HMPI

Academic Campuses, Super Spreader Events & Pandemics: Simulation Evidence from Reopening Indian Universities with COVID-19 (IIMA and IIT, 7/29)

Chirantan Chatterjee, IIM Ahmedabad and Hoover Institution, Stanford, and Aditya Bansal, Indian Institute of Technology

Contact: chirantanc@iima.ac.in

Abstract

What is the message? Universities in India need be cautious about reopening academic campuses. Otherwise, campuses face serious risk of widespread infections and deaths among faculty, staff, students, and families. The authors recommend that without a safe, efficacious vaccine deployed in the next few months, leaders of Indian academic campuses and public policy makers should go fully virtual for the next academic year.

What is the evidence? Analysis based on a simulation study on a stylized campus in India, IIMX.

Timeline: Submitted July 27, 2020; accepted after revisions: July 28, 2020

Cite as: Chirantan Chatterjee, Aditya Bansal. 2020. Academic Campuses, Super Spreader Events & Pandemics: Simulation Evidence from Reopening Indian Universities with COVID-19. Health Management, Policy and Innovation (HMPI.org), Volume 5, Issue 1, Special issue on COVID-19, June 2020.

Academic Campuses Face Major Risks of COVID Infections

As academic campuses worldwide consider reopening, they fear becoming the next hotspots for super spreader events during COVID-19 due to their nature as small worlds of closely interacting people. [1] Most evidence on the risks until now come from North American campuses, which often have a medical school on campus to scientifically inform and enable academic campus leadership towards their preparation in reopening. By contrast, evidence from developing economies is sparse. We fill this gap by undertaking a simulation study to enrich evidence in this space from India using the stylized setting of IIMX, a fictitious fully residential academic business school campus in India.

The simulation is based on plausible estimates. We assume that IIMX houses 1700 people, including students, faculty and staff, residents, and contractual employees. We assume that there will be 900 students in a two-year program and 140 students, plus 110 family members assuming approximately 80% will live with partners, in a one-year program. All students and family are resident in campus. We also assume 90 faculty members and 180 family members residential on campus; 60 administrative staff and 120 family members; and 100 contractual employees. We then use standard epidemiological models, as detailed below, to understand the impact of reopening.

Study Design

To understand the spread of COVID-19 within a college campus, we start with a simple compartmental model, the Susceptible, Exposed, Infectious, Removed (SEIR) model, which has been used by epidemiologists and governments globally to predict the spread of pandemic.[2] We use the SEIR model in a context where our total population size does not represent a country or a state but the small world of an academic campus like IIMX.[3] We further extend the SEIR model to factor in mortalities and hence create a SEIDR model where D captures death in our baseline SEIR model.[4] Using the SEIDR model, we divide the population of IIMX into five non-overlapping groups corresponding to stages of the disease as a function of time t as follows:

  • S(t) is the fraction of susceptible individuals: those able to contract the disease.
  • E(t) is the fraction of exposed individuals: those who have been infected but are not yet infectious.
  • I(t) is the fraction of infected individuals: those capable of transmitting the disease.
  • R(t) is the fraction of recovered individuals: those who have become immune.
  • D(t) is the fraction of dead people: those who have succumbed to the disease.

The variables give the fraction of individuals – that is, we have normalized them so that S + E + I + D + R = 1. We then assumed an incubation period of 5.1 days and infectious period of 3.3 days. [5]

We set the Infection Fatality Rate (IFR) at 1%, which is significantly lower than the current perceived Indian national mortality rate of 4%. The IFR is the ratio of deaths over all cases, including asymptomatic and undetected cases; by contrast, the anecdotally reported mortality rate in popular press is usually calculated as the ratio of deaths over detected cases. The value of IFR can be higher in university campuses with older faculty, who are more susceptible to the disease, and also with younger students who have prior comorbidities such as asthma and respiratory conditions that are exacerbated by air pollution and its health burden recently that is common in India.

Next, we iterate with several values of the basic reproduction number, R0, which is defined as the average number of people who will contract a contagious disease from one person with that disease. The population is said to have herd immunity when enough individuals are immune to the virus, which can thereby help to provide a measure of protection to individuals who are not immune. With reinfections rising from COVID-19, whether herd immunity is a tangible strategy for nations is also being debated. Nonetheless, assuming there is herd immunity, an outbreak may lead to new cases, but the size of the infected population always decreases. Reduction is achieved when the fraction of susceptible is less than 1/𝑅0.

We also make assumptions on the effectiveness of social distancing. In our context, zero percent effectiveness signifies that in-person classes are being held with no interventions such as masks, sanitizers, face shields, and ventilated classrooms. At the other extreme, one hundred percent effectiveness signifies that IIMX is holding completely virtual classes.

Using the above assumptions, we experiment with several simulations to predict the spread of COVID-19 under different configurations of R0, initial number exposed, and effectiveness of social distancing. We examine number infected, recoveries, and mortality rates while using the differential equation system as outlined in the supplementary material. For plotting the figures and running the simulations, we use the Matplotlib library from Python.

Findings

Our findings suggest that for IIMX, the numbers after reopening will manifest as follows. Take the most likely parameters: R0=3; initial number of people infected=10; 30% effectiveness of social distancing. Recent work has found that R0 expected from an average of past global studies is 3.32; also see our supplementary material for a defense of our R0, around which we experiment later in the paper. With these numbers, at the end of 20 weeks, 1392 of 1700 students (81.9%) will be infected, along with 14 mortalities. Within the first 10 weeks itself, 1321 students (77.7%) will be infected along, with 12 mortalities.

Figure 1 plots the rise in number of infections over 20 weeks and compares how the scenario could be vastly different if most classes are held remotely, all precautions are taken, and effectiveness of social distancing is 50%. In such a case, 963 (56.6%) of the total campus population would be infected at the end of 20 weeks, with 541 (31.8%) students being infected within the first 10 weeks. This scenario is significantly lower than the earlier case with 30% effectiveness of social distancing.

To understand the dynamics of how different parameters influence the final number of students exposed on campus, Figure 2 plots the number of infected cases (size representative of number) as we experiment with input parameters of R0 and social distancing effectiveness in a 3-dimensional figure. In most realistic scenarios, the pandemic is likely to blow up within a college campus with more than 50% students exposed.

A word on limitations of our study, while being conservative about lower bounds of potential true estimates conditioned by size of campus, is merited at this point. We do not model interactions within an IIMX system such as in classrooms, dorms, or people catching the virus from outside the campus. We also do not account for social interactions from canteens and during student events. Including such factors would increase infection probabilities.

Our simulations also do not incorporate a strong testing and contact tracing strategy, which could help control the spread within the campus. In addition, our simulation findings does not assess exposure campuses have to local healthcare ecosystems, which differ in their metropolitan or non-metropolitan location. This would be relevant for debates on bed availability in city hospitals, shortages in medicines supply, fake ventilators being deployed in hospitals, inadequate isolation wards, and stigma during COVID-19. Future work that incorporates these issues will add richness to our findings.

Figure 1: Simulated Findings of COVID-19 Spread in IIMX

Figure 2: Experiments with R0, Social Distancing and Resulting Infections at IIMX

Discussion and Policy Implications: Stay Virtual

Our simulations show that, if college administrations across India decide to go ahead with in-person classes, COVID-19 could sweep college campuses in India within 10 weeks. Our recommendation to Indian academic campuses would be to stay virtual for the foreseeable future. Remaining virtual is needed until India achieves adequate deployment of vaccines; campuses are prepared with isolation wards, testing capacities, and medicines; and information on these measures is transparently shared with stakeholders in the ecosystem. More broadly, given that risk preferences of individuals are heterogeneous, choice should be provided to students, faculty, and staff on blended models, although this might be operationally complicated and costly to achieve.

Although choice is important, institutes in India such as IIT Bombay have shifted completely virtual for the academic year. IIT Bombay is also raising funds to support students who have problems with digital access and computers. Following this lead, universities should marshal their high net worth alumni for special endowment funds to provide financial and non-financial support as they prepare as the coming academic year rolls out virtually.

In addition, whatever the ultimate decision, strong enforcement of social distancing is required, especially if some students return to campuses. Here, university faculty can play role models by themselves wearing masks. In addition, penalties both in monetary terms and in academic grades can be considered for all stakeholders involved.

Although some doctors in India are arguing for a herd immunity strategy[6] by reopening campuses, this idea is dangerous. New reports [7] have emerged that people become susceptible to the disease again in few months. If so, or if immunity is weak even in the short term, this would completely negate the idea of such a strategy.

Also, the heterogeneity in quality of healthcare available within and outside the college campus varies greatly in India. IIT Kharagpur in West Bengal, for instance, does not have a well-established hospital equipped with sufficient resources within 130 kms. University leaders need to be transparent about their preparations, while documenting information on health insurance for students, faculty, and staff perhaps through a COVID-19 committee and online website.

There also needs to be clarity on the legal liability implications in case things go out of control. With 6.0% [8] of infected cases needing hospitalization, our estimates suggest that a population size of 1700 would conservatively require at least 50 beds (based on 833 infections and a 6% hospitalization rate) with adequate healthcare equipment and staff. Until now, we cannot find evidence about preparation towards this goal on Indian campuses. As one model to learn from, Indian campuses and many other universities, especially in emerging economies, can look towards Taiwan[1] in their preparations and scientific arrangements towards reopening university campuses.

A final word is merited on co-morbidities arising from asthma and respiratory conditions. Even among the young in campus, students may be suffering from these issues due to India’s air pollution problems. Moreover, these concerns are amplified by the rising incidence of antimicrobial resistance in India over the last decade.

The concerns that the estimates in this analysis raise are serious. University leaders as well as central, state, and local policy makers need to be extremely cautious about reopening academic campuses in India.

 

References

[1] https://time.com/5867395/will-universities-be-next-covid-19-tinderboxes/

[2] https://www.nature.com/articles/d41586-020-01003-6

[3]https://colab.research.google.com/drive/1ddb_0swsq9MRKyHrzflCzeF8Tqqmp24H#scrollTo=YXQnHy66r4QD

[4] https://www.frontiersin.org/articles/10.3389/fpubh.2020.00230/full

[5] https://www.medrxiv.org/content/10.1101/2020.02.04.20020503v2

[6]https://www.indiatoday.in/education-today/news/story/schools-and-colleges-should-reopen-to-achieve-herd-immunity-say-aiims-professors-1702461-2020-07-20

[7]https://www.livemint.com/news/world/covid-19-reinfection-cases-increase-doctors-clueless-11595482420636.html

[8] https://cddep.org/covid-19/hospital-capacity-in-india/hospitalization-needs-in-india/

[9] https://www.jpmph.org/journal/view.php?doi=10.3961/jpmph.20.076

 

Supplementary Material

Basic Model Set-Up

Neglecting demographic processes of birth and death from other causes, and assuming a negligible death rate due to infectious disease at issue, the governing differential equations are as follows:

The rate processes are modeled as follows:

Properties of the SEIR Model

The SEIR model describes key epidemiological phenomena. Here is a brief summary of the key parameters appearing in the SEIR model:

The relationships of rate constants to time constants can be summarized as:

The SEIR model makes key predictions concerning the outbreak and eventual recovery from an epidemic. These are summarized as follows:

References for Supplementary Material

[1] https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf#:~:text=People%20with%20COVID%2D19,mild%20disease%20and%20recover

[2] https://sites.me.ucsb.edu/~moehlis/APC514/tutorials/tutorial_seasonal/node4.html

[3] http://dx.doi.org/10.2139/ssrn.3548753.

 

Word from the Editors

On behalf of the editorial team – Regina Herzlinger, Kevin Schulman, Lawrence Van Horn, and myself – I am delighted to welcome you to the second issue of Volume 5 of HMPI. We are proud to publish a set of new articles that contribute to HMPI’s core goal: drawing from the research and experience of scholars and practicing leaders to advance healthcare and health systems.

In this issue of HMPI, the authors report their research, insights, and case studies on a fascinating set of topics.

  • Camelia Ilie, Ramiro Casó, Guillermo Cardoza, Andrés Fernández of the INCAE Business School and the Universidad de Costa Rica report their study of workforce well-being in Latin America.
  • Navid Asgari and Will Mitchell of Fordham University and the University of Toronto highlight implications of the emerging trend toward integration of specialty pharmacies, healthcare insurers,, and pharmaceutical benefit managers that is transforming the role of value chain integrators in the U.S. pharmaceutical ecosystem.
  • Andrea Prado, Benjamin Gallo Marin, and Ramiro Casó from the INCAE Business School in Costa Rica and the Warren Alpert Medical School of Brown University in the U.S. describe the intriguing case of Speratum, Inc., a Costa Rican start-up focusing on a therapy for pancreatic cancer. The article describes the challenges of launching a science-based venture in an emerging market and the strategies that Speratum is using to overcome the challenges.
  • Markus Saba, Kati Schy, and Daniella Kapural of University of North Carolina at Chapel Hill highlight the way that high deductible health plans are affecting people with diabetes, particularly the low-income population, and suggest key changes that will overcome current barriers to access, efficiency, and health outcomes.
  • Aaron Baird, Andrew Sumner, and Yusen Xia of Georgia State University identify barriers to adopting electronic clinical quality measurement automation by U.S. hospitals and identify solutions to overcoming the barriers.
  • Pinar Karaca-Mandic, Ralph Hall, and Kimberly Choyke of the University of Minnesota report on the opioid crisis among Minnesota’s Native American and African American populations, highlighting positive steps toward solving the crisis.
  • And Stacy Wood and Kevin Schulman of North Carolina State University and Stanford University highlight a new case, in which Amazon’s Alexa offers innovative potential to help a hospital launch a new capitated-care program.

Although covering multiple geographies and a varied set of points along the healthcare and life sciences value chain, the articles have a common theme. All thoughtful leaders want their healthcare systems to achieve a strong balance of cost-effectiveness, broad-based access, and high-quality outcomes. Every health system faces systemic barriers to achieving this challenging three-part goal. Rather than give up the pursuit of the goal, thoughtful pathways exist to achieving important improvements. The articles in this issue of HMPI provide relevant tools and ideas for traveling along these pathways.

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

 

Regi’s ‘Innovating in Health Care’ Case Corner

This issue’s case features an opportunity to use Amazon’s Alexa for consumer-focused healthcare in a capitated payment environment.

Case: Amazon Alexa and Patient Engagement

Authors: Kevin Schulman, Stanford University, and Stacy Wood, North Carolina State University

Overview: The Palo Alto Health System had three hospitals, 1,600 physicians, and over 1 million patients each year – and a new risk-based model that incentivized the prevention of illness, as well as early intervention in patients with chronic illness. Engaging with patient populations involved clinical interventions, but also involved a marketing approach to encourage patients to adopt health behaviors and keep following those practices. This case study investigates this marketing aspect of healthcare, and looks at ways in which Amazon’s Alexa, an interactive speaker that connected people to the Internet and the cloud-based Alexa voice service, could enhance health care efforts. With patient permission, Alexa could collect specific health-related information, and could help schedule patients for medical screenings and then report back important laboratory results. One early adopter was the U.K. National Health Service, which contracted to use Alexa to provide health-related content to patients – the same information that was available on the public website. The case study considers healthcare privacy issues, and asks students to consider whether a marketing approach to healthcare, in this case using Alexa or another Internet personal assistant, would be a successful strategy for the Palo Alto Health System.

Abstract

Learning objective

The case study asks students to think critically about the concepts of marketing and consumer behavior, and apply them in the discussion of healthcare. Would a marketing-focused approach to health care enhance the experience and outcome for consumers – the 1 million patients of Palo Alto Health System? Specific aspects discussed include market segmentation, and whether an interactive personal assistant like Amazon’s Alexa can address segment-specific messaging.

Introduction

Becca Eskridge was excited. She had just been told she was promoted to vice-president of patient engagement at Palo Alto Health System (“Palo Alto”), a three-hospital system with 1,600 practicing physicians and over 1 million patients each year. This appointment was in response to Palo Alto’s new risk contract with the largest commercial health plan in the region. Under this agreement, Palo Alto would be financially at-risk for the care of 100,000 patients. This was a full-risk contract, so that the system was essentially capitated for the care of these patients. Under the historical fee-for-service contracts, the system made additional revenue for each additional service provided to patients. Now, under the new risk contract, each additional service provided was a cost with no associated revenue. This agreement was an enormous financial and cultural change for the organization.

Very quickly, the leadership of Palo Alto realized that they needed a new strategy to help care for this population of patients. If patients did not get access to primary care physicians, they would show up in the emergency room and cost the system even more for the clinical episode. As they looked at the situation, they realized that their fee-for-service world had essentially been turned upside down in this new risk-based world. Rather than a financial model built on the care of patients with illness, the new model would incentivize the prevention of illness and early intervention in patients with chronic illness. More broadly, the system leaders realized that individual health behaviors and choices of how and where to use the health caresystem would now be key factors in their future financial success. It is with this realization that they created the new position and recruited Eskridge. It would be up to her to map out a strategy for the future success of the organization.

As Eskridge went home, excited about the new position but also daunted by the challenges in front of her, she decided to celebrate with a glass of wine from the local Santa Cruz Mountains. To lighten the mood, she wanted to listen to some music. She saw her Amazon Alexa speaker, and said “Alexa,” to wake it up. But then the next words that came out of her mouth surprised her. Rather than request her favorite music, she said, “Alexa, can you help me?”

Case Questions

  1. What data would be required to fuel a marketing-based population health strategy? How would you acquire the data for this effort?
  2. For a given intervention, such as receipt of a flu shot, what is the maximum addressable population for Eskridge to consider (out of 100,000)? Why? How would you set up metrics to understand the success of her approach?
  3. Using these data, what services could you develop using the Primary Care model?
    The third-party strategy? The AVS strategy?
  4. Which strategy would you recommend for Palo Alto (and why):
    a. the Primary Care model,
    b. the third-party model, or
    c. the AVS model?

Stanford case: SM-328, November 4, 2019

HBS link: https://store.hbr.org/product/amazon-alexa-and-patient-engagement/SM328

Well-Being in the Workplace: An Exploratory Study in Latin America

Camelia Ilie-Cardoza, Associate Professor, INCAE Business School; Guillermo Cardoza, Professor, INCAE Business School; Ramiro Casó B, Senior Researcher, INCAE Business School;  Andrés Fernández, Senior Researcher, INCAE Business School 

Contact: camelia.ilie@incae.edu

Abstract

What is the message?

Understanding the role that work and work-related activities have on the development of personal well-being is a critical aspect for organizations and professionals in the workplace. This work provides evidence that, while the models of well-being proposed by researchers in developed countries are relevant to organizations in developing countries, well-being in the workplace in Latin America is still related to the absence of negative emotions or experiences. The research identifies the need to redefine the social script of the workplace and its role in people’s well-being.

What is the evidence?

A confirmatory factor analysis conducted on Positive Emotions, Engagement, Relationship, Meaning, and Accomplishment (PERMA) for workplace scale on professionals showed that Seligman’s model of well-being seems to have a good fit within a Latin American context. A preliminary estimation of a regression model and a decomposition of the variance showed that negative variables are the ones that contribute the most to current well-being in Latin America, calling for a need to redefine the social script related to work.

Submitted: April 16, 2020; accepted after review and revision: April 26, 2020

Cite as: Camelia Ilie, Ramiro Casó, Guillermo Cardoza, Andrés Fernández (2020). Well-Being in the Workplace: An Exploratory Study in Latin America. Health Management, Policy and Innovation (www.hmpi.org), Volume 5, Issue 2, Spring 2020.

Happiness is when what you think, what you say and what you do are in harmony.

Mahatma Gandhi

Achieving Work-Related Well-Being Is a Challenge 

Despite the amount of change and uncertainty that characterize the world today, optimistic scenarios in which innovative industries see accelerated growth and have a motivated and empowered workforce are possible, even in a post-coronavirus world. There is, however, another challenge that needs to be addressed for these scenarios to become a reality—namely, a new definition of work-related well-being.  According to a study conducted by Gallup in 142 countries, only 13% of the employees surveyed said that they were actively engaged with their work. Likewise, an alarming 24% said that they were actively “unengaged,” while the remaining 63% were indifferent. According to Gallup, these results represent losses of more than $500 billion in the US alone (1).

More recently, Campbell and Poquet (2) studied the relationship between decent work and life satisfaction.  Decent work, as defined by the International Labor Organization, can be understood as the “promotion of opportunities for women and men to obtain productive work in conditions of freedom, equity, security, and human dignity” (2). Their findings suggest that the material components of decent work account for roughly 50% of reported life satisfaction, leaving the other 50% to the non-material components of decent work.

The Gallup study illustrates a contradiction that raises essential questions. In an apparently favorable context with various powerful tools increasingly available to a greater number of people, why is there such a high level of detachment and demotivation in the workforce? What is the underlying psychological process, and how can it be measured? What is the impact on the results of an organization? Moreover, what are the implications for a developing region, such as Latin America? Campbell and Porquet’s study offers initial answers to those questions, but it is clear that further research is needed since the majority of studies have been conducted in developed nations.

Work-Related Well-Being: New Drivers, New Challenges

Understanding an apparent contradiction

The first step in finding answers to the questions concerning work-related well-being is to understand the apparent contradiction that emerges from the Gallup study. Why are so many professionals dissatisfied with their jobs even though they now have better training, management tools, and more resources overall? A possible response is that technological advances have changed the factors that drive labor motivation. Diener and Seligman (3) propose the following explanation:

Because goods and services are abundant today, our basic needs in modern societies are practically satisfied. People can afford to refocus their attention on the “good life” – an enjoyable life that has meaning and allows for fulfillment – by using the economy and other policies at their disposal. This “refocus” is justified because there is evidence that as societies become wealthier, they often experience an increase in mental and social problems, as well as a stagnation (or plateau) in the satisfaction of their life (p.2).

It is evident that in the past, work was a necessity, and people’s approach to it was rather pragmatic. Today, the same pragmatism does not necessarily hold. People seek much more than adequate and guaranteed remuneration in their work: they seek a “good life.”  What does that mean for today’s workforce, and what expectations do people have? Similarly, as mentioned above,  Campbell and Porquet (2) argue that the non-material dimension of work matters, and, therefore, a traditional, more economically oriented vision of work is not sufficient to promote work engagement and overall satisfaction.

Beyond happiness: Well-being as a multidimensional concept

Over the past 20 years, a group of pioneering psychologists, including Martin Seligman (4), Shawn Achor (5), Sonja Lyubomirsky et al. (6), and Tal Ben-Shahar (7),  have shaped a vast body of theories grouped under what is now known as Positive Psychology. This discipline can be defined as a field of science that studies the optimal functioning of individuals, groups, and organizations (8).

One of the central concepts in Positive Psychology is well-being, understood as positive and lasting characteristics that allow individuals and organizations to prosper (4). Well-being is a dynamic concept that includes not only subjective, social, and psychological dimensions, but also behavioral, health-related, and economic aspects.

One of the most widely cited theoretical models on well-being was developed by Martin Seligman and his colleagues at the University of Pennsylvania (4,9). According to Seligman, well-being is a construct, not a real thing. It is comprised of different measurable components, each of which is a real thing that contributes to well-being.

The five components of Seligman’s model are Positive Emotions, Engagement, Relationship, Meaning, and Accomplishment—known as “PERMA.” One of the most valuables aspects of the model is that it assumes that well-being is a comprehensive concept encompassing different but equally valuable aspects of human flourishing and life satisfaction. This becomes clear when each component is analyzed separately.

  • Positive emotions: Positive emotions refer to emotions such as joy or pleasure. The more often a person experiences a positive emotion, the more well-being he or she will have. This component improves work performance, promotes a better state of health, strengthens social relationships, and generates optimism for the future.
  • Engagement: Engagement refers to the attachment and concentration that a person experiences when performing a specific activity. A key concept related to engagement is Flow, proposed by the Hungarian-American psychologist Mihaly Csikszentmihalyi (10). Flow is defined as the psychological state of a person whose skills are aligned with the demands of a task she performs. Flow is a state of absolute immersion in the present, which contributes to well-being.
  • Relationship: Having a robust social network consisting of friends, colleagues, and family constitutes the Relationship component of well-being. The stronger these networks are, the more well-being a person will experience.
  • Meaning: Meaning refers to people’s need to be part of something bigger than themselves. When people invest time and energy in tasks that transcend personal goals, such as volunteer activities or doing something for the good of their community, they experience an increase in well-being.
  • Accomplishments: Lastly, to achieve well-being, people must be able to look back on their lives and feel that they have achieved their goals. Individuals pursue success or Accomplishments independently of other components, such as positive emotions or relationships. The feeling of being productive and being able to trace and reach goals is a significant component of well-being.

Seligman’s model allows for an objective approach to well-being. As such, it allows for the possibility of diagnosing and implementing corrective measures to improve each of the different components that generate well-being.

Is it possible to derive well-being from our jobs?

Extensive research has explored how employees’ level of well-being affects critical aspects of the working world. In that vein, several studies have shown that employees with low well-being are less productive, make poor decisions more frequently, miss work more often, and make substantially smaller contributions to the organization. Increased well-being helps reduce the risk of suffering from mental disorders and, at work, also increases performance, improves the quality of interpersonal relationships, and increases motivation and engagement (and/or flow) (8). Indeed, a  meta-study on the impact of well-being, which compiled more than 200 investigations conducted with more than 275,000 participants, showed that well-being is a predictor of success in almost all areas of people’s lives, especially in the workplace, in the development of professional careers, and in business (5).

The evidence seems to indicate that rather than being just a consequence of success, well-being may be one of its causes. Hence, as Diener and Seligman (3) point out, most of today’s organizations recognize the importance of cultivating and measuring well-being, as well as promoting employee prosperity, as one of the elements of great strategic importance in managing talent to achieve productivity and business growth. In turn, the study of well-being has become essential at several of the most prestigious universities in the world—including Yale,[i], [1] Harvard[2] and U.C. Berkeley[3]—which offer increasingly popular programs on the subject.

Therefore, it is essential to study and understand the impact that well-being has on organizations since their future success may depend on it. As Achor (5) points out, once organizations accept this new concept—that well-being enhances success—they can change how people work, interact with colleagues, and lead teams. Such changes, in turn, will give people and organizations a real competitive advantage.

Well-Being and Work in Latin America

Latin America is a region fraught with challenges due to enormous social and economic disparities and heterogeneity. Like other regions of the world, Latin America is involved in the immense process of transformation resulting from technological advances and the cultural and social changes that they entail. Therefore, more than ever, companies are pushed to adapt their business models and understand the key variables that increase their probability of success in a changing environment. As we have shown, well-being is one of these critical variables.

Unfortunately, thus far, little has been done in Latin America to understand this critical concept and its impact on the companies and industries of the region. For example, Ed Diener and colleagues recently published a review of subjective well-being, presenting evidence on the relationship between high subjective well-being and positive job outcomes, such as job satisfaction, less turnover, and increased citizenship.  However, as the authors put it: “We know only a little about the cross-cultural generality of the findings. Most of the research has been conducted in highly economically developed western nations. As happiness is highly valued in these nations, we do not know whether the findings might also apply in Africa, South America, or Asia” (11).

The current study in intended to be a step in that direction. Its objective is to make an initial assessment of the well-being of the region’s professionals to identify areas of opportunity and to start developing possible interventions. The road is long, but science provides the theoretical and methodological tools for facing this interesting challenge. There are reasons to be optimistic and confident that the findings of this study and future research will allow the region to move forward on the development path that it needs to follow in this changing world.

Study design

We gathered data using the PERMA for workplace scale developed by Butler and Kern (2014, 2016). The scale consisted of a 23-item measure that assesses well-being across five domains (positive emotion, engagement, relationships, meaning, accomplishment). Butler and Kern validated the instrument, which presented acceptable reliability, cross-time stability, and evidence for convergent and divergent validity in studies that had, in total, over 30,000 participants. In our study, respondents were taken from a non-probabilistic sample of 1757 individuals. The respondents agreed to answer the questionnaire through the Facebook application or via email. Our final sample was 41% male and 59% female.  All of the respondents were employed or had their own business, according to their account status.

Their managerial or work experience was distributed as follows: 28% had less than 3.5 years of managerial experience; 23% had between 3.5 and eight years; 25% had between 8.5 and 16.5 years; and, finally, 24% had over 16.5 years of managerial experience. Finally, 54% of the respondents were from Costa Rica, 11% from Perú, 10% from Ecuador, 9% from Colombia, and the remaining 17% from the other Latin Americas countries. The questionnaire consisted of 15 items that measured the five major components of well-being, according to the theoretical framework that was used for the study (13). The questionnaire also had an additional eight items that sought to measure complementary constructs that may be related to well-being.

The database collected in our study includes the variables required to approximately measure well-being in the workplace, according to the theoretical framework of the PERMA model (4,13). As outlined earlier, this well-being model divides the concept into five primary constructs: positive emotions, engagement, relationships, meaning, and accomplishment. Figure 1 shows these relationships:

We divided the analysis into two parts. First, we wanted to validate the model in Latin America. Although the model has been tested and used in other countries and contexts, we used a statistical technique called Confirmatory Factor Analysis (CFA) to validate the fact that it fits the data collected in Latin America. CFA is a data reduction technique used to find homogeneous groups of variables (14) in order to identify the latent variables described in the PERMA model.

Second, we used regression analysis to evaluate which of the variables contributed the most to the well-being of our participants in order to have an initial, but robust, approach for understanding well-being in the region.

Results: Analysis and model validation

The CFA confirmed that the factorial model we used adjusts to the data collected in the study. We summarize the results here.

The CFA showed that the most critical factors in the generation of the model are Positive Emotions and Meaning (with factorial loads greater than 0.90), which confirms the structure seen in the correlation matrix.  The matrix of correlations between the different factors (Table 1) shows a high correlation between Positive Emotions and all the other factors. Likewise, the matrix shows a high correlation between Meaning and all the others, except for the Relationships.

After verifying the structure, we generated the Well-Being construct using factorial scores specific to the factorial structure. This was done by taking the structure, weights, and other statistical relationships into account (Figure 2).

The results show an asymmetric distribution with a high concentration of values in the upper part of the distribution. This means that, in general, all the individuals in the sample have high levels of well-being. However, the distribution of the well-being variable according to the factor analysis has considerably more advantages when modeling the construct.

The Root Mean Square Error of Approximation (RMSEA), which is used to test model fit, was 0.051, with confidence intervals of 0.048 and 0.055, meaning that the model has a good fit. Each of the 21 subconstructs or items used in the model (three for each of the seven variables on the scale) had factorial loads above 0.3, showing a strong relationship among them.

Perhaps more importantly, all of the main subconstructs, or variables, presented factorial loads above 0.5 relative to well-being. Positive emotions, engagement, relationships, meaning, and accomplishment—the variables initially identified by Seligman—presented high factorial loads, showing a strong relationship with the main variable. As expected, negative emotions had a negative but still high factorial load. Finally, health presented the lowest factorial load, albeit still high according to CFA standards. Figure 3 summarizes these findings.

Variable contribution

We performed a preliminary estimation of a regression model and a decomposition of the variance of well-being. We also analyzed the relationship that other variables, such as demographic characteristics or variables external to the model, had with the construct of well-being. Results are in Table 2:

The model included eight variables to explain differences in well-being. Together, these variables explained 37.9% of the well-being variance. We found three variables with a high weight of explanation of the variability: negative emotions, health, and loneliness.  The synthesis of each of these is as follows:

  • Having negative emotions is the factor that most relates to a person’s well-being. The relationship is inversely proportional: the less negative a person is, the more well-being he or she enjoys (Figure 4).

  • A person’s self-perceived state of health is the second factor that most relates to his or her level of well-being. The relationship is direct: the better the state of self-perceived health, the more well-being the person enjoys (Figure 5).

  • Loneliness is the third most important factor related to well-being. The relationship is inversely proportional: the less loneliness the person experiences, the greater the well-being (Figure 6).

In addition to these three influences, a person’s position in the managerial hierarchy is the other factor that most relates to well-being (CEOs claim to have higher well-being than junior professionals, coordinators, or personnel supervisors). Figure 7 shows that the higher the manager’s position, the greater reported level of well-being (and less variability thereof).

Other variables, such as age and work experience, each contribute only approximately 1% to the total variability, while education, gender, and country of origin do not relate to well-being in a multivariate model.

Concerning the analysis of the results by gender, although women report slightly higher levels of well-being than men when the other factors are taken into account, these differences disappear regardless of the level of education or the country.

Finally, in a model with interactions, we found combinations that further enhance the differences in people’s perception of well-being:

  • When negative emotions and poor health act together, they trigger more significant deterioration in a person’s well-being.
  • Levels of loneliness vary according to managerial experience: individuals with more managerial experience exhibit higher levels of loneliness.

Four implications

This study identifies four preliminary findings of significant relevance to managers and companies in our region.

Relevance for Latin America: Seligman’s model of well-being seems to have a good fit within a Latin American context. Respondents showed overall high levels of well-being in the workplace. When we take factorial loads into consideration, positive emotions and meaning have the highest loads (1.02 and 0.99 respectively), which means that well-being in the workplace is defined mostly by these two factors. Conversely, health and negative emotions (0.5 and -0.64) have the lowest loads but are still considered high by CFA standards. It would be interesting to compare general well-being measures with workplace well-being to identify possible relationships among these variables. In other words, there is great value in determining the extent to which workplace well-being contributes to general well-being in our region.

Negative relationships: Well-being as a construct in our region seems to be mostly related to variables that are negative in nature. Indeed, negative emotions, health, and loneliness were responsible for the most significant amount of the explained well-being variance. One possible explanation is that people in the Latin American region still associate well-being in the workplace with the absence of problems. Also, external variables such as experience and managerial position presented very low explanatory power.

This calls into question the nature of work in our region and how much it contributes to people’s well-being. That is, people do not necessarily think of their jobs as a source of well-being, possibly because they do not associate their work with any of the five components defined in the model. Instead, they seem to have a sense of well-being as long as they experience fewer negative emotions, such as anger, sadness, or anxiety, and are less socially isolated. It is still common in the region to regard work as something one must do even if one does not like it or does not derive any pleasure from it.

It is not surprising, then, that the variation in well-being is related to the absence or presence of these kinds of emotions. However, most importantly, it is clear that there is room for change, as people will likely respond positively—that is, with a feeling of greater well-being—if organizations pay special attention to activities and decisions oriented toward promoting positive emotions, engagement, healthier and stronger relationships, a deep sense of meaning and purpose, and alternatives routes to success.

Building on these points, two additional implications are relevant for companies and professionals.

Organizations can shape well-being: Following Achor (15), these results serve as evidence of the importance for organizations to adjust the prevailing social script that undermines a positive environment and positive behaviors in the workplace. According to Achor, most organizations are focused on problem-solving and paying attention to things that are, or could be, wrong. This problem-oriented approach leaves little space for positive emotions and reinforces more-negative attitudes and behaviors. Instead, Achor argues that companies should incorporate a positive social script—one that prioritizes not mistakes, but successes, thus leaving room for positive emotions.

Seligman’s factors could serve as a guide. For instance, managers could design activities to increase the probability of experiencing positive emotions, such as joy or pride. Also, corporate communication efforts could be implemented to highlight the role of the company in society and how each employee contributes to fulfilling this role, thus generating a sense of meaning. Finally, companies should focus more on providing robust health services for their employees, as well as activities that enhance social networks within their organizations, breaking silos and fostering interaction.

Measuring well-being: Finally, Seligman´s model serves as a framework that managers could use to measure well-being in their organization. This will allow them to run diagnostics and design measures for specific components of well-being that need to be improved. Having systematic and continuous measures of well-being could also allow companies to determine the effect of well-being (or its lack thereof) on performance.

Limitations

Further research can address limitations of our study. First, it would be desirable to conduct research to determine the relationship between well-being and specific performance measures, such as revenue or cost reductions. Secondly, cultural differences among the Latin American countries are significant, therefore, a more detailed study on specific countries or sub-regions (Andean, for example) would be useful. In doing so, it would be interesting to see how cultural context relates to individual differences.

Looking Forward

Our results provide a first approximation of the study of well-being in the workplace in Latin America. Data suggest that the PERMA model serves to accurately assess well-being, providing new opportunities to study and design measures that could increase it. We are confident that our study will not only contribute to the debate on the importance of well-being in management, but will also encourage new research and, in that way, positively transform companies in Latin America.

References

  1. Deaton A. Income, health, and well-being around the world: eivdence from the Gallup World Poll. J Econ Perspect. 2008;22(2):53–72.
  2. Campbell D, Porquet RMG. Well-Being and the labout market from a global vier: it’s not just the money. In: Glatzer W, Camfield L, Moller V, Rojas M, editors. Global Handbook of Quality of Life: Exploration of Well-Being of Nations and Continents. New York: Sringer; 2015. p. 351–79.
  3. Diener E, Seligman M. Beyond Money Towrd and Economy of Well-Being. Psychol Sci. 2004;5(1):1–31.
  4. Seligman M. Flourish: a visionary new understanding of happiness and well-being. New York: Simon and Shuster; 2012.
  5. Achor S. The Happiness Advantage. 1st ed. New York: Crown Publishing; 2010.
  6. Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: Does happiness lead to success? Psychol Bull. 2005 Nov;131(6):803–55.
  7. Ben-Shahar T. Happier. McGraw-Hill Companies.; 2007.
  8. Kun Á, Balogh P, Krasz KG. Development of the work-related well-being questionnaire based on Seligman’s PERMA model. Period Polytech Soc Manag Sci. 2017;25(1):56–63.
  9. Seligman M. PERMA and the building blocks of well-being. J Posit Psychol [Internet]. 2018;13(4):333–5. Available from: https://doi.org/10.1080/17439760.2018.1437466
  10. Csikszentmihalyi M. Flow: the psychology of optimal experience. New York: HarperCollins. New York: HarperCollins; 2009.
  11. Diener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav [Internet]. 2018;2(4):253–60. Available from: http://dx.doi.org/10.1038/s41562-018-0307-6
  12. Butler J, Kern ML. The PERMA-Profiler: A brief multidimensional measure of flourishing. Int J Wellbeing. 2016;6(3):1–48.
  13. Butler J, Kern ML. The workplace PERMA Profiler: Margaret L . Kern, University of Pennsylvania. 2014;(October):1–2. Available from: http://www.peggykern.org/uploads/5/6/6/7/56678211/workplace_perma_profiler_102014.pdf
  14. Fernández Aráuz A. Aplicación del análisis factorial confirmatorio a un modelo de medición del rendimiento académico en lectura. Rev Ciencias Económicas. 2015;33(2):39.
  15. Achor S. Why can’t we all be happy at work. Training Magazine. 2015;(February 2015).

[1] Yale’s “The Science of Well Being” course is now available at https://www.coursera.org/learn/the-science-of-well-being

[2] Harvard’s “The Science of Happiness” can be explored at https://gened.fas.harvard.edu/classes/science-happiness

[3] Berkeley offers an online open program titled “The Science of Happiness” that can be accessed at https://ggsc.berkeley.edu/what_we_do/event/the_science_of_happiness

 

The Emerging Role of PPP Value Chain Integrators in the U.S. Pharmaceutical Ecosystem

Navid Asgari, Assistant Professor, Gabelli School of Business, Fordham University; Will Mitchell, Rotman School of Management, University of Toronto

Contact: William.mitchell@rotman.utoronto.ca

Abstract

What is the message?

Value chain integration in the U.S. pharmaceutical ecosystem is shifting from leadership by pharmaceutical manufacturers to a major role for three-part configurations of specialty pharmacies, healthcare insurers (payers), and pharmaceutical benefit managers, which we refer to as PPP combinations. During the past half decade, these PPP combinations have gained negotiating power relative to manufacturers, with the potential to influence both pharmaceutical net prices and complementary “beyond the pill” strategies. The ongoing disruptions might put manufacturers at risk or, instead, may improve the ability of actors in the system to work together to design and implement complementary services that lead to improved health outcomes and greater cost effectiveness in the healthcare system.

What is the evidence?

The analysis uses data concerning revenues, profits, entries, and acquisitions in the pharmaceutical ecosystem between 1999 and 2019. We consider five types of actors: pharmaceutical manufacturers (both major biopharma and moderate pharma), health insurers, wholesalers, pharmacies, and pharmaceutical benefit managers (PBMs).

Submitted: May 3, 2020; accepted after review and revision: May 20, 2020

Cite as: Navid Asgari and Will Mitchell. 2020. The Evolution of Value Chain Integration in the U.S. Pharmaceutical Ecosystem. Health Management, Policy and Innovation (www.hmpi.org), Volume 5, Issue 2. Spring 2020.

The Pharmaceutical Ecosystem in the U.S. is Complex

The pharmaceutical industry continues to attract both criticism and, with the threat of the coronavirus, hope for innovative tests, treatments, and vaccines. Although most attention is targeted at pharmaceutical manufacturers, the pharmaceutical ecosystem is much more complicated than simply manufacturers. To bring drugs from development to consumers also requires a sophisticated ecosystem of providers, wholesalers, payers, pharmacies, and pharmaceutical benefit management (PBM) services.

This article highlights the growing complexity and sophistication of the pharmaceutical ecosystem, while comparing revenue growth and profitability trends in different parts of the sector. We emphasize the current strategic challenge of determining which actors will be the value chain integrators that shape the evolution of the sector and distribution of profits within it.

A recent trend is the emergence of combinations of specialty pharmacies, payers and PBMs, whether within single firms or as alliances. [1] These PPP combinations are now competing with pharmaceutical manufacturers to be the value chain integrators that lead and shape the sector.

Actors in the Pharmaceutical Ecosystem

Seven categories of major actors

Figure 1 depicts a simplified version of relationships among seven actors in the pharmaceutical ecosystem: consumers, prescribers, manufacturers, wholesalers, pharmacies, health insurer payers, and PBMs. To maintain clarity, the figure omits other key actors such as firms and institutes that focus on drug discovery; contract research organizations and contract manufacturers; regulatory agencies; and academic institutions. Even with this simplification, the flow of drugs, money, and services that the figure depicts is complex.

Pharmaceutical manufacturers: Historically, drug companies based in North America, Europe, and Japan were the leaders of the pharmaceutical ecosystem. They developed drugs and/or acquired them from partners; brought them through the regulatory system; produced and/or coordinated production; set list prices; marketed their benefits to prescribers; and largely coordinated the flow of drugs through the system. In the past thirty years, however, as pharmaceutical prices have risen and drug regimens have become more complicated, other actors in the system have increased their power.

Wholesalers: Drug wholesalers such as McKesson, Cardinal, and AmerisourceBergen have long been visible players in the ecosystem, with a primary role in distributing drugs to hospitals and pharmacies. With the growth of biological and other complex medicines in the past two decades, the major wholesalers commonly also offer services required to help clinicians, insurers, and consumers prescribe, use, and pay for specialty pharmaceuticals.[i]

Pharmacies: Pharmacies such as CVS, Walgreens, and Rite Aid have a long-standing role in dispensing drugs. More recently, they have taken on more sophisticated activities involving group purchasing organizations and managing prescriptions for specialty pharmaceuticals.

Health insurers: Third-party health insurers such as UnitedHealth, Humana, Aetna, Cigna, Anthem, and Centene receive premiums from consumers and/or their employers, then manage payment for drugs. Like wholesalers and pharmacies, health insurers also commonly offer services for specialty pharmaceuticals.

Pharmacy benefit management services: PBMs began during the 1960s, primarily as mail-order services. During the past half century, they have become highly sophisticated – negotiating discounts with manufacturers that they in part pass on to payers; designing formularies and health management programs; providing administrative services to pharmacies and insurers; and organizing service networks.

Prescribers: Physicians and other prescribers traditionally were the primary actors in deciding what drugs were relevant for their patients, paying only limited attention to drug prices. During the past two decades, however, as drug prices have soared and pharmaceutical treatments have become part of multi-faceted health management regimens, insurers and PBMs have increasingly influenced providers’ prescription choices via formulary placement.

Consumers: Patients traditionally were “drug takers”, largely following the advice of their prescribers. Since at least the mid 1990s, though, patients have increasingly become decision-making consumers. Multiple trends have led to greater consumer agency, including the growth of direct to consumer marketing; access to on-line information about conditions and treatments; and the availability of mail and on-line channels for obtaining drugs.

Who are the value chain integrators?

This is a complex ecosystem. One of the learnings from strategic management research is that complex systems do not manage themselves. Instead, they benefit from having value chain integrators (VCIs) that coordinate activities throughout the system. [5] VCIs tend to shape the evolution of an industry and gather a substantial share of revenue and profits.

Historically, pharmaceutical manufacturers have played the VCI role in the pharmaceutical ecosystem. Over the past twenty years, though, there has been a substantial shift in power and revenues among the actors, as well as intriguing trends in profitability and corporate combinations that hint at ongoing changes in VCI power. We next report revenue and profitability trends. We then turn to the evolution of PBM entry ownership and to corporate combinations of insurers, specialty pharmacies, and PBMs.

Pharmaceutical Ecosystem: Revenue and Profitability Trends

Revenue trends

Figure 2 reports trends in U.S. revenue for five types of commercial actors in the pharmaceutical ecosystem: pharmaceutical manufacturers, wholesalers, health insurers, retail pharmacies and PBMs. The revenue data encompass most major players in each of the five categories from 1999 to 2019, including 18 PBMS, ten health insurers,[ii] five pharmacy chains, five wholesalers, and 154 pharmaceutical manufacturers.[iii] We obtained data from corporate annual reports and analyst discussions. The appendix lists the firms in the data.

Two points stand out in Figure 2. First, total revenue has grown massively in the past two decades. In 1999, total revenues across the five categories reached just over $400 billion. In 2019, the sum of the five categories exceeded $2.1 trillion: almost 5x growth in 20 years.

One must be careful here: the sums involve overlapping revenues as activities pass from one type of actor to another, so that the total substantially overstates the impact on pharmaceutical-related health expenditure in the country. Moreover, the health insurer total includes expenses other than pharmaceuticals, including med tech and medical services. Nonetheless, there is an unambiguous major increase in healthcare expenditure in the pharmaceutical ecosystem over the past two decades.

Second, in 1999, pharmaceutical manufacturers dominated the sector, with net revenues that accounte for almost 40 percent of the summed expenditure ($169 of $432 billion). By 2019, though, wholesalers and health insurers had passed the manufacturers and PBMs were close behind. Retail pharmacies (excluding their PBM revenue) lagged substantially at about $200 billion although this number is somewhat ambiguous about their pharmaceutical impact, because it includes sales of “front of store” consumer items but omits some of the companies’ specialty pharmaceutical service revenue. On a revenue basis, therefore, pharmaceutical manufacturers now share ecosystem leadership with insurers, wholesalers, and PBMs.

Profitability trends

The next question is who is profiting from this activity? Figure 3 reports profitability trends for the categories based on return on assets (ROA). The comparison splits out the pharmaceutical manufacturers into major biopharma firms (e.g., Merck, Janssen, Sanofi, Roche, Takeda, Astellas, Amgen, Biogen) and moderate pharmaceutical firms (e.g., Valeant, Shire, UCB).[iv] The ROA trends are based on data from ten health insurers, four pharmacy chains, four wholesalers, eight PBMs, 39 moderate pharma firms, and 35 major biopharma firms.

Trends across ecosystem categories in Figure 3 need to be compared cautiously, for two reasons. First, the data include corporate return on assets (ROA) from five categories (insurers, pharmacy retail, wholesalers, moderate pharma, major biopharma), while using operating return on assets (oROA) for PBMs. The oROA calculations (typically based on earnings before taxes as a ratio of reported segment assets) will somewhat overstate final profitability but are relevant for activities within corporations that report segment-level revenues but do not report post-tax profits for the segments, which is the case for PBMs.

Second, the two pharmaceutical categories report global profitability because the firms rarely break out geographic profits (revenues for the other four categories are almost entirely from the U.S.). Hence, the within-category trends are somewhat more relevant than across-category comparisons, although substantial differences between categories are meaningful.

Three patterns stand out in Figure 3. First, although wholesalers may enjoy high revenue (Figure 2), they have the lowest profitability, with declining and even negative ROA over the past few years. Second, although there is substantial year-on-year variance within categories, the twenty-year trend is relatively stable. The major exception is the moderate pharma firms, which had negative median ROA in the early 2000s and now are moderately positive. Third, major biopharma firms and PBMs commonly lie at or near the top of the ROA range, particularly in the past few years.[v]

Currently, beyond the financial trends, the strategic question is whether the value chain integrator leadership will shift from pharmaceutical manufacturers to other types of actors. To begin to answer this question, it is useful to consider the evolution of PBM ownership over time, which the next section discusses.

The Evolution of PBM Ownership

PBM services over time

PBMs began in the 1960s largely as basic prescription mail order services. By the early 1990s, they had become more sophisticated and have become increasingly so. [2, 6-10] In 1993, for instance, the Diversified Pharmaceutical Services (DPS) unit within UnitedHealthcare expanded to manage the costs of UnitedHealth’s clients in delivering pharmaceutical benefit programs. These services included creating a pharmacy network and formularies for it; creating a pharmacy claims processing system; maintaining a prescription database; and negotiating discounts with pharmacy retailers and pharmaceutical manufactures. [11]

Today, these services form the backbone of modern PBMs. The PBM industry association, Pharmaceutical Care Management Association (PCMA), highlights cost reductions from providing home delivery; encouraging the use of generics on formularies (generics now exceed 90 percent of prescriptions in the U.S.); reducing waste; improving adherence to drug regimens; helping to create broad-based health management programs; managing expensive specialty drugs; and negotiating discounts with both pharmacies and manufacturers. [12] While services vary among PBMs, these are the core activities, with particular emphasis on negotiating discounts and rebates.[vi]

The expansion of PBM services has involved a wide range of entries, exits, and changes in corporate ownership. The appendix reports more than 60 PBMs that have operated during the half century between 1969 and 2020. Of those actors, 25 operate in 2020.[vii] Perhaps the two most notable features of the list are the ongoing pace of acquisitions in the industry and the varied types of actors that have attempted to operate PBMs.

PBMs and other actors in the pharmaceutical ecosystem

An important pattern arises with the types of actors that have operated PBMs. The four categories that we discussed earlier in this article are particularly notable: wholesalers; pharmaceutical manufactures and other life sciences companies; health insurers; and pharmacies. Over time, the wholesalers and pharmaceutical manufacturers have largely retreated, while health insurers and pharmacies continue to engage with PBM activity. Table 1 summarizes the participation.

Wholesalers: The pharmaceutical wholesaler McKesson purchased PCS, perhaps the first PBM (founded in 1969), in 1972 and continued to operate the unit for more than 20 years. By the 1990s, though, McKesson concluded that PCS did not fit well with its wholesale activities, in part because customers and federal agencies were concerned about potential conflicts between PBM strategy and wholesale contracts, and sold PCS to Eli Lilly in 1994. This marked the last time that a pharmaceutical wholesaler undertook full scale PBM activity as neither of the other two major wholesalers, Cardinal and AmerisourceBergen, have operated major PBMs. Nonetheless, all three major wholesalers do currently offer pharmacy services administration organization (PSAO) services that coordinate how independent pharmacies contract with insurers and PBMs.

Pharmaceutical manufacturers and other life science companies: Between the late 1980s and early 1990s, multiple life sciences companies entered the PBM industry. In 1987, Baxter was the first, purchasing Caremark/Home Healthcare of America (about $250 million revenues) out of independent ownership for about $528 million. Caremark’s major business at that time was home infusion and other home health services, while adding basic PBM support over time. In the 1990s, three major pharmaceutical manufacturers acquired PBMs. In 1993, SmithKlineBeecham purchased DPS (about $200 million revenue) from UnitedHealthcare for about $2.3 billion. Also in 1993, Merck purchased Medco Containment Services (about $1.8 billion revenues) for about $6 billion. In 1994, Lilly purchased PCS from McKesson (about $120 million revenues), for about $4.1 billion. Thus, the life sciences companies made substantial investments – particularly the three pharmaceutical companies – to enter the PBM market.

A major incentive for the acquisitions of PBMs by the pharmaceutical companies during the 1990s was a response to the first Clinton administration’s attempt to create a national healthcare system. Drug companies wanted to position themselves to negotiate prices if a public payment system emerged. It soon became apparent, though, that national health insurance was not going to be politically viable at that time. Moreover, the drug companies struggled to fit the PBM and pharmaceutical business models together. Challenges similar to those that led McKesson to sell its unit to Lilly, including Federal Trade Commission demands to maintain open formularies, limited the expected strategic benefits of combining pharmaceutical production with PBM intermediation.[9]

All four life sciences companies quickly exited the PBM business, sometimes with substantial losses. Baxter lasted three years, then spun off Caremark in 1990. Lilly sold PCS to Rite Aid for about $1.5 billion in 1998, far below the $4.1 billion it had paid three years earlier, when the anticipated strategic fits in pricing and formulary preferences did not materialize. SmithKlineBeecham sold DPS to a leading independent PBM, Express Scripts, for about $700 million in 1998, again far below the $2.3 billion price five years earlier. Merck spun off Medco in 2002 (ten years later, Express Scripts bought a much larger Medco for $29 billion by); in ten years of operations within Merck, the Medco PBM had declined from an independent business with more than 7 percent operating returns to a drug company subsidiary with less than 2 percent operating returns. Thus, neither the wholesaler McKesson nor the life sciences companies could find a successful fit between their core businesses and the PBM market.

Health insurers: Health insurers, by contrast with wholesalers and pharmaceutical companies, have been more successful in adding PBM services. UnitedHealth became the earliest of the major insurers to add a PBM when it created DPS in 1976. United sold DPS in 1993 but re-entered the PBM market when it acquired AmeriChoice in 2002 and then expanded through other acquisitions, including Catamaran in 2015. UnitedHealth’s OptumRx is now the third largest PBM in the country. Cigna created a PBM in about 1994, then expanded substantially to become the number two PBM when it acquired Express Scripts for $54 billion in 2018. The PBMs are now core part of the insurers’ business portfolios.

Other major insurers also have been active. Wellpoint created a PBM during the 1990s, then was acquired by Anthem in 2004. Anthem created a PBM about 2000, then expanded with the acquisition of Wellpoint four years later. Aetna created a PBM in 2002, which it operated until being acquired by CVS in 2018. Centene bought U.S. Script in 2006 and expanded with the acquisition of Envolve Health in 2015. Humana created a PBM in 2006, which is now the number four PBM in the country. At this point, all the major health insurers operate successful PBMs, creating effective matches between their insurance and PBM services.

Pharmacies: The major pharmacy chains also have been active with PBMs, though with more mixed success. Eckerd/JC Penney created a mail order PBM during the 1990s, which it sold to CVS in 2004 as part of Eckerd’s corporate dissolution. CVS launched a PBM in 1996 and then expanded through multiple acquisitions, including the purchase of Caremark in 2006, to become the largest PBM in the country. Rite Aid purchased PCS in 1999 only to sell it to Advance in 2000, then attempted to purchase Express Scripts in 2007 (losing the deal to Cigna), and finally re-entered with the acquisition of Envision in 2015. Some of the smaller pharmacy chains also are present. Kroger pharmacies, for instance, have operated a small PBM since about 1993. The other two major pharmacies, Walgreens and Walmart, do not operate substantial PBMs themselves but instead contract for PBM services: Walgreens with PBMs including OptumRX, Prime, and Centene; Walmart with CVS Caremark. Thus, the major pharmacy chains all engage with PBMs, either internally or via contracts, though only CVS has become a market leader.

The key point here is that PBMs are increasingly combining with insurers and/or pharmacies. These combinations are creating counterweights to the pharmaceutical manufacturers.

Tripartite Combinations: PBMs, Insurers, Specialty Pharmacies

PPP combinations

The convergence of PBMs with other actors in the pharmaceutical ecosystem has become even more extensive in the past half decade. The deals that we summarize above have led to a strong set of three-party PPP combinations, including PBMs, health insurers (payers), and specialty pharmacies. PBMs have been the linchpins in these combinations, linking services across the segments. Table 2 summarizes the current state of leadership across these three market segments.

Five corporate combinations dominate the cross-segment PPP positions in 2020. CVS owns Aetna and CVS specialty pharmacy services, the Caremark PBM, and Aeta insurance. Cigna offers multiple specialty pharmacy lines, the Express Scripts PBM, and Cigna health insurance. UnitedHealth has BriovaRx and Diplomat specialty pharmacy, the OptumRx PBM, and UnitedHealth insurance. Centene provides Acaria specialty pharmacy, the Envolve PBM, plus Centene and Wellcare insurance. Humana operates in the specialty pharmacy, PBM, and insurance segments. These five are major integrated players in the pharmaceutical ecosystem.

Several two-part PP combinations also have been created during the past five years. The pharmacy chain, Rite Aid, has operated in both the specialty pharmacy and PBM segments through its EnvisionRx service, acquired in 2016. The pharmacy chain, Walgreens Boots, has had an alliance with the Prime PBM since 2016 (Walgreens has also been in and out of insurance alliances with Anthem). The insurer, Anthem, also operates a PBM through its IngenioRx service, created in 2019. It is highly likely that the PP pairings will seek to add the third leg of the PPP tripod in the near future.

Together, as the table shows, five PPP and 3 PP combinations dominate much of the combined market. As of 2020, the eight amalgamations account for 82% of the 2018 specialty pharmacy market, 96% of 2019 PBM revenue, and 46% of 2018 health insurance revenue. In practice, these combinations are competing with the drug manufacturers to be the value chain integrators of the pharmaceutical ecosystem. 

Disruption of traditional value chain integration

Figure 4 depicts the emerging relationships in the pharmaceutical ecosystem. The key point in the figure is that the PPP combinations are placing themselves at the center of the ecosystem. Pharmaceutical manufacturers continue to have substantial power. On the supply side, they bring new drugs to market. On the demand side, manufacturers continue to have meaningful market power in negotiating prices and services. The PPP combinations, though, now have substantial counter-balancing power as complementary value chain integrators.

Two major questions arise at this point, concerning prices and services. First, how extensively will PPP power bring down pharmaceutical profitability by negotiating even deeper discounts and rebates from list prices? While the major biopharma firms are comfortably profitable, on average, it would not take more than a moderate reduction in net prices to put this at risk, particularly for firms that tend to lie below the industry median profitability.[13] The moderate pharma firms, meanwhile, are even more at risk due to their lower average profitability. There is meaningful potential, therefore, for a substantial financially-driven disruption of the pharmaceutical manufacturer industry, particularly with the U.S. market accounting for about 40 percent of global pharmaceutical sales.

Moreover, because price reductions may be only partially passed on to consumers and their employers, there may be only limited concomitant financial benefits for end-users. Indeed, larger rebates commonly reflect higher list prices and service fees, again adding costs at multiple points in the system. [10]

Second, which players will take the lead in putting together bundles of services around pharmaceutical medicines. Players throughout the ecosystem are currently experimenting with multiple forms of complementary services, including pay for performance; infusion and other drug delivery support; compliance programs; nutrition and wellness programs; chronic disease management; patient tracking; and multiple forms of value-based healthcare designs. Some such services are integrated within existing players, while others involve partnerships with firms in other sectors, such as wearable devices; ingestible products; implantable devices; information technology providers; and artificial intelligence algorithms.

Such sets of complementary services have the potential to substantially improve health outcomes for consumers. They also may be able to increase cost-effectiveness by ensuring that expensive pharmaceuticals are used in the most effective contexts. Hence, beyond the pill innovations are critically important for the future of the pharmaceutical ecosystem, particularly for end-user consumers.

To date, beyond the pill strategies have had some effect but many attempts have taken hold more slowly than some expected. One of the reasons for the slow uptake has been the substantial fragmentation in the ecosystem that Figure 1 highlighted. A key question going forward is whether the more integrated ecosystem of Figure 4 will be more effective in designing and delivering such complementary strategies.

Two futures

There is no guarantee that the PPP configurations will be stable. Just as PBMs have moved in and out of other corporate configurations for the past half century, future misfits also are possible. Nonetheless, for the near term at least, the PPPs will affect competition, prices, and services in the pharmaceutical ecosystem.

Two near-term futures seem possible here. In one, leaders in the new ecosystem spends most of their efforts on price negotiations. In this first scenario, there is a lot to lose. The changes risk damaging the viability of pharmaceutical manufacturers, with no real gains in health outcomes. Moreover, even if the PPPs do negotiate deeper rebates, it is likely that only a limited amount of the reductions will reach consumers or their employers.[10]

In a second future, there is much more to gain. In this second scenario, the manufacturers and PPP combinations will find mutual benefit in working together to design and implement the complementary services that provide both cost effectiveness and improved health outcomes. Clearly, this is by far the superior path.

Looking Forward

The current challenge in the pharmaceutical ecosystem is to get past the historical obsession and political posturing about prices. Instead, we need to obsess about pursuing value in a way that respects the needs of each of the players in the system and, ultimately, the end user. Our primary goal needs to be creating strong cost-effective health value for consumers, with broad-based access to services.

Reaching this goal likely requires greater transparency of financial flows through the multiple parts of the pharmaceutical value chain. Perhaps counter-intuitively, the current lack of transparency does have benefits in maintaining the sustainability of the industry. It allows pharmaceutical manufacturers to negotiate higher net prices with some payers and so create price discrimination in which higher payers help cover the gap between average variable costs and average total costs that arise in the industry due to extensive need for fixed costs investments in research and related activities. [13] However, the extensive ambiguity in financial flows, partly reflecting the complexity of the sector and partly driven by confidentiality agreements between players, makes it difficult to evaluate what parts of the pricing chain are efficient, which mask systemic inefficiencies, and which reflect market power.

The current COVID-19 pandemic is teaching us about the importance of having a robust commercial life sciences sector, including pharmaceutical manufacturers. When we face crises such as this one, we need firms with the technical, organizational, and financial strength to respond in partnerships with public, non-profit, and academic actors across the globe. We also need sufficient clarity to be able to assess the robustness of the system.

The current shift to PPP value chain integration within the pharmaceutical ecosystem has the potential to either damage or strengthen the ability of life sciences companies to play the roles that we need them for. Reaching the positive potential will take thoughtful leadership by people throughout the ecosystem.

References

[1] Adam J. Fein. May 29, 2019. CVS, Express Scripts, and the Evolution of the PBM Business Model. Drug Channels. https://www.drugchannels.net/2019/05/cvs-express-scripts-and-evolution-of.html

[2] Patricia Danzon. 2015. Pharmacy Benefit Management: Are Reporting Requirements Pro- or Anticompetitive, International Journal of the Economics of Business, pages 245-261

[3] Economic Report on U.S. Pharmacies and Pharmacy Benefit Managers, 2017 [https://marketrealist.com/2017/10/walgreens-versus-cvs-discussing-pbm-strategies]

[4] Navid Asgari, Vivek Tandon, Kulwant Singh, and Will Mitchell. 2019. Dynamic Complementary Assets: Impact on Entrant and Incumbent Industry Leadership Duality. Working paper (available from the authors)

[5] Will Mitchell. 2014. Why Apple’s product magic continues to amaze–skills of the world’s #1 value chain integrator. Strategy & Leadership, 42 (6): 17-28, 2014.

[6] Robert F. Atlas 2004 The role of PBMs in Implementing the Medicare Prescription Benefit. Health Affairs, 23 (Supplement 1). https://doi.org/10.1377/hlthaff.W4.504

[7] Cole Werble. 2017. Pharmacy Benefit Managers. Health Affairs, September 14, 2017. https://www.healthaffairs.org/do/10.1377/hpb20171409.000178/full

[8] Helene L. Lipton, David H. Kreling, Ted Collins, Karen C. Hertz. 1999. Pharmacy Benefit Management Companies: Dimensions of Performance. Annual Review of Public Health. 20:361-401. https://doi.org/10.1146/annurev.publhealth.20.1.361

[9] Kevin A. Schulman, L. Elizabeth Rubenstein, Darrell R. Abernathy, Damon M. Seils, Daniel P. Sulmasy. 1996. The effect of Pharmaceutical Benefit Managers: Is it being evaluated. Annals of Internal Medicine, 124: 906-913.

[10] Kevin A. Schulman, Matan Dabora. 2018. The relationship between pharmacy benefit managers (PBMs) and the cost of therapies in the US pharmaceutical market: A policy primer for clinicians. American Heart Journal, 206:113-122.

[11] UnitedHealthcare. 1994. Form 10-K, Annual Report to the Securities and Exchange Commission, page 6.

[12] Pharmaceutical Care Management Association (PCMA). https://www.pcmanet.org/the-value-of-pbms/ [accessed May 2, 2020]

[13] Will Mitchell. 2018. Pharma Prices Are Not Too High (Usually). Health Management Policy and Innovation, Volume 3, Issue 2.

 

[1] “Specialty pharmaceutical” is a recent designation of medicines that involve high-cost, complex development and production, as well as complicated administration to patients commonly involving injections or infusions. These can include drugs based on biologicals, complex small cell therapeutics, regenerative medicine, vaccines, therapeutic peptides, and others. Specialty pharmaceuticals typically are prescribed and administered by a limited set of medical specialists [4].

[2] The health insurer data include about 2/3 of 2019 industry revenue. Other health insurers include Kaiser, HSCS, and Guidewell; as mutual holdings, they do not report public financials. Several general insurers, including Prudential, John Hancock/Manulife, State Farm, and Allstate also have smaller health insurance portfolios.

[3] The 154 pharmaceutical manufacturers with revenue in Figure 2 include 55 major biopharmaceutical firms; 39 moderate pharmaceutical firms; and 60 primarily generic pharmaceutical firms with sales in the U.S. from 1999 to 2019. The firms were based in North America, Europe, or Asia. The figure reports estimates of U.S. revenues.

[4] The “moderate pharma” category includes firms that sell portfolios of branded drugs commonly in-licensed or acquired from other companies; although they are often referred to as “specialty pharma” firms we use the term “moderate pharma” in this article to distinguish them from “specialty pharmaceuticals”, which may be sold either by major biopharma firms or by moderate pharma firms. The appendix lists the firms in each category.

[5] We also calculated return on sales (ROS) and operating return on sales (oROS) for the firms in the six categories. The patterns are similar, other than substantially higher ROS for major biopharma firms; the extensive asset investment by these firms brings the apparent high profitability generated by sales (ROS) down to earth when net income needs to cover the firms’ asset investments (ROA).

[6] See, for instance, the 2002 10-K from Health Net (page 6), which highlights pharmacy benefit design; clinical programs; claims processing; appropriate medication; and discounts with retailers and manufacturers.

[7] Both the list of 60 and the current set of 25 PBMs are a subset of total PBM activity. Some analysts place the aggregate total during the life span of the industry at over 150 PBM programs, while the current total may reach close to 50 programs. Nonetheless, the figures capture the vast majority of major programs, both over time and currently. In 2020, the primary industry association, PCMA, reports 16 members, which together encompass most reported income for the industry (we are aware of only four PBMs operating in 2020 with at least moderate revenue that are not members of the PCMA; one of those, which is in our data, just entered the sector).

Appendix 1: Firms Included in Analysis Data

A. PBMs in analysis data Data period
Advance PCS (acquired by Caremark) 2001-2003
Anthem Wellpoint (contracted to Express Scripts) 2000-2009
Anthem/IngenioRx 2019
Catamaran / SXC (acquired by OptumRx) 2008-2014
Cigna 1999-2019
CVS 1999-2019
Envision Rx (Acquired by Rite Aid) 2001-2014
Express Scripts (acquired by Cigna) 1999-2018
Humana 2009-2019
Magellan Health / Magellan Rx 2013-2019
Medco Inc. (acquired by Express Scripts) 2003-2012
MedPartners / Caremark RX / AdvancePCS (acquired by CVS) 1999-2006
Merck Medco (spun into independence) 1999-2002
OptumRx (UnitedHealth) 2005-2019
PCS / Rite Aid (sold to Advance Paradigm) 1999-2000
Prime (BCBS) 1999-2019
Rite Aid / EnvisionRX 2015-2019
Wellpoint (acquired by Anthem) 1999-2004

Note: Other PBMs operating during the 1999-2019 period (not in data) include Abarca; Accredo; Aetna (before being acquired by CVS); Argus; Catalyst; Centene/US Script/Envolve; CerpassRx; Diplomat; Eckerd; Envolve; First Health Group; First Health Coventry; Integrated Prescription Management; Kroger Prescription Plans; LDI; MaxorPlus; National Pharmaceutical; Navitus; Omnicare; PartnersRx; PerformRx; Pharmacy Gold; Prescription Solutions; ProAct; RxAdvance; Serve You; US Script; and WellDyneRx.

 

B. Retail pharmacies in analysis data Data period
CVS 1999-2019
Eckerd (acquired by CVS & Jean Coutu) 1999-2003
Jean Coutu / Brooks / Eckerd (U.S. operations acquired by Rite Aid) 1999-2008
Rite Aid 1999-2019
Walgreens 1999-2019

Note: Other pharmacies operating during the 1999-2019 period (not in data) include Walmart, food and retail store pharmacies; and multiple small to mid-size pharmacies.

 

C. Health insurers in analysis data Data period
Aetna (acquired by CVS) 1999-2018
Anthem 1999-2019
Centene 1999-2019
Cigna 1999-2019
CVS / Aetna 2019
Humana 1999-2019
Molina 1999-2019
UnitedHealthcare 1999-2019
Wellcare (2020: acquired by Centene) 2001-2019
Wellpoint (acquired by Anthem) 1999-2004

Note: Other insurers with health insurance coverage operating during the period (not in data) include HCSC; Kaiser; Guidewell; Allstate; Prudential; and John Hancock (Manulife) 


D. Pharmaceutical wholesalers in analysis data
Data period
AmerisourceBergen 1999-2019
Bergen Brunswig (merged with Amerisource) 1999-2001
Cardinal 1999-2019
Kinray (acquired by Cardinal) 1999-2010
McKesson 1999-2019

Note: Henry Schein also distributed pharmaceuticals during the period.

E1. Moderate pharmaceutical firms in analysis data Data period
Abraxis 2005-2019
Acorda 2001-2019
Alkermes 1999-2019
Allergan 1999-2014
Allergan-Watson 1999-2019
AMAG 1999-2019
Amarin 1999-2019
Auxilium 2002-2014
Bausch 1999-2019
Baxalta 2012-2015
Bracco 2003-2015
Dendreon 1999-2014
Dompe 2004-2018
Elan 1999-2011
Emergent 2004-2019
Endo 1999-2019
Forest 1999-2013
Grifols 2000-2019
Horizon 2008-2019
Incyte 1999-2019
Indivior 2011-2019
Ionis 1999-2019
Ipsen 2002-2019
Jazz 2015-2019
King 1999-2009
Leo 2008-2018
Lundbeck 1999-2019
Mallinckrodt 2011-2019
Meda 1999-2015
Medicines Co 1999-2018
Merz 2015-2019
Millenium 1999-2007
Nu Skin 1999-2019
Nycomed 2002-2010
Servier 2004-2019
Shire 2004-2018
Talecris 2005-2010
UCB 1999-2019
Vertex 1999-2019

 


E2. Major biopharma firms in analysis data
Data period ROA
Abbott 1999-2012 y
AbbVie 2013-2019 y
Alexion 1999-2019 y
Amgen 1999-2019 y
Astellas 2005-2019 y
AstraZeneca 1999-2019 y
Aventis 1999-2003
Bayer (pharma) 1999-2019
Beecham 1999
Biogen (Biogen Idec) 1999-2019 y
Biogen (pre Idec) 1999-2002
BioMarin 1999-2019 y
Boehringer Ingelheim 1999-2019 y
Bristo Myers Squibb 1999-2019 y
Celgene 1999-2019 y
Chiron 1999-2005
Chugai 1999-2019 y
Daichi 1999-2004
Daichi Sankyo 2005-2019 y
Eisai 1999-2019 y
Eli Lilly 1999-2019 y
Fujisawa 1999-2004
Genentech 1999-2008
Genzyme 1999-2010
Gilead 1999-2019 y
GSK 1999-2019 y
HGS 1999-2012 y
J&J (Janssen pharma) 1999-2019 y
Kyowa Hakko Kirin 1999-2019 y
MedImmune 1999-2006
Merck 1999-2019 y
Merck AG 1999-2019 y
Mitusbishi Tanabe 1999-2019 y
Novartis 1999-2019 y
Novo Nordisk 1999-2019 y
Otsuka 1999-2019
Pfizer 1999-2019 y
Pharmacia 1999-2002
Regeneron 2004-2019 y
Roche 1999-2019 y
Sankyo 1999-2004
Sanofi 1999-2019 y
Schering AG 1999-2005
Schering Plough 1999-2007
Seattle Genetics 1999-2019 y
Serono 1999-2005
Shionogi 1999-2019 y
Sumitomo Dainippon 1999-2019 y
Taisho 1999-2019 y
Takeda 1999-2019 y
United Therapeutics 1999-2019 y
Upjohn 1999
Warner Lambert 1999
Wyeth 1999-2007
Yamanouchi 1999-2004

 

 Appendix 2: Notable PBMs – Entries and Exits

Examples of PBM entry and exit Parent type PBM Started PBM exited Entry mode Exit mode
Anthem/IngenioRx Insurer 2019 operating Internal
Diplomat Pharmacy Services Pharmacy 2017 2020 Bought LDI & National Sold to OptumRx
CerpassRx PBM 2015 operating Internal
Rite Aid / EnvisionRX Pharmacy 2015 operating Bought EnvisionRx
Magellan Rx HC services 2010 operating Bought First Health; later bought Partners Rx
RxAdvance PBM 2013 operating Internal
Integrated Prescription Management (IPM) PBM 2009 operating Internal
Catamaran / SXC PBM 2008 2014 Internal Sold to UnitedHealth
Navitus Health (Dean Health / SSM Healthcare) HC services 2008 operating Bought Navitus Health
Humana Insurer 2006 operating Internal
Centene / US Script / Envolve Insurer 2006 operating Bought US Script; later bought Envolve (2015)
Abarca Health LLC (FL/PR) PBM 2005 operating Internal
UnitedHealth / OptumRx Insurer 2005 operating Internal; later bought AmeriChoice & Prescription Services
First Health / Coventry HC services 2005 2009 Bought First Health Group Sold to Magellan
Medco3: Medco Inc. (post Merck) PBM 2003 2012 Spun by Merck Sold to Express Scripts
Envolve Health HC services 2002 2015 Internal Sold to Centene
Navitus Health Solutions PBM 2002 2007 Internal Sold to Dean Health / SSM
Aetna Insurer ~2002 2019 Internal Sold to CVS
Envision RX PBM 2001 2014 Internal Sold to TPG (2013) & Rite Aid (2014)
PCS5: Advance PCS / Advance Paradigm (post Rite Aid) HC services 2001 2003 Bought from Rite Aid Sold to Caremark Rx / MedPartners
Partners Rx PBM 2001 2013 Internal Sold to Magellan
Anthem Wellpoint Insurer ~2000 2009 Internal; later bought Wellpoint (2004) Contracted PBM to Express Scripts (2009)
PCS4: Rite Aid (post Lilly) Pharmacy 1999 2000 Bought from Lilly Sold to Advance Paradigm
Perform Rx PBM 1999 operating Internal
ProAct (KPH Healthcare, Kinney Drugs) Pharmacy 1999 operating Internal
US Script PBM 1999 2005 internal Sold to Centene
Catalyst Health Solutions HC services 1999 2012 Internal Sold to SXC/Catamaran
Regence Rx / Cambia Health Solutions HC services 1999 operating Internal
Prime (BCBS) PBM 1998 operating Internal
First Health Group / Health Compare HC services 1998 2004 Bought First Health Services Sold to Coventry
Accredo Health / Nova Holdings HC services 1996 2005 Internal Sold to Medco
CM5: CVS Caremark Pharmacy 1996 operating Internal; later bought Caremark (2006) & others
CM4: Caremark Rx / MedPartners / AdvancePCS HC services 1996 2006 Bought Caremark Sold to CVS
PCS3: Lilly (post McKesson) Life science 1995 1998 Bought from McKesson Sold to Rite Aid
Pharmacy Gold / Aware Integrated (BCBS MN) Insurer 1994 operating Internal
Cigna Insurer ~1994 operating Internal; later bought Express Scripts (2018)
Alta Health Strategies / First Financial Management HC services 1992 operating Bought Alta
Medco2: Merck Medco Life science 1994 2002 Bought by Merck Spun by Merck
DPS2: SKB (post United) Life science 1994 1998 Bought from United Sold to Express Scripts
National Pharmaceutical Services PBM 1993 2017 Internal? Sold to Diplomat
Prescription Solutions (PacifiCare) Insurer 1993 2005 Internal Sold to United (became OptumRx)
Kroger Prescription Plans Pharmacy 1993 operating Internal
WellDyneRx / RxWest PBM 1992 operating Internal
MaxorPlus (Maxor) HC services 1991 operating Internal
CM3: Caremark International PBM 1991 1995 Spun by Baxter Sold to MedPartners
Health Net of California (Foundation Health) HC services 1990 2016 Internal? Sold to Centene
Wellpoint Insurer 1990s 2004 Internal Sold to Anthem
First Health Services HC services ~1990s 1997 Internal Sold to Health Compare (took First Health Group name)
Integrated Pharmaceutical Services (Foundation Health) HC services ~1990s 1999 Internal Sold to Advance PCS
LDI Integrated Pharmacy Services HC services ~1990s 2017 Internal? Sold to Diplomat
EcKerd Health Services / TDI Pharmacy ~1990s 2004 Internal Sold to CVS
CM2: Caremark / Baxter Life science 1988 1990 Bought Caremark Spun by Baxter
MedImpact Healthcare Systems PBM 1989 operating Internal
Value Rx PBM 1987 1998 Internal Sold to Express Scripts
Serve You Custom Prescription Management PBM 1987 operating Internal
Express Scripts PBM 1986 2018 Internal Sold to Cigna
Alta Health Strategies (UT) HC services ~1986 1992 Internal Sold to First Financial Management
Medco1: Medco Containment Services (pre Merck) PBM 1983 1993 Internal Sold to Merck
Argus / DST / SS&C Technologies HC services 1983 operating Internal
Omnicare HC services 1981 2015 Internal Sold to CVS
CM1: Caremark / Home Healthcare of America PBM 1979 1987 Internal Sold to Baxter
DPS1: United Healthcare Insurer 1976 1993 Internal Sold to SmithKlineBeecham
PCS2: McKesson Wholesale 1972 1994 Bought PCS Sold to Lilly
PCS1 PBM 1969 1971 Internal Sold to McKesson

Note: Dates and modes are based on public sources, which sometimes conflict; the tables report best estimates.

Speratum: Building a Life Science Startup in Costa Rica

Andrea Prado, Associate Professor, INCAE Business School; Benjamin Gallo Marin, Medical Student, The Warren Alpert Medical School of Brown University; Ramiro Casó, Senior Researcher, INCAE Business School

Contact: Andrea.prado@incae.edu

Abstract

What is the message?

Speratum, a Costa Rican startup determined to find an effective therapy for pancreatic cancer, is operating effectively in a developing country, despite financial and non-financial challenges that life science enterprises face in this context.

What is the evidence?

Speratum, based in Costa Rica, is close to finishing its preclinical trials for a treatment for pancreatic cancer.  Its work has been recognized internationally.  Marín-Muller won the Dutch Innovation for Health and the Innovadores de América—Science and Technology category—prizes in 2018 and was the only Latin American invited to present his project in the Buckingham Palace among a group of 23 entrepreneurs from 15 countries around the world in the event Pitch at Palace.

Cite as: Andrea Prado, Ramiro Casó, Benjamin Gallo Marin. 2020. Speratum: Building a Life Science Startup in Costa Rica. Health Management, Policy and Innovation (www.hmpi.org), Volume 5, Issue 2.

Leading Edge Life Sciences Innovation In Costa Rica

Latin America is rarely perceived as a region where scientific discoveries are channeled through the complex pipeline required for their successful commercialization, particularly in the context of the life science startup. Nonetheless, while funding limitations complicate such endeavors, some Latin American life science startups have made dramatic progress towards the commercialization of their biomedical innovations.

This paper highlights the intriguing case of Speratum, a startup based in San Jose, Costa Rica, that is currently advancing its mission to provide a novel microRNA-based therapeutic for pancreatic cancer. We explore the challenges that Speratum faces in its geographic location and identify strategies that are forwarding the company’s commercialization goals. In doing so, we identify key lessons in the management of life science startups based in non-traditional contexts.

Pancreatic Cancer: The Need For Treatments 

Speratum’s founder, Christian Marín Müller grew up in Costa Rica, where he developed an aptitude for science, particularly biology and chemistry. After witnessing family members and loved ones battling cancer, he was inspired to pursue a career in scientific research, with the hope to one day find a cure.

Christian, whose undergraduate training in biology directly exposed him to the challenges of commercializing novel biological products, decided to further his education with a graduate program in entrepreneurship and a focus on biotechnology. He went on to earn a PhD in Molecular Virology and Microbiology at Baylor College of Medicine (BCM) in Texas, where his graduate and postdoctoral training led to the development of new technologies that could potentially lead to better treatments for patients.

Pancreatic cancer

The pancreas is a tube-shaped organ located behind the stomach. It serves multiple key functions including the regulation of the metabolism and the control of blood glucose levels.1

Pancreatic cancer occurs when malignant cells grow unregulated to the point that tissues are structurally and functionally damaged. The disease accounts for about three percent of all cancers in the United States and about seven percent of cancer-related deaths. In 2020 in the United States, about 57,600 people will be diagnosed with pancreatic cancer and 47,050 people will die from it.2,3 Globally, pancreatic cancer is the 12th most common cancer in men and 11th most common in women.4

A 2017 study shows that pancreatic cancer cases worldwide have more than doubled since 1990.5 The average lifetime risk of suffering from pancreatic cancer is one in 64 and risk factors such as genetics, smoking, exposure to certain toxins, age, obesity, and diabetes, among others, can facilitate the onset of the disease.6,7

Often described as a “silent disease”, pancreatic cancer presents little to no symptoms during its earliest stages. As the cancer spreads, however, pain develops in the upper abdomen and symptoms such as weight loss, fatigue, and yellow discoloration of the eyes and skin may become apparent.8,9 Because pancreatic cancer is rarely detected during its earliest stages due to the absence of clinical symptoms, it is often diagnosed only after the disease has spread. The high mortality associated with pancreatic cancer is partially explained by the difficulty of obtaining an early diagnosis: less than one in 10 people are alive five years after being diagnosed with the disease.10

The discovery

In 2008, Christian initiated his PhD program at BCM in Houston, Texas. Christian worked under the supervision of Dr. Qizhi Cathy Yao, a prominent virologist and pancreatic cancer researcher.  During his time in the Yao Laboratory, Christian investigated the role that a type of oligonucleotides called microRNAs (miRNAs) play in pancreatic cancer. miRNAs are small molecules that usually turn off the expression of specific genes.

In a healthy state, microRNAs play an important role in regulating a cell’s biological functions and gene expression. In a disease state, however, microRNAs can function abnormally and dramatically alter cellular behavior and gene expression.12 In some illnesses, restoring the equilibrium of microRNAs can help bring a cell from the disease state to a healthy state.

In 2013, Christian and colleagues published a paper in the journal Clinical Cancer Research that suggested that a molecule called miRNA-198 suppresses cancer-inducing genes in pancreatic cancer cells in the laboratory.11 Because miRNA-198 is not present in human pancreatic cancer cells, Christian hypothesized that finding ways to introduce miRNA-198 in these cells could reduce the expression of genes that facilitated the disease and theoretically, cure the cancer.

The finding that Christian made on miRNA-198 inspired him to investigate a treatment option for pancreatic cancer that used this molecule. Christian decided to put his discovery to the test of entrepreneurship. Christian knew that further studying this molecule held the potential to provide patients suffering from this disease with a new treatment method.

Speratum: A Life Science Startup

Getting started

In 2014, Christian formally founded the life science startup, Speratum, which means “hope” in Latin.  He wanted to continue the preclinical phase of miRNA-198 research using animal models, while hoping one day to gain approval from the Food and Drug Administration (FDA) or European Medical Agency (EMA) and make the potential treatment option available worldwide.  Christian set up Speratum in his native country, Costa Rica, a small Central American nation with a stable sociopolitical climate and a rich pool of professionals in the sciences.

In 2015, Christian began building the company. He first established a scientific board, joined by experts in pancreatic cancer, biotechnology investment, clinical research experts, and his two mentors at BCM, including Qizhi Yao. The next step was to build a strong scientific team. Christian hired Osvaldo Vega, a master’s student in biomedical sciences and genomics at the University of Costa Rica, who eventually became the company’s Chief Science Officer (CSO). Together, they recruited top talent for different roles, including chemists, biologists, veterinarians, medical doctors, and engineers.

Speratum also opened its doors to top students in Costa Rica from various universities to complete their academic theses with the company. Christian is proud that Speratum has been able to hire promising young scientists, thus offering an avenue for serious professional work for local scientists in the making. Most of them have stayed on at Speratum post-graduation, while others have gone on to train in nanotechnology and biotechnology at top institutions worldwide—with the goal of returning with that training, as Christian did with Speratum.

By January 2019, Speratum had grown into a team of 14 full time employees and several rotating students and collaborators. Overseen by a Board of Directors, a Board of Scientific Advisors, the CEO, CSO, and Project Manager, Speratum is organized into six departments: Nanoparticle Development, Microbiology, Molecular Biology, Toxicology, and Animal Facilities.

Melvin Nuñez, who has been serving as Project Manager since November 2018, comments that the entire team—including Christian—meets weekly to discuss the results of each scientific experiment performed and the administrative needs of the startup. Christian estimates that he spends 60 percent of his time working on science, 25 percent on networking and fundraising for the company, and 15 percent on high level administrative tasks.

A disciplined experimental mindset

Juan Carlos Valverde, Scientific Leader of Molecular Biology, states that the company encourages its scientists to “replicate each experiment multiple times”, in order to confirm the validity of results. Scientific rigor is juxtaposed with freedom of scientific creativity, in some cases born out of a desire to conduct cost-effective drug development.

Many of the company’s most creative endeavors have led to new intellectual property and novel applications for its technologies.  For instance, the company is currently preparing a spin-off technology to treat cancer in dogs, born out of a desire to generate a low-cost production system for miR-198 that resulted in a manufacturing approach that can generate a low-cost alternative for the veterinary cancer market. Speratum’s resourcefulness plays an important role in the dramatic progress the company has seen in the preclinical research phase. Resources have been strategically allocated, and a culture of innovation has been implemented that has led to Speratum being considered an innovation leader in the region, with participation in some of the world’s largest technology entrepreneurship and cancer research forums.

Effective efficiency

Due to its location, Speratum can be highly cost effective. Although the company’s operating monthly expenses are “highly dependent on the experiments being performed”. These costs represent “about a fourth of what [they] would be spending if the entire operation would take place in the United States”.

Speratum has made dramatic progress in the preclinical phase with a fraction of the funding that most enterprises in drug development enjoy. Of 10,000 compounds with promising early results, only 250 successfully complete early preclinical testing, and only five enter clinical trials. Only one in those five drugs entering the clinic will make it to market, following a rigorous human testing regimen.

For example, Mirna Therapeutics based in Austin, Texas raised millions of dollars in venture capital starting in 2007 to fund oligonucleotide research for cancer. Their preclinical work alone had a cost of about $4 million per year. However, their product proved to be too toxic and the company ceased their investigations and the assets were acquired in 2017.

By contrast, Speratum has obtained encouraging data about their oligonucleotide product and has spent under $2 million in a span of roughly five years. The decision to focus on a highly promising molecule and practicing strategic resourcefulness has allowed Speratum to make great strides in their central project while keeping costs as low as possible.

Challenges of Building a Life Science Startup in Developing Countries

The recruitment

After Christian patented miRNA-198, the Costa Rican newspaper La Nación wrote about his discovery in September 2013. As a result, Christian states that “a lot of people invited [him] to Costa Rica to interview”. One of the individuals that contacted Christian was Allan Boruchowicz, founder and managing director of Carao Ventures, one of two venture capital firms that operate in Costa Rica and invests in early-stage Latin American startups. Christian states that after introducing miRNA-198 to Carao, “they asked [him] if [he] would like to start a business with them in Costa Rica, and the first thing that [he] told them is that it would be impossible in this country”.

Despite his early concerns about the location, Christian’s enthusiasm about Costa Rica grew after meeting with Franklin Chang-Diaz, a renowned Costa Rican scientist, entrepreneur, and former NASA astronaut. Chang-Diaz had established a company in Liberia, Costa Rica to develop novel propulsion technologies for deep space travel.  Chang-Diaz told Christian that “[he] had the potential to change many things in the country, and that [he] should seriously think about conducting his scientific work in Costa Rica”. Christian became convinced to further pursue the development of this technology in Costa Rica and fuel scientific innovation in his home country.

Costa Rica: Benefits and challenges for life sciences startups

Costa Rica is a small Central American nation bordered by Nicaragua and Panama. With a population of about 4.9 million, the country is a unitary presidential constitutional democracy and enjoys a stable sociopolitical culture. Costa Rica is known for its highly educated population: in 2016, the government spent roughly 6.9 percent of its budget on education, versus a global average of 4.4 percent.13,14 In recent years, the Costa Rican economy has diversified to include sectors such as finance, ecotourism, and the production of medical devices. Since Costa Rica is part of the Free Trade Zone (FTZ), foreign manufacturing and services companies can benefit from investments and tax incentives.15

In addition to the benefits of a highly skilled workforce and lower labor costs, Costa Rican entrepreneurs face important limitations that make it difficult to thrive in the life sciences. Key constraints include the need to import reagents and raw materials for research, as well as the challenge of maintaining small business status despite not having any sales or products in the short to middle term horizon. A major challenge is that the government offers little research funding to companies. While grants do exist, they are limited, and often times the regulations are such that one company cannot compete for multiple grant opportunities.

Life science companies seeking to perform research must rely primarily on angel investors and venture capital funds. However, it is difficult to attract foreign investors: while Costa Rica is well known for developing medical devices, it is not regarded as a hub for bringing novel drugs and pharmaceuticals to the global market. In addition, Christian comments that the lack of an investment track record in biotechnology, with its high risk and long-term returns, makes investment from locals in companies like Speratum more difficult.

Moreover, high level investors in Europe and the U.S. are often reluctant to invest in a Costa Rican based entity, not having a frame of reference for success in health-based biotech endeavors in the region. Christian believes this is a hurdle that can be overcome with early successes in the industry, such as the filing of a successful Investigational New Drug (IND) from Speratum, to foment biotechnology investment growth for the region. 

Initial funding

Carao Ventures provided Christian with the opportunity to raise $800,000 in initial funding, as well as provided office spaces, business support, and legal advice. Allan Boruchowicz commented in 2016 in La Nación that Speratum was interesting to Carao Ventures because “if the science is effective, not only will [they] obtain a great return but [they] also will have contributed in the development of a revolutionary drug that will save many lives, [which] has a big value for [them] and [their] investors”.16

Speratum and Carao Ventures offered their investors convertible notes, which is a strategy often used when it is difficult to define a company’s value in a given moment in time. Speratum’s future value—which can vary widely in the tens of millions of dollars depending on the results of their science—will depend on whether their research shows promising results for safety and efficacy leading up to and including to an IND approval. Essentially, at a later point in time, investors can choose to transform their investment into shares. If Speratum’s value is deemed to be high, the company will issue its shares at a premium. Christian argues that this agreement is “ultimately fair for those investing in this risk”.

Finding a laboratory

For some time, “the idea of the enterprise was to establish the company in Costa Rica but outsource a majority of the science abroad with the right people, assuming that [in Costa Rica] we would find limited infrastructure for conducting the necessary experiments”. Christian mentions that he “spent a lot of time — almost an entire year—evaluating all the possibilities for Speratum”. Christian and Carao Ventures came to the conclusion that the human capital in Costa Rica consisted of top-notch researchers who were more than capable of conducting the science and at a reduced cost in labor expenses. Therefore, the cheapest and most promising option would be to conduct experiments in the country. It would be critical, however, to find adequate laboratory space.

During the year-long process of due diligence on the capabilities within the country, Franklin Chang-Diaz connected Christian with Sergio Madrigal, who was the director of the Centro Nacional de Biotecnológicas (CENIBiot) in San Jose. The CENIBiot was funded with the support from the Costa Rica Government, CONARE (Consejo Nacional de Rectores) and the European Union with a donation of 11 million Euros to establish a world class laboratory for biotechnological innovations.

When Christian met Madrigal, “the CENIBiot was looking for a renaissance” and was interested in bringing new projects on board. Christian was able to negotiate the terms of his laboratory space contract, resulting in favorable conditions “with the caveat that [Speratum] will support Costa Rican innovation and it will make contributions to the country and [CENIBiot]” by bringing cutting edge science and collaborative programs to Costa Rica.

After signing a preliminary contract and beginning to operate at the CENIBiot, Speratum’s relationship with the Center has evolved to the extent that “[the organizations] apply to grants together and [are both] working on a nanotechnology project, as well as building together a unique animal facility”. Speratum has recently signed a new contract with CENIBiot that will “serve as a strategic alliance for both organizations, outlining how to develop independent projects that benefit both sides and a system to generate reciprocal licenses.” Christian describes Speratum’s relationship with CENIBiot as “a very interesting symbiosis”.

Speratum’s preclinical development requires working with specialized laboratory rodents as established models for cancer research and toxicology.  Christian was interested in establishing a world-class animal facility at CENIBiot. Christian and the CENIBiot team had to spend considerable time obtaining legal permits from Costa Rican authorities and educating logistics and customs processing companies on animal importation protocols in order to establish a safe and effective route to import these specialized animals into Costa Rica.

As part of establishing a facility that could support Speratum’s research needs, Christian initiated the process to establish necessary paperwork, protocols, and infrastructure to receive accreditation from the Assessment and Accreditation of Laboratory Animal Care. The AAALAC is a private non-profit organization based in the United States that promotes the humane and ethical treatment of research animals. A government grant for $35,000 helped make obtaining the grant possible—although later prevented Speratum from being able to apply for additional grants. Besides making Speratum the first organization to receive such accreditation in Central America, AAALAC accreditation is significant to the company because “it represents quality, promotes scientific validity and assurance in a global marketplace, and demonstrates accountability”.17 Speratum received AAALAC accreditation for its animal care and use program in May 2019.

Christian Marín Müller: “If we succeed, we could change more than just lives”

When it comes to why Costa Rica, Christian is perfectly clear: even though multiple challenges exist, the possibility of succeeding could bring change not only to the lives of patients with pancreatic cancer at a global scale but also to a country and its entrepreneurial ecosystem. “We found incredible talent, we have the support of important actors in the local ecosystem, such as Franklin Chang and Carao Ventures; we also have the support of important people who have been part of the medical research industry for decades. Now we have this fantastic accreditation and created an animal facility that did not exist. In sum, we are doing things that have never been done in the region”.

Future Challenges and Lessons Learned 

Next steps for Speratum

Christian and his team remain hopeful that their novel therapeutic will prove to be safe and efficacious and be launched to the global market, but, first, they must continue to perform the studies to meet the requirements for FDA and EMA approval. The ultimate goal remains years ahead as the company is close to finishing the preclinical investigation phase and making plans to successfully execute the very expensive clinical phases (Exhibit 1).

Although in February 2019 Speratum secured an additional $2 million in funding, Christian knows that Speratum will need far more capital to continue its progress. The difficulty of raising funds, particularly in attracting foreign investors to Costa Rica, and other limiting factors have made accelerating Speratum in Costa Rica challenging. Christian is proud Speratum has done well in Costa Rica with the preclinical research phase almost complete, despite a relatively limited budget.  As a result, Speratum’s leadership has considered establishing a presence for Speratum abroad.

Where will the money come from?

As we have shown, operating a life science startup from a developing country has its pros and cons.  Limited financial opportunities create an obvious challenge.  Access to local research grants tend to be non-existent or available only for local academic institutions rather than for companies.

An alternative to public financial support, accessing resources through investors, faces challenges in both global and domestic funding.  Globally, international investors are often concern about intellectual property issues when investing in startups registered in developing countries.  Domestically, the number of sophisticated investors in developing countries who might be familiar with supporting life science startups is low.  In Central America, for instance, most investors channel their resources either through real estate projects or traditional stock markets or financial instruments.

Life science startups have a relatively high probability of failure and a long term pay off—if any.  Thus, startups like Speratum are more likely to raise financial resources in ecosystems where more investors have some degree of expertise in this industry.  Thus, a life science startup from these countries has few options, especially after its preclinical research when resources needed for clinical trials become significant.

Institutional challenges and the need to find local resources

Adequate infrastructure and regulatory procedures are a challenge in developing countries that have not had experience with life science startups.  Infrastructure is often concentrated in public universities and governmental institutions, which limit access for private companies.  After a failed approach to obtain laboratory space at a public university, Speratum was fortunate to negotiate a deal for the rental space at CENIBiot, an institution that had just received resources to set up a world class laboratory for biotechnological innovations.  Nevertheless, Christian still had to drive the process to receive the AAALAC accreditation for its laboratories.

Developing countries also need to develop specific regulations and procedures to operate new ventures.  For example, importing inputs for laboratory research often requires the entrepreneur to coordinate efforts with governmental institutions to develop the necessary processes.  Hence, life scientists and entrepreneurs operating in developing countries often need to engage more in administrative and networking efforts than if the enterprise were to operate in a developed country.

Operating in a developing country might be significantly less expensive than in a developed country, but ventures need to obtain high quality staff and other resources.  In Costa Rica, access to highly skilled human resource was not a limitation for Speratum, but it will be in other developing countries.  Life science enterprises require highly specialized professionals, equipment, and infrastructure, as well as a supportive ecosystem where governments, academia, and investors provide the resources they need to operate successfully.  Nonetheless, entrepreneurs can sometimes find pathways to coordinating players and resources within a local ecosystem that others have not recognized.

Looking Forward

Christian is emotionally tied to Costa Rica. His aspiration to revolutionize the landscape of scientific innovation in his home country is serious. It has taken extraordinary efforts to make operations there possible, such as paving the way in working with local authorities about what life science enterprises need to succeed and then identifying solutions that address those needs. Christian wants to see the company’s future pancreatic cancer therapy succeed with as much of the development done in Costa Rica as possible.  But as much as he would like for it to be entirely investigated in Costa Rica to be given to the entire world, he states that, “at the end of the day, the most important thing is that the treatment reaches people as quickly and effectively as possible”. Although, over time, this will require both a local and global presence, Costa Rica and its resources will remain a cornerstone of Speratum’s strategy.

Exhibit 1 General description of the FDA new drug approval process.

Source: See references 18-20

References          

  1. Gittes, G. K. Developmental biology of the pancreas: a comprehensive review. Dev. Biol. 326, 4–35 (2009).
  2. Pancreatic cancer statistics. World Cancer Research Fund https://www.wcrf.org/dietandcancer/cancer-trends/pancreatic-cancer-statistics (2018).
  3. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 70, 7–30 (2020).
  4. Aier, I., Semwal, R., Sharma, A. & Varadwaj, P. K. A systematic assessment of statistics, risk factors, and underlying features involved in pancreatic cancer. Cancer Epidemiology vol. 58 104–110 (2019).
  5. Pourshams, A. et al. The global, regional, and national burden of pancreatic cancer and its attributable risk factors in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet Gastroenterology & Hepatology 4, 934–947 (2019).
  6. Raimondi, S., Maisonneuve, P., Löhr, J.-M. & Lowenfels, A. B. Early onset pancreatic cancer: evidence of a major role for smoking and genetic factors. Cancer Epidemiol. Biomarkers Prev. 16, 1894–1897 (2007).
  7. Lowenfels, A. B. & Maisonneuve, P. Epidemiology and risk factors for pancreatic cancer. Best Pract. Res. Clin. Gastroenterol. 20, 197–209 (2006).
  8. Holly, E. A., Chaliha, I., Bracci, P. M. & Gautam, M. Signs and symptoms of pancreatic cancer: a population-based case-control study in the San Francisco Bay area. Clin. Gastroenterol. Hepatol. 2, 510–517 (2004).
  9. Porta, M. et al. Exocrine pancreatic cancer: symptoms at presentation and their relation to tumour site and stage. Clin. Transl. Oncol. 7, 189–197 (2005).
  10. Yadav, D. & Lowenfels, A. B. The epidemiology of pancreatitis and pancreatic cancer. Gastroenterology 144, 1252–1261 (2013).
  11. Marin-Muller, C. et al. A tumorigenic factor interactome connected through tumor suppressor microRNA-198 in human pancreatic cancer. Clin. Cancer Res. 19, 5901–5913 (2013).
  12. Li, M. et al. MicroRNAs: control and loss of control in human physiology and disease. World J. Surg. 33, 667–684 (2009).
  13. OECD. OECD Economic Surveys: Costa Rica 2018. (OECD Publishing, 2018).
  14. Gonzales, M. THE ROLE OF EDUCATIONAL LEADERSHIP ON PARTICIPATION IN THE NATIONAL PROGRAM OF SCIENCE AND TECHNOLOGY FAIRS OF COSTA RICA. EDULEARN17 Proceedings (2017) doi:10.21125/edulearn.2017.0291.
  15. Monge González, R., Rosales Tijerino, J. & Arce Alpízar, G. Cost benefit analysis of the free trade zone system: the impact of foreign direct investment in Costa Rica. http://www.sidalc.net/cgi-bin/wxis.exe/?IsisScript=earth.xis&method=post&formato=2&cantidad=1&expresion=mfn=019609 (2005).
  16. L, M. V. Tico buscará apoyo europeo para terapia contra cáncer de páncreas. La Nación, Grupo Nación https://www.nacion.com/tecnologia/innovaciones/tico-buscara-apoyo-europeo-para-terapia-contra-cancer-de-pancreas/FYDZCTSQ5FAVPBS6WXUYEOGJEU/story/ (2016).
  17. What is AAALAC Accreditation? AAALAC https://www.aaalac.org/accreditation-program/what-is-aaalac-accreditation/.
  18. Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).
  19. DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 47, 20–33 (2016).
  20. Center for Drug Evaluation & Research. Drug Development & Approval Process. U.S. Food and Drug Administration https://www.fda.gov/drugs/development-approval-process-drugs (2019).

 

Assessing the Impact of High-Deductible Health Plans on People with Diabetes

Markus Saba, Kati Schy, and Daniella Kapural, University of North Carolina at Chapel Hill

Contact: Markus_Saba@kenan-flagler.unc.edu

Abstract

What is the message? The evidence concerning high-deductible health plans (HDHPs) suggests three key patterns involving people with diabetes (PWD): (1) a disproportionately negative impact on low-income individuals; (2) low healthcare literacy as a predictor of poor health outcomes; (3) the influence of insurance design on consumer and health behavior. The patterns suggest that HDHPs can be customized to the diabetes disease state in order to reduce cost and improve health outcomes. Changes include policy advances, special initiatives for PWD, and leadership by employers.

What is the evidence? The conclusions are based on an extensive literature review plus deliberations of an expert panel.

Link to white paper: https://cboh.unc.edu/index.php/ki-research/?researchID=11208

Timeline: Submitted: June 2, 2020; accepted after revisions: June 16, 2020

Cite as: Markus Saba, Kati Schy, and Daniella Kapural. 2020. Assessing the impact of high deductible health plans on people with diabetes. Health Management, Policy and Innovation (HMPI.org), volume 5, Issue 2, June 2020.

Many People with Diabetes Have High-Deductible Health Plans

Since the introduction of high-deductible health plans (HDHPs) to the U.S. in 2004, there has been much ambiguity surrounding how HDHPs affect access, costs, and outcomes of health care.  This topic is especially relevant to patients with chronic conditions, such as diabetes mellitus, who have more frequent health-monitoring demands. Understanding the net impact of HDHPs on people with diabetes (PWD) will determine what changes and improvements to HDHPs should be implemented.

HDHPs are defined as a plan with a deductible of at least $1,300 for an individual or $2,600 for a family1.  The intent behind HDHPs is to reduce overall healthcare costs and utilization by incentivizing individuals to be more conscious of unnecessary medical expenses, while making healthcare coverage more affordable as a result of lower insurance premiums.

While HDHPs can be beneficial under certain circumstances, the impact of HDHPs on PWD is uncertain. HDHPs may have negative health and financial consequences for people with chronic illnesses such as diabetes because the out-of-pockets costs are high and continue over time.  This is particularly relevant for PWD as this group of patients is growing, costs are increasing, improved outcomes take time, and complications emerge over time.  People with chronic conditions on HDHPs may end up deferring necessary healthcare which can result in poor outcomes and higher costs in the long run2,3.

The analysis explores how healthcare costs change under different circumstances, whether health outcomes for PWD are better or worse with HDHPs, and if there are other implications and patient behaviors that should be taken into consideration.

Research Design

The research project included three phases.

Phase I: Literature Review – Review of thirty-nine studies from reputable medical journals and other scholarly publications.  Seven of these studies focused specifically on HDHPs and PWD.

Phase II:  Advisory Board – We facilitated a roundtable discussion held on February 12, 2020 with eight leading experts in the diabetes field representing multiple perspectives.

Phase III: White Paper – A detailed report captured the output, conclusions, and recommendations of the literature review, data analysis, and advisory board.

Literature Review: Three Themes

Thirty-nine research publications were selected based on inclusion of studies on the association of HDHPs and outcomes in PWD. Thirteen of the initial studies were selected from the honors thesis of Pooja Joshi, a UNC student who partnered with UNC Kenan-Flagler Business School’s Center for the Business of Health (CBOH) in 2018 to explore trends in diabetes care and HDHPs. Of the thirty-nine, seven publications focused on PWD, five focused on chronic illnesses and mentioned diabetes, and the other twenty-seven focused on the broader scope of health in the context of HDHPs and healthcare costs.

The relationship between HDHPs and health outcomes is complex. While most studies found an association between HDHPs and negative health outcomes for PWD, some studies found no difference. Some variables overlapped so that HDHPs were associated with one variable (e.g., decreased healthcare utilization), but showed no statistical association with another variable (e.g., health outcomes).

Despite the complexity, three themes emerged from the literature review, concerning income disparity, health insurance literacy, and consumer and health behavior.

Income Disparity 

Although previous literature suggest that HDHPs may result in cost savings from reduced healthcare utilization, much of the research analyzed in this review suggests that lower-income individuals may be adversely affected by the HDHP insurance design. Health care and maintenance costs for PWD are substantial, even for patients who are insured4,5.  The literature demonstrates that HDHPs do decrease utilization among PWD6; however, the lower utilization may result in severe medical consequences for low-income subgroups3,7.

Compared to families enrolled in traditional plans, families whose members have chronic conditions commonly reported financial burden related to healthcare costs, especially when enrolled in HDHPs8; this number doubled for low-income families in HDHPs9. Although HDHPs offer lower premiums, they generally result in higher out-of-pocket expenses when medical events occur. For chronic diseases such as diabetes, higher numbers of medical events are nearly certain to occur, driving out-of-pocket expenses up for PWD.

Uninsured PWD are predominantly from low-income households or belong to a minority group, and receive less preventive care than insured PWD10. Even for the insured population, though, lower income has substantial relationships with HDHPs and inferior outcomes.

Low-income PWD who are insured privately — and have high deductibles – are more likely to report forgoing needed medical services, such as outpatient visits or diabetes management medications11,12,13. Forgoing primary or preventive medical treatment puts these patients at risk for higher severity emergency department (ED) visits and poor health outcomes in the long-term. Studies showed that after an employer-mandated switch to HDHPs, low-income patients experienced concerning increases in high-severity ED visit expenditures and hospitalization days14,15,16.

Health Insurance Literacy

Health insurance literacy is the degree to which patients have the ability to access and understand information about health insurance plans, select the most appropriate plan for their circumstances, and utilize the plan effectively to maintain good health.  While only a few studies focused specifically on health insurance literacy, the relevant research reflected that many patients were unsure or uninformed about their health insurance plan and its benefits.

Our research validated the importance of health insurance literacy in optimal healthcare utilization. One study evaluated comprehension of insurance plans and plan choice separately. This 2013 study showed that only 14 percent of Americans correctly identify the four basic components of health insurance plans: deductible, copays, coinsurance, and maximum out-of-pocket costs and only 11% of Americans adequately understood the cost of hospitalizations17.

It appears that consumers do not understand their current insurance plans, and studies suggest that plan simplification could be beneficial to the optimization of HDHPs17,18. Another study found that consumers “frequently reported changing their care-seeking behavior” due to cost, despite having limited knowledge about their deductibles18.

While HDHPs were designed to reduce overall utilization, poor health insurance literacy limits the intended effects of deductibles and adversely impacts health outcomes for patients with chronic illnesses19. Across the studies, three attributed unused benefits to poor health insurance literacy. A 2017 study of low-income PWD, for instance, found that patients enrolled in HDHPs might experience increased severe health outcomes by forgoing primary care that is covered by their plans3. Another study found patients enrolled in HDHPs are likely to reduce preventive care use, even when covered without cost-sharing, and they are largely unaware of the fact that preventive care is free or low cost1.

Three studies elaborated on how the limitations of insurance markets (e.g., adverse selection) and inadequate health plan choices by consumers can lead to negative health outcomes for patients20,21,22.  Better patient awareness and decision support is a key aspect of optimizing plan usage and helping consumers understand their insurance benefits (e.g., making a distinction between when care is necessary, unnecessary, or discretionary)18.  When provided the resources, consumers seem to utilize health and cost information to make their decisions23.

Consumer and Health Behavior

The majority of the research reported broadly on consumer and health behaviors—how patients prioritize or maintain their health depending on their healthcare plan.  As HDHPs were designed to do, they shape the patients’ consumer behavior. However, these changes in consumer behavior also translate to changes in health behavior.

Our research demonstrates that these changes can have damaging health effects on people with chronic illnesses such as PWD. Additionally, health and consumer behavior are interconnected. For example, consumers’ health status plays a key role in their consumer behavior, specifically their choice of plan; the probability of choosing high-premium health plans increases with more chronic comorbidities among family members24.

Consumer Behavior

Consumer behavior is a patient’s responses to the design of the insurance plan and how such behaviors impacted the utilization of healthcare services.  Among the studies, topics ranged from consumer elasticity in healthcare to moral hazard to attitudes about preventative care. It appears that higher deductibles significantly decrease opportunities for early detection, management, and care coordination of chronic diseases25,26.

Four publications focused on the need for competition in healthcare to change patient behaviors27,28,29,30.  Some of the recommendations included putting patients at the center of care, creating choice, and standardizing value-based methods of payment31. Others explored solutions related to bundled payments or value-based insurance designs32.  Another publication focused on providing financial incentives to patients with diabetes, hypertension, and high cholesterol, to better manage their health33.  However, one study showed that switching to an HDHP did not change medication availability or reduce use of essential medications34.

Health Behavior

Health behaviors are actions people take to maintain or enhance their health, or prevent disease.  Diabetes is largely a behavioral disease, as it can be improved, managed, or prevented with good health behaviors.  Good health behaviors such a healthy diet, regular exercise, and adherence to medical regimens (e.g., monitoring insulin) are critical components of diabetes management.

Studies found that patients under insurance plans with less coverage show a lower likelihood of exercising regularly, modifying their diet, and using oral medication for chronic illnesses9,35. Another study found that insurance type had a significant effect in determining which insulin management plan patients followed, showing increased use of insulin pumps, and lower HbA1cs among privately insured patients36. Similarly, it is generally shown that those enrolled in HDHP with chronic conditions such as diabetes adhere five percent less to their prescribed medication than those that did not switch plans37.

The research suggests that costs associated with high deductibles provide a financial incentive for families to make certain sacrifices to their health. Delayed and forgone care due to health care costs is higher among families with chronic conditions enrolled in HDHPs38. Families with lower incomes are also at higher risk to delay or forgo necessary care, making them an especially vulnerable population3,7,13.

Advisory Board Conclusions 

On February 12, 2020 an advisory board was held in which the research team presented the findings from the literature review to a panel of eight national experts representing various fields in the treatment and management of diabetes. The conclusions drawn include recommendations on potential modifications to the HDHP paradigm that might reduce costs while improving health outcomes for PWD.

The advisory panel consisted of the following members:

Recommendations: Policy, Initiatives, And Employers

Based on the literature review plus the discussion with the advisory board, our recommendations are centered around the following three categories: (1) policy implications, (2) special initiatives for PWD, and (3) role of employers.

Policy Implications

The advisory board’s discussion on policy of HDHPs for PWD focused on the benefits of preventative care and patient awareness of what preventative care is covered by insurance. Below is a summary of the main points that were considered.

Preventative Care

 In order to achieve better health outcomes and overall cost savings, prevention services and lifestyle interventions must be integrated into care for chronic conditions39.  The Affordable Care Act (ACA) requires insurers to cover medical expenses without cost-sharing for the screening of depression, diabetes, cholesterol, obesity, various cancers, HIV and STIs, as well as counseling for drug and tobacco use, healthy eating and other common health concerns.  The costs of immunizations and reproductive health are covered at no costs as well.

The Trump administration’s recent mandate (Notice 2019-45) expands the list of preventative care benefits required to be provided by HDHPs without a deductible. See the chart below.

Despite the extended preventive care coverage under the ACA and Notice 2019-45, some concerns and challenges still remain specifically for PWD in a HDHP.  Two main issues are the coverage limitations for PWD and the awareness of this coverage among patients and providers.

Coverage

The advisory board noted that PWD experience a larger burden of cost with regards to intervention, prevention and maintenance than is recognized.  The above-mentioned screening services and preventative care, such as A1C testing and retinopathy screening, are of particular importance to PWD; however, they are not comprehensive.

In addition to the services covered in the recent policies, several prevention services are required for PWD, including kidney disease screening for CKD, foot exams for DPNP, and neuropathy screening.  Additionally, multiple ongoing and necessary costs are associated with screening, prevention, testing, monitoring, and maintenance for PWD. These costs accumulate quickly, leaving patients looking for ways to reduce the out-of-pocket spending required by their disease on a regular basis.

Furthermore, as PWD obtain treatment and medication, they must pay at the full list price and are not benefiting from the negotiated discounts if they have not met their deductible.  Strong consideration should be given to covering maintenance treatment for PWD in HDHPs. At the least, the net prices should be charged even while PWD are in the process of paying down their deductible.

Awareness

An urgent concern is that prevention and screening services that are covered under the ACA and Notice 2019-45 for HDHPs are underutilized.  This creates a critical gap for PWD.

Many HDHP insurance plan enrollees are unaware that the ACA covers preventative care office visits, screening tests, immunizations and counseling with no out-of-pockets charges.  Furthermore, it appears patients are also unaware that Notice 2019-45 expands coverage for PWD in a HDHP to include insulin and other glucose lowering agents, retinopathy screening, glucometers and HbA1c testing. We estimate that the majority of patients, and even some healthcare providers, are unaware what these services are and if they are partially or fully covered for people in HDHPs.

We recommend developing two public service awareness campaigns: one campaign targeted to patients and caregivers and another designed for providers.  A solution should include a consortia of government, insurers and employers to develop and implement the communication plans.

Special Initiatives For PWD

The advisory board addressed whether specific plans customized for PWD on HDHPs can result in better outcomes while reducing costs, and if so, what it might look like.  The discussion focused on two key points: directed care and incentives.

Directed Care

PWD should be offered directed care that is designed to improve health outcomes through financial incentives that reduce costs to patients. One challenge to this approach is that many patients with diabetes are likely to have comorbid conditions.  This leads to the issue of prioritizing directed care and determining what are the most important healthcare elements to focus on.

For PWD, key factors include monitoring HbA1c, following dietary guidelines, increasing exercise, and adjusting insulin and other medications.  These are daily responsibilities that require time, energy and money.

The impacts of diabetes are not only physical and financial, but also psychological.  For PWD that have comorbid conditions, the physical, financial, and emotional burdens associated with chronic illnesses are compounded.

A more specific proposal would be to offer financial rewards such as lower premiums and/or reduction in deductibles for PWD that follow the directed care regimen.  If a patient follows a customized protocol as determined by their physician, in turn the patient would receive discounts in their premium, deductible and/or copay.

Research suggests that following directed care would result in better outcomes, which would result in reduced costs.  While there are some challenges with validation, the concept should be explored and tested more.

Advances in technology can help in the monitoring of adherence to directed care. Employer Human Resource departments working together with insurers, physicians and health benefits analysts have developed specific patient care.  To date, most of these solutions do not change patient behavior, other than to make them consume less, leading to the conclusion that improvements are still needed.

Current best practices of directed care in the primary care space provide an excellent analogue that could be successfully applied to the chronic care space.  In primary care, once services are authorized by a third-party/payer, then the services are covered by insurance.  The third-party determines where the patient will get the care and what kind of care to get.

Applying this model to patients with chronic disease such as diabetes would be a way to increase the uptake of preventative care, which is already covered, and adherence to continuous treatments to promote better health outcomes. For example, for routine lab tests associated with diabetes, a patient will be notified of a lab nearby that is the most cost effective.  The cost is fully covered by the insurer and free to the patient.  If the patient goes to a lab that is more expensive, then the insurer pays 80% of the test fees and the patient pays the difference.  This insurance design would result in changes in behavior, leading to lower overall costs and, eventually, lower premiums and deductibles for PWD.

In addition to pharmacological approaches, behavioral intervention is a crucial component of diabetes treatment and maintenance.  Guidance for various groups of PWD should be provided to inform patients of which behaviors to focus on and will be accompanied with a financial incentive, such as a reduction in the amount of deductible.  These elements need to be aligned with having the best pay-off with regards to health outcomes.

If such an ‘incentivized directed care’ program had alignment and buy-in from the broader healthcare ecosystem, then the buy-in and commitment from patients and caregivers would be very high.  Ideally a more sophisticated model would include primary and secondary prevention and screening elements as well.  Including peer-support and caregivers is an important aspect in adherence.

An incentivized directed care program should be customized to PWD needs; result in   better health outcomes; incentivized by a cost-reduction in HDHPs; and address comorbidities.  This requires alignment with providers, payers, pharma, and employers.

Incentives

Another topic of discussion centered on what incentives the payer might provide to the patient to help reduce the deductibles.  For example, many policies reduce a premium and/or deductible if the person can verify that they are a non-smoker.  Some policies tie the body mass index (BMI) to insurance rates.  In the past, there were more aggressive incentives in place around lipid management, weight, and disease management programs.

HDHPs should offer an incentive for PWD to maintain good health.  For example, if a person’s HbA1c is within a healthy range for a certain amount of time, then their deductible is reduced.  This incentive would benefit both parties, as it would (a) result in better health outcomes and reduced out of pocket costs for the patient, while (b) reducing the overall and long term costs for the insurer.

An example is the University of North Carolina’s affiliated health plan which has a program for PWD. If patients join and adhere to the program, the plan will waive copays for their diabetes medications.   This offers a demonstration of a value-based benefit design solution.

One potential pitfall of these plans is that some people view the plans as discriminatory. For instance, providing a lower deductible for keeping your weight in check could be discriminatory against those who are genetically predisposed to obesity.  Policies should consider putting in place incentives that offer financial rewards of either reducing the deductible or waiving copays for PWD in HDHPs given patients achieve certain treatment and outcome measures.

Role of Employers

The ACA expanding coverage to include preventive care does not mean that health-literate PWD will necessarily engage in preventive health behaviors, such as getting a diabetes screening.  There is a need for fundamental changes to educate consumers and provide direction for their care, which could be accomplished through their employer.  Patients should not be expected to navigate HDHPs, co-insurance and copays, all while managing their disease, treatment, and other life circumstances. Due to the chronic nature of diabetes, this support is most essential for PWD.

HDHPs have some drawbacks for patients, insurers, and/or employers. As mentioned in the findings from the literature review, patients under HDHPs are financially incentivized to practice poorer health behaviors with their disease, such as forgoing necessary care to save on healthcare costs. Patients that neglect their health have increased absenteeism, implying lower productivity for the employer. As patients with low medical adherence tend to incur more ED visits and hospitalizations that could be avoided, HDHPs create more costs for insurers.

At least two options are available for employer-led policies: zero-dollar copays, and employer benefit plans.

Zero-Dollar Copays

Besides broad PSAs reminding their employees to receive preventative care, employers could also opt into a zero-dollar copay model for all maintenance medications. CVS Caremark materialized this idea with the Rx Zero program, where diabetic patients pay $0 out of pocket for diabetes medication. The National Business Group on Health advocates this idea as Caremark was named one of the best employers for healthy lifestyles.

At present, this program is limited by the types of medications covered under first-dollar coverage in the current HDHP design. A potential solution to this limitation is a pending law that enables insurance companies to design HDHPs that pay for medication for chronic diseases, including diabetes medication (e.g., insulin). Nevertheless, PWD would still pay a net price for their diabetes medication under this law.

Lack of access to employees’ health records limits the employer’s ability to create individual communications and interventions.  Employers could follow a similar plan to CVS Caremark and work around the need to provide individualized solutions.  A policy with a HDHP for PWD that has a zero copay for maintenance medicine (e.g., generic orals and injectables) would change the paradigm in achieving better outcomes at lower costs.

Employer Benefit Plans 

While healthcare should be directed from a patient and provider perspective to improve health outcomes, employers should guide HDHP conversations and policy interventions. There are several opportunities for employers to be more engaged in health promotion, with an added incentive to reduce healthcare costs incurred by the employer.

Employers also have a role in working with insurers to develop ways to reduce the overall cost while improving outcomes.  For PWD, adherence is a key variable in reducing ED visits, which in turn, lowers costs.  Our literature review suggests that PWD may forgo needed care, which might result in increased absenteeism and lower productivity.  In addition to health promotion, it is in both the employer and insurers’ best interests to offer plans that support the best medical adherence, which could result in lower costs, better outcomes, and better work performance.

Employer benefits managers should play a larger role in developing plans that are designed to resolve the major challenges for PWD.  The plans need to be simple, communicated, and have incentives that are aligned with reduced costs and better outcomes.  Many employers do not have the expertise nor information to develop such solutions. This underscores the importance of collaborating with diabetes experts, insurers, and the pharmaceutical industry to develop a comprehensive solution that is specific to PWD, especially those in HDHPs.

As the employer pays the largest portion of the bill and has the most to gain, employer benefits managers should take a leadership role in coordinating this initiative.  In order to maximize the impact, employers should ensure that the policy solutions are informed from a patient perspective.  This can be accomplished by including the voice of the employee as well as others in the healthcare ecosystems such as physicians, insurers, pharmaceutical manufacturers, and pharmacies.

Summary Recommendations

Overall, the literature review and expert panel lead to five recommendations.

  1. Coverage: Policy should be adjusted to allow for maintenance medication and treatment for PWD in HDHPs to be free or low cost.  At the very least, PWD should not be charged at list price until the deductible is met.  PWD should benefit from the negotiated discounts that are realized after the deductible is met resulting in better outcomes.
  2. Awareness: Two PSA awareness campaigns should be developed: one targeted to patients and caregivers and another designed for providers.  A solution should include a consortium of government, insurers, and employers to develop and implement the communication plan.
  3. Incentives and Directed Care: Policies should consider putting in place incentives that offer financial rewards of either reducing the deductible or waiving copays for PWD in HDHPs as long as they achieve certain treatment goals and comply with their directed care.
  4. Zero Copay: A policy with a HDHPs for PWD that has a zero copay covered by employers for maintenance medicine (e.g., generic orals and injectables) would change the paradigm in regards to achieving better outcomes at lower costs.
  5. Employer Leadership: Benefit managers should lead an initiative to rethink HDHPs and work with patients, providers, and insurers to develop a specific solution for PWD.

Looking Forward

The literature review offered insights about three key themes:  (1) income disparity; (2) health insurance literacy; and (3) consumer and health behavior. During the advisory board portion of this project, our advisors investigated those themes further, exploring potential causes, possible solutions, implications on policy, special initiatives for PWD, and the role of the employer. The roundtable led us to identify policy modifications that could be made to accommodate PWD in HDHPs. PWD requires customized solutions, developed with patient-centered approaches, and employers have a key role as catalysts for such solutions.

More research is needed to show that preventative care does indeed result in clear savings for PWD, so that insurers and employers can fully embrace this initiative. Furthermore, directed care is a concept that should be adopted more across the healthcare ecosystem. Patients, providers, and employers should determine where money will be spent, what should be OOP vs. covered for PWD, and in what manner this money will be spent in order to receive the greatest value in return.

Moreover, informed care is a critical success factor for any patient under a HDHP, but especially those with chronic conditions. Distinguishing between different insurance plans and understanding what they cover allows patients and physicians to determine what care is necessary, unnecessary, and discretionary. Based on our findings, diabetes treatments should be more focused on outcomes rather than cost savings. With better health outcomes, PWD could potentially decrease high-cost healthcare expenses such as emergency department visits.

As is the case with many improvement initiatives, these are most effective when the approaches occur concurrently. For example, the directed care and the communications from the employer need to convey the same information and at the same time. All the recommendations have multiplier effects. Together, these recommended improvements on policy, specific initiatives for PWD, and the expanded role of employers, may increase access, reduce costs, and improve outcomes for people with diabetes who have high deductible health plans.

 

References

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This research was supported by the UNC Kenan-Flagler Business School’s Center for the Business of Health.  Funding was provided by Eli Lilly and Company.

 

How U.S. Hospitals Can Overcome Barriers to Automation of Electronic Clinical Quality Measurement (eCQM)

Aaron Baird, Associate Professor, Georgia State University; Andrew Sumner, Director of the Institute of Health Administration, Georgia State University, Yusen Xia, Director, Institute of Insight, Georgia State University

Contact: abaird@gsu.edu

Abstract

What is the message?

While electronic clinical quality measurement (eCQM) automation by U.S. hospitals is progressing, significant barriers remain. After identifying U.S. hospital characteristics associated with eCQM automation and top reported barriers to eCQM automation, we advocate for: 1) policies that require better alignment between electronic health record (EHR) product designs and provider workflows, and 2) incentives for entrepreneurs to leverage EHR application programming interfaces (APIs) to develop innovative natural language processing (NLP) and machine learning (ML) solutions for extracting, aggregating, and analyzing eCQM data from semi- and unstructured data, such as clinical notes, diagnostic notes, and potentially even images, in the future. We contribute by providing analyses and recommendations at the national level.

What is the evidence?

We analyze U.S. hospital responses to eCQM questions in the American Hospital Association (AHA) Information Technology (IT) Supplement for 2017, the first year for which eCQM questions were asked and available in this survey, as well as hospital characteristics reported in the AHA Annual Survey for 2016.

Submitted: January 17, 2020; accepted after review: March 17, 2020

Cite as: Aaron Baird, Andrew Sumner, Yusen Xia (2020). Overcoming Barriers to Automation of Electronic Clinical Quality Measurement (eCQM) by U.S. Hospitals. Health Management, Policy and Innovation (www.hmpi.org), Volume 5, Issue 2, Spring 2020.

The Current State of Quality Measurement and Reporting Automation

As the U.S. healthcare system moves toward an emphasis on value and accountability, a key underlying mechanism that facilitates this shift is healthcare quality measurement. Healthcare quality is, “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge,”1 as well as “whether individuals can access the health structures and processes of care which they need and whether the care received is effective.”2 Quality measurement is the process of collecting data related to prescribed quality measures and reporting this data to requesting agencies and payors.3 For instance, the U.S. Centers for Medicare and Medicaid Services (CMS) now requires or incentivizes, depending on the program, participating U.S. hospitals to collect and report data related to prescribed quality measures.4

While there is general agreement among healthcare stakeholders that healthcare delivery should be of high quality, there is considerable debate about how to best facilitate quality measurement and reporting.5-15 This debate is becoming especially heated and poignant as value-based and accountable care programs impose significant administrative burdens on healthcare providers.5,7,8,16 Such burdens are not likely to abate any time soon, as quality measurement is now beginning to shift toward more specific measures that require use of clinical data, which requires even more effort on the part of providers and administrative staff.6

This is in contrast to using primarily administrative data already submitted to payors as a basis for quality measurement. For instance, if clinical data is required to calculate one or more quality measures, clinicians must be sure to input the required data correctly, at the right point and time in the process. Administrative staff must then dedicate time and effort to extracting and aggregating this data, as well as developing and submitting complete reports to requesting agencies in a timely manner.3,5,6 Further complicating matters is that many quality measurement standards are still in their infancy or adolescence. Such variation in maturity results in considerable complexity for providers, especially when managing requirements between different value-based and quality management programs.3

A potential resolution, which is garnering significant interest, is to work toward automation of the calculation and reporting of quality measures.3 Automation of quality measurement and reporting is based on the idea that once clinical data is collected and stored electronically (digitally) in an EHR, automated procedures—such as data pipelines—can be developed. Since 2009, the proliferation of certified EHRs in the U.S. has significantly increased due to incentives offered by the Meaningful Use program17 and the 21st Century Cures Act.18 Through a combination of incentives (“carrots”) and penalties (“sticks”), these programs have resulted in a remarkable increase in infrastructure for collecting and sharing health information, particularly related to patient health records.

As a byproduct of digitizing health information, this EHR and interoperability infrastructure can now also be used by health systems to digitally track quality. For instance, it is now possible to objectively determine when evidence-based guidelines were or were not followed for processes where clinical process information is entered into an EHR. If such information is collected and available to extract, automated procedures and pipelines can be designed to extract and aggregate relevant data and even automatically calculate quality measurement attainment levels as well as electronically submit the results.4

Given that much of the foundational technology is already in place and the incentives to reduce burdens are high, one might assume that eCQM automation should be relatively straightforward, even if initial setup costs are high. This assumption makes intuitive sense, as many other industries have seen significant productivity and quality benefits from automation. Interestingly, though, eCQM automation has proven difficult and, while progress is being made, significant barriers remain.5,7 For instance, studies conducted in ambulatory settings have found that EHRs often must be retrofitted or paired with third-party software to meet quality measurement requirements and such efforts are not always successful.7

U.S. Hospital eCQM Automation Levels and Barriers to Automation

Interestingly, while use of health information technology (health IT) to support quality measurement and reporting has been discussed and gradually implemented for the past 10 years or so,10,19 extant research is scant on U.S. hospital eCQM automation levels and associated barriers to increasing eCQM automation, especially at the national level. Thus, the purpose of this article is to evaluate the current levels of eCQM automation in U.S. hospitals, the characteristics of U.S. hospitals at each level of eCQM automation, and to evaluate barriers to eCQM automation. To achieve this goal, we evaluate U.S. hospital responses to the American Hospital Association (AHA) Annual Information Technology (IT) Supplement Survey from 2017 and U.S. hospital characteristics from the AHA Annual Survey from 2016 (n=3,513 U.S. hospitals).

We specifically focus on U.S. hospital responses to two questions: 1) To what degree does your hospital use automated, EHR generated measures (versus manual processes such as chart abstraction) for each of the following programs (Medicare Inpatient Quality Reporting, Physician-Specific eCQMs, Hospital-Specific eCQMs)?, and 2) What barriers—if any—has your hospital experienced in the transition from manually to fully or partially automated reporting? These questions were newly added to the 2017 version of the AHA IT Supplement Survey, which means that 2017 is the first year in which this data could be analyzed. Our results are summarized in Table 1 and Figure 1, and are further described in the following paragraphs.

First, we find that a majority (86.34%) of U.S. hospitals responding to this survey report engaging in either full or partial automation of eCQMs in 2017 for at least one of the three programs asked about in the survey (Table 1). The percentage of hospitals engaging in full or partial automation for all three programs is slightly lower, at 72.23%. The percentage of hospitals engaging in the maximum level of automation, full automation for all three programs, is significantly lower at 28.87%.

These results illustrate that at least partial eCQM automation is in place at a large majority of U.S. hospitals, which is promising, but also that full automation is currently lacking. Less than one-third of U.S. hospitals report full eCQM automation for all three programs asked about in the survey.

Further, as shown in the descriptive characteristics in Table 1, the U.S. hospitals that report full (no partial) automation for all three programs are: significantly larger (more discharges), more likely to be system owned, more likely to engage in automation in slightly less competitive markets, and are more likely to have engaged in other value-based programs including Accountable Care Organizations (ACOs) and Patient Centered Medical Home (PCMH) programs.[1] The implications of these results are that economies-of-scale, reduced market pressures, and prior experience with other value-based programs enhance the likelihood of automating eCQMs. These findings also suggest that smaller, less resource- and technology- intensive U.S. hospitals will likely face significant barriers to reaching full automation in all eCQM programs.

Second, we evaluated the barriers to eCQM automation reported by responding U.S. hospitals in tandem with their reported eCQM automation level (Figure 1). Overall, we find that the top barriers to automation are: 1) problems with clinical workflow leading to missing data or incorrect information being collected, and 2) EHR data not mapping correctly to eCQM measures, leading to missing or inaccurate information. For U.S. hospitals reporting no automation (or do not know for all three categories), we find that resources and data issues associated with EHRs dominate, as the top three barriers for hospitals with no eCQM automation are: 1) Difficulty extracting data from EHRs, 2) EHR does not possess capability to automatically generate measures, and 3) Lack of IT staff needed to generate reports.

In our view, underlying these barriers is: 1) the complexity involved in extracting data from very complex underlying databases, which then either requires technical staff very knowledgeable in the data structures from which the data must be extracted from or reliance on the vendor to extract the data, and 2) EHR vendors trying to create CQM pathways (for monitoring and reporting) and automation on their own, rather than fully enabling others to do so through APIs. We return to this point in our next section.

For U.S. hospitals reporting anywhere from at least some automation for at least one program to full automation for all three programs, the primary barriers were mixed between EHRs and clinical workflow challenges. In particular, these hospitals reported EHR data not mapping correctly and problems with clinical workflow as their primary barriers to eCQM automation. These barriers are followed closely by difficulty of extracting data from the EHR and poor EHR usability or design issues, leading to missing or inaccurate information.

Interestingly, for the U.S. hospitals that report full (no partial) automation for all three programs, automated generation of quality measures by the EHR and poor EHR data quality are the barriers with the lowest reported percentages. Further, follow-up analyses of the effect of EHR vendor choice on whether or not such barriers are more likely to be reported found that some EHR vendors, particularly the smallest and lowest market-share EHR vendors, are significantly more likely to be associated with higher reported quality measure generation and data extraction barriers.[2] In sum, primary barriers include: collecting data within an EHR at the right point in the process and in the right format, as well as mapping collected data to quality measurement calculations and EHR choice (or ability to work with your EHR vendor to customize eCQM automation).

These findings primarily imply that U.S. hospital EHRs, while vital to eCQM automation, are also currently standing in the way of automation, particularly in that needed data is often difficult to effectively collect and extract. In combination with the results described above from Table 1, we can also surmise that economies-of-scale, that often are a benefit of larger hospital size and system ownership, likely help to distribute eCQM automation effort and costs among hospitals within systems and units within larger hospitals, and thus reduce eCQM automation burdens.

Looking to the Future: Considerations for Overcoming Automation Barriers

While much of the current health policy debate is focused on interoperability, coordinating care for patients across the continuum, cost containment, and, of course, improved quality,20-23 we argue that equally as important is enabling automation of eCQMs (passive voice). While closely-related efforts, such as standardization of quality measures, will help to enable automation of eCQMs,7 we have yet to see a concerted policy effort focused specifically on reducing barriers to eCQM automation. As discussed by Mandl and Kohane in their excellent article on how to enable the next generation of innovation in health IT,18 the information within EHRs is the true source of value and the potential value of these data must be unlocked. However, we find, ironically, that EHRs are a primary impediment to attaining full eCQM automation.

To overcome these barriers, we suggest that significant policy efforts be focused on two areas: 1) better alignment of EHR structured data collection and extraction of quality measure data with quality measure requirements, particularly associated with making it clear to end-users within the EHR user-interfaces where and how data must be entered if it is to be included in required quality measures, and 2) developing methods, possibly including NLP and ML methods, to effectively extract quality measurement data from unstructured and semi-structured data formats, such as clinical notes, diagnostic notes, and even images, in the future.

Furthermore, artificial intelligence (AI) such as deep neural network methods can take advantage of these data to generate various inferences and recommendations that help health professionals make decisions more effectively and efficiently. As an example, by combining the medical history of different patients with clinician’s notes and images, an AI system can provide recommendations that may potentially mitigate near-future health complications of a patient or even suggest more effective medicines or procedures for the patient.

In regard to our first point focused on alignment of data (and data collection methods and architectures) with quality measures, we believe that such policy efforts should be focused directly toward EHR vendors, rather than only indirectly on data requirements imposed upon hospitals. Some efforts in this regard are already underway, such as the inclusion of specific interoperability requirements in EHR certification processes,24 which will indirectly impact eCQM automation, as well as technical efforts such as those associated with clinical quality language (CQL),[3] the health quality measure format (HQMF),[4] and use of qualified clinical data registries (QCDR).[5] We nevertheless suggest that more could be done in this area.

Consider the following example. Data for one quality measure can be collected from multiple places within an EHR, such as deciding whether to obtain diagnosis data from the problem list, diagnoses directly entered into charts, or even diagnoses received from other healthcare facilities. Complexity for providers would be significantly reduced if EHR vendors’ data structures were coordinated with quality measurement and reporting requirements, as well as with the providers who need to extract needed data. Further, once such requirements are clarified and coordinated, if EHR user-interfaces more clearly identify how or why certain clinical data is needed in specific places (e.g., make it clear that if a chronic diagnosis is not included in the problem list, corresponding quality measures will be missed or potentially reported as not met for such patients[6]), integration of clinical workflows and quality automation processes would likely be significantly improved.

In regard to our second point focused on addressing the challenges of semi- and unstructured data, we note that structuring data for every quality measure imposes significant burdens on providers (and even EHR vendors), such as when adding checkboxes or picklists for all data elements that must be included in quality measures (e.g., a checkbox that the provider must check if smoking cessation counseling was provided for patients who report tobacco usage[7]). Such burdens would be significantly reduced if needed information could be extracted from unstructured or semi-structured notes, documents, and images. One solution to this approach would be to again ask or require EHR vendors to make investments into unstructured data such as NLP and images as well as ML and AI methods to find and extract such data.

Many advances are being made in unstructured data and ML and AI that greatly increase the efficiency and efficacy of these methods, but we note that these methods are evolving rapidly, and these areas are not the traditional core competencies of EHR vendors. Further, much of the focus of NLP, ML, and AI by EHR vendors and healthcare providers has been on predictive algorithms, such as for predicting patient health and cost risk.25,26 While investments into predicting risk certainly overlap with eCQM efforts, interestingly, we have not seen a significant increase in eCQM automation as a result of these efforts. Thus, another solution, rather than only pushing EHR vendors (and healthcare providers) to incorporate new ways to automate eCQM, is to also incentivize entrepreneurs and third-parties to develop innovative NLP/Images and ML/AI applications that can be built on top of existing EHRs.27-29

In other words, promoting smaller scale or more focused NLP and ML innovations30 via market-based incentives and API use,18 versus only healthcare organization- or EHR vendor-based regulations, may more quickly and effectively result in solutions that can eventually scale to meet broad quality measurement automation needs. This is especially important as future quality measures are likely to require even more granular measurement through analysis of different types of data, including potentially even image data.

Further, if APIs are standardized, as advocated for by the Substitutable Medical Applications and Reusable Technologies (SMART) initiative31 and health standards such as Fast Health Interoperability Resources (FHIR) support bulk data access,[8] such innovative apps would potentially be scalable across many or even all EHRs, irrespective of vendor or underlying proprietary data structures.32 Even further, frictionless health information flows that enable automation of eCQM could also then be leveraged to enhance disease surveillance, deep learning, and advance the goals of a learning health system.33-35

Conclusion

In sum, we find that eCQM automation is progressing, but significant barriers remain. Given that healthcare providers who can take advantage of economies-of-scale are more likely to fully automate eCQM, we provide policy ideas for increasing the likelihood of all U.S. hospitals reaching full eCQM automation, rather than just the largest and resource-rich. In particular, we think that policymakers should work more closely with EHR vendors to align data collection interfaces and extraction procedures with eCQM requirements. We also suggest that policymakers incentivize entrepreneurs to innovatively solve unstructured and semi-structured data challenges associated with eCQM measurement and reporting.

References

  1. Lohr KN, Schroeder SA. A strategy for quality assurance in Medicare. New England Journal of Medicine. 1990;322(10):707-712.
  2. Campbell SM, Roland MO, Buetow SA. Defining quality of care. Social Science & Medicine. 2000;51(11):1611-1625.
  3. Hayford TB, Maeda JL. Issues and Challenges in Measuring and Improving the Quality of Health Care. Congressional Budget Office;2017.
  4. CMS. Clinical Quality Measures Basics. 2019; https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/ClinicalQualityMeasures. Accessed 1/10/2020, 2020.
  5. McConnell RA, Kane SV. The Potential and Pitfalls of Using the Electronic Health Record to Measure Quality. The American Journal of Gastroenterology. 2018;113(8):1111-1113.
  6. Tang PC, Ralston M, Arrigotti MF, Qureshi L, Graham J. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. Journal of the American Medical Informatics Association. 2007;14(1):10-15.
  7. Ahmad FS, Rasmussen LV, Persell SD, et al. Challenges to electronic clinical quality measurement using third-party platforms in primary care practices: the healthy hearts in the heartland experience. JAMIA Open. 2019;ooz038:1-6.
  8. Casalino LP, Gans D, Weber R, et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Affairs. 2016;35(3):401-406.
  9. McGlynn EA, Adams JL, Kerr EA. The quest to improve quality: measurement is necessary but not sufficient. JAMA Internal Medicine. 2016;176(12):1790-1791.
  10. Conway PH, Mostashari F, Clancy C. The future of quality measurement for improvement and accountability. Journal of the American Medical Association. 2013;309(21):2215-2216.
  11. Kaplan RS, Porter ME. Mandate Outcomes Reporting. Health Managment Policy and Innovation. 2019;4(3):1-7.
  12. Liu X, Schulman K, Scheinker D. Private and Public Incentives for Hospitals to Improve the Quality and Reduce the Cost of Care. Health Managment Policy and Innovation.4(3):1-12.
  13. Eisenberg F, Lasome C, Advani A, Martins R, Craig P, Sprenger S. A study of the impact of meaningful use clinical quality measures. American Hospital Association;2013.
  14. Scales CD, Schulman KA. Triggering management for quality improvement. Health Services Research. 2014;49(5):1401-1406.
  15. Leape LL, Berwick DM. Five years after To Err Is Human: what have we learned? JAMA. 2005;293(19):2384-2390.
  16. Casalino LP. Pioneer Accountable Care Organizations: Traversing Rough Country. Journal of the American Medical Association. 2015;313(21):2126-2127.
  17. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. New England Journal of Medicine. 2010;363(6):501-504.
  18. Mandl KD, Kohane IS. A 21st-Century Health IT System-creating a real-world information economy. N Engl J Med. 2017;376(20):1905-1907.
  19. Anderson KM, Marsh CA, Flemming AC, Isenstein H, Reynolds J. An Environmental Snapshot: Quality Measurement Enabled by Health IT: Overview, Possibilities and Challenges. Agency for Healthcare Research and Quality;2012.
  20. Adler-Milstein J, Embi PJ, Middleton B, Sarkar IN, Smith J. Crossing the health IT chasm: considerations and policy recommendations to overcome current challenges and enable value-based care. Journal of the American Medical Informatics Association. 2017;24(5):1036-1043.
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  24. CMS. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Policy Changes and Fiscal Year 2019 Rates; Quality Reporting Requirements for Specific Providers; Medicare and Medicaid Electronic Health Record (EHR) Incentive Programs (Promoting Interoperability Programs) Requirements for Eligible Hospitals, Critical Access Hospitals, and Eligible Professionals; Medicare Cost Reporting Requirements; and Physician Certification and Recertification of Claims. Final rule. Federal Register. 2018;83(160):41144.
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  27. Khalilia M, Choi M, Henderson A, Iyengar S, Braunstein M, Sun J. Clinical predictive modeling development and deployment through FHIR web services. Paper presented at: AMIA Annual Symposium Proceedings2015; San Francisco.
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  33. Etheredge LM. A rapid-learning health system: what would a rapid-learning health system look like, and how might we get there? Health Affairs. 2007;26(Suppl1):w107-w118.
  34. Friedman C, Rubin J, Brown J, et al. Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. Journal of the American Medical Informatics Association. 2015;22(1):43-50.
  35. Norgeot B, Glicksberg BS, Butte AJ. A call for deep-learning healthcare. Nature Medicine. 2019;25(1):14-15.

[a] These variables were determined to be significant at P<0.05 via a logistic regression with full eCQM automation as the dependent variable, the characteristics described in Table 1 as the independent variables (lagged by one year to address issues with contemporaneous effects), and further controlling for physician intensity, teaching hospital, and region (West, Midwest, South, and Northeast).

[b] These analyses were conducted with logistic regressions with the reported eCQM barrier as the dependent variable, the reported EHR vendors as indicator variables (with the highest frequency EHR vendor reported as the base variable), and controlling for all characteristics described in Table 1 (lagged by one year) as well as physician intensity, teaching hospital, and region (West, Midwest, South, and Northeast).

[c] For more information on CQL, please see https://cql.hl7.org/01-introduction.html.

[d] For more information on HQMF, please see https://ecqi.healthit.gov/hqmf.

[e] For more information on QCDR, please see https://qpp.cms.gov/mips/quality-measures.

[f] For example: https://www.ahrq.gov/pqmp/measures/management-of-chronic-conditions.html

[g] For more information on this quality measure, please see https://qpp.cms.gov/docs/QPP_quality_measure_specifications/Claims-Registry-Measures/2019_Measure_226_MedicarePartBClaims.pdf.

[h] See FHIR Bulk Data Access (Flat FHIR) at https://hl7.org/fhir/uv/bulkdata/ for more details.

Key Lessons From a Minnesota Multi-Stakeholder Opioid Policy Forum

Pinar Karaca-Mandic, Professor, University of Minnesota; Ralph Hall, Professor, University of Minnesota, Principal, Leavitt Partners; Kimberly Choyke, Program Director, MILI, University of Minnesota

Contact: pkmandic@umn.edu

Abstract

What is the message?

Solving the opioid crisis among Minnesota’s Native American and African-American populations requires five actions: (1) addressing local contexts; (2) innovative funding models; (3) addressing structural determinants of health; (4) the active coordination among relevant stakeholders; (5) concurrently addressing other substance abuse and recovery issues.

What is the evidence?

Conclusions of a multi-sector forum on “Combating Minnesota’s Opioid Epidemic” hosted at the Carlson School of Management’s Medical Industry Leadership Institute (MILI) of the University of Minnesota, October 2019. 

Submitted: February 11, 2020; accepted after review and revisions, March 28, 2020

Cite as: Pinar Karaca-Mandic, Ralph Hall, Kimberly Choyke, 2020. Key Lessons from a Minnesota Multi-Stakeholder Opioid Policy Forum. Health Management, Policy and Innovation (HMPI.org), Volume 5, Issue 2, Spring 2020.

Minnesota’s opioid overdoses, while well under the national average1,2, are disproportionately concentrated among Native American and African American populations.  In 2016, Native Americans were six times more likely and African Americans were twice as likely to die of drug overdose compared to white Minnesotans. This is the worst race-based disparity in the United States. 3,4

In order to adequately address these and other opioid use issues in Minnesota, in October 2019, the Carlson School of Management’s Medical Industry Leadership Institute (MILI) at the University of Minnesota convened a multi-sector forum, “Combating Minnesota’s Opioid Epidemic”. The forum had active collaboration from the University’s Office of Academic Clinical Affairs, Law School, School of Public Health, State Health Access Data Assistance Center as well as Mayo Clinic, Leavitt Partners, and other organizations.  The forum included federal and state policy makers; local, tribal, state health department and judicial representatives; state legislators, healthcare and payer systems; and recovery organizations.  The participants and panelists coalesced around five key learning points.

Point 1. Local contexts: While national policies and strategies are critical, unique state and local conditions must be identified and addressed

Minnesota has unique challenges regarding opioid abuse among its Native American and African American populations.  Other jurisdictions have their own specific local issues and challenges. While national policies are vitally important, one size solutions do not fit all situations.

The Twin Cities has the most Native Americans per capita in the country, and Minnesota is home to 13 tribal nations and communities. Tribal representatives emphasized that tribes do not necessarily fit into the standard mold of evidence-based medicine, and new solutions are needed to include tribes in policy development and to address tribal needs. These communities need authentic listening and direct involvement in policy development.

Another example of local challenges was identified when local representatives in the forum emphasized their inability to sufficiently work on prevention because their resources are often largely consumed by efforts to get used syringes off the streets. City officials report this has been an increasing issue, particularly across Minneapolis, as the opioid crisis continues.  Fire departments, city health workers, and the city and state health departments are working to clean up the syringes on the streets and implement new initiatives to encourage safe disposal.

The participants also stressed that state and local initiatives can serve as test beds for identifying successful innovative strategies to substance abuse.  This approach allows areas to pilot new strategies, gather data, and closely monitor the impact of strategies specific to their populations.  Local officials can then make a case to expand these strategies with evidence and as a model for others. 

Point 2. Funding: Innovative public and private funding sources and payment models need to be explored

Currently, much of the financial support for initiatives to combat the opioid epidemic come from federal and grant funds; however, these sources can be limited and uncertain.  Therefore, new, innovative funding sources are needed, and existing treatment/recovery reimbursement concepts rethought in order to ensure critical efforts continue.

As an example, forum panelists highlighted the recently passed Minnesota Statute 151.066.  This law was the first in the country to impose licensing fees on pharmaceutical companies. These fees are earmarked to a special stewardship account for opioid abuse and recovery/treatment efforts. Activities include the following targets:

  • The Opiate Epidemic Response Advisory Council
  • A five-year opioid initiative leveraging $20 million in annual revenue
  • Physician training in opioid prescribing and opioid alternatives,
  • Non-pharmaceutical pain management mapping,
  • Funding for Native American communities to help support their tribal healing programs.

Payer organizations discussed the need to improve access and coverage for non-opioid pain management strategies. Participants also discussed whether to waive prior authorization to facilitate access to medication assisted treatments. Other payers addressed variations in front-end insurance coverage for detox; and increased access to telehealth services particularly for low-access communities.

Point 3. Structural challenges: Additional efforts are needed to identify and resolve structural determinants of health that exacerbate opioid issues

There must be consideration of specific structural determinants of health impacting opioid abuse, treatment and recovery.  Often these are unintentional barriers or reflect past practices.

Due to the workforce disparity among care professionals, many Native Americans and African Americans do not have access to the addiction care they need within their communities.  For example, a college degree is required to have an alcohol dependency counselor license, yet this well-intentioned requirement is currently a structural barrier to finding Native American alcohol and drug abuse counselors. By refining requirements for addiction counseling qualifications, otherwise qualified peers without traditional credentials could provide treatment and recovery services to their communities.

Point 4. Coordination: On-going coordination efforts, including sharing of best practices, successes and failures must accelerate

One constant theme among panelist is the need to coordinate across the public and private sectors, to share best practices, and to create new approaches to opioid abuse prevention and treatment. The following points were central to this part of the discussion.

  • Physicians in the forum noted that often a person will present with opioid use disorder, but is not yet ready for treatment. New approaches are needed to provide these patients with immediate and longer-term treatment options.
  • Licensing, credentials and business models need to revamp and leverage coordination between recovery workforce, community health workers, community health centers, and sober living facilities.
  • Pain management and recovery care should include a holistic approach including:
    • Comprehensive training programs on addiction treatment and prescribing practices
    • Patient education on treatment options and risks
    • More point of care support including prescribing limits and increased efforts in opioid avoidance
    • Increased utilization of telemedicine, medical home and other team-based approaches
    • Development and access to non-opioid pain management tools
    • Patient education on availability of alternative treatments

Point 5. Beyond opioids: Addiction and recovery issues are broader than opioids and must address other substance abuse issues such as meth and alcohol abuse

While the focus of the policy forum was on opioids, all stakeholders emphasized that this is a substance use disorder, not just an opioid use problem.  Some policies may, for example, push patients from one substance being abused (e.g., prescription opioids) to another substance (e.g., heroin or meth).  A meaningful fraction of individuals in the Twin Cities are getting addicted directly to heroin rather than through prescription opioids, and we need policies to address this phenomenon. In other communities, meth is a critical issue. National, state, and local policies need to address substance abuse broadly and not just prescription opioid abuse.

Looking Forward

The opioid crisis is pervasive and cannot be fought in isolation. A one-size fits all approach will not work to resolve the opioid epidemic.  Without cross-disciplinary, cross-sector partnerships to develop and implement new policies, practices, training, and education, we will not overcome the crisis.  Collaborative discussions must not only include practitioners, policy experts and law makers, but also individuals.  The work must consider the communities served and the policies that impact them.

 References

  1. Opioid overdose prevention, Minnesota Department of Health, www.health.state.mn.us/communities/opioids/index.html, Accessed January 24, 2020.
  2. SHADAC analysis of opioid-related and other drug poisoning deaths, State Health Compare, SHADAC, University of Minnesota, statehealthcompare.shadac.org, Accessed January 24, 2020.
  3. The opioid epidemic in Minnesota, Minnesota Department of Human Services, mn.gov/dhs/assets/federal-opioid-briefing_tcm1053-336378.pdf, Accessed February 3, 2020
  4. Race Rate Disparity in Drug Overdose Death, Minnesota Department of Health, https://www.health.state.mn.us/communities/opioids/documents/raceratedisparity.pdf, Accessed February 8, 2020

Flattening The Mental Health Curve: Three Strategies for Medical and Psychiatric Care During the COVID-19 Pandemic (6/15, Stanford, UCSF)

Jimmy J. Qian, Stanford University School of Medicine, Khashayar Nattagh, University of California San Francisco School of Medicine, and Robert Pearl, MD, Stanford University School of Medicine and Graduate School of Business, Stanford University

Contact: jimmyqian@stanford.edu

Abstract

What is the message? Three strategies can address mental health challenges that are being exacerbated during the COVID-19 pandemic: (1) implement mental health screenings in conjunction with viral detection and serology tests; (2) leverage rapid adoption of novel technologies, such as the expanded use of telemedicine for mental healthcare delivery; (3) use peer-to-peer support and collaborative care models to meet demand.

What is the evidence? The authors’ experience in multiple clinical facilities.

Timeline: Submitted June 12, 2020; accepted after revisions: June 13, 2020

Cite as: Jimmy J. Qian, Khashayar Nattagh, Robert Pearl. 2020. Flattening the mental health curve: Three strategies for medical and psychiatric care during the Covid-19 pandemic. Health Management, Policy and Innovation (HMPI.org), Volume 5, Issue 1, Special issue on COVID-19, June 2020.

Mental Health Also Requires A Flatter Curve

Throughout the COVID-19 pandemic, physicians and policymakers have focused efforts on “flattening the curve.” This approach spreads the number of infected patients over a longer time interval to prevent overwhelming hospitals and critical care capacity. Much attention has been given to flattening the curve for the respiratory illnesses caused by SARS-CoV2.

By contrast, relatively little has been done about the mental health crisis and the urgent need to flatten the curve for the psychiatric burden of this pandemic. In some ways, flattening one curve can do the opposite to the other: social distancing can save lives from SARS-CoV2 infection while prolonging and worsening mental health issues.

COVID-19 Is Exacerbating the Mental Health Crisis In The U.S.

The United States already had a mental health crisis prior to the pandemic, with nearly 20% of Americans living with a mental health condition and nearly 50,000 suicides in the US each year [1]. A pre-pandemic shortage of mental health providers has also been well-documented. During and after COVID-19, it is expected that the mental health crisis will be exacerbated by a sharp rise in depression, anxiety, and post-traumatic stress disorder.

In addition to the deaths and suffering caused by the coronavirus itself, economic factors and social isolation have negatively impacted people’s mental well-being. Unemployment, which has been shown to correlate with suicide rates, has affected millions of workers as many families struggle to pay rent or buy critical food and supplies [2]. In one study, nearly half of U.S. adults reported that their mental health had been negatively affected by the pandemic [3].

Demand for therapy has skyrocketed, while research has shown after major events like natural disasters and economic downturns, rates of suicide, overdose deaths, and substance use disorders go up [4]. Indeed, rates of depression, domestic violence, alcoholism, overdoses, and suicides have already increased [2].

Three Solutions to Flatten the Mental Health Curve

The American healthcare system must brace for heightened demand for mental health services this year and for several more to come. Yet little funding and attention has been allocated for this purpose and no additional trainees are in the training pipeline. We argue that elected officials and healthcare leaders must devote themselves to flattening the mental health curve just as they did for coronavirus-associated medical illnesses.

Unlike a viral infection, there is no vaccine or curative therapeutic for psychiatric conditions. The US healthcare system must pursue a coordinated effort to proactively address these problems, rather than once again addressing a crisis too late. To this end, we propose three specific solutions.

Screen: First, we should screen for mental health conditions when people get tested for SARS-CoV2 infection or resulting antibodies. In the same way that patients may not realize they have the coronavirus, they may be unaware of mental health issues. A unique opportunity to achieve population-scale mental health screening arises from the tremendous effort dedicated to testing for viral spread.

The Patient Health Questionnaire-2 (PHQ-2) with an additional question on suicidality could work well: the PHQ-2 questionnaire is rapid and has been validated as a depression screening tool [5]. Because it does not very reliably detect suicidality, an extra question about suicidal ideation would be helpful and simple to add. This concise mental health screen can be directly added to any patient-facing paperwork or digital interface associated with the viral infection test.

In addition to being able to help individuals, widespread mental health screening will allow the medical community to quantitatively understand the country’s state of mental health during this pandemic in the same way we study transmissibility and lethality of the coronavirus itself. An additional screening question could also help identify individuals who might not feel safe in their shelter-in-place locations due to domestic violence or other factors.

Workflow innovation: Second, leaders must design novel workflows and incentives to meet the increased demand for mental health care. The United States has a shortage of mental health personnel.

Just as the healthcare system has embraced technology, virtual care, and novel infrastructures for COVID-related medical illnesses, it must do the same for psychiatric conditions. For instance, both asynchronous and synchronous virtual psychiatric care expands capacity and increases efficiency.

Healthcare systems and payers must move quickly: they should rapidly scale their virtual mental health services if they have the capability to do so in-house. If not, they can collaborate with digital health companies that can offer virtual mental health care at scale.

Payers must also expand coverage of mental health services to beneficiaries, ensuring widespread access and coverage during the pandemic and also for the years to come. Restrictions such as caps on the number of covered therapy sessions should be abrogated, and provider reimbursement must increase.

Many healthcare systems showed immense speed innovating in care delivery for coronavirus-induced respiratory illness and comorbidities. This is the time for them to do the same for psychiatric care.

Multiple stakeholders: Third, just as medical care has taken an all-hands-on-deck approach during the pandemic, psychiatric care should also utilize a variety of stakeholders to meet increased patient demand. An approach that works particularly well with virtual mental health care is the use of peer counseling and patient communities, allowing people with similar experiences to support one another online. Group counseling has been shown to achieve similar results to individual sessions and is a way to rapidly increase access.

Another useful strategy is to utilize the collaborative care model (CCM), a clinical protocol that has been shown through randomized controlled trials to consistently improve mental health outcomes [6]. CCM allows primary care providers to provide mental health care in conjunction with a psychiatric consultant and a behavioral health manager. This leveraged model allows mental health care providers to care for more patients, alleviating the psychiatric provider shortage. Research has shown that telemedicine-based CCM yields better outcomes than on-site CCM [7], making CCM an even more attractive model in the setting of the pandemic.

Looking Forward

COVID-19 has exacerbated the American mental health crisis. The best way to combat this pandemic-induced psychiatric burden is to adopt three strategies similar to those used for the outbreak’s medical burden. First, we must implement mental health screenings at-scale in conjunction with viral detection and serology tests. Second, we must leverage rapid adoption of novel technologies and infrastructures for mental health care delivery, such as expanded use of telemedicine. Third, just as overwhelmed healthcare systems enlisted residents, various medical specialists, and mid-level providers to help treat respiratory illness, so mental healthcare systems can use peer-to-peer support and collaborative care models to meet demand.

We must flatten the curve – both for medical and psychiatric care – to save lives and help patients.

 

Acknowledgements

Financial support and sponsorship: none

Conflicts of interest: none

 

References

  1. Statistics. Mental Health Information, National Institute of Mental Health. Accessed May 22, 2020. https://www.nimh.nih.gov/health/statistics/index.shtml
  2. Pearl R. How the 80/20 Rule Can Save Your Life During the Coronavirus Reopening. Forbes. Published May 26, 2020. Accessed May 27, 2020. https://www.forbes.com/sites/robertpearl/2020/05/26/the-80-20-rule/#58b7ec0627f8
  3. Panchal N, Kamal R, Orgera K, Cox C, Garfield R, Hamel L, Munana C, Chidambaram P. The Implications of COVID-19 for Mental Health and Substance Use. Kaiser Family Foundation. Published April 21, 2020. Accessed May 22, 2020. https://www.kff.org/coronavirus-covid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substance-use/
  4. Wan W. The coronavirus pandemic is pushing America into a mental health crisis. The Washington Post. Published May 4, 2020. Accessed May 22, 2020. https://www.washingtonpost.com/health/2020/05/04/mental-health-coronavirus/
  5. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41(11):1284‐1292. doi:10.1097/01.MLR.0000093487.78664.3C
  6. Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS. Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. American Journal of Psychiatry. 2012 Aug;169(8):790-804. doi: 10.1176/appi.ajp.2012.11111616
  7. Fortney JC, Pyne JM, Mouden SB, et al. Practice-based versus telemedicine-based collaborative care for depression in rural federally qualified health centers: a pragmatic randomized comparative effectiveness trial. Am J Psychiatry. 2013;170(4):414‐425. doi:10.1176/appi.ajp.2012.12050696