funding from Congress for healthcare workers.

Bailey indicated that burnout among physicians was high and was of concern before the pandemic. The problem was only compounded by the pandemic. She indicated that burnout is a systems issue, not a reflection of the individual. As per Bailey, “Many physicians are reluctant to ask for help due to the stigma. So, we are helping institutions understand there are things they can do to reduce the stresses on physicians, such as scheduling their coverage and having adequate supplies of personal protective equipment (PPE).” The burden of documentation was also mentioned in the discussion.

Fischer-Wright spoke to the concern regarding the significant increase in retirements of physicians and nurses with the potential for significant negative impact on delivery of health care nationally, with particular concern for the rural communities.

Having said that, several panelists indicated that both medical and nursing school applications are on the rise. “The media has shown how heroic and meaningful it is to take care of a patient at those crucial moments of life or death,” said Bailey. “Despite all the stresses, being a doctor is still an incredibly rewarding profession.”

Grant added that there is a strong uptick in the people interested in entering the nursing profession. The problem is that there is not enough capacity in terms of faculty, buildings and clinical spaces. As Grant indicated, investments are needed in the academic infrastructure.

Reflecting on the upsurge in student interest, Bailey said, “Today’s young people are altruistic, going into the health care professions for all the right reasons. It’s great to see this trend.”

Telehealth Services

One of the very few silver linings coming out of the pandemic was the growth in acceptance of telehealth as a source of safety, convenience, and access fostered further by Medicare now reimbursing for telehealth encounters.

Geraghty noted that telehealth has become very popular for remote mental health visits, especially if the patient already had a relationship with a therapist. “It will be interesting to see how that model fares as we move forward,” he said.

There was consensus that for telehealth to be truly effective in the future, health care providers need to become comfortable and proficient with the technology. Further, with the realization that COVID-19 in general has only highlighted the impact of health disparities in our society, until broadband is widely available in disadvantaged communities, health care access through telehealth will continue to be a reflection of health disparities in this country. Panelists agreed that now more than ever, broadband internet access is a social determinant of health.

Value-Based Care Models

The panelists turned their discussion to the pandemic’s impact on value-based care. Fifer said providers who shared financial risk with an insurance company have done better than practices dependent on fee-for-service methodologies. However, having dozens of value-based care models muddies the waters. “Providers don’t know how the value computations are made, and don’t like financial surprises,” he added. “CFOs like the predictability of fee-for-service arrangements.”

Hospitals are focused on immediate financial concerns, rather than experimenting with new reimbursement models, according to Hrobsky. She added that large health systems typically have more options than rural hospitals in regard to payments.

“COVID-19 has pulled providers out of value-based care,” said Fischer-Wright. “When medical practices have a 90 percent drop in volume, they have no bandwidth for trying something new. Right now, many medical practices are in survival mode, rather than moving to Maslow’s self-actualization phase.”

For physicians, value-based-care can be problematic. “Who decides value? How do you measure it? How do you reward it?” said Bailey. “Small practices can’t negotiate with payers and are forced into a take-it-or-leave-it situation.”

Vaccine Hesitancy

Vaccine hesitancy is one of the pressing health issues of 2021, said Geraghty, who asked the panelists for their thoughts. Bailey said the American Medical Association has seen an improvement in vaccine confidence in almost all demographic groups.

While many nurses have also been hesitant about vaccines, the American Nurses Association has provided extensive educational materials about how they work, Grant added.

Fifer noted that a handful of adverse reactions to the Johnson & Johnson and AstraZeneca vaccines have been given extensive media coverage. However, he indicated that science and the data should drive vaccination decisions, rather than emotions.

Some final thoughts revolved around insurance premiums. When asked what the impact of COVID-19 would be on insurance costs, Eyles indicated that things are uncertain. “On the one hand, there is pent-up demand for care, which would push premiums up. However, telehealth services may help the plans manage affordability. It is a tough time being a health care actuary right now.”

Last year, Florida Blue spent more than $200 million covering claims for members who were unable to pay their premiums, said Geraghty. “We extended a lot of credit rather than leaving the medical community without funds. It was the right thing for us to do.”

The University of Miami’s Business of Health Care conference is an annual event. As we see what develops over the upcoming year, we will be quite interested as to what these industry leaders have to say one year from now. The theme for the next conference on April 1, 2022 is “Business of Health Care: Technology, Access, and the New Normal.” We look forward to their future insights.

CU Denver Study Finds More than 64% Health CEOs are Concerned About Pandemic Challenges

In ground-breaking research released today, researchers found that the “new normal,” brought on by the COVID-19 pandemic, provides both disruptions and unique growth opportunities for health systems. The report highlights that health system CEOs are concerned about challenges in the new normal. Specifically, their concerns include keeping up with technology, regulatory changes, fiscal burden, and cyber threats. Nevertheless, CEOs believe that digital and intelligent technologies, new strategic mindsets, and hiring a diverse workforce can sway these challenges into growth opportunities.

According to the research, only 35% of CEOs believe the “new normal” is presenting new opportunities for health systems and less than half believe it will increase opportunities to identify new revenue growth. However, as noted in the research, the CEOs noted that there were many lessons learned and industry advances during the last year that could not have happened were it not for the pandemic, specifically those advances surrounding development and utilization of new technology.

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Relative-Risk and the Assessment of School Safety in the COVID-19 Pandemic: Schools May Offer Students Shelter from the Storm (2/25)

Yeganeh Alimohammadi, Kirankumar Shiragur, Ramesh Johari, David Scheinker, Kevin Schulman, and Kristan Staudenmayer, Stanford University 

Contact: kristans@stanford.edu

Abstract

What is the message? The debate around school closures has focused on the question of whether schools are safe in the midst of the pandemic. Most studies look at this issue from the perspective of absolute risk. We consider the perspective of relative risk: in other words, whether children are safer at school or at home. Our model shows, under a reasonable and robust set of assumptions about testing strategies and compliance with CDC guidance on countermeasures, in-person learning can be safer for children than being at home. Interestingly, the model suggests that this benefit increases with rates of community spread. Our model-based findings are in concordance with a broad range of empirical studies suggesting that students are safe at school.

What is the evidence? Literature review and decision model.

Timeline: Submitted: February 19, 2021; accepted after revisions: February 24, 2021

Cite as: Yeganeh Alimohammadi, Kirankumar Shiragur, Ramesh Johari, David Scheinker, Kevin Schulman, and Kristan Staudenmayer. 2021. Relative-Risk and the Assessment of School Safety in the COVID-19 Pandemic: Schools May Offer Students Shelter from the Storm. Health Management, Policy, and Innovation (HMPI.org), volume 5, Issue 1, special issue on COVID-19, 2021.

 

School Safety: Absolute Vs. Relative Risk 

The COVID pandemic has disrupted the lives and education of the children all over the world. Returning children to in-person school has become a national priority under President Biden. The opening of K-12 schools is essential for developing the next generation of our country, especially the most vulnerable.[i] The Director of the CDC has recently issued statements on the safety of school reopening.[ii] Schools have safely re-started in-person learning in many regions in the United States and in countries across the world, providing strong empirical evidence that it is possible to do so safely [iii] [iv] [v] [vi] [vii] [viii]. Yet, there remains a lingering health concern about school safety among some members of the public and some school teachers and staff.[ix]

Making sense of the health concern depend on the type of risk that one considers. The most common approaches to looking at the question of school safety focus on absolute risk.  By contrast, we take the perspective that the situation warrants an approach that evaluates school safety from the perspective of relative risk: i.e., are open schools more or less safe than the surrounding community?

In looking at the problem from the perspective of relative risk, we have found a fascinating result. Under most scenarios, students would be as safe or safer in school as in their community. This is particularly true in the highest-risk communities, where many students live in households were families lack the ability to ensure socially distancing, compared to a school environment that can enforce CDC public health protocols.

Even more importantly, taking a relative risk perspective suggests that the higher the prevalence in the community, the greater the advantage offered by schools in which appropriate prevention measures are taken.  This is different from much current guidance that suggests that in-person learning should stop in communities hardest hit by the pandemic. The advantage is enhanced through self-selection in which the students with the resources to remain safely at home would be more likely to do so, while those without the resources would be more likely to attend and benefit as a result.

Modeling School Safety 

In this paper, we report the results of a modeling exercise to address the issue of school safety, considering three major parameters of the pandemic: community incidence, inbound infection rate, and in-school spread. Community incidence is a measure of the burden of the infection in the community. Inbound infection rate is the rate at which students infected in the community would be presenting to schools. In-school spread represents on-campus transmission of the virus. The values for these variables represent estimates that can be influenced by test accessibility, local testing culture, and vulnerability of children to become infected compared with adults[x].

“Offense” strategy: Limiting infections from entering schools

In an “offense” strategy, schools can make efforts to decrease the chance that infected students and staff come to the physical campus. Here, symptom surveys and fever checks can be deployed. However, data suggest that a large number of people remain asymptomatic after becoming infected, and even people who become symptomatic can spread the virus before symptoms appear, and might be most contagious before the onset of symptoms; symptom surveys and fever checks are inadequate to detect these infected individuals. To address this issue, testing can be deployed to identify students with asymptomatic infections. We report results based on three different testing strategies: every 3 days, every 7 days, and every 14 days.

Defense” strategy: Mitigating risk within schools

Unfortunately, there is no foolproof testing strategy to guarantee that no infected student or staff member comes into the school environment.[xi] So, even with a testing strategy, schools need to implement a “defense” strategy applying “countermeasures.”

A defense strategy should include five mitigation approaches recommended by the CDC: (1) Universal and correct use of masks; (2) physical distancing; (3) handwashing and respiratory etiquette; (4) cleaning and maintaining healthy facilities; and (5) contact tracing in combination with isolation and quarantine.[xii]  Beyond these five tactics, defense could also include assigning students to pods to limit the spread of infections, as well as environmental protections such as improved ventilation systems and ultraviolet radiation (UV-C) systems. In our model, we assess three different levels of compliance with these countermeasure bundles: low, medium and high.

Model results

In Figure 1, we present the results of our analysis for different levels of community spread. In terms of relative risk, a school was considered to be “safer” when students would be less likely to be infected if they attend school than if they remain in the community, and was considered “less safe” if they were more likely to be infected in school than at home.

Applying this definition, when a three-day testing strategy was employed, schools were safer than the community for all levels of countermeasure bundle compliance, and for all values of the average adjusted case rate per day between 1 to 100 per 100,000.  When a seven-day testing strategy was employed, the same result held, with only one exception: with an average adjusted case rate per day of 1 per 100,000 and low countermeasure bundle compliance, school and home risk would be similar.

We also considered less frequent (14 day) testing strategies. In these models, there were more scenarios where school and home would be similar risk.

We found a few scenarios in which schools were less safe than home. These primarily involved the combination of three factors”: 14-day testing, low countermeasure compliance efforts, and an average adjusted case rate per day in the community less than 10 per 100,000.

Further analysis reveal that these results are robust to a variety of assumptions. In Figure 2, we report the results of whether students are safer at home or in school with weekly testing across three different rates of school transmission.

There is a strong and intuitive implication here. When very high rates of community burden increased the community risk, schools were safer under all testing scenarios and across all compliance rates for countermeasures.

Looking Forward 

The dialogue around school closures have been focused on asking the question of whether schools are safe. Our analyses strongly suggest we instead look at the issue not from an absolute risk but from a relative risk perspective. In other words, we should ask the question of whether our children are safer at school or at home. Our model is relatively robust that under a reasonable set of assumptions about testing strategies and compliance with CDC safety measures, in-person school is safer for children than being at home. Interestingly, the model suggests that this benefit increases with rates of community spread, especially for the most vulnerable children. Our model-based findings are in concordance with a broad range of empirical studies suggesting that students are safe at school.

We note two concerns that might arise in applying this relative risk framework.  First is the possibility that students may spread infection into the community, having acquired it at school; this is referred to as “outbound spread” from schools.  However, as the rate of school-acquired spread among students is low in nearly all scenarios with adequate testing, and testing itself detects infection among students, with resulting isolation and quarantine, the rate of outbound spread from schools would also remain low even in communities with high inbound rates.

Second, there may be some households, e.g., with particularly vulnerable individuals at home, that may wish to mitigate absolute risk by keeping kids at home.  As long as schools continue to accommodate such needs, such as by continuing to offer a distance learning option, the self-selection of who does and does not attend in-person will increase the benefits of reopening schools.

 This model reinforces an emerging understanding of the risks and benefits of school opening. Independently of the particular model used in this work, the results of our analysis accord with numerous reports of reported empirical data from schools that have reopened, various simulation models of school reopening, and studies finding that communities with the lowest prevalence of the virus are those in which the rates of masking, social distancing, and testing approach the levels we modeled. [xiii] [xiv] These analyses should provide reassurance to parents and teachers that our children are safer at school than at home at this point in the pandemic, but also suggest the need for careful attention to CDC guidance on countermeasures to achieve this result.

 

References:

[i] https://joebiden.com/reopening/

[ii] https://www.cnbc.com/2021/02/03/cdc-director-says-schools-can-safely-reopen-without-vaccinating-teachers.html; https://www.nytimes.com/2021/01/26/world/cdc-schools-reopening.html

[iii] Incidence and Secondary Transmission of SARS-CoV-2 Infections in Schools

Kanecia O. Zimmerman, Ibukunoluwa C. Akinboyo, M. Alan Brookhart, Angelique E. Boutzoukas, Kathleen McGann, Michael J. Smith, Gabriela Maradiaga Panayotti, Sarah C. Armstrong, Helen Bristow, Donna Parker, Sabrina Zadrozny, David J. Weber, Daniel K. Benjamin

Pediatrics Jan 2021, e2020048090; DOI: 10.1542/peds.2020-048090

[iv] Yoon Y, Kim KR, Park H, Kim S, Kim YJ. Stepwise School Opening and an Impact on the Epidemiology of COVID-19 in the Children. J Korean Med Sci. 2020 Nov 30;35(46):e414.; Otte Im Kampe E, Lehfeld AS, Buda S, Buchholz U, Haas W. Surveillance of COVID-19 school outbreaks, Germany, March to August 2020. Euro Surveill. 2020 Sep;25(38):2001645.

[v] Falk A, Benda A, Falk P, Steffen S, Wallace Z, Høeg TB. COVID-19 Cases and Transmission in 17 K–12 Schools — Wood County, Wisconsin, August 31–November 29, 2020. MMWR Morb Mortal Wkly Rep 2021;70:136–140. DOI: http://dx.doi.org/10.15585/mmwr.mm7004e3

[vi] Ludvigsson, JF. Children are unlikely to be the main drivers of the COVID‐19 pandemic – A systematic review. Acta Paediatr. 2020; 109: 1525– 1530. https://doi.org/10.1111/apa.15371

[vii] Hobbs CV, Martin LM, Kim SS, Kirmse BM, Haynie L, McGraw S, Byers P, Taylor KG, Patel MM, Flannery B; CDC COVID-19 Response Team. Factors Associated with Positive SARS-CoV-2 Test Results in Outpatient Health Facilities and Emergency Departments Among Children and Adolescents Aged <18 Years – Mississippi, September-November 2020. MMWR Morb Mortal Wkly Rep. 2020 Dec 18;69(50):1925-1929. doi: 10.15585/mmwr.mm6950e3. PMID: 33332298; PMCID: PMC7745952.

[viii] COVID-19 in children and the role of school settings in transmission – first update. Stockholm: ECDC;&nbsp;2020.

[ix] https://www.vox.com/22254942/covid-19-schools-reopening-cases-cdc-opening

[x] Tönshoff B, Müller B, Elling R, et al. Prevalence of SARS-CoV-2 Infection in Children and Their Parents in Southwest Germany. JAMA Pediatr. Published online January 22, 2021.

[xi] Letizia AG, Ramos I, Obla A, Goforth C, Weir DL, Ge Y, Bamman MM, Dutta J, Ellis E, Estrella L, George MC, Gonzalez-Reiche AS, Graham WD, van de Guchte A, Gutierrez R, Jones F, Kalomoiri A, Lizewski R, Lizewski S, Marayag J, Marjanovic N, Millar EV, Nair VD, Nudelman G, Nunez E, Pike BL, Porter C, Regeimbal J, Rirak S, Santa Ana E, Sealfon RSG, Sebra R, Simons MP, Soares-Schanoski A, Sugiharto V, Termini M, Vangeti S, Williams C, Troyanskaya OG, van Bakel H, Sealfon SC. SARS-CoV-2 Transmission among Marine Recruits during Quarantine. N Engl J Med. 2020 Dec 17;383(25):2407-2416.

[xii] Centers for Disease Control and Prevention (CDC).  Operational Strategy for K-12 Schools Through Phased Mitigation.  https://www.cdc.gov/coronavirus/2019-ncov/community/schools-childcare/operation-strategy.html

[xiii] Otte Im Kampe E, Lehfeld AS, Buda S, Buchholz U, Haas W. Surveillance of COVID-19 school outbreaks, Germany, March to August 2020. Euro Surveill. 2020 Sep;25(38):2001645. doi: 10.2807/1560-7917.ES.2020.25.38.2001645. PMID: 32975186; PMCID: PMC7533620.

[xiv] Exclusive: Kids catch and spread coronavirus half as much as adults, Iceland study confirms, https://www.nationalgeographic.com/science/article/we-now-know-how-much-children-spread-coronavirus

 

Behavioral Dynamics Affecting Covid-19 Vaccination Uptake in India (Indian School of Business, Ontario Health, 1/29)

Aman Kabra and Ashish Sachdeva, Indian School of Business, and Ami Sheth, Ontario Health

Contact:  ashish_sachdeva@isb.edu

Abstract

What is the message? Although widespread vaccinations can help address the COVID-19 pandemic in India, multiple barriers may limit vaccine uptake. Strategies from the studies of behavioral economics can help overcome the barriers. The strategies can address the intention-action gap, thus increasing not only the initial willingness to take the vaccines but also the subsequent action in actually doing so. The paper highlights multiple recommendations for policy.

What is the evidence? The authors review studies from behavioral economics and apply the insights to COVID vaccination needs in India.

Timeline: Submitted January 21, 2021; accepted after revision January 23, 2021.

Cite as: Kabra, A., Sachdeva, A., and Sheth, A.2021. Behavioral dynamics affecting COVID-19 vaccination uptake in India. Health Management, Policy and Innovation (HMPI.org), Volume 5, Issue 1, special issue on COVID-19, January 2021.

 

India Needs to Overcome Challenges that Could Limit Uptake of COVID Vaccines

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, India has the second-highest number of confirmed cases globally, with more than 10 million confirmed cases and 150,000 people having lost their lives because of the disease. Due to the COVID-19 pandemic, India’s GDP growth is forecasted to decline to 4.8 percent as per the United Nations’ recent estimates[1]. Both lives and livelihood hinge on successfully developing herd immunity, which can be partly attained by vaccination.

Addressing the bottlenecks in the supply and demand for vaccines can help India in its fight against COVID-19. The components associated with the vaccination supply include procurement of large quantities of vaccination doses, storage of these vaccines, managing supply chain and vaccine delivery logistics, building human capital, and coordinating delivery operations. These components can be optimized using principles from operations management and operations research.

This article focuses on the uptake of the COVID-19 vaccine in India. Vaccination is widely recognized as one of the most successful public health measures. However, within the Indian context, there are several challenges for rapid mass vaccination uptake.  We list some of the prominent ones here. First, India is a vast and populous country; its demographic and geographic diversity makes it virtually impossible to develop a one-solution-fits-all approach to nudge the public toward COVID-19 vaccination uptake. The presence of various leaders and religious groups, each bringing their own opinion about vaccination for COVID-19, coupled with the diversity in literacy rate across the country, adds to the difficulty in coming up with a universal approach for publicity campaigns.  Second, a typical vaccine development process takes 10-15 years. The expedited development of the COVID-19 vaccine and the limited information regarding the vaccine approval process is bound to raise questions regarding its safety and efficacy in the public’s mind. Third, the unavailability of data on long-term effectiveness and side-effects from vaccination may not only deter vaccine supporters, including early adopters, but also give anti-vaccinators support for avoiding vaccination. Fourth, the public in India is not accustomed to choosing a brand of vaccine. There are currently multiple brands of vaccines for COVID-19 approved in India with conflicting and incomplete results, resulting in a dilemma for the public on which brand to trust.  This may lead to decision paralysis, and people might end up avoiding vaccination altogether.  Fifth, there exists a lack of adequate and equitable access to vaccination centers equipped with medical facilities in small towns and rural areas in India to address any immediate life-threatening side-effects resulting from vaccination.  This can further discourage people from getting a COVID-19 vaccine.

Insights from behavioral economics can help alleviate several of the challenges and increase vaccination uptake.  In this article, we apply those insights in the context of vaccination hesitancy and acceptance in India and provide tangible recommendations to policymakers and healthcare practitioners to help reduce the frictions in scaling of COVID-19 vaccination uptake. 

Uptake of Vaccines

An individual’s decision to vaccinate depends on multiple factors.  From a behavioral perspective, the process of getting vaccinated can be subdivided into willingness and action.

  1. Willingness

We list below some of the key factors affecting an individual’s willingness to get vaccinated:

  1. Perception
  2. Confidence/Trust
  3. Norms
  4. Altruism

Perception

Public health experts widely use the health belief model to predict health-related behavior in terms of certain belief patterns[2]. The health belief model proposes that the uptake of a vaccine will depend on the perceived susceptibility of the disease; severity of the disease; benefits of the vaccine; and risks of the vaccine22.  A meta-analysis of previous vaccination studies shows that people are more likely to seek vaccination if there is an increased “perceived severity” and “perceived likelihood” of the disease[3].

Note, the word “perceived” here refers to perceptions which might be significantly different from reality. Vaccination data regarding efficacy and safety is conveyed with numbers and statistics. However, relating to numbers at an individual level is difficult. People will make perceptions based on statistics, and at times fill the void of information with their own stories, and so fall prey to biases, such as confirmation bias (tendency to seek out information that supports something you already believe); availability bias (tendency to use the information we can quickly recall); in-group bias (people are more likely to support or believe someone within their own social group than an outsider); and optimism bias (overestimating the chance of getting a favorable outcome, or underestimating the chance of getting an unfavorable outcome).

For an instance, since April 2020, the COVID-19 pandemic has spread widely across India. Many people may now know of someone within their close circle who may have either met with an unfortunate incident due to the disease or may have recovered from the disease. Due to an in-group bias, these close contact stories will play a major role in shaping people’s perceptions of the disease’s susceptibility and severity, hence influencing their vaccine uptake. If someone close to them recovered easily from COVID-19, they might believe the disease is not severe and may not decide to get vaccinated.  Similarly, a particular section of the society may underestimate their own risks due to “optimism bias”.

Policy implication: The key point here for policy is that asking people to act as outside observers and consider the vaccination decision for someone like them may improve the assessment of their own risk and lead to higher vaccination rates[4].

Another important consideration related to perception shaping vaccination behavior is the “perceived benefit and risk” associated with vaccines. A study on vaccination showed that the decision to vaccinate is positively correlated with perceived benefit and inversely correlated to the severity of vaccine-associated adverse events3. The vaccine will be given to millions of people. There are bound to be cases where the vaccine is ineffective, where a person gets infected even after getting vaccinated, or when someone has a serious side-effect or dies after getting vaccinated. Some of these adverse incidents might be unrelated to the vaccination. However, these stories will affect people’s perception of risk and benefit of vaccination and their demand for the vaccine.  For an instance, recent news from Norway reported that 29 people died after receiving Pfizer COVID-19 vaccine[5]. Although such outcomes typically have little or nothing to do with the vaccine, they shape public perceptions.  These reports, as well as any other uncommon stories sensationalized by the media, may discourage vaccination uptake.

Policy implication:  Systems should be in place to avoid or correct for these perception biases. Common side effects, such as muscle fatigue and headache can be easily managed by providing information.  However, managing any serious side-effects such as hospital admission and death will require an extensive planning and proactive approach. One strategy to address vaccination side-effects is to continually monitor the health status of the vaccinated population and accordingly modify the recommendations, as done in a case from Norway.  Another way to influence perception regarding vaccination and support optimal decision-making is to provide easy-to-understand, clear and transparent information, allowing people to understand the benefits and risks of vaccination and discern the differences in causation and correlation.

Furthermore, in the cases where marginal cost increases due to the perceived risk associated with side-effects from the vaccine, leveraging regret theory can be an effective strategy. Invoking regret can be initiated through loss framing messages in terms of getting seriously ill or encountering death from vaccine-preventable disease than being more focused on the vaccine’s potential side effects[6]. Policymakers and medical practitioners should carefully tailor messages to address peoples’ fears and misinformation.

Confidence/Trust

The demand for any healthcare intervention depends on the confidence the public has in that intervention. COVID-19 vaccination is no different. Vaccine confidence can be measured by perceived benefits and risks along with the trust in the providers of vaccines3. Here, the term “providers” refers to not only the medical community advocating for the vaccine but also the decision and policymakers involved in approving the vaccines. People’s confidence in vaccination may be lower, resulting in a low uptake, if (a) the manufacturing and distribution for the vaccine are rushed, (b) manufacturing vaccines in countries where people have low-confidence, and (c) lack of transparency related to vaccine’s approval process, benefits, and side-effects.

A recent global survey on vaccination intent revealed that 87% of Indians are willing to seek vaccination, but only 34% of participants will seek vaccination as soon as it is available[7]. This difference in intentions can be explained by people’s concerns regarding the safety and the efficacy of the vaccine7. India recently approved a vaccine for emergency use whose efficacy is yet to be proven and another vaccine with 62-90% efficacy[8]. In contrast, Europe, the United States, and Canada approved vaccines with greater than 90% efficacy.  Such actions may be perceived as hasty and raise questions regarding policy-makers intentions for public safety.

Policy implications: Confidence can be built and maintained based on trust through periodic and transparent dissemination of information on the approval process and rigorous surveillance measures, of cases of ineffectiveness, plus possible COVID-19 reinfection despite vaccination. Communication should be done concretely and consistently, through which information feels immediate, proximate, feasible, and likely to directly affect people or those they care about. E.g., in the case of the vaccine approval process, instead of saying phase 3, say, “the vaccine has been tested on X number of people”6. Moreover, the rapid spread of miss-information (infodemic) on social media channels should be rigorously monitored, contained and addressed to gain public’s trust.

Public confidence can be gained by increasing the vaccine’s attractiveness by highlighting the clinical endorsement of vaccines by people involved in the manufacturing and approval of the vaccines, and by showcasing medical professionals and politicians getting vaccinated. Recently, many key leaders in the US, such as Kamala Harris and Joe Biden, received COVID-19 vaccines to display their own confidence in the vaccine’s safety. On the contrary, the Indian government asserted that politicians would not be considered a priority group and will not be vaccinated in the first phase of the rollout, perhaps instilling a sense of doubt and fear in the minds of the public[9]. Government leaders should be put at the forefront of vaccination intake to instill the trust of the public in the vaccination.

Another challenge that India faces is the market for fake, counterfeit, or substandard medicines, accounting for 25% of India’s drugs[10]. The presence of fake medicines reduces people’s trust in the system and makes them apprehensive about seeking treatment.

Policy implications: To build confidence, India will have to take decisive steps to identify and control any potential fake vaccine market.

While India has made vaccination voluntary, the decision to pick and choose among the approved vaccines is currently not available. This policy is similar to other countries, but in India, a vaccine was approved despite incomplete data. The unavailability of choice here raises several ethical and safety concerns and may harm people’s trust and confidence in the vaccine.

Policy implications: Confidence in vaccination can be increased by providing people with a choice and control over their decisions.

Norms

The theory of planned behavior proposes that uptake of the vaccine will depend on subjective and social norms. Subjective norms refer to the belief about whether most people approve or disapprove of the behavior. It relates to a person’s beliefs about whether peers and people of importance think they should engage in the behavior. Social norm refers to the customary codes of behavior in a group of people or a larger cultural context. This means that people tend to fit their actions to others’ behavior and expectations, and vaccination is no exception3. As more people vaccinate, the act of vaccination will become a social norm and eventually a default action.

In this age of social media, influencers play a key role in increasing vaccination uptake and influencing social norms. These influencers can range from a close family member to a group influencers as immunization programme managers, community and religious leaders, health workers, civil society organizations, media outlets, and digital platforms.

Policy implications: Favorable vaccination behavior can be attained through consistent messaging, which iterates based on the context and the audience.  Drawing parallels from the ALS bucket challenge or i-voted campaign, showing society influencers taking the vaccination jab can create a powerful ripple effect and help establish vaccination as a social norm. 

Moreover, childhood vaccination and flu vaccination experiences show that a medical practitioner, especially a family doctor, plays a significant role in influencing social norms.

Policy implications: Relevant practitioners can be provided with a toolkit that contains messages and FAQs, which can be used as a reference material to address patients’ concerns regarding vaccination.  Similarly, using important nodal links such as the school system, or large employers to spread vaccination information may influence subjective norms and intended behavior.

Altruism 

Getting a vaccine benefits not just an individual but others as well. An externality of vaccination is that if enough people vaccinate, then it can lead to herd immunity. This benefits people who are unable to vaccinate due to medical or any other reasons. Several studies from the flu vaccination campaign for health care workers indicate that the uptake for vaccination increased when the vaccination behavior was made salient and social with messages such as, “I vaccinated from influenza to protect you”3.  A recent vaccination intention study indicated that younger age group participants between the ages of 18 and 24 were less likely to vaccinate than other age groups[11].

Policy implications: One way to influence a change within hesitant groups is to invoke the feeling of altruism by conveying how a vaccination decision impacts the wellbeing of more vulnerable segments of the population with messages such as, “thank you for vaccination, you have saved a life of a cancer patient and a pregnant woman”.

  1. Action

Once an individual is willing to get vaccinated, there can still be an intention-action gap. Addressing the last mile challenge to access the vaccine is dependent on two factors:

  1. Barriers
  2. Affordability

Barriers

Barriers entail the effort required to perform an action. Reducing the effort required can increase vaccination uptake[12].  Humans are far less likely to search for, pay attention to, understand, use information, or choose wisely when hassle costs are substantial. Even slight amounts of extra hassle can have significant adverse effects on our behavior11.

The World Health Organization states, “Everyone, everywhere who could benefit from safe and effective COVID-19 vaccines should have access as quickly as possible, starting with those at highest risk of serious disease or death”[13].  In terms of COVID-19 vaccination, if the vaccination location is not easily accessible, it will fail to reach the masses. Additionally, suppose scheduling for and getting a vaccination requires intensive effort such as lengthy wait times, requiring several ID proofs, then people will be more likely to not seek vaccination due to increased hassle cost.

Furthermore reading and thoroughly understanding information related to the vaccine, such as vaccine efficacy, side-effects, and follow up at the time of vaccination, can create a further impediment to action.  These constraints are particularly strong if the information is not presented in an easy and clear format.  This is because humans cannot process vast amounts of information all at once, which may be exaggerated with large variations in literacy levels. The government should try to reduce these barriers to uptake of vaccination.

Policy implications: Vaccination centers should be easily reachable, accessible for differently-abled, familiar to the target public, approved for safety as per vaccination guidelines, and rigorously sanitized.  The process of scheduling and getting vaccinated should be easy, take little time, and require minimal paperwork.

One strategy to make the process easy, hassle-free, and straightforward would be to design a seamless encounter experienced by travelers at airports. The facilities can use similar approaches such as pre-check-in, pre-read and understandable instructions before coming to the facility and should be able to locate the nearest facility with the help of ‘Co-WIN App,’ a mobile application8. Using the principles of choice architecture, the vaccine provider can pre-register the user after the first appointment for the second dose and send them a reminder requiring an acknowledgment to decrease the no-show rate.

In addition, the health care workers at vaccination centers should be knowledgeable in educating the public about the benefits, side-effects, what-to-dos, costs, and where-to-go facts on COVID-19 vaccination and pay close attention to customer service and de-escalation skills.

Patients could also be provided with allowable flexibility in the visit timings. Despite the benefits of flexibility, this would require careful planning to avoid long wait times, which might otherwise result in the patients walking away.  Long wait times could also result from patients deciding to commute to the center in groups, which must be prohibited.

Policy implications: Proven strategies from operations management can help minimize over-crowding at the vaccination facilities and ensure respectful time management, thus decreasing an overall no-show rate. The higher the consistency, availability, and quality of care service, the more motivated the public will be to visit the centers.

Affordability

One other key factors related to vaccine uptake is the out-of-pocket cost burden carried by the end-users. Indian vaccination programs have been hugely lauded across the globe due to the country’s home-grown manufacturing facilities, along with robust storage and tracking mechanisms. Critically, 90% of costs are covered under India’s Universal Immunization Programme (UIP)[14]. The Indian government can take advantage of pandemic pricing policies through which the vaccine developers cannot generate extraordinary profit.

Policy implications:  Care must be taken that the price reduction should come from cost-efficiency rather than merely through price controls. Additionally, the public should have an option if they want to get a vaccine of their choice at a higher price with some degree of governmental control.  

Looking Forward

It is crucial to understand the determinants of individual vaccination decisions to establish effective strategies to support the success of country-specific public health policies. Interventions should be carefully targeted by first understanding underlying reasons of “why” certain people might refrain from COVID-19 vaccination uptake, which can then inform “what” suitable interventions can be appropriately put in place to increase vaccination uptake and developing that much-needed herd immunity to save lives and to jump-start the economy.

Vaccine refusal can occur due to fears related to vaccination side effects, lack of trust in policy and decision-makers, and abundant misinformation or ambiguous information. We believe that policymakers and essential government task forces need to play a significant role in influencing vaccination perception and thus creating much-needed trust and confidence in the process and benefits of vaccination. As shown by several studies, the accelerated development of vaccines has caused reluctance for vaccination decisions. The behavioral tools of choice architecture, message framing, addressing cognitive biases, and creating norms can be powerful arsenals in the policy-makers tool kit to influence pro-vaccination behavior for the COVID-19 disease. Selection of these tools requires understanding the root causes of vaccine hesitancy and will need an experimental mindset to determine the best course of action.

Lastly, addressing the last mile challenge related to the accessibility of safe vaccination sites and affordability of vaccines can be tackled through thorough planning and coordination. The goal of inoculating approximately 1.3 billion people within a year is ambitious. India can draw from the effective interventions employed by the western countries and its own world-recognized past vaccination efforts, including the eradication of polio and smallpox to ensure uptake of COVID-19 vaccination within its population. The fight against COVID-19 is not over, but we will get there.

References

[1] Unknown. (2020, April 09). India’s GDP for FY21 projected at 4.8%, says UN report. Bloomberg. https://www.bloombergquint.com/business/india-s-gdp-for-fy21-projected-at-4-8-covid19-to-have-adverse-economic-impact-globally-un-report

[2] Corace, K. M., Srigley, J. A., Hargadon, D. P., Yu, D., MacDonald, T. K., Fabrigar, L. R., & Garber, G. E. (2016). Using behavior change frameworks to improve healthcare worker influenza vaccination rates: a systematic review. Vaccine, 34(28), 3235-3242.

[3] Brewer, N. T., Chapman, G. B., Rothman, A. J., Leask, J., & Kempe, A. (2018). Understanding and increasing vaccination behaviors: Putting psychology into action. Psychol. Sci. Public Interest, 18, 149-207.

[4] Chen, F., & Stevens, R. (2017). Applying lessons from behavioral economics to increase flu vaccination rates. Health promotion international, 32(6), 1067-1073.

[5] Taraldsen, L.E. (2021, January 16). Norway raises concern over vaccine jabs for the elderly. Bloomberg. https://www.bloomberg.com/news/articles/2021-01-16/norway-vaccine-fatalities-among-people-75-and-older-rise-to-29

[6] Center for Public Interest Communications, University of Florida College of Journalism and Communications. (2021). Guide to COVID-19 vaccine communications.

[7] Boyon, N. (2020, November 05). COVID-19 vaccination intent is decreasing globally. Ipsos.

https://www.ipsos.com/en/global-attitudes-covid-19-vaccine-october-2020

[8] Unknown. (2021, January 21). Covaxin and Covishield: What we know about India’s Covid vaccines. BBC News. https://www.bbc.com/news/world-asia-india-55748124

[9] Express Web Desk. (2021, January 19). India’s Covid-19 vaccination drive begins: All your questions answered. The Indian Express.  https://indianexpress.com/article/india/india-covid-19-vaccination-drive-7147254/

[10] Krishnan, A. (2018, September 26). How are e-Pharmacies beating the fake drugs issue in India? The Times of India. http://timesofindia.indiatimes.com/articleshow/65936562.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst

[11] Lazarus, J. V., Ratzan, S. C., Palayew, A., Gostin, L. O., Larson, H. J., Rabin, K., … & El-Mohandes, A. (2020). A global survey of potential acceptance of a COVID-19 vaccine. Nature medicine, 1-4.

[12] Owain, S., Hallsworth, M., Halpern, D., Algate, F., Gallagher, R., … & Kirkman, E.  (2015). Four simple ways to apply behavioral insights- The EAST framework. Behavioral Insights Team. https://www.bi.team/wp-content/uploads/2015/07/BIT-Publication-EAST_FA_WEB.pdf

[13] Unknown. (2020, October 28). Coronavirus disease (COVID-19): Vaccine access and allocation. World Health Organization. https://www.who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccine-access-and-allocation

[14] Chatterjee, S., Pant, M., Haldar, P., Aggarwal, M. K., & Laxminarayan, R. (2016). Current costs & projected financial needs of India’s Universal Immunization Programme. The Indian journal of medical research, 143(6), 801–808.

 

 

 

 

 

Word from the Editors

We are delighted to launch the Issue 6.1 of HMPI. The articles feature a mini special issue on bundled care, together with an exciting set of discussions of current challenges in heath and healthcare.

Four papers highlight opportunities and challenges in bundled care. Lauren Bell and Jiayan Chen describe a bundled care initiative led by a shared service organization in Ontario Canada that is beginning to make a difference in the quality and cost of services such as hip and knee replacements. Vivian Lee points to emerging opportunities in U.S. healthcare to pay for results, not action. Brian Golden and Rosemary Hannam report on a failed attempt to implement bundled care in an environment that would seem to be particularly suited to this strategy, the Canadian province of Ontario. Jiayin Xue and Kevin Schulman describe the history of Medicare Advantage Medicare and the ways in which it operates through capitated payment models.

Three papers in this issue address issues with relevance for dealing with pandemics. Blair Gifford provides a history of how voluntary hospitals in Chicago responded quickly to the influenza epidemic of 1918, which has implications for how facilities with a community service orientation are now responding to COVID. Hoyt Gong and Cecilia Wang describe how China’s emergency response system at the initial outbreak of COVID-19 in the country left unintended fallout on non-COVID-19 patients. Steven Ullmann and Richard Westlund describe insights that emerged from the annual conference of the University of Miami Center for Health Management and Policy, including challenges of access to COVID-related services that arise from social determinants of health.

Two papers highlight issues at the forefront of health care initiatives in the U.S. and globally. Mark Pauly and Koushal Rao investigate incentives for sellers to provide transparent information about healthcare prices. Kristan Staudenmayer, Courtenay Steward, Clare Purvis, and Kevin Schulman highlight governance structures that limit innovation in digital health care.
Finally, Regi’s Case Corner describes a new case about Google’s Verily Life Sciences and its initiative to harness machine learning for healthcare.

Health systems around the world face a hugely challenging dual set of challenges. They are under extreme stress as they deal with the COVID pandemic. At the same time, the health systems face the parallel need to continue to provide their traditional services, independent of COVID. The research and perspectives from our authors provide ideas that will help health system leaders walk this balance of pandemic and traditional services.

As always, the authors of the articles that we publish in HMPI are committed to improving management practices in health systems around the world. We welcome your comments about the ideas that the articles spark and your ideas for subsequent articles. Please send us your comments to info@hmpi.org. We also welcome discussion on the BAHM Forum on LinkedIn, the BAHM LinkedIn page and on Twitter .

If you have an idea that you would like to explore for HMPI, please send an outline of your article to our editorial team at info@hmpi.org.

Will Mitchell
Professor of Strategic Management
Anthony S. Fell Chair in New Technologies and Commercialization
Rotman School of Management, University of Toronto

 

Light Under a Bushel: Medical Price Transparency Regulation and Low-Priced Seller Behavior

Mark V. Pauly, Koushal Rao and David Futoran, The Wharton School at the University of Pennsylvania 

Contact: pauly@wharton.upenn.edu

Abstract

What is the message? If medical care consumers are to make better choices among competing sellers of well-defined services, they will, we are told, need more transparency on the price they will pay, on quality, and on ease of access. Some states have established programs to mandate such information, and the federal government has recently required hospitals to disclose all prices charged and received. This paper explores the novel issue of the power of and interest by sellers themselves in furnishing information on price when they have decided to charge low prices in their local market—along with information on quality or access. We provide a conceptual discussion of why such information may or may not be supplied.

What is the evidence? We illustrate actual seller behavior by extracting data from provider websites in New Hampshire and Maine for a number of common procedures. We provide evidence that, as might be expected, those sellers charging lower prices in their markets are more likely to mention price or some proxy for it in their website, while higher priced sellers are silent about price but mention quality or convenience. However, we find that many low-priced sellers do not draw potential buyers’ attention to this fact, and consider some possible reasons for this apparent paradox.

Timeline: Submitted July 27, 2020; accepted after review August 3, 2020

Cite as: Mark Pauly, Koushal Rao, David Futoran. 2021. Light Under A Bushel: Medical Price Transparency Regulation And Low Priced Seller Behavior. Health Management, Policy and Innovation (HMPI.org), Volume 6, Issue 1, Winter 2021.

How Can We Achieve Price Transparency? 

Hospitals and healthcare providers are caught in the crossfire between two groups that want to use very different methods to reduce spending on their services. Some critics want to move public policy toward stricter regulation of prices or reimbursements received, having them controlled or set by a single payer. Others envision a more aggressive competitive market in which consumer-patients searching for better deals put pressure on all sellers to keep down what consumers have to pay. The Trump administration recently won court approval for a regulation that requires hospital disclosure of prices received from all buyers, taking the view that consumers, especially those with higher deductibles in their insurance plans, can benefit from transparency of prices different buyers charged for medical services and that those prices should neither be obscured before services are rendered nor kept secret afterwards. [1]

Markets for medical services do not work the same as other potentially competitive markets because of the presence of health insurance, whose form can strongly affect the potential gain to consumers from knowing about lower priced sellers. Transparency is not of great value for heavily insured services, ones whose price exceeds the typical deductible, or ones for which there is little opportunity for patient choice. However, though it still only covers a minority of the population, the striking growth in high-deductible health insurance has generated interest in consumer price information for commodity-type-services priced high but below the deductible.

Variation in list prices for such medical services (where patient severity or other characteristics should have minimal effects on cost), such as MRI scans or routine colonoscopies appears to be large in most markets, and variation across payers transacting with a given provider are common. This evidence on price variation has led to proposals and legislation designed to bring about greater price transparency for these medical services, in order to assist consumers who could save by choosing lower priced sellers. In this policy discussion the presence of price variation and the absence of good information about prices have been taken as given, thus motivating the need for public regulation of transparency and public support for dissemination of that information.

In this paper we argue that there is another vehicle for price transparency which has been ignored—the firms that charge low prices may have an incentive themselves to bring that fact to the attention of consumer buyers. Sellers need not be viewed as passive price setters, some greedy and others neglectful of profit maximization, who just somehow generate the large variation in prices. Instead, some of those firms who choose to set below average prices may benefit from having buyers know about the bargains they offer.

Some Firms That Charge Low Prices Have Incentives To Publicize That Fact

We first describe the economic models in which some — but by no means all — firms might choose to provide information on their below-average prices.  We then use data from states that have been most aggressive in the push for price transparency to show that in practice firms with prices at the lower end of the distribution of prices in a market do not just wait for buyers aided by government to arrive.  Instead many of them take an active role. However, not all sellers make their prices transparent, and not even all low priced seller push out information about that fact.  Sometimes there are good theoretical reasons for them to conceal, but sometimes government assistance might help.

It is important to add that price information alone is not sufficient for good consumer decisions; there also needs to be adequate information about the quality and amenities supplied by different providers, to be compared to prices. So, multidimensional transparency is a reasonable policy goal.

There is, of course, an insurer model alternative to the consumer directed high-deductible plan in which the consumer’s insurer, not the individual, does the searching and bargaining over price and quality; this is still the more common arrangement in low-deductible plans and even in some high-deductible plans where insurers make their networks and discounted prices available to insureds who are under the deductible. There clearly is growth in plans where patients are supposed to take responsibility for price shopping and there  does seem to be bipartisan  sentiment for, at a minimum, disclosure of what the consumer will be expected to pay.

Current Policy Goals: Demands For Price Transparency

Current policy discussions by the Trump administration and industry critics have argued that more price transparency is needed. The Administration has proposed rules to use Medicare data to improve transparency [2]. The president claims this is important – “this is bigger than anything we have ever done in this particular realm.” [3].  The ultimate goal appears to be that of letting each consumer know where the use of lower priced sellers can lead to lower out of pocket payments, given the deductibles and coinsurance in each consumer’s policy.

However, because of the nature of insurance coverage and the emotional issues that often surround decisions about medical care, especially that for immediate health needs, so far it appears that many consumers in high deductible plans may not themselves choose to seek or shop for lower prices for such services [4]. Recent research using data from individual employers and plans suggests that individual consumers do not regularly search in their local markets to find lower priced sellers [5]. Still, it seems a matter of simple economics that somehow making it both important and easy for consumers to compare prices for standard services could increase effective competition that might lower spending on those services [6].

Why Transparency Policy Is Not Always Best

Despite the current demands for price transparency, there are two important issues here that have not been well considered. First, as George Stigler noted in his classic work [7], the absence of a perfectly competitive market structure and the presence of oligopolistic interaction between sellers may mean that better information might lead average prices actually paid to increase, as dominant firms more easily detect and punish price cutting by smaller rivals. We have treated the possibilities and the circumstances in which this might happen elsewhere [8].  This turns out to be a complex question with few a priori answers, with some empirical support for other industries in other countries (e.g, cement in Denmark) but no bulletproof evidence for the US insured medical care markets.

The second issue is much simpler but even more neglected in the discussion. If at least some semblance of competition is at work, there should be another source of information to consumers about prices: the sellers themselves [9]. This is because there is little financial advantage to those firms charging lower prices than others unless their prices attract a larger volume of business than would higher prices – and enough additional volume to offset the lower prices.

To outline the obvious, here is how that would work. Suppose there are five firms in a community selling MRI scans of the back. Seller A charges the lowest price, though we might wonder why.  But if that seller seeks profits or net income, or even just a greater role in providing services to the community, it should be eager to inform buyers of its price and that its list price is the lowest in town – information that it could collect relatively easily. That should lead the second-lowest priced seller (seller B) to inform buyers that it is less costly than the other three sellers C, D, and E.  This process will continue until only seller E is silent about its price, but consumers will know that silence must mean that it is the most expensive facility in town.

In short, a number of incentives should prompt sellers to furnish price information, with no necessary need for laws or grants or external agencies to compile buyers’ guides – though having public sector help in disclosing price would still help. Nor would rules compelling disclosure of lower prices be needed. In the words of one commentator, “One might think that providers who can deliver a comprehensive set of services at a (low) negotiated price would relish the notion of having that price fully disclosed.”[10]. In economics, the sellers should not only relish someone’s help in publishing low prices, they should actually publicize the low prices themselves.  So do actual medical markets work the way economics suggests?

Economic Models of Price Dispersion 

Logic

In perfectly competitive markets, economic theory proves that the Law of One Price will hold: all firms and all buyers will pay the same price which just generates normal profits. If there is a reasonably large number of sellers, in order to get a model that allows for dispersed prices, one needs to assume some impediment to buyer search, either “search costs” or “switching costs.”  Such models have been proposed by Steven Salop and co-authors. [11.12].

Even then, if buyers can also provide information, as in the spirit of our introductory remarks, no equilibrium may exist or no search may exist [9]. Depending on the kinds of searching and the methods of searching, there can also be models where dispersed prices emerge but may or may not generate an equilibrium, as opposed to permanent churning [13]. Generally speaking, as long as at least some buyers only sample one seller while others sample more than one, an equilibrium with dispersed prices is possible.

These models all build on the early work of Stigler (1961) [7] on search costs, primarily on his theory but also on his empirical observation that price dispersion is smaller for consumer big ticket items where search provides more benefit. Stigler was concerned about oligopoly behavior (e.g., few sellers because of barriers to entry like small market size), noting that better information to competitors about rivals’ prices in such a situation may paradoxically lead to higher prices if it threatens secret discounts offered by some sellers, a prediction for which there is some theoretical evidence in markets for industrial products like concrete [14,15].

In the application to medical services, the key assumption that some buyers search more than others seems eminently plausible. Among other reasons, if some buyers will pay out of pocket, or a larger proportion out of pocket, while others have more complete insurance coverage, there should be differences in search behavior.

The purpose of government efforts to improve price transparency is to help the high-deductible subpopulation, although it has proven challenging to do so. The reason is that data on payments to sellers by insurers often bear little relationship to what a consumer who has to pay will be charged. List prices that would be paid by a well-off but uninsured person almost always exceed insurer payments, while lower income uninsured may get discounts, and some of those with high deductible plans have access to insurer negotiated prices while others do not. [16,17].  In addition, different insurers may pay quite different amounts to the same seller for the same service. Discovering that someone else (or someone else’s insurer) paid less than you are being charged by a healthcare provider may make for irritation and a good argument but is unlikely to move the provider to change behavior if you have fewer options or lower bargaining skills than the bargain hunter. You are not going to be able to pay the Medicaid price.

These reasons may account for the general failure to find large positive impacts from state programs for price transparency, at least so far. [18, 19]. To illustrate these points further, we now present an empirical model of the relationship between prices firms set and actions firms take, and illustrate it with the oldest and most comprehensive publicly mandated price data on medical procedures, that from New Hampshire and Maine.

Conceptual Model

In order to develop a model with price dispersion in equilibrium, there must be some impediment to buyer ability to know about and patronize different sellers. Otherwise all buyers will move to the lowest priced sellers and price will be uniform. As noted above, one common assumption therefore is that buyers differ in terms of search costs or search strategies—some may choose or sample only one seller while others obtain information and consider using different sellers. A firm that sets a high price will therefore tend to sell to those who do little search, and will have low volume, while a firm with lower prices will attract more buyers among those who search.

Of course, a buyer searching – or even just ending up at – only one firm might by chance hit the lowest price seller, but the average price paid by low searchers will be higher than that paid by high searchers. However, profits per firm may be equal across firms with different prices, under plausible assumptions about economies of scale, as high-priced sellers make more profit per unit but sell fewer units than low-priced sellers.  If profits are the same regardless of price chosen, within limits, there will be no incentive for a firm to change pricing to increase profits.

In the study to follow we needed to find settings in which price information was available to us as researchers. New Hampshire and Maine are two of the three states with the longest history and most explicit government efforts to create price transparency [20]. The other state (Massachusetts) has an array of other price regulation devices that may confound efforts to identify the role of price information alone, while the many states with public data files on hospitals generally do not make that information easy for consumers to use. If anything is going to happen, it should happen in New Hampshire and Maine.

For those buyers using state information, search costs are reduced, and so any seller provided with information will have an effect only if it calls attention to or adds to the information buyers can use. In the spirit of Stigler’s original work, making price information available to buyers also makes it available to other sellers and thus increases the ability of dominant firms in an oligopoly to detect and punish low-priced sellers, thus driving them out of business or getting them to fall in line with a higher price.

Hence it is an empirical question whether low-priced firms will find it advantageous to advertise their price; are they in an oligopoly game where almost every buyer gets a special confidential low price, or are they in a more competitive setting where large firms (or all other firms) do not automatically respond to individual firm price reductions? However, it will always be the case that it will be disadvantageous for high-priced firms to volunteer that information, unless they have some quality or convenience offset they want to publicize.

So we would expect seller efforts to publicize prices when their prices are low to characterize some though not necessarily all low-priced firms. High-priced firms will continue to have an incentive, perhaps even a stronger one, to publicize higher quality or convenience if they can furnish high enough above-average values to justify their prices. High-priced firms with mediocre quality or access will stay quiet in hopes that some foolish low searching buyers will happen their way.

Illustrating the Theory:  Motivation and Design

Therefore, we look at information on what efforts sellers make to disclose price, using data on prices and disclosures from New Hampshire and Maine. We test the hypothesis that low-priced sellers provide information on their pricing behavior. Both states have programs in which a state agency collects seller- and service-specific price information and makes it available to the public. The New Hampshire program was graded as the best program in the country in a report card released by the Catalyst for Payment Reform and Health Incentives Improvements Institute in 2015 [21].  The New Hampshire state pricing website lists prices paid by different payers, including self pay or “uninsured.”  The Maine site lists only insurer prices.

To explore how sellers view the program and how they set and publicize prices, we reviewed seller websites to see whether information to suggest lower prices was present or not. We made follow up phone calls if the information was unclear. This review was conducted in May and June 2016, and was updated in June 2017. This information allows us to see whether sellers themselves call attention to their pricing and which kinds do so. We also see whether higher quality sellers bring those facts to consumers’ attention, potentially as a countervailing influence if their price is high. We look at what sellers, charging higher prices, say about convenience and wait time, since time cost can offset money cost.

After a test exercise using older posts, a trained reader tabulated mentions of price, quality, or access on each site, blind to the relative price of other sellers. It was not difficult to identify specific mentions of price or affordability, as indicated in Exhibit A.

Exhibit A

The key words used to identify price were “low cost,” “save,” and “affordable.” Those for quality included “state-of-the-art,” “ACR Gold Seal,” and “quality.” Access was flagged if “same-day,” “walk-in, or “ER wait time” were mentioned.  A given site could mention all or some of these characteristics.

For Maine data, only one price, that facing insurers, was published.  For New Hampshire, different prices for the two largest insurers (Anthem and Harvard Pilgrim) along with the list price for those reporting self pay or no insurance coverage for a particular service. We analyzed the New Hampshire data and found high correlation between the three price schedules. We therefore used only Anthem prices for the main analysis. Unfortunately, the New Hampshire sample was too small to allow meaningful analysis of differences between insurer paid prices and prices for the self-paying public. Tables 1A and 1B provide descriptive information on the numbers, types, locations, and median prices of sellers in our analysis sample.

Results

The main purpose of this study is to show the relationship between price charged and the type of information provided. As can be seen in Table 2, provision of price information was almost strictly limited to non-hospital organizations in the bottom quartile or bottom half of the price distribution for all three services.

This difference in the proportion of sellers advertising price as a function of the quartile of price  is statistically significant versus the null hypothesis of equal proportions in each price quartile for all three procedures (at the 0.001 level for MRI, and at the 0.05 level for the other two procedures). It also shows that the frequency of advertising price was zero in the highest quartiles but most common in the lowest priced quartile.

However, even among these low-priced sellers, almost none of which were hospital based, many did not advertise price. Many sellers who chose to set prices much lower than average did not mention that fact as a main way they communicate with potential buyers, although more of them did so than their higher-pricing counterparts. So, a puzzle remains, one we will discuss but not resolve further below.

For the other dimensions of care that might matter to consumers – quality and convenience – mention was more common on websites than was mention of price, and mention of either dimension was either unrelated to price or only weakly (and directly) related. Higher priced sellers sometimes were more likely to tout quality. Information on either of these dimension, in contrast to price, was usually not comparative—e.g., “our goal is to provide high quality and convenient care” — but without information on ranking or performance relative to competitors.

Discussion

The most striking finding with this modest data, though the best available, here is that, consistent with the assumptions of Peter Diamond’s model: sellers do not just passively set prices.  Instead, if they set low prices they are more likely to publicize that fact, explicitly or subtly, in the information they provide on websites. This relationship is strongest for MRI imaging, consistent with the high volume of demand for that test and its relatively high fixed cost and price, but it also is apparent for the other services we examined. There is also a strong hint that high-priced firms turn to publicizing their quality, whatever it is, to offset their high prices. Some mention greater convenience or say nothing at all on their website other than characteristics of the entity and its address and phone number.

Why do some low-priced sellers not mention that fact on their websites? We do find strong evidence that free-standing sellers mention price more often than low-priced hospitals, who are less common. There is some suggestion that price mention is more likely in more populous markets where there are presumably more alternatives.

Perhaps some organizations do not have net revenue maximization or fiscal growth as a goal; they just want to offer low-priced care in their communities even though they could charge more and sell about the same amount. Others may not be so attentive to fiscal matters as long as they are breaking even. We cannot determine these motives from this data, obviously, but they deserve to be explored with more targeted inquiries.

Other Issues

It is also possible that some insurance companies may play a role in why many firms do not advertise their low prices. Some insurance companies have adjusted their benefit-design plans in a way that uses the newly available information about the low-priced firms to incentivize their patients to see the low-priced providers. Accordingly, providers may care more about making their price information available to insurance companies rather than to patients via advertisements.

Perhaps, as well, low-priced firms feared customers would judge quality by price, though we found little evidence that low-priced firms offered reassurance on quality to any greater extent than other firms. If that hypothesis were true, however, it would pose the further question of why then the firm charged a low price if doing so would only deliver the wrong message to buyers. Indeed, the most serious gap in our understanding of how price transparency works is the absence of an explanation of why there are low-priced sellers in the initial case.

Another possible explanation for low-priced sellers is that they may not have a choice. Large hospitals that service tens of thousands of patients have a strong negotiating standpoint to demand higher payments. Smaller independent facilities may lack that same negotiating power and be forced to accept lower payments from insurance companies as a result. It was observed, in fact, that although the prices across no insurance, Anthem insurance, and Harvard Pilgrim insurance were correlated, the prices for uninsured individuals were invariably higher than those for insured individuals.

Still, there was significant variation across firms among the prices offered to uninsured individuals, which returns us to the question of why the low-priced sellers exist in the market. Perhaps, however, some low priced sellers decided to specialize in attracting Anthem or Harvard Pilgrim customers. While some firms have emerged to attempt to help consumers seek better buys (e.g. Castlight, https://www.castlighthealth.com/), breaking the code of silence that seems to affect many sellers may be the key to making price transparency matter.

Policy Issues

As noted earlier, there has been bipartisan support for regulations that require all hospitals or other sellers of medical services to be fully transparent about the prices they charge and receive from different buyers. Some sellers who would have wanted buyers to know anyway about their lower prices, or higher value for a given price, may benefit from such actions. However, others who have pursued a strategy of secret price concessions to foil large oligopolistic competitors may lose from such a one-size process.

Perhaps it is reasonable to consider an alternative policy, one that begins with a template or model of full price disclosure set up by government and then permits different sellers to decide voluntarily whether they want to participate.  That is, sellers can choose to be certified as low priced but they are not required to disclose low prices.  In addition, public policy would provide clear and easy to access information on the prices charged by those sellers who participate, as well as explicit identification of those firms that have decided to keep their prices hidden.

There are complex legal issues that would have to be addressed if such a policy were to be formulated for concrete application. However, we believe that this kind of regulation/deregulation might generate less opposition and more realized consumer benefits than other approaches. Of course, any steps that could be taken to reduce oligopoly power — access to larger numbers of sellers, limitations on mergers and acquisitions — would also be desirable.  Sometimes it may be desirable to permit some sellers to keep present and especially future bargains confidential. Nonetheless, as long as those consumers who must function as individual buyers can work around such limits by using other sellers, the potential of hidden discounts to offer better deals to those whose insurers shop for them may be of value.

Looking Forward

The conclusion is that the pattern of firm-provided information in two states is broadly consistent with what one would expect if profit-seeking firms were competing for business. However, the pattern is by no means precise and universal. Price disclosure by all such firms so far does not seem to be something that sellers can be shown to relish; a sizeable fraction of firms do not communicate their low prices.

It is plausible to assume that seller provision of information about prices, quality, and convenience does help consumers looking for low prices and high value, especially those with high- deductible health plans who are not assisted by their insurers.  The generally small or zero effects thus far of state efforts, like those of these two states, to improve price transparency suggests that federal regulation requiring disclosure may not be safe and effective in all settings.

Since policy efforts to date have been primarily directed at consumers, it might be time to broaden the focus to include sellers. If an imaging center was willing to go with the slogan “fifteen minutes can save you 15% on the cost of your MRI scan,” there might be a disruptive change in the market at last.

 

References

  1. Kliff, S., Sanger-Katz, M. Hospitals sued to keep prices secret. They lost. The New York Times, 2020 June 23. Retrieved July 12, 2020 from https://www.nytimes.com/2020/06/23/upshot/hospitals-lost-price-transparency-lawsuit.html
  2. Commins, J CMS unveils sweeping proposed mandates on hospital pricing transparency. Health Leaders. 2019 July 29.  https://www.healthleadersmedia.com/finance/cms-unveils-sweeping-proposed-mandates-hospital-pricing-transparencyAccessed November 6, 2019
  3. CBS News. Trump signs executive order to make health costs more transparent. 2019 June 25. https://www.cbsnews.com/news/trump-signing-executive-order-health-care-costs-2019-06-24-live-updates/. Accessed November 6, 2019.
  4. Mehrotra, A, Chernew, ME , Sinaiko, AD. Promise and reality of price transparency. New England Journal of Medicine. 2018; 378(14): 1348-1354.
  5. Brot-Goldberg, Z, Chandra, A, Handel, BR, Kolstad, J. What does a deductible do?  The impact of cost-sharing on health care prices, quantities, and spending dynamics. NBER Working Paper 21632; 2015.  http://www.nber.org/papers/w21632.  Accessed November 6, 2019.
  6. Sinaiko, AD, Kakani, P, Rosenthal, MB. Marketwide price transparency suggests significant opportunities for value-based purchasing. Health Affairs (Millwood). 2019; 38(9).  https://www.healthaffairs.org/doi/10.1377/hlthaff.2018.05315. Accessed November 6, 2019.
  7. Stigler, George J. The economics of information. Journal of Political Economy. 1961; 69(3): 213-225.
  8. Pauly, MV, Burns, LR. When is medical care price transparency a good thing (and when isn’t it)? Advances in Health Care Management. In press.
  9. Diamond , P. A model of price adjustment. Journal of Economic Theory. 1971; 3(2): 156-168.
  10. De Brantes, F. HCI3 Update from the field: The cost of hope. Newtown, CT: Health Care Incentives Improvement Institute; 2013. Available at: http://www.hci3.org/content/hci3-update-field-cost-hope. Accessed 2013.
  11. Salop, S, Stiglitz, J. Bargains and ripoffs: A model of monopolistically competitive price dispersion.  Review of Economic Studies. 1977; 44(3):  493-510.
  12. Perloff, J, Salop, SC. Equilibrium with product differentiation. Review of Economic Studies. 1985; 52(1): 107-120.
  13. Burdett, K, Judd, KL. Equilibrium price dispersion. Econometrica 1983;51(4).
  14. Albaek, S, Møllgaard, P, Overgaard, PV. Government assisted oligopoly coordination? A concrete case. Journal of Law and Economics. 1997; 45 (December): 429-443.
  15. Austin, DA, Gravelle, JG. Does price transparency improve market efficiency? Implications of empirical evidence in other markets or the health sector.” Washington, DC: Congressional Research Service; 2007.  https://fas.org/sgp/crs/secrecy/RL34101.pdf.  Accessed November 6, 2019.
  16. Melnick, GA, Fonkych, K. Hospital pricing and the uninsured. Do the uninsured pay higher prices?  Health Affairs. (Millwood.) 2008; 27(1).  https://www.healthaffairs.org/doi/full/10.1377/hlthaff.27.2.w116.  Accessed November 6, 2019.
  17. White, C, Whaley, C. Prices paid to hospitals by private health plans are high relative to medicare and vary widely. Santa Monica, California:  The Rand Corporation; 2019. https://www.rand.org/pubs/research_reports/RR3033.html.  Accessed November 6, 2019.
  18. Gustafsson, L, Bishop, S. Hospital price transparency: Making it useful for patients. New York:  The Commonwealth Fund.  February 12, 2019. https://www.commonwealthfund.org/blog/2019/hospital-price-transparency-making-it-useful-patients.  Accessed November 6, 2019.
  19. Tu, H, Lauer, J. Impact of health care transparency on price variation: The new hampshire experience.  Washington, DC:  Center for Studying Health System Change; 2009.
  20. Agency for Healthcare Research and Quality (AHRQ). Public reporting of cost measures in health. Washington, D.C.: AHRQ; 2015. Retrieved July 11, 2020 from https://effectivehealthcare.ahrq.gov/products/public-reporting-cost-measures/technical-brief
  21. Delbanco, SF. The payment reform landscape: States show little progress in past year.  Health Affairs. (Millwood) 2015 July 9. https://www.healthaffairs.org/do/10.1377/hblog20150709.049227/full/.  Accessed November 6, 2019.

Regi’s “Innovating in Health Care” Case Corner

Case: Verily Life Sciences and Machine Learning

Authors: Kevin Schulman, Stanford University, and Kevin Ho

Abstract: Verily Life Sciences, an independent subsidiary of Alphabet, Inc., set out to harness machine learning in the healthcare field. The company sought partnerships with academic research institutions, legacy life sciences companies, and hospitals and health systems to develop tools to collect and organize health data, with the goal of creating platforms that utilized the insights from that data to enhance patient care. The case study discusses these broad partnerships and goals, the illness-specific health monitoring and care tools in Verily’s project pipeline, and efforts by competitors like Apple and Amazon, as well as a growing number of start-ups. The case provides insights into decision-making in the largely uncharted territory of machine learning in the health care industry, providing an in-depth look at Verily’s diabetic retinopathy project, which screened for eye disease.

Abstract

Learning objective

The case study aims to expose MBA and other students in the healthcare industry to broad opportunities in machine learning, as well as the many questions a company might face in developing a business model in a largely new field that was attracting a growing number of competitors. How would companies like Verily acquire and curate millions of piece of data, and develop potentially life-saving technologies? How would they monetize and market these algorithms?

Stanford case: SM-335 (July 28, 2020)

HBS link: https://hbsp.harvard.edu/product/SM335-PDF-ENG?Ntt=verily+life&itemFindingMethod=Search 

 

A Narrative Review of Traditional Medicare and Medicare Advantage

Jiayin Xue and Kevin Schulman, Stanford University 

Contact: Kevin.Schulman@Stanford.Edu

Abstract

What is the message? Medicare Advantage (MA) plans have been gaining popularity in recent years, but how do they differ from traditional fee-for-service Medicare programs, and how do MA plans impact patient care? We outline a brief history of traditional Medicare and Medicare Advantage, and a comparison of cost, quality, patient care outcomes, and impact on care delivery innovations in the last decade.

What is the evidence? Published medical literature, reports from the government and policy institutions, publicly available information on select care delivery organizations

Submitted: December 30, 2020; accepted after review: January 7, 2021.

Cite as: Jiayin Xue, Kevin Schulman. 2021. A Narrative Review of Traditional Medicare and Medicare Advantage. Health Management, Policy and Innovation (HMPI.org), Volume 6, Issue 1, Winter 2021

Please see an Addendum below in response to the growing debate over the true cost of Medicare Advantage.

What is Medicare Advantage and How Is It Changing?

Medicare is a blend of the traditional fee-for-service program (TM) and separate programs run by commercial health plans. In recent years, commercial plans (Medicare Advantage or MA plans) have been gaining popularity. MA enrollees now account for over 34% of Medicare beneficiaries.1 Medicare expansion proposals have also garnered much attention during the 2020 Democratic primary campaign, some of which proposed elimination of all commercial payers, others retained MA and other types of private options. Given the increasing interests from both patients and policy makers, we explore the origin of MA and what is known about the impact of the program on Medicare beneficiaries in this narrative review.

Previous studies have indicated that the quality of care between traditional Medicare and MA plans are similar, with MA plans incentivizing better resource utilization but tend to perform worse in access and patient satisfaction.2,3  To our knowledge, the most recent review was largely based on data from more than a decade ago.3 In this paper, we focus on evidence published since 2010 as well as recent practice innovations in the healthcare market.

History of Medicare

Overview of benefits & coverage

Medicare was created in 1965 as a U.S. federal insurance program to cover the cost of healthcare services for the people aged 65 and older. Under the Social Security Amendments of 1972, this coverage was expanded to younger people with certain disabilities and anyone with end-stage renal disease needing dialysis or transplant. Originally, Medicare only included Part A which covered hospital services, and Part B which covered outpatient medical services. The 2003 Medicare Modernization Act (MMA) added Medicare Part D (the Medicare drug benefit) starting in 2006.4 These plans are fee-for-service insurance plans, with beneficiary premiums required for Part B and later for Part D. Together, the fee-for-service program is considered Traditional Medicare (TM).

Since the beginning, Medicare has also contracted with private plans such as health maintenance organizations (HMOs) such as Kaiser Permanente and the Health Insurance Plan of NY to provide coverage for enrollees.5,6 In 2003, the MMA renamed the private plan option from Medicare + Choice (M+C) to Medicare Advantage (MA).

Currently, Medicare has nearly 60 million enrollees, two-thirds of whom are covered by TM, and the remaining 34% are covered through Medicare Advantage.1 Today, Part A covers not only hospital stays but also certain types of long term services such as short-term skilled nursing facility stays, hospice, and home visits. Part B includes outpatient services and preventive care. Part C, or Medicare Advantage, includes all benefits within Parts A and B and often Part D, as well as additional non-Medicare benefits such as dental, fitness, and vision coverage. Medicare Advantage enrollment has doubled in the last decade from 10.5 million people in 2009 to 22 million people in 2019.1

Payment models & cost of care

Since 1966, Medicare has contracted with HMOs, which are individual networks of physicians and hospitals that provide service at a fixed monthly or annual payment, modeled after organizations such as the Kaiser Foundation Health Plan in California. In the beginning, these contracts were few due to the small number of HMOs and Medicare’s unattractive retrospective payment model.7 In the late 1970s to 1980s, though, there was a significant increase in the number of HMOs following the HMO Act of 1973 under President Nixon, which provided federal funding to catalyze the growth of this sector.

Setting the payment rate for Commercial health plans in Medicare has been a challenge over time. The 1982 Tax Equity and Fiscal Responsibility Act (TEFRA) set prospective risk-based capitated payments at 95% adjusted average per capita cost (AAPCC) of traditional Medicare based on the county of residence and demographics of the enrollee with the concept that the Medicare program would share in the efficiencies of the commercial market.7,8  Unfortunately, this payment model created strong incentives to recruit enrollees heathier than traditional Medicare enrollees, a practice known as favorable selection or cream skimming. Because risk-adjustment mechanisms were inadequate at this time, Medicare was found to be paying more for the care of a patient through Medicare Advantage than under traditional Medicare.8

The Balanced Budget Act (BBA) of 1997 adjusted payments rates for private Medicare plans in attempts to contain cost, increase access, and correct for favorable selection. The BBA established the Medicare + Choice program and payment floors for rural counties. It also included enrollees’ demographic status in Medicare’s payment rate adjustments for private plans. New plan options such as preferred provider organizations (PPOs), private fee-for-service (PFFS), and high-deductible plans were created.8  BBA changes to the payment rules slowed the growth of M+C plan payments and led to increased beneficiary premiums and reduced benefits. This financial model proved unattractive to many commercial health plans who left the market as a result.9

Adjustments have continued during the past two decades. In 2000, the Benefits Improvement and Protection Act (BIPA) raised the payment rates for private plans in both rural and urban counties and added diagnostic information to the risk adjustment models. In 2003, the MMA again raised payments and established a new bidding system in which private plans bid on the cost of delivering care for a county, and if the bid is lower than benchmark TM spending, the plan would receive a rebate that can be used to expand enrollee benefits.8 These various adjustments led to higher average per capita federal payments for enrollees in private plans. By 2009, payments to MA plans were as much as 14% higher than that for an equivalent enrollee in TM.10

The 2010 Affordable Care Act (ACA), commonly known as Obamacare, took further steps to risk adjust for geography & enrollee health statuses in MA plans. Other changes included adjusting MA plans’ risk scores to account for increased coding intensity, and a new payment model where plans received bonuses based on quality.8,11 As a result of the ACA, payments to MA plans are now more aligned with TM spending, and MA enrollments have gradually increased. From 2017 to 2019, the per capita MA to TM payment ratios, after adjusting for risk and including quality bonuses, was close to 100% (Figure 1). However, MedPac estimates that if coding intensity had been fully considered beyond the 5.9% statutory rate, it would add another 1-2% to the relative MA payment rate.12

While federal spending is thought to be roughly equal by formula between the two models, a comparison of claims level data between several private MA plans and traditional Medicare found that actual per capita healthcare spending of enrollees in MA plans is much lower, largely due to lower utilization of services among MA enrollees.13 Enrollees in Medicare Advantage also tend to pay lower premiums than for traditional Medicare since they no longer need supplemental insurance and/or a separate prescription drug plans. Average premiums paid by MA enrollees was $29 per month in 2019, down from $44 per month in 2010,1 while the Part B premium is $144.6014 and part D premiums vary by plan in TM. Traditional Medicare does not have an out-of-pocket maximum as it is required for MA plans. However, little is known about the total out-of-pocket costs for enrollees in traditional Medicare and Medicare Advantage.11

Evidence on Quality, Outcomes and Utilization

In addition to reviewing information from CMS and the Kaiser Family Foundation on Medicare Advantage care quality and utilization patterns, we searched PubMed for comparative studies of MA and TM fee-for-service (FFS) using key words such as “Medicare Advantage”, “HMO”, “health maintenance organization”, “Medicare fee-for-service” or “traditional Medicare.” Regional or national studies published from 2010 to 2020 were included. Single-institutional studies or studies utilizing data from before 2000 were excluded. Commentaries or studies not relevant to quality, outcomes, or utilization of care were also excluded.

Overall, comparisons across studies are difficult due to differences in study designs, data sources, timing, and specific outcomes measured. With the exception of patience experience data captured through CAHPS, most of the studies we reviewed draw data from administrative claims or other types of secondary data which may limit characterization of the patient populations or services delivered to beneficiaries. Unlike HEDIS measurements collected for MA plans, TM claims data often do not enable direct measurement of quality. Analysis of large population samples are relatively overpowered, so even small effects are found to be statistically significant while clearly not achieving clinical significance.

Performance measures and outcomes

Each year, CMS attempts to measure the quality of services provided by Medicare Part C (MA) & Part D (prescription drugs) plans using the Star rating system. The ratings are on a scale of one to five, with a score of five being the highest quality rating. The Star ratings are calculated from clinical measures reported by the plans and patient experience data. For MA contracts that offer prescription drug plans (MA-PDs), 52% of the 210 contracts that will be offered in 2020 had a rating of 4.0 or higher, 82% of MA-PD enrollees are in such contracts currently.15 The ratings may not reflect the actual care received for a specific enrollee, as the MA contracts often draw enrollees from multiple geographic areas with varied levels of care quality.12

In general, the limited available evidence continues to suggest mixed results with respect to quality and patient outcomes between MA plans and TM. [Appendix Table 1] A number of studies have demonstrated higher performance on ambulatory and preventive care quality measures for MA plans compared to TM. These data include process measures such as disease screening and vaccination rates, as well as medication treatment adherence.16–20 Positive performance differences between MA and TM plans can be more pronounced in MA-HMO plans than for MA-PPO plans, suggesting the impact of better care coordination on these measures.20 In contrast, there is also some evidence that TM beneficiaries may be more likely to receive care from higher quality facilities or providers.21–23

While MA plans appear to be more successful at implementing preventive efforts, these successes sometimes do not directly translate into improved clinical outcomes. In a recent secondary prevention study, MA plan beneficiaries with coronary heart disease were slightly more likely than TM patients to receive secondary prevention treatments, but both populations achieved similar results in terms of blood pressure control and lipid management.24 Overall, studies comparing risk-adjusted mortality rates between TM and MA beneficiaries again show mixed results, and there is evidence that over time the mortality rates converge between patients in these two types of Medicare.25–27 Studies on differences in hospital readmission rates also vary and appear to be sensitive to risk adjustment methodologies, geography, and data sources.28–31

Based on data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS), patient experiences across TM and MA plans are also mixed.  There is evidence that MA patients tend to report more ease in getting medications and in getting information on cost and coverage, but TM may outperform MA in patient reported access to care.20,32 A longitudinal national study of Medicare beneficiaries from 2003-2009 suggested that MA patient ratings of their physicians are improving over time.16  In both MA and TM, higher intensity of care appears to be associated with worse care experiences, and patients with depressive symptoms tend to also have more negative experiences, and the differences may be larger in MA than in TM.33,34

Access and utilization

One major feature of MA plans is that they offer members more limited provider networks, although Medicare has implemented network adequacy requirements that all plans must meet. The Kaiser Family Foundation examined hospital network access for ~400 MA plans in 2015, it found that plan networks on average included roughly half of all hospitals in a given county. The range of narrow vs. broad networks varied significantly across MA plans, with the former including up to 30% of hospitals and the latter including 70% or more hospitals.35 In a follow up study of physician networks within MA plans, it was found that on average the plans included 46% of all physicians in a given county, and again the network size varied greatly. Across 26 medical specialties that MA plans are required to include, access to psychiatrists was the most restricted. Access to cardiothoracic surgeons, neurosurgeons, plastic surgeons, and radiation oncologists were also restricted for some plans.35

With respect to utilization, studies suggest that beneficiaries in MA plans tend to have more appropriate use of services than beneficiaries in traditional Medicare.36–40,18,41–44 Better utilization of resources is reflected in the lower rates of specific interventional procedures, fewer preventable hospitalizations, shorter lengths of stay in hospitals, as well as more efficient use of post-acute care services. [Appendix Table 2] At least three recent studies suggest that MA beneficiaries tend to have shorter rehabilitation stays with better outcomes.42–44

Innovative Care Delivery Models

Under the capitated payment model, MA plans are financially incentivized to find innovative ways of delivering better quality of care at lower cost. MA plans have the advantage of being able to implement new care models at scale across providers and geographic areas. TM has also implemented novel payment models such as Accountable Care Organizations, Primary Care Transformation, and Episode-based Payment Initiatives, though these models largely rely on individual provider organizations for design and execution of care models within the program constraints.

Recently, the increase in MA enrollment has spawned the growth of a number of new primary care provider models focused on improving care for the elderly and/or high-risk populations under fully capitated payment arrangements. These programs often feature integrated team-based care and use custom technology for health records, decision support and care management. Several have experimented with physician panel sizes related to patient acuity, with the mix of providers on the clinical team, and with ways in which technology can support the overall care goals of the organization.

Many of these new models have demonstrated clinical improvements in their target population. ChenMed is an example of a high-touch primary care center for seniors that has been able to lower per member per month spending and hospital admission rates.45 Iora Health is another rapidly growing network of primary care clinics that targets the Medicare population, and it has reported better hypertension and diabetes control, and reduced hospital admissions and ER visits for their patients.46 Oak Street Health provides care for Medicare, Medicaid, and dual-eligible patient, and despite having a panel of clinically and socially complex patients, Oak Street reported large reductions in hospital admission rates, ER visits, and 30-day readmission rates.47 CareMore, which is now an integrated health delivery system under Anthem, grew out of a novel care model targeting high-risk populations in the early 90s and became a MA program by 1997, reported that it has been able to lower hospital and skilled nursing facility utilizations, 30-day hospital readmissions, and provide care at a lower cost than other MA plans.48 Another example of a fast-growing MA company is Devoted Health. Its model is built upon technology-enabled decision making as well as partnerships with existing care providers, but it also has its own in-house care coordination and care delivery teams that focus on the highest risk populations.49

Looking Forward

In the past, payment rules resulted in Medicare overspending on patients enrolled in MA compared to TM. This risk of overpayment has been reduced, but not eliminated, due to implementation of more sophisticated risk-adjustment mechanisms and increased accountability for quality. CMS now spends a near equal amount for enrollees in TM and FFS, and there is evidence that the cost of care delivery in MA plans are less than in TM. At least some of these savings are passed on to MA enrollees as lower premiums and out-of-pocket costs. In sum, recent comparative data on quality, outcomes, and utilization remain largely unchanged since previous reviews.

The growth of MA is partially a reflection of the market competitiveness of private health plans based on the perception of value by consumers. Many MA plans have developed and recently introduced innovative care delivery models. While there is early evidence of success of some of these models, whether they can maintain their performance as they scale remains an important question for further research.

Appendix

Author Publication Year Category Data Year(s) Geography Data Sources Sample size Outcomes of Interest Direction of positive outcome
Huesch21 2010 Quality 2003-2006 Regional Florida Department of Health’s Agency for Health Care Administration 33,840 FFS, 6,626 MA patients Quality of physicians used by Florida coronary stent patients FFS
Brennan et al.50 2010 Quality 2006-2007 National HEDIS 35,176,538 – 34,842,196 FFS and 5,978,584 -6,454,358 MA beneficiaries 11 Quality measures (med management, breast cancer & CAD screening, diabetes care) Neutral or Mixed
Ayanian et al.17 2013 Quality 2009 National Medicare Beneficiary Summary File, HEDIS, claims data 453,820 FFS patients and 495,836 MA-HMOs patients, 81,480 MA-PPOs patients Mammography screening among racial/ethnic groups MA
Hung et al.19 2016 Quality 2009 National Medicare Current Beneficiary Survey (MCBS) data 5,417 FFS beneficiaries and 1,875 MA beneficiaries Mammography screening for beneficiaries MA
Chang et al.51 2016 Quality 2011 National CMS beneficiary enrollment files, quarterly long-term care MDS files 1,800,193 FFS patients and 371,641 MA patients Nursing home quality Neutral or Mixed
Meyers et al.22 2018 Quality 2012-2014 National MBSF, MDS, HEDIS, Online Survey Certification and Reporting System (OSCAR) for SNF-level characteristics 3,335,476 FFS and 1,248646 MA patients Quality of skilled nursing facilities entered by beneficiaries FFS
Gidwani-Marszowski et al.52 2018 Quality 2008-2013 National VHA and Medicare administrative data 295,605 FFS and 1,172,230 MA decedents Hospice care & hospice duration for veterans MA
Teno et al.53 2018 Quality 2000, 2005, 2009, 2011, 2015 National Medicare enrollment and claims data, MDS, MBSF 1,361,870 FFS and 871,845 MA decedents Site of death, place of care, and care transitions during last 3 days of life Neutral or Mixed
Schwartz et al.23 2019 Quality 2015 National Outcome and Assessment Information Set (OASIS), MBSF 3,316,163 FFS and 1,075,817 MA patients Quality of home health agencies used, based on patient care star ratings FFS
Meyers et al.54 2020 Quality 2012-2016 National Medicare Provider and Analysis Review (MedPAR) 5,059,508 TM and 2,071,102 MA patients Quality of hospitals enrollees enter, as measured by star ratings Neutral or Mixed
Li et. al.30 2016 Quality and Outcome 2009-2012 Regional HCUP New York State Inpatient Databases 64,357 total patients in 2009 to 56,445 in 2012 30-day readmission rates and racial disparity for AMI, CHF, or pneumonia in New York MA
Rivera-Hernandez et al.55 2019 Quality and Outcome 2015 National MDS, MBSF, Long-Term Care: Facts on Care in the United States and Nursing Home Compare Five-Star Ratings database, U.S. Census 1,291,133 FFS patients and 522,830 MA patients Racial disparity in 30 -day readmission rate in SNFs Neutral or Mixed
Friedman et al.25 2010 Outcome 2006 Regional HCUP-SID 3,478,947 FFS and 670,019 MA discharges Risk-adjusted mortality rates and postoperative safety event rates in 13 states Neutral or Mixed
Ward et al.56 2010 Outcome 2005-2007 National The National Cancer Database 843,177 Total patients Relationship between stage of diagnosis and insurance status for eight types of cancer MA
Lemieux et al.29 2012 Outcome 2006-2008 National MedAssurant Medical Outcomes Research for Effectiveness and Economics Registry, Medicare 5% sample files 812,869 FFS and 907,704 MA patients 30-day medical and surgical readmission rates MA
Friedman et al.28 2012 Outcome 2006 Regional HCUP-SID 870,335 FFS and 266,577 MA discharges Likelihood of readmission after hospital discharge in five states FFS
Basu et al.57 2013 Outcome 2002 Regional HCUP-SID 2,971,673 FFS, 509,413 MA patients Adverse events during hospitalization in Florida FFS
Beveridge et al.26 2017 Outcome 2010-2013 National MA claims data, 5% randomly selected Limited Data Set samples from the CMS for 2010-2012 4,313,885 person-years FFS and 5,477,976 person-years MA Mortality rate of beneficiaries MA
Newhouse et al.27 2019 Outcome 2007-2017 National MBSF Unavailable Mortality rate of beneficiaries over time Neutral or Mixed
Figueroa et al.24 2019 Outcome 2013-2014 National Practice Innovation and Clinical Excellence (PINNACLE) registry 172,732 FFS and 35,563 MA patients CAD secondary prevention treatment and intermediate outcomes Neutral or Mixed
Panagiotou et al.31 2019 Outcome 2011-2014 National HEDIS, Medicare Provider Analysis and Review (MedPAR) 4,159,840 FFS and 1,218,236 MA discharges 30 -day hospital readmissions for AMI, CHF, and pneumonia patients FFS
Mittler et al.33 2010 Care Experience 2003 National CAHPS 120,974 FFS and 135,757 MA beneficiaries Patient care experiences based on market service intensity FFS
Elliot et al.32 2011 Care Experience 2007 National CAHPS 201,444 FFS and 132,937 MA beneficiaries 11 measures of patient experience with care Neutral or Mixed
Martino et al.34 2016 Care Experience 2010 National CAHPS 135,874 FFS and 220,040 MA beneficiaries Disparities in care between patients with depressive symptoms and those without FFS
Ayanian et al.15 2013 Quality and Care Experience 2003-2009 National HEDIS, CAHPS, MBSF Clinical quality: 742,976 MA-HMO enrollees, comparable number of FFS enrollees. Patient experience: 103,254 FFS and 128,706 MA-HMO enrollees Ambulatory care quality and patient experience MA
Timbie et al.20 2017 Quality and Care Experience 2010-2012 Regional HEDIS, Part D measures, MCAHPS, claims data 6,352,239 FFS and 3,571,743 MA beneficiaries Clinical quality measures & patient experience measures among beneficiaries in CA, NY, and FL MA

 

Appendix Table 1:  Studies comparing MA and TM fee-for-service (FFS) on quality, outcomes, care experience.

Notes: CAHPS = Consumer Assessment of Healthcare Providers and Systems

HEDIS = Healthcare Effectiveness Data and Information Set

HCUP-SID = AHRQ’s Healthcare Cost and Utilization Project State Inpatient Databases

MBSF = Master Beneficiary Summary File

MDS = Minimum Data Set

Author Publication Year Category Data Year(s) Geography Data Sources Sample size Outcomes of Interest Direction of positive outcome
Basu et al.36 2012 Utilization 2004 Regional AHRQ’s Healthcare Cost and Utilization Project (HCUP-SID) 936,698 Total admissions Preventable and referral-sensitive hospitalizations in NY, FL, CA MA
Landon et al37 2012 Utilization 2003-2009 National HEDIS, CAHPS, Medicare Beneficiary Summary files 103,162-152,444 FFS and 66,813-131104 for MA Service utilization for surgical procedures, ambulatory care, and inpatient care MA
Stevenson et al.38 2013 Utilization 2003-2009 National Medicare Beneficiary Summary File, HEDIS, claims data 206,754 FFS patients in 2003 to 189,721 in 2009; 150,679 MA patients in 2003 – 248,676 in 2009 End-of-life service use MA
Nicholas LH 39 2013 Utilization 1999-2005 Regional Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) matched to Medicare enrollment data >1,500 MA enrollees and 5,000 FFS Rates of hospitalizations in Arizona, Florida, New Jersey and New York MA
Matlock et al.58 2013 Utilization 2003-2007 Regional Medicare Enrollment Database; Administrative, claims, and clinical electronic health record data from the Cardiovascular Research Network 5,013,650 FFS and 878,339 MA patients Rates of coronary angiography, percutaneous coronary intervention (PCI), and coronary artery bypass grafting (CABG) Neutral or Mixed
Raetzman et al.40 2015 Utilization 2013 Regional Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) for 13 States 4,165,200 FFS and 1,650,200 MA hospital stays Hospital lengths of stay MA
Landon et al.18 2015 Utilization and Quality 2007 National Medicare Beneficiary Summary files, HEDIS, claims data 4,207,433 MA-HMO enrollees, 318,293 MA-PPO enrollees, matched to FFS Utilization and quality of ambulatory care for diabetes and cardiovascular disease patients MA
Waxman et al.41 2016 Utilization 2010-2011 National CMS OASIS file, American Community Survey 30,837,130 FFS and 10,594,658 MA beneficiaries Proportin of beneficiaries receiving home health and duration of use MA
Huckfeldt et al.42 2017 Utilization and Outcome 2011-2013 National Medicare Provider Analysis & Review File, Master Beneficiary Summary File, Minimum Data Set, Inpatient Rehabilitation Facility Patient Assessment Instrument 1,630,214 FFS and 582,024 MA care episodes Post-acute care utilization for patients with lower extremity joint replacement, stroke, and heart failure MA
Henke et al.59 2018 Utilization 2013 Regional HCUP State Inpatient Databases, AHA Annual Survey data, Medicare Enrollment Denominator File 1,926,712 MA and 5,907,956 TM discharges length of stay and cost per hospital discharge Neutral or Mixed
Li et al.60 2018 Utilization 2007-13 National Medicare claims data, HEDIS, Outcome and Assessment Information Set (OASIS), Minimum Data Set, Medicare beneficiary summary file 54,000,000 total Medicare beneficiaries Geographic variations in the use of home health, SNF, and hospital care between Medicare Advantage and traditional Medicare. Neutral or Mixed
Kumar et al.43 2018 Utilization and Outcome 2011-2015 National Master Beneficiary Summary File, Medicare Provider and Analysis Review data, Healthcare Effectiveness Data and Information Set data, the Minimum Data Set, and the American Community Survey 211,296 FFS and 75,554 MA patients Rehabilitation service use, length of stay, and outcomes in hip fracture patients MA

 

Appendix Table 2 – Studies comparing MA and TM fee-for-service (FFS) on utilization, including some studies examining both utilization and outcome or utilization and quality.

Notes: CAHPS = Consumer Assessment of Healthcare Providers and Systems

HEDIS = Healthcare Effectiveness Data and Information Set

HCUP-SID = AHRQ’s Healthcare Cost and Utilization Project State Inpatient Databases

MBSF = Master Beneficiary Summary File

MDS = Minimum Data Set

 

Acknowledgement

We want to thank our research assistant Sohini Guin for help in proofreading the appendix summary tables.

 

References

(Note references 50-60 are for appendix tables only)

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Addendum to A Narrative Review of Traditional Medicare and Medicare Advantage

Since the publication of our review, there has been a growing debate over the true cost of Medicare Advantage. While CMS payments to MA plans have been lower in the last decade, MedPac continues to estimate that the per capita payments for MA enrollees are higher than Traditional Medicare when coding intensity is considered. Critics have pointed out that this higher intensity of coding underlies the business model of many MA plans, and it is the reason for the rapid growth and the outsized investments into health plans and primary care startups that focus on the MA population.[1]

Profits resulting from upcoding allow for the lowered premiums and more comprehensive enrollee benefits, but much of the profits also remain with the investors and commercial entities at a high cost to CMS and to taxpayers. Furthermore, the launch of Direct Contracting Entities (DCEs), which enables varying degrees of capitated pay-for-performance payments in organizations that served Traditional Medicare enrollees, allows the MA plans to bring the same coding practices into the fee-for-service Medicare market and threatens the growth of Accountable Care Organizations (although the ACO program has other issues affecting growth).[2]

Access to medically necessary care under MA is also under scrutiny. A recent report from the Office of Inspector General (OIG) found that some MA organizations have denied beneficiary requests that fell within Medicare coverage rules. In reviewing a random sample of hundreds of denied prior authorization and payment requests, the OIG reported that some of the unnecessary denials stemmed from applying clinical criteria outside of Medicare coverage rules, which most frequently affected imaging, post-acute care, and injection services. Other denials and delays were due to requesting additional documentation even when records are sufficient, and from making either human or system review errors.[3]

Counter arguments contend that there remains evidence that MA costs are lower, that precise risk adjustments in MA plans are necessary for performance tracking, and that DCEs enable primary care providers to deliver more coordinated and better-quality care under performance-based contracting (although without providing evidence that the quality improvement is occurring in these models).[4] They emphasize that enrollees in MA plans are often of lower socioeconomic status, and the focus on quality led to innovative solutions for these higher risk

[1] Medicare Advantage, Direct Contracting, And The Medicare ‘Money Machine,’ Part 1: The Risk-Score Game. Sept 29, 2021. https://www.healthaffairs.org/do/10.1377/forefront.20210927.6239

[2] Medicare Advantage, Direct Contracting, And The Medicare ‘Money Machine,’ Part 2: Building On The ACO Model. Sept. 30, 2021. https://www.healthaffairs.org/do/10.1377/forefront.20210928.795755/full/

[3] Some Medicare Advantage Organization Denials of Prior Authorization Requests Raise Concerns About Beneficiary Access to Medically Necessary Care. https://oig.hhs.gov/oei/reports/OEI-09-18-00260.pdf

[4] The Important Roles Of Medicare Advantage And Direct Contracting: A Response To Gilfillan And Berwick. Feb 7, 2022. https://www.healthaffairs.org/do/10.1377/forefront.20220203.915914/

 

Innovations in Technology: Perspectives of Hospital CIOs

Kristan Staudenmayer, Courtenay Stewart, Clare Purvis, and Kevin Schulman, Stanford University

Contact: kristans@stanford.edu

Abstract

What is the message? Digital transformation in healthcare faces three key issues. First, a substantial proportion of the IT budget is consumed by maintaining multiple separate technology systems. Second, systems lack interoperability. Third, governance structures commonly limit innovation.

What is the evidence? Interviews with nine hospital-based CIOs conducted in 2019.

Submitted: September 18, 2020; accepted after review: December 10, 2020.

Cite as: Kristan Staudenmayer, Courtenay Stewart, Clare Purvis, Kevin Schulman. 2021. Innovations in Technology: Perspectives of Hospital CIOs. Health Management, Policy and Innovation (HMPI.org), Volume 6, Issue 1, Winter 2021.

Healthcare Lags in Digital Transformation

Healthcare has not yet achieved the level of digital transformation involving web services, big data, artificial intelligence, and other capabilities that has reshaped many industries. According to a study conducted by the McKinsey Global Institute, the healthcare’s industry “digitization index”, a measure of digital progress, is only slightly above those of the hospitality, construction and agriculture industries.1 In contrast, those industries leading in digital transformation, including technology, media, and finance, have used technology to create new business models for their industry. It is essential to understand why this sector lags if we are to implement the solutions needed to advance healthcare along the road of digital transformation.

The Chief Information Officer (CIO) of a healthcare system lies at the heart of the technological ecosystem for provider organizations. In this article, we explore the current state of the CIO office, particularly as it relates to its role in digital transformation. We report our findings from nine hospital-based CIO interviews conducted in 2019. These hospitals were drawn from all four U.S. census regions (Northeast, South, Midwest, and the West).

The interviews contextualize digital transformation in relation to recent historical events, trends, and current challenges. The interviews were conducted using a nine-question structured interview guide and included information on an institution’s health IT-specific vision, reporting structure and strategy, and innovation. We created a rubric by which to code the free-form conversations to standardize and thus compare responses. We limited our interviews to CIOs of hospitals who had implemented certified electronic health records (EHR) according to the Office of National Coordinator for Health Information Technology.

Historical Context of the CIO Office and Factors Shaping the Current State

 Three major factors are shaping current needs and opportunities for digital transformation in U.S. healthcare.

HITECH Act of 2009. First, over the last decade, the CIO’s role has rapidly changed and continues to evolve. Much of this change is a direct result of the HITECH Act of 2009 and the mandate for EHR adoption. Before HiTECH, CIOs’ efforts focused primarily on communication infrastructure and other technology projects. New technologies were added piecemeal as they emerged in the market or they arose from home-grown solutions, which were often developed in a decentralized fashion. HiTECH propelled the role of the CIO in the organization to one that was highly visible and touched almost all business processes.

Hospital consolidation. Second, after the pressure to install EHR systems came another industry-wide trend that further complicated matters—consolidation of hospital systems. Mergers brought with it a need for integration of technology systems across newly formed hospital networks. However, because each EHR installation at each site was highly customized, lack of interoperability prevented integration. One CIO noted that, at one point, their physicians needed to have nine separate logins to nine different systems.

The burden of consolidation continues. Over 50% of the CIOs we interviewed listed the merger of EHR systems as a top priority in the last 12 months.

Fragmented marketplace. As the CIO’s responsibilities grew, a third trend created additional pressure. The evolution of technology in other industries led to an explosion in the number of companies approaching hospitals with new digital solutions. The CIOs we interviewed found themselves increasingly hosting numerous disparate pilots sponsored by various stakeholders, all with different agendas and results. These pilots are not often well-coordinated and lack an overall strategy for dissemination and scale, as has been previously described.2

CIOs have adjusted to these demands – most now require an internal needs assessment before supporting a pilot project. Even still, there is a lack of standardization in how pilots test can be evaluated and supported. Many of the CIOs also expressed a need for an industry-wide set of standards to better vet technology that touches healthcare.

Taken together, the CIO’s office today is a result of large-scale trends in regulation, technology changes, and industry-wide strategic shifts. Within this context, two major constraints are shaping the potential for digital transformation in healthcare – legacy systems and organizational charts.

Two Constraints: Legacy Systems and Organizational Charts

Legacy systems: the ball-and-chain for the CIOs’ innovation agenda

The healthcare CIOs in our survey reported that the majority of their department’s time and money were spent on maintaining their EHR systems.  Our respondents suggested that 80% to 90% of the IT budget was spent on maintenance, vs 10% to 20% towards innovation and developing new technology. One CIO commented that while he spends 70% of his time thinking about transformation, he must spend 70% of his team’s resources on maintenance.

All of the CIOs reported overseeing large teams that are required to keep the EHR systems and legacy subsystems functioning at current levels. With each passing year, maintenance challenges become more daunting. One CIO stated “the cost of carrying legacy systems [i.e., electronic health records] is an unexplored and big risk for health systems. I liken it to an iceberg. There’s the small, shiny white piece that is above water, and there’s the big, dark, cold piece. Legacy systems are the big, dark, cold piece. And unless you are investing appropriately in them (e.g., networks and servers and data centers and upgrades and licenses) you can quickly run into trouble.”

Compared to other industries, the effort spent on maintaining legacy systems has an outsized role in health care.3 This is due to the fact that many systems in hospitals reflect older technologies that have not been updated.  Newer systems are added on to older systems, creating complex workflow and technological dependencies that increase the costs and barriers to replacement.

The burden of maintenance also results in a high degree of dependency on existing EHR systems, shaping the innovation strategy of hospitals. Several CIOs mentioned that they had a narrow bandwidth for innovation that is not consistent with an EHR vendor’s interests and “roadmap.” One CIO stated: “If you put advanced technology in a new bucket, and leave all your old technology in a different bucket, it is a recipe for failure because you don’t have your best people working on your core technology, and there’s this shiny new stuff.” As a result, most of the CIOs surveyed are reluctant to test new technologies.

Furthermore, there are economic disincentives to work outside of existing EHRs. Vendors use their leverage to price technology and services, leaving little room for investments in potentially competitive technologies. Many CIOs expressed concerns that adding any new systems that were not integrated with existing technology exponentially increased the burden of work and costs.

Beyond consuming time, money and energy, there is another way in which legacy systems prevent CIOs from investing in new technology. In most health systems, the EHR is based on out-of-date client-server technology, compared to the cloud-based technology that has become the dominant foundation of digital solutions since 2010. Adopting these more modern cloud-based solutions is difficult as the older systems tether healthcare to this older technology. Therefore, despite new requirements under 21st Century Cures Act that allow for solutions to be built along-side of the EHR, within the EHR, or from the data warehouse, most of the organizations we interviewed remain focused on within-EHR strategies.

The Organizational Chart and the Impact on Innovation

Flexible digital platforms could serve as a critical strategic asset for organizations facing new market challenges such as a shift towards value-based payment models. For this to occur, digital solutions need to be at the heart of the global strategic decisions for an organization, rather than existing as disconnected “IT projects.” While most CIOs in our survey reported that they were involved with organizational strategy discussions, it was not clear whether participation led to alignment with organizational goals and mission.

Furthermore, there was substantial variability between institutions with regards to the organizational reporting structure for CIOs. While some CIOs report to their hospital Chief Executive Officers (CEOs), others report to the Chief Operating Officer (COO). At academic centers, there is also another chain of command, which is the medical school. We found that some CIOs report to multiple academic leaders such as the chancellors, deans, and provosts. One CIO summarized this variability as: “If you’ve seen one IT organization you’ve seen one IT organization.” This variability speaks to the industry’s overall lack of alignment between technology and strategic decision-making.

A new mechanism by which the CIO might be able to engage in business strategy has arisen with the establishment of a new hospital role, the Chief Innovation Officer. This new role has the potential to bridge the divide between organizational strategy and technology. Other organizations have created roles to offload the CIO’s plate.

One CIO described such a solution:  “I spend most of my time devoted to new technology. We have a separate role on our team of a chief operating officer. He spends most of his time maintaining what we already have today.” Another CIO has a Chief Innovation Officer directly reporting to him. Together the two C-level executives can share resources to innovate with other leaders to solve business problems.

However, there is still a significant degree of variability in the reporting structures of chief innovation officers, similar to CIOs.4 And despite the title suggesting the importance of the role in high-level decision-making, only 36% of innovation officers in that survey reported to the CEO.

Looking Forward

In this survey of health system CIOs, we gained in-depth insights into operational challenges faced by organizational leaders as well as the tensions between the aspirations for a digital transformation and the crushing reality of maintaining legacy systems.  Consistent themes emerged around the challenges, opportunities, and needs that CIOs and healthcare organizations should consider in order to leverage new technologies.

Challenges centered on three key issues.

  • The first theme is that a substantial proportion of the IT budget is consumed on maintaining multiple separate existing technology systems. This leads to an over-reliance on legacy vendors to drive the innovation agenda and represents an existential threat to healthcare organizations.
  • The second theme is the lack of interoperability. Not only does this limit innovation and ensure vendor-lock, but it also becomes an all-consuming project for IT to solve during systems mergers and acquisitions.
  • The third type of challenge was around governance structures. There was a wide array of reporting structures for CIOs. Ultimately, most are tasked to focus on implementing an organization’s business strategy after it has been developed, rather than helping to direct the strategy and incorporating digital transformation into the overall plan.

The CIOs we interviewed have recommendations to address some of these challenges that are well within the scope of most organizations. For example, organizations can change governance structures to prioritize innovation, such as by carving out a separate role within their office for innovation. Such a role should partner closely with the CIO so that innovation and maintenance are coordinated and balanced. Governance changes would also involve closer alignment between digital transformation and the overall business plan. Another solution was to create streamlined, standardized processes for approaching new technology so that limited resources could be used while allowing for a broader array of new solutions to be tested. In addition to within-organization changes, the CIOs identified needs from the technology space for them to transform.  These focused on a need for standardization and interoperability.

While the current study is limited by the number of interviews conducted, we believe the patterns are reliable. We observed limited heterogeneity in responses to our questions. Furthermore, we did not identify best-performing organizations for our interviews. All adapted with their limited bandwidth to their local needs, but all saw a significant opportunity to improve their experiences. We are unlikely to see any organization break from the crowd as the legacy technology of healthcare falls further and further behind the capabilities of other service industries.

Digital transformation in healthcare has many barriers. From the perspective of the CIO, these range from technologic barriers to governance structures to the competing challenges of maintenance vs. innovation. Creative solutions by CIOs have been demonstrated, but much of the innovation agenda lies outside their realm of control. At an organizational level, developing a strategy to separate technology maintenance from technology and service innovation was a key theme emerging from our discussions.

References

  1. Which Industries Are the Most Digital (and Why)? McKinsey Global Institute, 2016. (Accessed July 28, 2020, at https://hbr.org/2016/04/a-chart-that-shows-which-industries-are-the-most-digital-and-why.)
  2. A Roadmap To Welcoming Health Care Innovation. Health Affairs Blog, 2019. (Accessed July 27, 2020, at https://www.healthaffairs.org/do/10.1377/hblog20191119.155490/full/.)
  3. Technology budgets: From value preservation to value creation. 2017. at https://www2.deloitte.com/us/en/insights/focus/cio-insider-business-insights/technology-investments-value-creation.html.)
  4. We Interviewed Health Care Leaders About Their Industry, and They’re Worried. 2016. (Accessed July 28, 2020)

Chicago Hospitals Response to the 1918 Influenza Pandemic: Legitimating Their Identity as Community Institutions

Blair D. Gifford, University of Colorado Denver

Contact: Blair.Gifford@ucdenver.edu

Abstract

What is the message? Voluntary hospitals in the Chicago area responded quickly to the influenza epidemic that appeared in September 1918 and quickly surged to a high number of contagious patients. The active response reinforced the original community service orientation that led to the quick proliferation of hospitals at the turn of the 20th century and crystallized what it meant to be a voluntary hospital.

What is the evidence? Extensive archival evidence from multiple sources.

Timeline: Submitted October 25, 2020; accepted after review: December 10, 2020.

Cite as: Blair D. Gifford. 2021. Chicago Hospitals Response to the 1918 Influenza Pandemic:  Legitimating Their Identity as Community Institutions. Health Management, Policy and Innovation (HMPI.org), Volume 6, Issue 1, Winter 2021.

Dedication: This paper is dedicated as a memorial to my grandmother, Rita Demarest Gifford.  Rita lived in the Kenwood neighborhood (University of Chicago) on the southside of Chicago.  As a young newlywed in her 20s, Rita succumbed to the second wave of the influenza epidemic in 1919.  She was pregnant at the time.

U.S. Hospitals Were Gaining Credibility When the 1918 Influenza Epidemic Began

Hospitals were just starting to gain the public’s trust for medical efficacy at the turn of the nineteenth century. With support from poor and wealthy alike, hospitals started to consider how they might combine a charitable tradition with the medical advances that brought top doctors and paying patients to their doors. It was during this early period of increasing appreciation and use of hospitals that the 1918 influenza epidemic suddenly struck and brought an overwhelming surge of patients who needed hospital care.

The number of influenza cases in Chicago began to increase and become noticed in early September, 1918. By late September, it was clear that an epidemic was at hand.  Public health officials scrambled to find ways to lessen contagion in public spaces. At its height, during an eight-week period from late September until mid-November, there were two thousand plus new cases of influenza and pneumonia each day. These cases resulted in over five hundred deaths a day in Chicago at the peak of the epidemic.

This research will first provide a social-historical context of the burgeoning hospital industry in Chicago at the turn of the nineteenth century. This history is not just about hospitals. It is also about immigration and community change; how various religions went about providing a health safety net for their members; and, how doctors influenced the development of hospitals into their professional workshop in the 1910s. Given these various influences, the paper will describe how hospitals, during a moment of crisis, responded to heightened need in an effort to maintain their legitimacy as community institutions.

Urbanization, Immigration and the Growth of Hospitals at the Turn of the Twentieth Century

The first census of hospitals in the U.S. was done in 1873. It listed only 178 hospitals nationwide, and of these, most were long-term care institutions, many of which were state and private asylums for the insane (1). However, hospitals were just beginning to move to the forefront with the ongoing urbanization and immigration of the period. From 1870 to 1890 the population of Chicago quadrupled from 300,000 to 1.1 million, and by 1890, 78% of the population were foreign born or children of foreign born.  By 1910, the population reached almost 2.2 million, with continued growth to 2.7 million by 1920.

The growing population faced many medical needs. In response to the multi-cultural growth, smaller charitable hospitals were built throughout Chicago’s communities to accommodate the special needs of the various religious and ethnic populations. As part of this evolution, a hospital could be a place of comfort to various beliefs, customs, languages, and races, as well as a site of medical care.

Many working-class Chicagoans endured economic uncertainty and hardship. Unemployment always seemed to be lurking around the next corner, and family illness or an unexpected death could shatter an already fragile existence. Foreign-born populations shied away from most public charity because there was a deep distrust of public assistance (2). In the “old country” there was no such thing as public relief, and they did not expect it or look for it in America. Other forms of charity were still too close to home and could be found with the community setting.  But medical care was different.  It represented a new and exciting world, and, as such, accepting medical charity was less of a humbling experience and more of a new society that lay beyond the confines of family and community (3).

The science of charity was an attempt to distinguish among those who were in a temporary state of poverty or made dependent by diseases and disability from those who took advantage of charity and would arguably become dependent on charity. Charity hospital patients, although poor and often immigrants, were perceived to be morally redeemable and medically curable. While patients at the large public hospitals were generally terminal or not seen to be “worthy” due to diseases like alcoholism, insanity and venereal diseases (4).

Hospitals in the 1910s: The New Medical Workshop

The introduction of aseptic medicine, anesthesia, and x-ray technologies in the 1890s and clinical labs and early 1900s greatly enhanced the capabilities of hospital-based care.  Physicians took advantage of these medical science advances and began to open small, proprietary hospitals, usually with a strong surgical orientation. Ethnic, foreign-born physicians, who had been discriminately excluded from other hospital staffs, were often the enterprising forces behind these hospitals. As a result, a variety of hospitals developed. Hospitals were often started by religious groups, communities, individual doctors and even companies such as railroads. Cities and factories provided a population base to support new health care institutions, and the establishment of a hospital became the goal of every civic-minded community (5, 6).

In view of the government’s expanding role in providing medical care, the availability of free care in many hospitals and dispensaries, and competition from irregular physicians (7), American physicians grew increasingly concerned about their financial status. Medical journals across the country published editorials bemoaning the low income of doctors, which they sometimes estimated to be as low as $500 or $750 a year, scarcely more than that earned by manual laborers (8). In 1913, the Judicial Council of the American Medical Association reported “that hardly more than 10 percent of the physicians in the United States are able to earn a comfortable income” (9).  Although further evidence indicates that the medical profession was probably not as badly off as it imagined (7), concerns about income shaped physicians’ views of charitable care. As more and more workers, unable to afford private physicians, but otherwise self -sufficient, turned to charitable inpatient care and dispensaries, the medical profession grew increasingly suspicious that hospitals and dispensaries were treating for free patients who were fully capable of paying.

Medical practitioners also became increasingly upset with the lack of regulation and standards in the industry. Anybody, it seemed, could get a medical degree and practice at hospitals which ranged radically in terms of quality care. To protect itself, the medical profession was instrumental in working with the Carnegie Foundation to research the condition of medical schools in the U.S.  The resulting Flexner Report led to closure of almost half of the medical schools in the U.S. and the standardization of medical education in the others. These actions and others indicated that the medical professional, itself aglow in the light of a medical science ethos, was beginning to redefine medical care away from a charitable orientation (4).

The Development of Voluntary Hospitals: Growing Emphasis on Medical Science

In the early 1900s, private hospitals in the United States had begun to move away from a primary charitable orientation to become “voluntary.” At the time, the use of voluntary to describe a hospital meant that it had developed a dual mission of charity and medical science. Voluntary hospitals did not want to break away from their charitable, community-oriented past. However, they needed extra resources to maintain the continuance of their charitable mission. With the excitement of new medical capabilities, hospitals began to embrace a medical science orientation, too.  Such an orientation was the new modernism of the day.  This new dual orientation allowed voluntary hospitals to maintain community support while increasing revenues (10, 11).

By attracting talented doctors, hospitals were able to gain an increasing number of paying patients especially shorter-term medical and surgical patients. By keeping a charitable orientation, hospitals were able to gain donations from their communities of support and patients to afford the care of those who were less financially able, but worthy. Hospitals had clearly become needed community and medical institutions. As such, the growth in the number of hospitals was exceptional. By 1910, there were almost five thousand hospitals in the U.S., and the ratio of hospital beds per person was equivalent to what it is today.

The dual orientation of voluntary hospitals even became a foundation for some government hospitals. In 1910, Cook County Hospital’s accounted for almost a quarter of all hospital beds and its $642,000 of expenses accounted for about a quarter of general care hospital expenses in Chicago. As a governmental hospital, Cook County provided much charitable care, but it also was a hospital of great distinction for the medical care that it provided. Indeed, it was quite an honor to be a physician with admitting privileges at Cook County Hospital.  lso, the hospital served as a primary source for residencies for recent medical college graduates.

Members of the medical profession in Chicago felt that some patients should not be admitted to Cook County because they had financial means, but still tried to receive free services.  In 1911, a County Agent was installed in the hospital to examine the financial circumstances of all applicants for admission. These efforts led to restricted admission to those “needing and entitled to service” and led to hospital attendance “materially below would it would have been with a wide-open policy. (12).

Voluntary Hospitals Limit Care for Contagious Disease Patients

The increasing prominence of technology and the physicians who employed these impressive new tools expressed itself in another particularly tenacious way.  Voluntary hospitals maintained a charitable mission, advanced a medical science orientation, but were starting to move away from admitting chronic patients, including those with contagious diseases.  Such patients would have to seek services at governmental hospitals.  As a result, patients at public hospitals started to be seen as wards of the state and generally terminal.  That is, they were not seen to be “worthy” due to socially-framed diseases like alcoholism, insanity, and sexually transmitted diseases such as syphilis and gonorrhea, as well as contagious diseases like tuberculosis.

Whether to admit contagious patients or not was a difficult decision for voluntary hospitals to consider. A 1910 survey by the US Government of Benevolent institutions indicated that many voluntary hospitals in Chicago had moved away from admitting contagious diseases (13). As Table 1 shows, over one-half of voluntary hospitals maintained a non-contagious admittance policy, including all the Catholic hospitals and the Jewish hospital and many prominent protestant hospitals.

By contrast, a line was drawn at Cook County Hospital.  It had been acting much like a voluntary hospital, but when it came to admitting contagious patients, it needed to be true to its public and government orientation. Yet, it should be pointed out that since Cook County was almost always filled, they probably re-directed many contagious patients to nearby governmental contagious disease facilities (12).

The 1918 Influenza Epidemic in Chicago

The exceptional virulence of the influenza strain of 1918 first became apparent during August outbreaks in Africa, Europe, and North America.  No other modern strain of influenza led so frequently to deadly pneumonia. Unlike the 2020 Coronavirus pandemic, younger adults were very susceptible to succumbing to the 1918 influenza as general good health seemed to provide no defense against the virus. Further, the continued wartime mobilization of soldiers and civilians created optimal conditions for the spread of the highly contagious virus. Global fatalities exceeded twenty million and may have approached forty million. In the U.S., influenza and pneumonia deaths exceeded half a million (14).

In Chicago, city health officials became alarmed about a marked rise in deaths in the suburbs to the north. On September 21st, Chicago had its first recorded death due to the acute respiratory problems that the virus presented. By September 30th, there were at least 260 known cases of the influenza virus (15).

This surge led the Health Commission, Dr. John Robertson, to order patient isolation at the large and renowned Cook County Hospital.  Military officers, at nearby Great Lakes Naval Training Station, instituted isolation and quarantine controls for those who became sick. All 50,000 sailors on hand were to be given daily nose and throat sprays. Overall, 1000 men were soon put in isolation, another 4,000 sailors were put under quarantine, and liberty leave was canceled for all (16, 17).

On October 11th, the new and quickly formed Illinois Influenza Advisory Commission banned public dancing and public funerals (18).  At this point, the spread of the flu had gone parabolic reaching upwards of 2,000 cases and 500 deaths a day.  On October 15th, city leaders closed theaters and night schools. Churches and schools were left off the closure list, but clergy were asked to shorten services and students were beginning to not show up for classes. Mischievous students even took to sniffing pepper in order to induce a coughing or sneezing fit, knowing that the school health officer would send them home for a week. On October 16th, the Commissioner ordered that all non-essential public gatherings be banned and that social distancing be practiced. However, exceptions were made for saloons and restaurants so people had a place to go for meals (19).

Doctors and nurses worked around the clock while citizens were trying to understand and cope with the crisis. Morris Fishbein, a prominent Chicago doctor who later became the editor of the Journal of the American Medical Association, wrote in his memoirs that most Chicago physicians visited some “sixty to ninety patients each day” at the height of the epidemic (20).

It was a dangerous time to be a health professional. For example, prominent physician, Harold Dwyer, died suddenly on October 21st.  He had worked “incessantly” at Chicago’s public Contagious Disease Hospital since the beginning of the epidemic (21).

Little could be done. Physicians and nurses were generally unable to do much besides isolating influenza patients in large ventilated wards and trying to make them comfortable. In mid-October, a new vaccine serum, made from the blood of influenza-pneumonia convalescents collected at Chicago’s hospitals and known as the Rosnow formula, was made available, but it ultimately showed no effect (19).  The front page of the Journal of the American Medical Association stated as much. “Unfortunately we as yet have no specific serum or other specific means for the cure of influenza, and no specific vaccine or vaccines for its prevention…the physician must not allow himself to be led into making more promises than the facts warrant (21).” Besides collecting blood for serum, surgical cases were stopped at hospitals and emergency beds were set up in hallways. Hospitals also promised to make many of the beds free. (22)

By late October, new case reports indicated that the epidemic might be on the decline.

With danger seemingly passing, the Chicago Tribune newspaper started putting pressure on the health commissioner to loosen all restrictions.  The editors sarcastically called the Commissioner “his highness” and “his eminence,” and wrote: “outside of the fact that you mustn’t cough, sneeze, expectorate, or osculate, mustn’t smoke on street cars or elevated trains, can not visit sick friends and must continue to observe food and fuel regulations and keep up your installment payments on Liberty bonds, you can get up tomorrow and do as you please” (23). At first, the Health Commissioner did not budge from his strict and unprecedented position, but as cases dwindled he allowed all restrictions to subside on November 16 (24), before subsequent waves of cases reoccurred in 1919 and 1920.

Overall, more than fourteen thousand Chicagoans, in a city of almost 2.7 million, died of influenza or pneumonia between mid-September of 1918 and March of 1919. During this period, the weekly death rate leaped from 10.8 per thousand to 63 per thousand at its height in late October (i.e., one in 16 people).  During the critical period of the crisis – September 22 to November 16 – the Department of Health received reports of 37,921 influenza cases and 13,109 pneumonia cases. Of all these cases, there were about 8,500 deaths, an almost 17% rate of death for those who fell ill in an eight week period at the height of the epidemic (25). Officials acknowledged, however, that sickness was far more widespread than their statistics indicated, and that thousands of cases went unreported (16).

Database of Chicago’s Hospitals, 1910 to 1920

Trying to determine how hospitals responded to an influenza epidemic over 100 years ago requires archival work, especially since many of the 1910 and 1920 hospitals in Chicago no longer exist.  Hospital survey data were collected from the U.S. Bureau of the Census, Survey of Benevolent Institutions (13), American and Canadian Hospitals (26), the Chicago Medical Society, Blue Book (bi-annually 1905 through 1953) (27), the History of Medicine and Surgery:  Physicians and Surgeons in Chicago (28), the Annual Survey of Hospitals (American Hospital Association) (29), and Medicine in Chicago, 1850-1950 (30). Using all sources available, the author’s dataset is more complete than any of the individual sources listed above.

Voluntary hospitals were determined by the extent to which a hospital provided charity care or not. It was decided to use a 5% cut off level for charity patients. That is, those hospitals which provided 5% or more of charity care were labeled “voluntary,” while those below 5% were labeled “proprietary.” Note that almost all voluntary hospitals were well above the cut-off level in a range of 15 to 35 or even 50%, while those categorized as proprietary generally offered no charitable care at all (Gifford, 1993). Historical data was collected from annual reports at the few archives of hospitals still available in Chicago in late 1980s and early 1990s, including: St. Elizabeth, Presbyterian, Englewood, Michael Reese, Lutheran Deaconess, Cook County, Passavant, and German/Grant Hospitals, Cook County, Isolation Hospital.

Chicago Hospital Census, 1910 and 1920

As the gathered data indicate (Table 1), there was a large increase in the capacity of Chicago hospitals in the 1910s as measured by hospital beds. Voluntary hospitals increased their bed capacity by almost 25%, which is line with the population growth of Chicago in the 1910s. Proprietary and private specialty hospitals increased their capacity at a faster rate, but their overall total number of beds was relatively low. The increased capacity of specialty hospitals – both private and public – is probably best explained by the increasing number of births that had moved away from being done privately at home and into hospitals (6).

What is especially clear from Table 2 is that the government hospital capacity had increased dramatically by 1920. That is, the number of public health hospitals in Chicago increased fourfold (1 to 4) and the bed capacity of these hospitals increased well over 1000% (125 to 1820 beds by 1920). When the influenza epidemic arrived government hospitals re-purposed buildings with temporary beds and quickly built public health hospitals to handle the surge in patients and for injured troops coming back home from the conclusion of World War 1. (Note that more American troops died from the influenza epidemic [58,000] than died from battling in the war [53,000], which ended on November 11, 1918, during the pandemic.) For example, Cook County Hospital doubled its bed size from 1350 to 2700 by 1920, but the hospital was almost always filled so it might not have been able to do much extra for the influenza epidemic surge. This would have put even more pressure on quickly making public health beds available in the fall of 1918.

 

Voluntary Hospitals Respond to Heightened Community Need to Care for Contagious Patients

The influenza epidemic put a severe strain on the capacity of all hospitals. When looking at Table 2, it appears that since many voluntary hospitals had a non-contagious patient policy that this group of hospitals might not have done as much as other hospital types to help handle the surge of patients that appeared at hospitals during the influenza epidemic. However, a closer look at archival data from individual voluntary hospitals provides a different story. That is, although many voluntary hospitals reported that they would not receive non-contagious patients in 1910, many of them reversed this decision and did take contagious patients during the 1918 influenza epidemic. Seven of the eight voluntary hospitals described below clearly opened their doors and, in a few cases, were heroic in their response to providing care for contagious patients.

Catholic hospitals. Despite increasing the size of their outpatient capacity, St. Elizabeth’s Hospital had trouble meeting patient demand during the epidemic: “It can readily be seen by the casual observer that the amount of space supplied by the present building is not sufficient to care for the ever increasing number of patients, who apply for admission. . . at times the institution was taxed to its capacity and compelled to turn patients away (32).  Such comments illustrate how the hospital did the best that it could at the time.

Mercy Hospital, the large Catholic teaching hospital on the near south side of Chicago, had its “most difficult nursing assignment for a six to eight month period” during the 1918 Influenza epidemic. Beds were put throughout the hospitals corridors to handle the surge in cases and nurses had to deal with “the sight of flushed patients, with foam forming on the their lips, dying before they could be admitted.” Gordon reported that for prevention purposes, many nurses drank “an ounce of whiskey each day before breakfast.” Amazingly, no Mercy nurses contacted the virus.  (33).

Protestant hospitals.  The large protestant teaching hospitals in Chicago – Wesley, St. Luke’s and Presbyterian – all were accustomed to providing high levels of charitable care (34). For example, at Presbyterian, 69% of 121,249 patient days in 1917 were free or part-pay, and in 1920, 66% of 134,620 patient days were free or part-pay.  Smaller protestant hospitals also provided charitable days of care, but at lower levels.  For example, 12% of 17,838 patient days Lutheran Deaconess Hospital were “charitable” in 1910. This increased to 14% of 29,008 patient days in 1919. (35)

In at least some cases, protestant hospitals’ responses to the influenza epidemic was heroic. Passavant Hospital, a medium size hospital on the near north side of Chicago’s downtown business district, stated the following in their 1918 annual report: “All the physical parts of the hospital were overtaxed…that there should be a deficit – and that, a large one – will be no surprise…all epidemic patients were admitted, irrespective of their ability to pay.” (36)

Jewish hospitals.  Michael Reese, on the near south side, was the main Jewish hospital in Chicago until Mt. Sinai started operations on the southwest side in 1919.  Michael Reese had a strong scientific medicine orientation and was the home to many of Chicago’s Jewish doctors. Both Michael Reese and Mt. Sinai provided very high levels of charitable care. United Hebrew Charities provided significant funding to Michael Reese and Mt. Sinai to cover the costs of charitable patients. As a result, the hospitals provided significant charitable care. (37)

Community hospitals.  Grant Hospital, which had just changed its name from German Hospital in light of the fight against Germany in World War 1, increased its percentage of free days to almost 30% in 1918.  This compares with 24% in 1914 and 13% in 1909.  (38). At Englewood Hospital, the percent of charity patients increased to over 15% in 1919 in comparison to less than 10% in 1912. In Englewood’s case, such an increase in charitable costs was especially difficult since the hospital had no endowment money from which to cover the costs of charitable care.  (39)

Government hospitals. Except for Cook County Hospital and the Isolation hospital, archival records could not be located for most government hospitals in Chicago during the 1910s. During the 1910s, Cook County moderately increased admissions from 26,791 in 1910 to 27,095 in 1915 and uptick to 29,418 patient admits in 1918 and back to 26,626 patients in 1920. (40) Presumably, some patients were instructed to go to other public hospital facilities that were quickly set up for the epidemic. For example, admissions at the relatively small Isolation hospital (i.e., contagious diseases) increased from 75 in 1916 to 423 in 1918 and then decreased to 190 in 1920. (41)

Discussion

An influenza epidemic suddenly appeared in the Chicago area in September 1918 and quickly surged to a high number of contagious patients. Although there was little effective care that could be provided outside of comfort and isolation, hospitals had to quickly make a difficult choice. Should they turn back to their community foundation or stand aside as medical science institutions for non-contagious patients?

As archival data indicate, voluntary hospitals in Chicago chose to manage the surge of patients from their neighborhoods.  In some cases, their efforts were heroic. Physicians and nurses worked extended hours and hospitals did what they could to provide extra beds for the surge of neighbors that came to their doors. As is noted in archives of Passavant Hospital, “all epidemic patients were admitted, irrespective of their ability to pay.” Although these hospitals might have seen their admittance of poorer, contagious patients as a short-term issue, there is little evidence of this in the archives. Voluntary hospitals continued to provide community-based care during the subsequent waves of the influenza epidemic that occurred in 1919 and 1920. Indeed, well beyond the influenza epidemic, there is additional evidence that voluntary hospitals were heroic in providing for their communities during the Depression years (31).

The active response by Passavant and other voluntary hospitals to the 1918 influenza epidemic served to re-inforce the original community service orientation that led to the quick proliferation of hospitals at the turn of the 20th century. In effect, the epidemic crystallized what it meant to be a voluntary hospital. This identity included the capacity to choose their primary function while maintaining their institutional legitimacy as a community institution (42).

References

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2. Vogel, M.J. 1980. The Invention of the Modern Hospital: Boston 1870 to 1930. University of Chicago Press.

3. Rosner, D. 1982. “Health Care for the “Truly Needy”: Nineteenth-Century Origins of the Concept,” Millbank Memorial Fund Quarterly, 60 (#3): 355-85.

4. Rosenberg, C.E. 1987. The Care of Strangers: Rise of the American Hospital System. New York: Basic Books.

5. Starr, P. 1982. The Social Transformation of American Medicine. New York: Basic Books.

6. Stevens, R. 1984. In Sickness and in Wealth: American Hospitals in the Twentieth Century. New York: Basic Books.

7. Numbers, R. 1978. Almost Persuaded. Baltimore: Johns Hopkins University Press.

8. Van Sickle, F.J. 1916. “Social Insurance Against Accidents.” Journal of Sociological Medicine, Vol. 17: 292.

9. Report of the Judicial Council. 1913. American Medical Association, Chicago.

10. Klarman, H.A. 1963. Hospital Care in New York City: The Roles of Voluntary and Municipal Hospitals. New York: Columbia University Press.

11. Rosner, D. 1988. “Heterogeneity and Uniformity: Historical Perspectives on the Voluntary Hospital.” Pp. 87-126 in In Sickness and in Health: The Mission of the Voluntary Health Care Institutions, edited by J. David Seay and Bruce C. Vladeck. New York: McGraw-Hill.

12. Cook County Commissioners. 1926-27. “Proceedings of the Board of Commissioners.” Chicago: Cook County.

13. U.S. Bureau of the Census. 1904 and 1910. Survey of Benevolent Institutions. Washington D.C.: Government Printing Office.

14. Patterson, K.D. and Pyle, G.F. 1991 “The Geography and Mortality of the 1918 Influenza Pandemic,” Bulletin of the History of Medicine, 65, #1 (Spring): 4-22.

15. Chicago Tribune. 1918. “Grip Shuts off Jackie Liberty at Great Lakes.” September 20.

16. Ruth, D.E. 1991. “Don’t Shake–Salute!” Chicago History, XIX, #3-4 (Fall, 1990 & Winter, 1991): 4-23.

17. Chicago Tribune. 1918. “You Can’t Smoke on Street Cars till Flu End” and “Influenza Shuts All Chicago.” October 13.

18. Chicago Tribune. 1918. “Churches Open, But Influenza Reduced Crowds.” October 21.

19. Chicago Tribune. 1918. “Flue Epidemic Passing; Death Rate Declines.” October 26.

20. Fishbein, M. 1969. Morris Fishbein, MD: An Autobiography. New York: Doubleday.

21. Journal of the American Medical Association. 1918. “Serums and Vaccines in Influenza” Page 1408, Editorial. October 26.

22. Chicago Tribune. 1918. “Chicago Gets Vaccine to Aid in “Flue” Fight.” October 21.

23. Chicago Tribune. 1918. “Wouldst Dance? Then Snooze as J. Dill Snoozes.” November 2.

24. Chicago Tribune. 2014. “Flashback 1918: Influenza Epidemic Struck Hard, Fast.” R. Grossman, October 18.

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27. Chicago Medical Society. 1905-1953. Blue Book. Bulletin of the Chicago Medical Society.

28. Chicago Medical Society. 1922. History of Medicine and Surgery: Physicians and Surgeons in Chicago. Chicago: Biographical Publishing Co.

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32. Sisters of Poor Handmaids of Jesus Christ. 1920. Annual Report, St. Elizabeth’s Hospitals, Notre Dame University archives.

33. Gordon, S. 1985. No Service To Small: Mercy Hospital. Random Books, Chicago.

34. Bowman, J. 1987. Good Medicine: The First 150 Years of Rush-Presbyterian-St. Luke’s Medical Center. Chicago: Chicago Review Press.

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38. Grant Hospital Archives, Chicago.

39. Englewood Hospital Archives, Chicago.

40. Cook County Hospital Archives, Chicago.

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42. Friedland, R. and Alford, R. 1990. “Bringing Society Back In: Symbols, Practices, and Institutional Contradictions,” Pp. 232-66 in The New Institutionalism in Organizational Analysis edited by Woodrow Powell and Paul DiMaggio. Chicago: University of Chicago Press.