U.S. voters in the November 2024 election expressed concerned about economic challenges for the American family. While there was much discussion about inflation and housing prices, healthcare costs also contribute to economic anxiety. The cost of healthcare increases well above the rate of inflation, and health insurance continues to fail to provide access and financial security to covered lives.

If Americans voted for change, how might the healthcare market change? Several of the papers in this issue provide some insights for readers.

One of the greatest areas of concern with the U.S. healthcare market is the administrative costs of our multi-payer system. While this concern has been raised repeatedly, there has been less focus on solutions and how to improve the system. In an exciting application of a powerful new business framework, Precedent Thinking, Istvan and colleagues highlight 82 different firms and markets that have transformed in ways that can be applicable to healthcare. They offer a novel solution of computable contracts and a standard platform to reduce administrative waste and increase innovation.

Another exciting innovation is AI. Jain and colleagues are some of the country’s leading experts in AI in healthcare. Their concern is to ensure that the AI we adopt helps us improve our clinical paradigm and is not a new source of error and waste in the system.

Saba et. al. examine one of the signature initiatives of the Biden administration — drug price negotiation in Medicare. The authors suggest that for all the attention this effort has received, it will likely have little impact on the cost of medications covered by the Medicare program.

Value-based healthcare has been a rallying cry for healthcare reformers since the passage of the Affordable Care Act. However, there is little evidence that this approach has had a meaningful impact on the cost of healthcare. I suggest that this a failed approach as it does not consider the financial incentives driving consolidation of healthcare delivery in this country, incentives that are more powerful than the financial incentives of value-based care.

Ernest Ludy is an entrepreneur who, as the CEO on founder of Medstat, built a career on holding down healthcare costs. Ludy used data to understand variation and costs and apply these insights to improve the quality and efficiency of care. He discusses how his original vision can be applicable in today’s healthcare market.

The United States has seen an explosion of interest in a novel class of anti-obesity medications, the GLP-1 Receptor Antagonists. Information on the medication has spread virally through social media and influencers, transforming the idea of pharmaceutical marketing. Ray and Chatterjee examine marketing and “Direct to Consumer Prescriptions” in their analysis of an emerging trend in the pharmaceutical market.

In a new health management faculty series, we highlight the career of one of our member faculty, Pinar Karaca-Mandic, from the University of Minnesota. She is is both a scholar and now an entrepreneur, and offers her insights into leveraging and managing those roles.

Finally, we highlight some new teaching tools. Tal Gross, faculty member of the Questrom School of Business at Boston University, provides an overview of a book he co-authored that has been described as an “ideal entry point into health economics for everyone from aspiring economists to healthcare professionals.” In addition, a new teaching case examines GoodRx and the U.S. pharmaceutical marketplace.

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

Kevin Schulman, Graduate School of Business, Stanford University; Clinical Excellence Research Center, Stanford School of Medicine

Thank you to Regina Herzlinger for prompting this evaluation of value-based payments. She has been a true thought-leader in health care management. Thanks also for Crystal Tin-Tin Chang for reviewing an earlier version of this manuscript.

Contact: kevin.schulman@stanford.edu

The Evolution of Value-Based Care

Healthcare delivery in the United States has long been characterized by high costs and inconsistent quality of services for patients. A little over a decade ago, policymakers designed an innovative solution to these challenges: they would shift payment models from the provision of services (often described in a derogatory manner as payment for volume), to a new concept called value. If we paid for services based on value, then the delivery system would respond by reorienting the business model to lower-cost, higher-quality services. (1) Leading advocates of this new model were certain that it would finally drive to a higher-performing healthcare system. An article in a business magazine reflected this perspective, “The Strategy That Will Fix Healthcare.” (2)

Value-based healthcare (VBC) was adopted by the Centers for Medicare and Medicaid Services (CMS) through the Affordable Care Act and later, by the private healthcare market. Value would be driven by: a) financial models such as Accountable Care Organizations (ACOs), b) value-based care demonstration models across service lines such as oncology and orthopedic surgery, and c) new programs offered by the CMS Innovation Center. In the private sector, most major health insurers have value-based payment models. For example, United Healthcare touts, “…we are accelerating the transition from a fee-for-service to a value-based system of care delivery. Value-based care arrangements are designed to manage health care costs and improve the patient experience.” (3) Aetna has a different take on the model: ” VBC’s triple aim is to improve the health care experience, improve the health of individuals and populations and reduce the costs of health care. To do this, VBC moves beyond sick care and adopts a proactive, team-oriented and data-driven approach to keeping people healthy.” (4)

Value-based care models still dominate discussion at the policy level and across private health insurance markets. Yet, there is little evidence that value-based payment has had a meaningful impact on healthcare delivery. Evaluations of the ACO program found little evidence of benefit from hospital-based ACOs, and at best, extremely modest evidence of benefit from other models. (5) Even proponents of these policies have begun to acknowledge this failure. (6)

“Apart from conceptual reservations, a decade of empirical evidence on the effects of pay for performance is not encouraging. Less charitably, it is damning. There have been some scattered gains, but studies of major programs have consistently found little to no improvement—even on targeted measures—and revealed plenty of cause for concern.” (7) The government’s assessment is consistent with these assessments, albeit with an optimistic spin: “CBO concluded that some ACO models produced small net savings.” (8)

How could such a perfect solution result in such disappointing results? From the beginning, the policymakers promoting value ignored the business model of the U.S. healthcare delivery system. Hospitals embarked on a strategy of scale starting in the late 1990’s, finding that scale at a local level would provide significant pricing leverage in negotiations with private health insurance plans. This strategy was successful, driving up prices for those with private health insurance, and driving up the costs of healthcare delivery as hospitals competed for profitable, privately insured patients with newer and more elegant facilities. While the American Hospital Association reported that private health insurance paid only a modest premium to Medicare in 2000, (9) a 2022 study from RAND found that private health plans now pay 224% of Medicare prices, and in 19 states, health plans pay more than 300% of Medicare prices for outpatient care. (10)

The premise of value actually allowed hospital systems to double-down on this strategy of consolidation by extending their leverage over physician services. In 2012, only 6% of physicians worked directly for hospitals. (11) But federal policy shifted to a value framework that ignored the impact of hospital consolidation on prices in the private healthcare market. In the name of “value,” we witnessed relaxed anti-trust enforcement, CMS’s pursuit of hospital-friendly payment policies including generous facility fee payment models, and expansion of the 340B drug purchasing program for outpatient hospital-based services. At the same time, physicians struggled to adapt to new rules of electronic health records and the cacophony of “value” payments models in the market. The net result saw physicians flock to hospital-based employment. By 2022, 52% of physicians were hospital employees. (11)

Where Are We Today?

The value movement is steamrolling ahead unabated despite its dismal record. Even negative evaluations of value programs in academic literature suggest that these programs require time to mature rather than recognizing them for what they are — a failure. This policy battle is not without cost. The real cost of private health insurance has risen from 13% of median family household income in 2000 to 25% in 2021, (12) and our life expectancy has fallen to a rank of 33 among 49 nations tracked by the OECD, with average U.S. life expectancy now 6.5 years lower than in Switzerland. (13)

How did policymakers get this whole concept so wrong? In August of every year, U.S. economists meet in Jackson Hole, Wyoming for the Jackson Hole Economic Symposium. There, looking at the grandeur of the mountains, attendees fiercely debate economic data and policy, and the outlook for the coming year.

We have no such gathering in healthcare. We have experts in pieces of the elephant — experts in Medicare policy, experts in Medicaid, experts examining hospital consolidation, and experts examining the pharmaceutical market. One state Secretary of Health explained that they were in charge of Medicaid and the state employee health plan. They did not even know about the alignment of hospitals and physicians in the state.

Some of these experts meet at different disciplinary conferences to present their research, but nowhere are all of these perspectives brought together to achieve an overarching economic understanding of the market and the inter-relationships between hospital strategy, payment models, and health outcomes. Rationally, how could a “value” payment program even be considered in one sector of the market when it is not in sync with, or is possibly even in conflict with, value models in other sectors of the economy, and still hope for changes in the market?

The U.S. healthcare system is a market, and our policymakers need to take on a market-level view and understanding. Yet, we have no such mechanism for market oversight in place. (14) The Biden administration competition policy initiative could have provided the rationale for such an effort. The Trump administration market disruption agenda could also provide a impetus for this wholistic approach. We could tackle this entire agenda, or start with the segment of the market with the greatest potential for leverage, by focusing on the payment process. (15) By examining all of the pieces of the market concurrently, we can begin to understand the strategies driving disparate actors and develop market and regulatory solutions to drive the market in the direction of value for individual patients.

References

1. Burns LR, Pauly MV. Transformation of the Health Care Industry: Curb Your Enthusiasm? Milbank Q. 2018 Mar;96(1):57-109.
2. Porter, M., and Lee T. “The Strategy That Will Fix Health Care.” Harvard Business Review 91, no. 10 (October 2013): 50–70. https://search-ebscohost-com.ezp-prod1.hul.harvard.edu/login.aspx?direct=true&db=heh&AN=90325428&site=ehost-live&scope=site
3. https://www.unitedhealthgroup.com/driven-by-our-mission/what-we-do/value-based-care.html
4. https://www.aetna.com/employers-organizations/resources/value-based-care.html
5. McWilliams JM. Pay for Performance: When Slogans Overtake Science in Health Policy. JAMA. 2022 Dec 6;328(21):2114-2116.
6. Schulman KA, Richman BD. Reassessing ACOs and Health Care Reform. JAMA. 2016 Aug 16;316(7):707-8.
7. Song Z, Fisher ES. The ACO Experiment in Infancy–Looking Back and Looking Forward. JAMA. 2016 Aug 16;316(7):705-6.
8. https://www.cbo.gov/system/files/2024-04/59879-Medicare-ACOs.pdf
9. Schulman KA, Milstein A. The Implications of “Medicare for All” for US Hospitals. JAMA. 2019 May 7;321(17):1661-1662
10. Whaley, Christopher M., Brian Briscombe, Rose Kerber, Brenna O’Neill, and Aaron Kofner, Prices Paid to Hospitals by Private Health Plans: Findings from Round 4 of an Employer-Led Transparency Initiative. Santa Monica, CA: RAND Corporation, 2022. https://www.rand.org/pubs/research_reports/RRA1144-1.html.
11. Bowling D 3rd, Richman BD, Schulman KA. The Rise and Potential of Physician Unions. JAMA. 2022 Aug 16;328(7):617-618.
12. Schulman KA, Narayan, A. Employer-Based Health Insurance and Employee Compensation. JAMA Health Forum. 2023;4(3):e225486.
13. https://data.oecd.org/healthstat/life-expectancy-at-birth.htm
14. Burns R. The U.S. healthcare ecosystem: payers, providers, producers. New York : McGraw Hill. 2021.
15. Richman, Barak D. and Schulman, Kevin, Healthcare Administrative Costs and Competition Policy (May 2023). Competition Policy International Antitrust Chronicle, (May 2023), Duke Law School Public Law & Legal Theory Series No. 2023-36, Available at SSRN: https://ssrn.com/abstract=4488625

 

 

 

 

Brooke Istvan,  Graduate School of Business; Perry Nielsen Jr,; Megan Eluhu, School of Medicine, Bryan Kozin, Walt Winslow, Graduate School of Business, David Scheinker,  Kavita Patel, School of Medicine, Kenneth Favaro, Stefanos Zenios, Graduate School of Business, and Kevin Schulman, Graduate School of Business, Hospital Medicine and Clinical Excellence Research Center, School of Medicine; Stanford University

Professors Zenios and Schulman are co-senior authors.

Contact: kevin.schulman@stanford.edu

Introduction

Administrative costs represent a considerable burden in the U.S. healthcare market.^{1,2,3,5,6,7,8,9,10,11,12,13} Administrative costs account for nearly one quarter of the $4.8 trillion spent on healthcare services.¹⁴ Among OECD countries, the United States spends nearly ten times the average on healthcare administrative functions per capita.¹⁵ Estimates suggest that we can readily eliminate at least $265 billion annually in spending from reducing this administrative burden.^1,2

While the burden of administrative costs in the U.S. healthcare system has been well recognized, developing solutions to this challenge has proved vexing. Efforts to understand why these administrative costs are so high highlight the complexity of the market (317,987 different health plans, 599,204 codes for products or services, and 57 billion negotiated prices¹²). Other efforts have described the high billing and insurance-related costs resulting from the architectural complexity of the contracting process, the complexity of the health plan contracts with providers, and compliance costs.¹³ Moreover, much of the administrative effort in healthcare is based on digitized analog processes, with requirements for phone calls, faxes, and transmission of paper (or PDF) documents. Other challenges include the lack of a regulatory body overseeing the market. The academic and policy literature assessing the problem of healthcare administrative costs suggests little opportunity to change given the lack of a catalyst for improvement, no market forces demanding change, and no oversight mechanisms holding the market accountable for improving this situation.

However, an alternative perspective is to consider that this is not an intractable problem. There are many markets which have faced daunting challenges such as we have described for the healthcare market and have seen significant change.  

How firms and markets change is an exciting area of research in the business literature. The challenge is to ascertain an underlying strategy for reproducible and predictable innovation processes. If such an approach can be articulated, it would offer a new way to address seemingly intractable issues such as the administrative cost issue in the U.S. healthcare market.

Precedents Thinking is one of the newest advances in this field of research.⁴ It builds from the observation that all innovative solutions are creative combinations of prior innovations, “precedents”, found in different businesses and markets that faced similar challenges. If we can find the best precedents, then we can increase our chances of developing an innovative, workable solution to the problem at hand. One barrier to large-scale innovation, or addressing intractable problems, is that it is hard to generate the investment required (money, time and effort, or policy interest in a crowded legislative/regulatory space) to tackle daunting issues such as administrative costs at the required scale. The Precedents Thinking methodology offers innovators a set of proven solutions across firms and markets. One theory is that by limiting innovation to proven solutions, we may have de-risked the problem sufficiently to attract investment required to tackle the problem.

In this paper, we apply Precedents Thinking to the problem of administrative costs in U.S. healthcare.

Methods

Precedents Thinking is a method where past solutions to similar problems are used in new situations to come up with innovative ideas. We applied the Precedents Thinking methodology to the issue of U.S. healthcare administrative spending. Precedents Thinking methodology has three distinct steps: 1) framing the problem statement and its key elements, 2) searching for prior innovations, “precedents”, that are relevant to one or more of the problem’s key elements, and 3) combining the precedents into the best possible workable solution to the problem.

1. Problem Statement and Deconstructions

The problem statement and deconstructions identify the core elements of the problem to highlight generalized features that could be used in the precedents search process. The problem statement and its key elements known as “deconstructions”, were developed through a workshop using a modified Delphi consensus process featuring expert facilitation. Participants were research team members and selected outside advisors, including the two developers of the Precedents Thinking methodology (see appendix exhibit a1). Participants were provided prereading materials that included explanations and examples of the Precedents Thinking methodology.  

The group divided into two breakout sessions. Each breakout group was charged with narrowing the problem statement and defining its elements, the “deconstructions”. The workshop resumed as a whole to refine the two problem statements and deconstructions of each group into a workshop consensus statement and deconstructions. The problem statement and deconstructions continued to be refined over the course of the effort, but, for readability, only the final version is reported in the results section below.  

2. Precedent Generation and Selection

Based on the workshop consensus problem statement and deconstructions, the research team began a search for precedents. The goal of this step was to develop an exhaustive list of firms or markets that had successfully implemented solutions to the problem statement within and outside of the healthcare market. Precedents were included if they were: 1) highly relevant to at least one problem deconstruction, 2) had strong evidence of success beyond luck, and 3) were more detailed than a common best practice. The precedent generation process included brainstorming among workshop participants, interviews with a broader group of industry experts and steering committee members, and a final step of using ChatGPT with a prompt of the problem statement alone and with each of its deconstructions until the output was hallucinatory or nonsensical.¹⁶

Each precedent was systematically described with background, insights, and outcomes.

Precedents were classified by industry, governance (public, private, public-private) and primary mode of change (digitization, centralization, standardization).

An initial item reduction step was taken by the research team to arrive at a shorter list of priority precedents based on impact, feasibility, trust building capacity, and applicability to the problem statement. Impact was assessed via capacity to simplify the system and reduce complexity of processes; feasibility was assessed by governance structure (i.e., private, public-private, public) and primary mode of change (centralization, standardization, digitization). Building trust across stakeholders was a binary categorization that held the same weight as the other categorizations. To determine applicability to the problem statement, we used a graphical approach to item summarization where precedent summaries were applied to the problem statement and deconstructions in 2×2 matrixes where each dimension was the level to which the precedent solved each deconstruction. The precedents were then ranked by impact, feasibility, and trust and selected to include a variety of industries and unique insights around the theory of change. A subset of 26 high-priority precedents emerged.  

The 26 high-priority precedents were summarized into written briefs including background, key insights, business model, ownership, and impact on heterogeneity, complexity, trust, cost, user experience, productivity, and profitability.  

3. Creative Combinations

The final step was to refine the precedents into an actionable set of solutions from the 26 high-priority precedents. This step allowed for the aggregation of precedents into composite solutions applicable to the healthcare market.  

Each working group member was assigned to create at least two creative combinations of two to three precedents that solve the problem statement through at least one of its deconstructions. The full team then met and summarized the individual responses into a final summary consensus solution for the healthcare market. The group reviewed the creative combinations generated by each participant and used breakout groups to further refine the individual assignments into consensus sets of precedents and creative combinations to address the problem statement and deconstructions. The breakout groups’ solutions were then compared against each other and combined into a final set of precedents and creative combinations for each deconstruction of the problem statement.  

Data Analysis

We used descriptive statistics to summarize the precedents developed from the precedent generation step of this effort.

Results

Problem Statement and Deconstructions

The final problem statement defined by the workshop was: How to create the standardization and infrastructure that’s needed to reduce administrative waste in healthcare? We developed two deconstructions of this problem statement:

Deconstruction 1: How to reduce heterogeneity and complexity of contracts that results in administrative burden

Deconstruction 2:  How to create the necessary payment infrastructure to support an efficient healthcare transaction ecosystem

Precedent Generation and Selection

We were able to generate 82 precedents for this stage of the research (see Appendix Exhibit a2). A majority (72%) were drawn from Finance, Healthcare, Public Services, & Technology. In terms of governance, 57% of our precedents were private sector solutions, 22% were public sector solutions, and 21% were developed through public-private partnerships. Regarding mode of change, 49% used standardization as a primary mode of change, 29% used digitization, and 22% used centralization. Finally, 35% of precedents were thought to have improved trust in the market. Descriptive data for all precedents and a subset of 26 high priority precedents are reported in Exhibit 1.

Exhibit 1: Summary characteristics of precedents
Industry	Full precedent list (N=82)		High priority precedent list (N=26)
Consumer Products	2	(2%)	2	(8%)
E-Commerce	7	(9%)	3	(12%)
Entertainment	4	(5%)	1	(4%)
Finance	18	(22%)	8	(31%)
Food Services	3	(4%)	1	(4%)
Healthcare	10	(12%)	2	(8%)
Logistics	5	(6%)	1	(4%)
Public Services	18	(22%)	3	(12%)
Real Estate	2	(2%)	0	(0%)
Technology	13	(16%)	5	(19%)
Ownership
Private	47	(57%)	17	(65%)
Public	18	(22%)	3	(12%)
Public-Private	17	(21%)	6	(23%)
Primary form of change
Centralization	18	(22%)	10	(38%)
Digitization	24	(29%)	5	(19%)
Standardization	40	(49%)	11	(42%)
Trust
Improved Trust	29	(35%)	14	(54%)
Not Impacting Trust	53	(65%)	12	(46%)

Legend: Full precedent list is the result of the precedent generation exercise. High priority precedent list is the final list of precedents used in the creative combination exercise.

Creative Combinations

The final creative combinations aimed to solve both problem statement deconstructions (contract standardization and payment infrastructure) by applying the learnings from a consensus set of our precedents narrowed in on by our working team. The precedents that informed our core solution were:

• Modularized machine-readable contracts:
  • Standardized mortgages
  • Mobile phone standard setting organizations (SSOs)
• Payment Infrastructure:
  • Society for Worldwide Interbank Financial Telecommunications (SWIFT)
  • Stripe
  • SMART on FHIR
  • State utility commissions
  • FAA

See Exhibit 2 in the Appendix for the complete summary of relevant precedents.

Solution Part 1: Modularized machine-readable contracts

Part one of the solution aims to implement modularized machine-readable contracts in a digital and unified manner. In the current market, each payer (or individual health plan) must negotiate a contract for services with each in-network provider organization. While features of these agreements refer to a similar set of business processes, they do not follow any standardized structure or standard set of fully digital processes.¹² Further, novel features such as value-based payment models are further individualized for payers or plans (often resulting in entirely analog transactions).

Building from our learnings on mortgage standardization, we propose a single, modularized digital contract format. In the early 1970s, Fannie Mae and Freddie Mac standardized to a modular format to improve the mortgage process and to allow syndication of these now standard mortgage products.³ Our working definition of a modularized machine-readable contract is one that is designed to be digitally adjudicated. Such a contract will have a standard structure and set of contract terms terms (See illustrative example in Exhibit 3). For example, items that are typically addressed in these agreements include billing processes, payment terms, additional requirements such as prior authorization processes, quality reporting, confidentiality, and regulatory compliance. We have defined these agreements as modularized, not uniform. In other words, agreements could be customizable by health plans under this structure, but customization could not alter the requirement for complete digital adjudication.

Exhibit 3: Modularized Machine-Readable Contracts

Legend: Each insurer could design new contracts, or reproduce the logic, requirements, and processes of each of their current contracts, with the Modularized Machine-Readable Contracts framework. Each care provider could use a single operational and technical framework to interact with every contract from every insurer. Differences between contracts would be captured with standardized categories of inputs and outputs, variables, and functions. To resolve edge cases not captured in the contractual logic, insurers and providers could continue to work as in the current state.

Single Sign-Ons (SSOs) are industry or public-private partnerships that bring together competing firms that collectively select and adopt uniform technical standards to ensure compatibility and interoperability among products. This approach has allowed for standardization and innovation that supports the enormous mobile phone market. The SSO process could be used to determine the final content of the modularized machine-readable contracts, as well as technical supporting details (such as a requirement for SMART on Fast Healthcare Interoperability Resources (FHIR) APIs for digital transactions). The SSO for such a process could build from industry (America’s Health Insurance Plans (AHIP), for example) or could be constructed through the Federal government (the U.S. Department of Health and Human Services or the Department of Commerce, for example).  

The process of selecting a modularized and machine-readable contract structure would drive innovation in contract design and build engagement from across the industry. In the mobile phone SSO process, each industry partner submits their proposal for mobile phone standards. The best option available is selected by the SSO body and becomes the standard for the industry (for a fixed time, this is an ongoing innovation process). Firms are incentivized to contribute their intellectual property (in the mobile phone market this in in the form of patents) because when one firm’s technology is selected, their relevant patents are deemed standard-essential patents (“SEPs”), generating royalty payments from the other firms in the market. Since contract elements may not be patentable, the SSO process may have to develop other compensation schemes to support collective engagement with the process.

Solution Part 2: Uniform Payment Infrastructure

Standardized contracts would enable the construction of a uniform digital transaction platform for the U.S. healthcare market. Such a platform should be seen as critical core infrastructure supporting the market. Currently, each health plan utilizes their own platform to process healthcare claims or relies on a limited set of “clearinghouses” in the market. Given the heterogeneity in transaction processes in the current healthcare market, there is significant underinvestment in this infrastructure (an issue that was highlighted by the recent cyberattack on Change Healthcare¹⁴).  

Stripe has built a comprehensive, digital-first backend payment infrastructure that has created a trusted and centralized payment process for vendors across industries with easy access through APIs. Given a standard contract, it is easy to envision the development of a consistent payment processing infrastructure for all payers and providers to use, eliminating the payment inconsistencies that exist in the market today. This infrastructure would house (and implement) the digital contracts to ensure the integrity of the payment process.

SWIFT is a consortium of financial institutions that developed a digital communication system that underlies most banking transactions. The SWIFT network demonstrates that a digital transaction platform can be reliable, secure and robust, even at enormous scale. It is also an example of such a platform emerging out of collective industry action that later expanded to include the Federal Reserve and other central banks as opposed to one created through regulation.

Financing critical infrastructure such as this transaction platform usually follows a pattern of initial investment followed by a self-sustaining financial model (say, by collecting a transaction fee for each payment). In this case, the required transaction fees are likely to be substantially lower than the cost per payment transaction under the current model. Currently, there are no governance mechanisms in the market to support the development of this infrastructure. While the private sector could deploy the capital required for this effort, a purely private transaction platform could be subject to rent-seeking by the platform owner over time, limiting the economic benefits of this initiative. Creating a public or a public benefit corporation to develop and oversee this platform could be a pathway to addressing this challenge. Creating a public oversight mechanism would help to ensure transparency and accountability across the market and possibly avoid rent-seeking. For example, another precedent is how the Federal Aviation Administration (FAA) centralized the infrastructure and agencies needed to support commercial aviation in the U.S. and how state utility commissions regulate public utilities and their profits.   

Discussion

The high administrative cost burden of U.S. healthcare is a seemingly intractable problem. These costs result from tremendous complexity in transactions across the diversity of health plans, and the lack of oversight and attention to this issue at the federal and state levels. This is not just an abstract concern about the market. Complexity and administrative challenges are a burden to patients and cost consumers an enormous amount of time by having to negotiate insurance terms and conditions, prior authorization, and appeals processes. These challenges interrupt patients’ access to care, resulting in delayed diagnoses and treatment. ^18,19

The Precedent Thinking methodology described in the business literature suggests one model for developing predictable and scalable innovation. This model requires the identification of a key problem statement, developing and refining a set of firms and markets which have successfully addressed a business challenge similar to the core problem, and then adapting these precedents to a solution for the market of interest.^20,21 We challenged ourselves to understand how to standardize transactions and create an infrastructure that would be needed to reduce administrative waste in healthcare. We identified 82 firms or markets that have successfully addressed challenges of this magnitude, focused on a subset of 26 of these innovations, and developed a proposal for contract standardization and payment infrastructure development that could address transaction costs in healthcare.

The Precedent Thinking methodology helped us to understand how other firms and industries have successfully addressed challenges of the magnitude faced in the healthcare market. In developing the idea for modularized machine-readable contracts, we identified home mortgages and mobile phone standard setting organizations as key precedents. In developing the idea for a digital transaction infrastructure, we have identified the work of the firm Stripe in financial markets, the SWIFT infrastructure for the banking system, the APIs available through SMART on FHIR, the role of the FAA in the aviation market, and state public utilities commissions. These precedents provide critical insights for solving the administrative cost challenge of the U.S. healthcare market.

In a focused exploration of business precedents, we found that other industries have solved large, seemingly intractable problems like an analog to digital transition. From this effort, we discovered that standardization and digitization have been successfully deployed in several markets, generating key insights that can be applied to the U.S. healthcare market. We identified that large-scale market change does not require initial government initiative (private initiatives have been successful), though government involvement can drive adoption across stakeholder groups.

Another important insight from Precedents Thinking is how solutions such as standardization and digitalization can create positive network effects in a market. Developing modularized, machine-readable contracts and, correspondingly, standardizing a transaction platform, can lead to transaction efficiency, market entry, enhanced liquidity, competition, and value in a manner that can continue to build over time through investments in improved infrastructure and transaction processes (Exhibit 4). For example, it could enable the securitization of insurance contracts to enhance liquidity in the market.

Exhibit 4: Catalyzing a Virtuous Cycle in Healthcare

Legend: Adoption of modularized machine-readable contracts and the unified digital payment infrastructure would enable a virtuous cycle of follow-on impacts across the healthcare market over time. The platform would lower transaction costs thanks to standardized and centralized digital payments. Reducing transaction costs and “friction” associated with the payment infrastructure would ease entry of new firms and products into the market. These new entrants would drive increased competition and investment that would improve the value of the healthcare provided by the system. Clear improvements to value would generate increased investment in the digital payment infrastructure that would allow the virtuous cycle to continue.

 

Our work is not the only effort to understand the high administrative costs in healthcare. Several authors have identified the high administrative costs in the U.S. healthcare system^{1,2,4,5,6,7,8,9,10,11,12} and some have proposed ideas to help reduce waste.^{2,3,6,9,10,22,23,24,25,26,27} Their work validates the enormous waste in the market and focuses on an overlapping set of potential solutions that can be deployed to address these challenges. However, the path to achieving such solutions remains unclear. Precedents Thinking has allowed us to think deeper about how truly transformative solutions at scale could be implemented.

One result from this work is a better understanding of the critical role of standardization and infrastructure investment in addressing the high transaction costs in healthcare. Many of the precedents studied that have successfully addressed these challenges come from the finance industry where government structures such as the Federal Reserve Bank have helped to establish, catalyze, coordinate, and regulate different aspects of the financial markets. Obviously, we lack such a coordinating entity in the U.S. healthcare market. Using our formulation of a solution, it would be possible to examine how existing legislative authority can be used to implement our solution, including legislative authority under HIPAA, the Affordable Care Act, and through the Centers for Medicare & Medicaid Services. Additionally, broad executive actions around AI could be leveraged to develop a contractual and payment infrastructure environment for safe AI use. An alternative governance structure could include the role of agencies such as the U.S. Labor Department (through the employer health plan fiduciary obligations) or the Commerce Department. New legislative authority might be required to fully implement the complete set of precedents we have identified in the healthcare market. For example, federal legislation could establish a centralized authority overseeing healthcare payment transactions similar to the federal reserve and state legislation could mandate use of a standard transaction platform by physicians and hospitals licensed within a state when engaging with health plans.

States could also play a key role in addressing high administrative costs because of smaller scale and faster implementation times. Programs like Medicaid, managed at the state level with federal funding, could provide a means of scaling successful standardization efforts.

One challenge for the governance structure is the inherent conflict between standardization and heterogeneity in the market. While it is possible to build technology that can implement enormous complexity in an algorithmically-driven payment process model, the more we enable complexity, the more we risk diluting some of the economic benefit of standardization. Health plans have built their marketing efforts on facilitating health plan customization, even with little economic support for this approach (for example, the Medigap market has 10 health plan structures,²⁸  while the individual insurance (Obamacare) exchange plans have the same benefit structures but differ in cost-sharing provisions²⁹). At the extreme, it’s easy to postulate that there is little economic or market rationale to support the current 317,987 different health plan structures in the new infrastructure, but the degree to which plan customization is a required design element should be a matter of further discussion.

Even with substantial government involvement, industry participation is a prerequisite for the successful adoption of modularized contracts and a digital infrastructure. One possibility is that SSOs provide a platform through which industry partners agree to details such as the degree of plan customization. SSOs could also play a role in making final decisions about digital infrastructure across the industry. Besides SSOs, private entities could collaborate with government on policy options through working committees and nonprofit coalitions. Beyond the initial adoption of new contracts and infrastructure, industry partners could also provide critical insights to guide change management and improvements over time. Incumbent industry leaders unsettled by the potential for a new transaction model that disrupts their core business model might need to be pulled into this effort by government or customers.

Limitations

The strength of the Precedents Thinking method is the robustness of the three steps in the process. While we convened an outstanding research team and steering committee, other efforts to apply the same methodology to this problem could have identified a different set of solutions. Further, while we tried to be exhaustive in developing precedents for discussion, we could have missed key innovations in other markets in the U.S. and globally. Finally, our assumption behind the Precedents Thinking approach is that the solutions can generalize to the healthcare market and scale, and both assumptions are untested.

Conclusion

We applied Precedents Thinking methodology to the challenge of high administrative costs in the U.S. healthcare market. Using business precedents from markets and firms inside and outside of healthcare, we identified contract modularization and the development of a digital payment infrastructure as a solution than can address this challenge at scale. There are remaining questions about the governance model for implementing these solutions and the potential to pilot and scale, but overall, we conclude that high administrative costs need not be an intractable feature of the U.S. healthcare market.

 

Acknowledgements:

Funding provided by The Ludy Family Foundation, the Hirsch Family Foundation, Gates Ventures, and the Government, Business and Society Initiative at the Stanford Graduate School of Business.

 

Appendix

Exhibit 2: Summary of Relevant Precedents

Deconstruction	Precedent	Background	Key insights	Industry	Governance	Primary form of change
1) Modularized machine-readable contracts	Standardized Mortgages	Fannie Mae and Freddie Mac wrote and mandated standardized mortgage contracts in the 1970s	Government backed private agencies that created mortgage forms divided into two components: 1) uniform mortgages accepted by every state and 2) those that could not reach consensus called non-uniform.	Real Estate	Public-private	Standardization
	Mobile Standard Setting Organizations (SSOs)	Information and Telecommunication (ICT) standardization efforts apply standards across the entire industry through the work of standard-setting organizations (SSOs) (i.e., a public-private partnership to crowdsource and implement common technical standards across competing firms)	SSOs are self-governed industry associations of competing firms that collectively select and adopt uniform technical standards to ensure compatibility and interoperability among products. To set a new standard, SSOs typically require members to disclose related IP. The SSO then determines the best solution to implement as the common standard across the market allowing them to achieve scale while incentivizing individual innovators to compete in the creation of better technology. SSO processes are revised and improved with government input, through membership of multiple agencies in the SSO and enforcement actions from the DOJ and FTC.	Finance	Public-private	Centralization
2) Payment infrastructure	Society for Worldwide Interbank Financial Telecommunication (SWIFT)	Cooperation from many different players to work together to create a shared messaging service that provides improved services to customers and enables swifter transactions around the globe	A group of 239 private financial institutions came together to develop a centralized communication system with codes that allowed banks to digitize process around transferring money. It was a member owned cooperative institution owned by shareholders (~3,500 financial institutions across the world are shareholders) but operated as a company with full time employees and CEO. The governance is a board of 25 representatives from the member banks, overseen by the ECB and central banks of the G10 countries where each nation’s usage determines the number of board members that each country is allowed.	Finance	Public-private	Centralization
	Stripe	Revolutionized digital payment systems by creating a secure, standardized infrastructure that simplified bank connections for developers, enabling easier and more uniform tool development and reducing complexity in payment processes.	The solution worked because they targeted a specific consumer (the developers) and need (payment infrastructure) and offered tailored benefits that worked. By offering simple, well-documented APIs, Stripe made it easier for developers to integrate payment processing into applications making it more convenient for consumers to make purchases and reducing cart abandonment rates. This precedent enabled a shared technological base across competing firms via a third party that enabled swifter innovation and development in an industry with large, slow-moving incumbents.	Finance	Private	Standardization
	HL7 SMART on FHIR	Influence of government regulation in driving towards technological standardization within healthcare and the interoperability of systems that the change has created.	With the goal to create a modern standard for healthcare data exchange, it aimed to overcome the limitations of previous HL7 standards like versions 2 and 3, which were known for being inflexible and cumbersome. FHIR leveraged modern web technologies to enable flexible and lightweight data exchange. The development of HL7 FHIR required collaboration from various stakeholders, which ensured real-world applicability and industry best practices. HL7 International maintains FHIR, regularly releasing updates and extensions to address emerging healthcare challenges.	Technology	Private	Standardization
	State utility commissions	Public utility commissions began with regulatory activity to reign in the railroad monopoly in the late 19th century. States have since created similar commissions to regulate a broader range of public utilities, including electricity, gas, and water, to ensure fair rates and reliable service.	Public utilities balance the interests of consumers and utility companies by overseeing operations, approving rate changes, and enforcing policies. They protect consumers while ensuring the financial health of utility providers and present a model of regulation of necessary transactions infrastructure.	Public services	Public-private	Centralization
	Federal Aviation Administration (FAA)	Before the FAA was created in 1958, the CAA and CAB shared oversight of civil aviation regulation and safety measures, and regulatory responsibilities between the two agencies. When the FAA replaced the CAA and CAB, the US established a common civil-military system for air navigation and air traffic control and assumed broader authority to reduce aviation hazards.	The FAA consolidated fragmented regulatory functions of the CAA and the CAB into a single authority, providing a comprehensive and standardized approach to aviation oversight. The FAA has embraced technological advancements, improved safety and security measures, and navigated various challenges, demonstrating the FAA’s ability to adapt to and address the evolving technological landscape of the aviation industry. The FAA has been actively involved in redesigning the National Airspace System (NAS) to accommodate the growing demand for air travel. This initiative involves optimizing airspace, improving navigation procedures, and implementing advanced technologies to enhance capacity and reduce delays. The FAA’s role has since expanded to the regulation of drones and commercial space flights, responding to the rapid growth of drone technology, commercial space travel, and cybersecurity.	Logistics	Public-private	Centralization

Legend: These precedents were used to directly support the final creative combination solutions. Descriptors were developed by the research team and described on the precedent briefs.

 

Exhibit a1: Workshop Participants and Steering Committee Members

Workshop Participants

Name	Description
Kenneth Favaro, MBA	Developer of precedents methodology
Stefanos Zenios, PhD	Developer of precedents methodology, Stanford Graduate School of Business
Kevin Schulman, MD	Stanford University Schools of Medicine and Business
David Scheinker, PhD	Stanford School of Medicine
Michael Murray, MS	Former CFO of Blue Shield of California
Meghan Eluhu, MCiM	Research team member
Bryan Kozin, MBA	Research team member
Brooke Istvan, MBA	Research team member
Perry Neilsen	Research team member
Walter Winslow, MBA	Research team member

 

Steering Committee Members
Name		Description
Jacob Asher, MD		Former CMO, multiple health plans
Matt Eyles, MPP		Former CEO, AHIP
Kenneth Favaro, MBA		Developer of precedents thinking methodology, Chief Strategy Officer, BERA Brand Management
Goutham Kandru		Gates Ventures, associate director US healthcare
Robert Kaplan, PhD		Harvard Business School
Ernest Ludy		Former CEO, Medstat
Michael Murray, MS		Former CFO of Blue Shield of California
Kavita Patel, MD, MSHS		Stanford University School of Medicine
Barak Richman, PhD, JD		George Washington School of Law
David Scheinker, PHD		Stanford School of Medicine and Engineering
Kevin Schulman, MD		Stanford Schools of Medicine and Business
Will Shrank, MD		Former CMO, Humana
James Weinstein, MD		SVP Microsoft Healthcare
Stefanos Zenios, PhD		Developer of precedents thinking methodology, Stanford Graduate School of Business

 

Exhibit a2: Full Precedents List

Precedent	Explanation	Digitization, Standardization, or Centralization?	Public vs private vs partnership?
ATM Machines	ATM Machines allow consumers to withdraw cash from any machine in the country using their debit card	Digitization	Private
ATM Networks	ATM networks allow banks to communicate across regions and contracted networks in order to validate and process ATM requests for an additional fee	Standardization	Private
P&C insurance	Property and casualty insurance to consumers is structured with standard minimum and fault formulas. Individual contract rates are not typically negotiated	Standardization	Private
Medicare PPS	The Medicare BBS determines fixed bundled payments to hospitals based on geographic factors, patient case mix, and DRGs	Standardization	Public
State of MD all-payer rate setting	State (the HSCRC) sets rates for healthcare services that all providers receive from all payers	Standardization	Public
Medicare Advantage Generally	Privately administered Medicare plans reimbursed through capitation at the federal level, allowing private payers to manage the plans locally in whatever way will maximize cost savings	Standardization	Public-Private
NHS standard contracts	All of NHS uses the same contracts (single payer and single provider system makes this easy)	Standardization	Public
Direct contracting employer – provider (i.e., centers of excellence)	Large employers are contracting directly with large providers to get guaranteed rates especially for specific high-cost procedures	Centralization	Private
CMS 1500 form	CMS’s attempted common / standard claims form (used for all Medicare FFS and suggested to be used by private payers but it is mostly not used)	Standardization	Public
Uniform Mortgage forms for Fannie Mae/Freddie Mac	In 1971, the two held the first public meeting to begin their efforts to standardize. This proved to be an iterative process with public meetings and community comment periods. There was disagreement over all components so both provided similar standardized mortgage forms and have specific pieces tailored to their guidelines	Standardization	Public-Private

 

OTC derivatives contracts	In 1985, the ISDA and published a list of agreed-upon definitions and terms for contracts, covering a wide range of topics including floating amounts and default and termination provisions. ISDA also published a Master Agreement (MA) template in 1987, with updates in 1992 and 2002.	Standardization	Private
Tax forms	1040 form created in 1917, IRS created in 1953 which audited and ensured up to date standard forms	Standardization	Public
Credit card applications	Credit card applications are not standardized. Different credit cards are allowed to use different components of information to make a decision on approval. However, there are standard elements. For example, all lenders may consider a FICO score and all credit card agreements must include a “Schumer box” which details fees associated with the card as required by the Truth in Lending Act to be presented in a standardized format	Standardization	Private
Walt Disney World Ticketing	Rather than pay for each experience at Disney individually (like FFS), Disney Goers will pay for a general ticket upfront with “special” experiences and perks being paid on an individual basis. Tickets and experiences can be bundled for a few days or seasonally depending on the park goers preference.	Digitization	Private
Search Engines Algorithms	Search engines use a variety of factors and algorithms to predict which searches are most relevant to the user’s request; this process has become increasingly sophisticated and sponsor-based as these platforms have developed. However, many firms will “hack” these algorithms by using SEOfavorable components on their websites in order to get higher rankings	Digitization	Private
Life Insurance policies	Purchasers of life insurance are the people being directly insured themselves, no network negotiations	Standardization	Private
Online Gambling	Originating as digitally posted sports books in 1995, private companies took advantage of lax gambling restrictions in Caribbean countries to establish online betting exchanges. CryptoLogic in 1995 allowed monetary transactions over the internet, which allowed the entire betting transaction to occur automatically on client websites. Note: the legality of online betting remains controversial	Digitization	Private

 

Streaming Services Analytics	Large streaming services need to perform “content validation” in order to determine which content is worth purchasing/financing and what can be cut from their portfolio without losing a large percentage of subscribers	Digitization	Private
TV Residuals Standards/Structure	Residuals are paid to union members for continuously shown media. Residuals are calculated based on a variety of factors, including guild membership, initial payment, time spent, type of production, and foreign vs domestic market	Standardization	Private
TV Residuals Payments (SAG AFTRA)	SAGAFTRA Unions administer and negotiate TV residuals for its members who appear on TV	Centralization	Private
Fast Food Franchising	Brand identity, trademarks, suppliers, and products are licensed to investors for a percentage of revenue in order to establish a local chain	Standardization	Private
Eventbrite	Consolidates contracts with artists and vendors on a centralized platform and derives revenue from a percentage of the ticket sale	Centralization	Private
Residential Lease Agreements	Property managers and landlords use standardize lease agreements	Standardization	Private
Banking clearinghouses	Established between 1750 and 1770 as a place where the clerks of the bankers of the city of London could assemble daily to exchange with one another the cheques drawn upon and bills payable at their respective houses. Meant to reduce the risk of a member firm failing to honor its trade settlement obligations.	Centralization	Public
Digital ACH infrastructure	Computer-based electronic network for processing transactions, usually domestic low value payments, between participating financial institutions, automating the clearinghouse concept developed in the 1700s in London	Digitization	Public-Private
Apple Wallet	Digital passes etc. collected across arious apps, emails, etc. into one central digital wallet	Centralization	Private
Stripe	Stripe’s focus was to make it easier for developers to integrate payment processing into their websites and applications. They gained popularity and expanded its services globally at the forefront of developing and implementing new technologies in the payment space (i.e., simple checkout, support for various payment methods, tools for managing subscriptions and recurring payments)	Standardization	Private

 

TurboTax	TurboTax has become the premier source for compiling and issuing annual tax payments for both federal and state filings. Consumers can use a tool at no cost to help with filing tax returns	Digitization	Private
CommonApp	CommonApp served to simplify the college application process by enrolling multiple institutions to the same college application questions and formats to make it easier on students and families	Centralization	Private
The Bar exam / association	The Bar serves as a standardized set of requirements for legal professionals to be certified by in order to practice. Standardized nationally and tailored at the individual state level. The bar creates a repository of all certified lawyers	Standardization	Public
Online marketplaces (Indeed, amazon)	Proliferated in the 21st century as a simple way to shop or share data online in standard locations/sites with standardized formats	Centralization	Private
Credit scores & loan preapproval	Credit scores created by centralized providers serve as the measurement for financial services providers. FICO created in 1989, which is the basis for a credit score to determine approvals and preapprovals	Standardization	Private
Student loans / FAFSA	FAFSA is a standardized form by which student loan decisions are made with key data elements that are shared to loan providers	Centralization	Public
Railroad infrastructure (Amtrak)	A combination effort from government subsidized players and private entities enabled passenger rail transportation across the US to grow significantly	Centralization	Public-Private
Spam email (Phishing)	Ever since the first spam email was sent over ARPANET in 1978, email clients have been trying to sort spam email using all sorts of sophisticated algorithms and big data analytics. However, spam emailers have used equally sophisticated systems in order to evade detection which has driven increasing reliance on technological innovation on both sides of the “spam war’	Digitization	Private

 

Federal Direct Cost Reporting	Within a higher ed institutions, “direct” costs for sponsored projects are individually itemized and tracked per project, even under the same principal investigator. When those costs are reported to the federal government for grant reimbursement, they are concatenated under 8 categories for billing simplification	Standardization	Public
Tech modularization (Hardware – HP printers, Software – Enterprise software offerings)	HP printers are a famous operations case study of modularization in production where HP can easily mass produce a bunch of printers and then just change the charging cable to sell them around the world	Standardization	Private
Quality metrics (state-based efforts to standardize)	Healthcare quality metrics have exploded over the past 2 decades with thousands of different quality metrics providers are required to report to specific payers and regulatory bodies. There have been several states that have taken legislative action to standardize quality metrics and require that health plans use the standardized measures. For example, Minnesota’s 2008, Massachusetts 2010, and Oregon’s 2013 laws direct the development of standard sets of quality measures and mandate healthcare providers report on these measures and health plans do not require other metrics	Standardization	Public-Private
Eliminating upcoding in MA	There is a history of providers and payers “upcoding” in MA to get more money for a more risky population. The federal government reviewed MA codes compared to FFS codes and found a bunch of codes that were higher $ reimbursement that were overutilized and cut those codes / reduced their payment to be in line with average, etc. where medically appropriate	Standardization	Public
Government contracts / RFPs / RFIs	Government has a standard Request for proposal process for hiring vendors / contractors that allow the government to evaluate on set criteria and also a request for information (RFI) process to solicit input into law making from private sector associations as well as nonprofits and research institutes	Standardization	Public
Class pass / Doordash / Eventbrite	A centralized app and payment that allows a consumer to choose from many options at many providers (e.g., for food, for workout classes)	Centralization	Private
Drinking water standards / wastewater standards	The Federal Water Pollution Control Act of 1948 was the first major U.S. law to address water pollution. Growing public awareness and concern for controlling water pollution led to sweeping amendments in 1972. As amended in 1972, the law became commonly known as the Clean Water Act (CWA).	Standardization	Public

 

FAA / air traffic control regulations	There was some regulation from the 1930’s to the 1950’s with the FAA creation taking place in 1958 to ensure safety and things were initially fragmennted. The FAA consolidated and became part of DOT in 1967 to ensure a coordinated transportation system.	Centralization	Public-Private
GAAP / Capitalization standards	The SEC was created after the crash of 1929 with the first mention of GAAP in 1936. The goal was to achieve conformity with proper accounting, full disclosure and comparability.	Standardization	Public
Car emissions standards	Congress passed the landmark Clean Air Act in 1970, which gave the newly formed EPA the legal authority to regulate pollution from cars and other forms of transportation.	Standardization	Public
Gas octane levels	Combination of private marketing in the 1960’s to standardize offerings to consumes and the Clean Air Act from the EPA phasing out lead gasoline.	Standardization	Public-Private
W2 vs. 1099 / employee vs. contractor distinction	The 1099 tax form has been around since 1917. Labor laws in the 1930’s and additional regulation in the 1970’s was passed focused on contractor vs. employee distinction with separate forms	Standardization	Public
Fishing (Fish and Wildlife) as technology increased	As early as 1871, Spencer Fullerton Baird, Assistant Secretary of the Smithsonian Institution flagged depletion and created the National Marine Fisheries Service (NMFS). Various regulation pre-dated the formation of the NOAA but then NMFS was consolidated under NOAA which was more focused on conservation and established regulations and quotas that reduced overfishing	Centralization	Public
AI in finance / open banking rule	CFPB issued RFI on AI in 2021; 2022 issued notice highlighting discrimination in models; June 2022, the American Data Privacy Protection Act (ADPPA); also in 2022 Biden issued AI Bill of Rights, which set out provisions to give consumers more control over and protetction of their data	Standardization	Public-Private
Voting machines (Analog to digital transition)	Ballots were originally paper and have been converted to digital in many places. Mail-in voting is still analog but counted by machines. This is an example of a hybrid system	Digitization	Public-Private
Shopify	Collation and tracking of online purchases. Helped centralize online shopping and shipping information for consumers	Standardization	Private
Blockchain identity verification (Truework)	Traditionally, identity and employment verification were extremely tedious for processes like mortgages. Truework created a digital verification of information and maintenance of that information for future use	Digitization	Private
Online air tickets	Tickets used to be purchased at counters in airports and travel agencies then moved online and centrtalized by Google flights	Digitization	Private

 

DocuSign	Example of digitizing an analog process of signing documents but doing so in secure and trusted environment	Digitization	Private
RFID in Retail Inventory Management	Example of multiple stakeholders coming together in the private sector to develop the technology in a lab at MIT and then commercialize it for digital tracking and tagging of inventory and shipped goods that can be interoperable	Digitization	Private
Online retail return	As shopping moved digital, so did returns. Amazon is a great example of simplification for the user on top of this digital process (you can just walk into a UPS store with whatever item you want to return and scan a QR code, don’t even have to box anything up).	Digitization	Private
DSCSA for drug tracking	Enacted in 2013 to focus on transparency and tracking of drugs through the supply chain. It improved safety, visibility, tracking and availability data	Standardization	Public
Digital fast food menu boards and OS (e.g., Toast)	Analog process made digital. There was also standardization and cataloging of items. Tedious process with tons of combinations became streamlined through an easy to update digital platform.	Digitization	Private
HTML/early internet architecture	The first internet was invented by Tim Berners-Lee, a physicist at the European Laboratory for Particle Physics (CERN), who wanted to share research ideas freely with his collaborators in other countries. This was the first rendition of “hypertext” which later became HTML, the language of coding internet websites. HTML was further developed and legitimized by the Internet Engineering Task Force, led by other scientists and engineers trying to standardize HTML to maximize its benefit to the academic community. This is an example of private standards slowly incorporated by larger working groups until it established as the global standard	Standardization	Public-Private
Lean Manufacturing	Lean manufacturing is a production method that tries to eliminate waste by limiting excess production and inventory to match total demand and focus on quality control and efficiency at individual steps in the manufacturing process	Standardization	Private
Automated passport control	Automated border entry for travelers meeting entry requirements improves user experience and reduces manual bureaucratic steps	Digitization	Public-Private
Usage-based Billing for Utilities	Automated billing and payments options offered by utility companies that can be set up directly with customer bank accounts or credit cards. This improves efficiency, likelihood of utilities getting paid and hassle for users	Standardization	Public-Private
Accounts Receivable Securitization	Been in place since the 1980s, but low penetration vs. mortgages. Reliable and cost efficient funding through accounts receivable securitization + receivables insurance can reduce credit performance uncertainty, mitigate catastrophic risk and enhance cash flow	Centralization	Private
Digital Identity Verification (e.g., face scans)	Fast form of secure identity verification (i.e., hard to copy a whole face). There are systems sharing information used in more and more locations like airports to expedite security processes.	Digitization	Private

 

ICT Cellphone	TIA has a history of encouraging disclosure of IP to ensure standards to accelerate interoperable / connected development	Centralization	Private
Tesla (DTC marketing)	Tesla eliminated the dealer as a secondary margin taker to increase their ability to make their cars more affordable	Standardization	Private
Tesla (supply chain innovation)	Unlike traditional automakers, Tesla vertically integrated several aspects of its supply chain, including manufacturing key components like batteries and electric motors in-house	Standardization	Private
Enterprise resource planning systems in manufacturing	Enterprise Resource Planning (ERP) systems in manufacturing emerged in the 1990s as a response to the need for integrated solutions that could manage various business processes, from production and inventory to finance and human resources. ERP systems aimed to eliminate data silos and enhance overall operational efficiency. ERP systems revolutionized manufacturing by providing a unified platform for managing and analyzing business processes. They streamlined operations, improved communication between departments, and enhanced decision-making through real-time data insights.	Digitization	Private
Two-factor authentication	Two-Factor Authentication (2FA) has its roots in the information technology and cybersecurity domains. The concept gained prominence as a response to the vulnerabilities associated with traditional username and password systems. The idea is to add an additional layer of security by requiring users to provide a second form of identification beyond just a password. It has enhanced cybersecurity by adding an extra layer of protection against unauthorized access. By requiring users to provide a second form of identification, such as a temporary code from a mobile device, 2FA has reduced the risk of data breaches, identity theft, and unauthorized system access.	Digitization	Private
SWIFT (Society for Worldwide Interbank Financial Telecommunications)	Global provider of secure financial messaging services. It facilitates standardized communication and transactions b/w financial institutions worldwide and streamlines financial processes (e.g. fund transfers, payment instructions, etc.). It was started by a group of private banks who recognized the need for a standard messaging service and then grew to include government central banks	Centralization	Public-Private
Contactless fare payments (MTA in NYC, BART in SF, etc.)	Public transportation systems allow for contactless cards/mobile payment apps enabling better customer experience	Digitization	Public-Private
Freelance Platforms (Upwork, Fiverr, etc.)	Marketplaces for businesses to find and hire independent professionals for temporary jobs or projects based on select criteria (skills, experience, location, etc.) secures transactions and ensures payment and quality work	Centralization	Private
Hotel express checkout	Hotels allowing guests to skip traditional checkout process and receive an electronic invoice instead to improve user experience and reduce work for the hotel	Digitization	Private
Minor software updates	Software companies conduct automatic updates for minor software releases (e.g. iOS updates). This streamlines the software maintenance process without requiring explicit prior authorizations for security updates or bug fixes	Digitization	Private
Common Course Registrations	Direct enrollment for classes that don’t require prior authorization to reduce burden for schools and students	Digitization	Private
Peoplesoft / HR software	Simplified authorization processes for routine time off requests and low-risk HR processes	Digitization	Private
Renewal of government licenses	Government implemented automatic renewal processes for licenses with straightforward renewal criteria (e.g. driver’s licenses, business licenses, hunting/fishing licenses, etc.), reducing burden for both the government and users	Standardization	Public
Napster	User created content from centralized data (i.e., playlists from central repository of songs)	Centralization	Private
HL7 FHIR	Industry created interoperability standards that enable easy data exchange and developer consensus	Standardization	Private
Roth IRA	Innovation on the 401K that offers tax advantages	Standardization	Public-Private
State utility commissions	Regulation to ensure fair rates, reliable service, and compliance with standards. They balance the interests of consumers and utility companies by overseeing operations, approving rate changes, and enforcing policies.	Centralization	Public-private

 

Exhibit a3: Summary of 26 Precedent Briefs

 

Exhibit a4: Example of 2 Pager Precedent Briefs

 

Exhibit a5: Original prompt used for ChatGPT precedents brainstorm

“I am trying to create a list of examples where industries have innovated to improve nonstandard administrative processes to streamline a set of services and remove costs. I want to focus on administrative spend reduction in particular with target reductions in contract complexity, billing process complexity, and documentation & regulation standards as examples. I don’t want to focus on applications to patient care. I want to apply a set of takeaways from these other industry examples to healthcare to try to figure out how to remedy the rising healthcare costs in the US. Please provide examples with a title of the precedent, a quick summary of the history/definition of the change, the industry it was relevant to, the impact to that industry, and the potential application to healthcare. Please provide 25 examples.”

 

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15. 2023 Peterson-KFF tracker. Peter G Peterson Foundation. Available from: https://www.pgpf.org/blog/2023/07/how-does-the-us-healthcaresystem-compare-to-other-countries. Accessed 2024 Mar 7.
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19. Kyle MA, Frakt AB. Patient administrative burden in the US health care system. Health Serv Res. 2021 Oct;56(5):755-765. doi: 10.1111/1475-6773.13861. Epub 2021 Sep 8. PMID: 34498259; PMCID: PMC8522562.
20. Han E. What Is Design Thinking & Why Is It Important?. Business Insights Blog. Harvard Business School Online; 2022. Available from: https://online.hbs.edu/blog/post/what-is-design-thinking. Accessed 2024 Mar 20.
21. Shepherd, D.A., Patzelt, H. (2018). Prior Knowledge and Entrepreneurial Cognition. In: Entrepreneurial Cognition. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-71782-1_2
22. Sandling J, Richman BD, Favaro K, Zenios SA, Schulman KA. Ensuring Access To Generic Medications In The US. Competition Policy International (pyments.com). 2024.
23. National Research Council (US) Committee on the Future of Emergency Care in the United States Health System. Hospital-Based Emergency Care: At the Breaking Point. Washington (DC): National Academies Press (US); 2007. Chapter 6, Building a 21st-Century Emergency Care System. Available from: https://nap.nationalacademies.org/read/12750/chapter/7
24. Himmelstein DU, Lawless RM, Thorne D, Foohey P, Woolhandler S. Medical bankruptcy: Still common despite the Affordable Care Act. BMC Health Serv Res. 2014. 14:556. Available from: https://bmchealthservres.biomedcentral.com/track/pdf/10.1186/s12913-014-0556-7.pdf
25. Tsai TC, Orav EJ, Jha AK. Care Fragmentation in the Postdischarge Period: Surgical Readmissions, Distance of Travel, and Postoperative Complications. Ann Intern Med. 2020. 172(4):289-297. Available from: https://www.acpjournals.org/doi/10.7326/M19-2818
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Markus Saba, UNC Kenan-Flagler Business School, UNC Center for the Business of Health, Tanya Aggarwal, UNC Kenan-Flagler Business School, Ashwin Ramanathan, UNC Kenan-Flagler Business School, Stewart Spanbauer, UNC Kenan-Flagler Business School

Contact: markus_saba@kenan-flagler.unc.edu

Introduction

As the United States approaches the implementation phase of the Drug Price Negotiation policies of the Inflation Reduction Act (IRA) in 2026, the discourse within health policy and pharmaceutical industry circles continues to intensify. This article aims to shed light on the implications of these policies, focusing on the 10 drugs selected for price negotiations. While the legislative framework promises significant price and cost reductions, it is crucial to examine the actual impact once these measures are in effect. This review does not seek to assess the policy aspect, but rather to present an objective analysis of the consequences of the law’s practical implications.

As we delve into the specifics, it becomes apparent that the law’s theoretical benefits may not fully materialize in practice, resulting in minimal impact on reducing drug prices, minimal reductions to Medicare patients out-of-pocket (OOP) costs, minimal reductions to Medicare’s budget, and minimal impact to pharmaceutical companies’ margins and R&D investments. In fact, the unintended consequences could very well result in higher costs to Medicare patients in premiums and OOP costs. Through this analysis, we will explore the broader implications for healthcare management, industry innovation, and patient affordability. The policy sounds promising on paper, but its real-world impact falls short, leaving the stakeholders to question its overall effectiveness.

Components of the IRA Related to Healthcare

The IRA’s legislative framework focuses on five main areas of Medicare drug pricing; this analysis focuses primarily on the government’s ability to negotiate drug prices for Medicare patients, which will be reviewed in the second part of this paper. The other four areas are the following:

1. Monthly insulin prices capped at $35 OOP expenditure for Medicare patients
2. Medicare patients’ drug prices capped at $2,000 OOP annually
3. More vaccines fully covered by Medicare
4. The ability of the Centers for Medicare & Medicaid Services (CMS) to demand an inflation rebate from pharmaceutical companies.

Insulin Price Cap at $35: Starting in 2023, the cost of insulin purchased under a prescription drug plan, or a Medicare Advantage Part D plan, will not exceed $35 per month. The Trump Administration in 2021 originally introduced a voluntary $35 monthly cap, covering specific insulin types obtained via Medicare Part D drug plans. The Biden administration rescinded the Trump Administration’s pricing rule, after which insulin reverted to the previous average OOP of $54. The IRA policy then made the voluntary $35 monthly cap mandatory, encompassing all insulin users on Part B and Part D plans. Approximately 5% to 7% of Medicare patients use insulin. Of those patients, 85% use Part D, which covers traditional insulin pens, and less than 15% of Medicare insulin users fall under Part B, which covers insulin pump users.  Finally, not all insulin is priced above or even at $35 for a monthly supply. Many insulin offerings are currently less than $33/month OOP for patients (Lilly has a $25 Wholesale Acquisition Cost (WAC) price and California will provide $30 insulin), with some as low as $16 per month. Patients can also receive necessary insulin for less than $35/month through drug company-sponsored patient assistance programs.

$2,000 OOP Cap: Starting in 2025, Medicare enrollees will pay no more than $2,000 OOP for prescription drugs covered under Part D, and this will be indexed annually to the rate of change in Part D costs. This effectively lowers the catastrophic coverage from $3,300 OOP for those enrolled in Medicare to $2,000. In 2021, roughly 1,537,000, or 2.2% of Medicare enrollees, spent more than $2,000. Thus, this new provision will save approximately 1.5 million Medicare enrollees about $1,000 annually.

Expanded Vaccine Coverage: Effective January 1, 2023, the IRA eliminated cost sharing and deductibles for adult vaccines recommended by the Advisory Committee on Immunization Practices (ACIP) covered under Medicare Part D. This saved Part D enrollees an average $38.84 on both RSV and Shingles shots if they elected to receive those vaccines. There was no impact on the vaccines already covered by Part B or by the public health programs that provide vaccines at no cost to adults who wanted, but could not afford, vaccinations.

Inflation Rebate: The Medicaid inflation rebate has been in place since 2019; it requires manufacturers to pay a rebate to the government if their drug prices for the program rise faster than inflation. The IRA expands this policy to Medicare. The primary expected result is that new drugs will have higher list prices at launch, which could end up costing Medicare Part B even more as drug price negotiations won’t be possible under the IRA until the selection criteria have been met.

The inflation rebate impacts the cost of the Part D benefit, but it may not impact the product prices individuals pay given the different formulary structures under Part D, as well as the OOP payment cap.

The Drug Price Negotiation Program

The centerpiece of the IRA is the provision allowing the government to negotiate drug prices for Medicare Part D (starting in 2026) and eventually, Part B (starting in 2028), a policy shift that has been heralded as a significant step toward controlling pharmaceutical costs. However, a closer examination reveals that the practical implications of this policy may be limited as many of the drugs selected already face strong competition from other branded medications, are currently sold to Medicare at heavily discounted prices from the List Price, and will experience patent expirations in coming years that will open the market to generics. Under the IRA, the Centers for Medicare & Medicaid Services (CMS) negotiates the list prices of the drugs (the list, or WAC, prices are the basis of prices paid by distributors for inventory but do not reflect drug rebates that are later paid by the manufacturer for each product sale), and not the net price (the prices booked as revenue by the manufacturer after the rebate is paid). Six of the 10 drugs selected in the 2026 negotiation are in classes with a rebates of more than 60%, so the list price is not the price paid by CMS. Further, at least eight, and potentially all 10 of the drugs, will be off patent by the time the policy is implemented in 2026*.  The 10 drugs selected, medications from the same or overlapping therapeutics areas, as well as the timing of the implementation, means that these drugs would face both therapeutic and generic competition regardless of the new negotiation provisions, leading to substantial price reductions independent of the IRA’s influence. In fact, the reduction in prices from the impact of the brands going generic will be much more substantial than the newly negotiated price reductions, with prices naturally decreasing as much as 90% once generics are on the market.

*Patent expiration dates for the selected drugs are complex with various extensions, formulations and ongoing litigation. Reliable sources like the FDA’s Orange Book and other patent databases were used. The sources for the patent expiration dates and other dates that have been found in our research are noted below.

1. Imbruvica (Ibrutinib): March 30, 2032 – GreyB, additional sources indicate a 2026 patent expiration date.
2. Eliquis (Apixaban): February 28, 2025 – GreyB, additional sources indicate April 21, 2026, as the expiration date.
3. Januvia (Sitagliptin): April 16, 2025 – GreyB, additional sources indicate November 24, 2026, as the expiration date.
4. Entresto (Sacubitril/Valsartan): March 1, 2025 – GreyB, additional sources indicate May 27, 2027, as the expiration date.
5. Jardiance (Empagliflozin): May 15, 2025 – GreyB, additional sources indicate April 15, 2027, as the expiration date.
6. Farxiga (Dapagliflozin): June 30, 2025 – GreyB,
7. Fiasp (Insulin Aspart): July 20, 2025 – GreyB
8. Enbrel (Etanercept): February 28, 2029 – GreyB, sources show an expiration in 2023, pending legal disputes.
9. Stelara (Ustekinumab): August 15, 2025 – GreyB
10. Xarelto (Rivaroxaban): February 28, 2025 – GreyB

Orange Book: Approved Drug Products with Therapeutic Equivalence Evaluations

The impact on the pharmaceutical industry appears to be minimal. The initial price cuts seem manageable as the first 10 drugs under IRA price negotiation were already facing near-term headwinds due to competition, newer medications, and anticipated patent expiry. Stock prices have also not shown any considerable change as a result of the legislation (with January 1, 2023 as a basis), indicating no real impact on sales or profits for the industry. The stock prices for all eight companies facing the first round of drug price negotiations have either gone up (5) or declined modestly, except for a decline in Bristol-Myers Squibb stock that is due to the steep “patent cliff,” i.e. the expiration of drug patents, and other challenges faced by the company.

Company Name	Ticker	Stock Price (Adjusted Closing)
		Prices at the start of 2023	IRA Selection 29th Aug 2023	IRA Prices Announced 15th Aug 2024	Price as on 30th Aug 2024
AstraZeneca	AZN	$64.99	$67.68	$84.90	$72.83
Amgen	AMGN	$248.37	$252.06	$323.14	$315.54
BMS	BMY	$66.87	$59.89	$49.11	$52.66
Johnson & Johnson	JNJ	$167.52	$159.27	$157.89	$160.61
Lilly	LLY	$360.51	$550.20	$931.58	$846.83
Merck	MRK	$105.90	$106.42	$112.56	$104.83
Novo Nordisk	NVO	$66.08	$94.15	$137.06	$113.24
Novartis	NVS	$83.74	$99.48	$113.31	$109.91

Source: Yahoo Finance – Historical Data

Historically, the patent cliff has led to significant revenue losses for pharmaceutical companies with the introduction of lower-cost generic alternatives. Between 2010 and 2015, approximately $250 billion in sales were at risk from patent expirations, prompting companies to adopt strategies such as price increases, mergers, and diversification into biologics and diagnostics to maintain profitability.

The anticipated savings from negotiated prices are thus overshadowed by current market forces. CMS’s list for Medicare drug price negotiation includes drugs that accounted for $50.5 billion in gross Part D costs, affecting about 8.2 million Medicare enrollees. However, since these drugs are approaching their loss of exclusivity, the impact on actual cost savings will be limited. The Congressional Budget Office (CBO) concurs with this assessment, estimating that the IRA will have minimal impact on Part D spending.

Pharmaceutical companies are expected to adapt to the IRA program in ways that may mitigate the financial impact of this program. One likely response is the adjustment of launch prices for new drugs. Anticipating future price negotiations and inflation-based rebates, companies may set higher initial prices to “bake in” their anticipated price decreases into the launch price. This preemptive pricing strategy would allow them to maintain profit margins and continue funding robust research and development (R&D) efforts, which historically account for a significant portion of their expenditures.

The uncertainty introduced by the IRA also plays a critical role. Pharmaceutical companies face increased risk due to the unpredictability of whether their drugs will be selected for future price negotiations. This uncertainty may influence investment decisions, particularly in areas where the return on investment becomes less certain. Companies and investors generally prefer stable market conditions, and this added risk could lead to a more cautious approach in drug development pipelines.

Part D plans may respond to reduced rebates by increasing premiums and deductibles, effectively passing costs back to consumers. This result is the perverse logic of the design of the Part D program, which has those using the benefit subsidizing the premium cost for Part D coverage. Currently, consumers pay inflated drug prices at the retail pharmacy (higher list prices associated with rebates leads to higher patient cost-sharing), while the rebate dollars from these sales decreases the premiums for those purchasing Part D coverage. Lower list prices and rebates will decrease the costs at the pharmacy for individuals, but depending on the math, they could result in higher premiums for the coverage.

The policy may also inadvertently reduce patient access to certain medications. As Medicare Advantage plans adjust their formularies to control costs, they might limit the range of covered drugs, leading to fewer choices for patients. Pharmaceutical companies might deprioritize developing new indications for existing drugs if the financial incentives for drug development diminish, potentially slowing innovation and the availability of new treatments.

Furthermore, CMS has outlined a complex negotiation process involving data submissions, meetings, and offers between CMS and drug manufacturers, targeting the establishment of maximum fair prices by 2026. While the process aims to balance cost reduction with the maintenance of innovation incentives, participation by drug companies is technically voluntary, with significant penalties for non-participation. This framework adds another layer of uncertainty and operational burden for pharmaceutical companies.

Finally, as the current policy is written, our analysis indicates that future drugs selected for price negotiation will also be those facing imminent patent expiration. The criteria set for drug selection state that medications must be on the market for nine years (small molecule) or 13 years (biologics), leaving little to no patent life in most cases after implementation of the negotiated price, as seen with the first round of drugs selected. Again, this reinforces the CBO perspective about the modest potential impact of this program.

Conclusion

Overall, the IRA represents a significant policy development. However, based on the current analysis, its real-world impact on drug prices, patient OOP costs, Medicare spending, and pharmaceutical innovation appears limited. Stakeholders across the healthcare spectrum must consider these findings as they plan for future change, recognizing that additional efforts may be necessary to achieve meaningful progress in reducing cost while enhancing patients’ access to healthcare.

Avik Ray, Department of Medicine, Brigham and Women’s Hospital, Chirantan Chatterjee, Department of Economics, University of Sussex Business School

Contact: aray13@bwh.harvard.edu

Introduction

The landscape of prescription drug advertising in the United States has undergone a significant transformation, with direct-to-consumer advertisements (DTCA) becoming ubiquitous on American television. The trend began in 1983 with the first such ad, which was promptly taken down but gained momentum in 1997 when the U.S. Food and Drug Administration (FDA) relaxed its guidelines around DTCA.¹

A New Model of Drug Sale

After four decades, the introduction of anti-obesity medicines (AOMs) and Lilly’s 2024 direct-to-consumer pharmacy (DTCP) strategy with Zepbound, which offers medications directly to patients, is likely to reignite discussions on the welfare effects of drug advertising. There have been past instances of pharmaceutical companies selling medicines directly to consumers who already have prescriptions. But the launch of Lilly’s DTCP service, LillyDirect, comprising direct-to-consumer along with a referral network of independent telehealth providers (including Form Health and 9amHealth) with prescribing powers, will likely give rise to a new category of DTCA-DTCP. Lilly Direct joins firms such as Hims & Hers who combine prescribing and product dispensing. We discuss the regulatory challenge of this emerging practice. While there have been discussions about direct-to-consumer drug company pharmacies,² we aim to emphasize how DTCA-DTCP is poised to play a potentially concerning role in this emerging model.

One of the most significant concerns arising from DTCA, especially for drugs with a mass appeal like AOMs such as Wegovy (semaglutide) by Novo Nordisk and Zepbound (tirzepatide) by Lilly, is the shift in patient behavior from relying on evidence-based medical discussions to seeking medications based on social media influences. Social media platforms, particularly TikTok, have become influential sources of information for patients. Doctors report instances where patients request specific medications, such as the “skinny jab“³ or weight loss shots, based on what they have seen influencers or celebrities discuss online.

Control over Advertising

In the United States, the FDA exercises control over advertisements from the pharmaceutical industry, mandating the acknowledgment of drug risks and side effects (the major statement according to the FDA). However, advertisements by telehealth companies are not subject to FDA promotional rules since telehealth companies are not a regulated drug manufacturer.

The DTCA-DTCP model exists in a regulatory vacuum. The First Amendment limits the ability of government to control advertising (since it is deemed speech). While the FDA has power over DTCA by manufacturers,⁴ it does not regulate the practice of medicine or physician prescribers. The Federal Trade Commission (FTC) has broad authority to ensure that claims in advertisements are truthful, are not deceptive or unfair, and are evidence-based.⁵ Thus, telemedicine companies need to meet the FTC standard in DTCA, which importantly does not include the FDA requirements on presentation of risks in advertisements.

The FDA is collaborating with external partners, such as the FTC, to tackle apprehensions related to the marketing practices of telehealth companies concerning prescription drugs across diverse platforms, including social media and public billboards. Despite these policies, marketing campaigns by several of these telehealth providers have exploited this regulatory gray area, even mentioning brand names in simple pitches such as “Wegovy to lose weight” without the requirement to provide a fair balance of information. Many of these companies aim to reduce the stigma associated with obesity while also advocating for the use of anti-obesity medications, which may influence individuals to consider these treatments.

The phenomenon extends beyond traditional medical settings, with spas offering drugs like semaglutide often without adequate medical support. Some providers and telehealth companies offer ‘compounded‘ semaglutide, which carries some potential downsides, including the possibility of contamination. Weight loss clinics also promote unconventional additions to these drugs, contributing to the complexity and potential risks associated with their usage.

Internationally, regulatory bodies are becoming more active in combating unethical pharmaceutical advertising on social media. Instances in the United Kingdom demonstrate efforts to scrutinize and regulate misleading drug promotions, underscoring the global nature of the issue.⁶ While the focus is on pricing and supply chain optimization to meet the increasing demand for AOMs, it is more critical than ever to revisit the DTCA policies and create rules suited explicitly to this emerging category of DTCA-DTCP with a sponsor referral network of providers that lacks the usual prerequisite of a neutral physician prescription to get access to medicines.

A Long-Standing Debate

The impact of manufacturer DTCA on medicine uptake and prescribing practices has been a longstanding question with mixed evidence. A recent review on DTCA practices highlights how complicated it is to understand the impact of DTCA on health systems.⁷

The potential consequences of DTCA extend beyond legislative solutions. A pertinent worry is that these ads exacerbate the situation by fostering a culture of demand for lifestyle drugs such as Wegovy and Zepbound and higher-cost options, even when lower-cost alternatives are available. The concern surrounding misleading direct-to-consumer advertising extends to other medications, such as ketamine, which could potentially increase the risk of misuse. As the healthcare system grapples with issues of accessibility and affordability of AOMs, the role of DTCA-DTCP in shaping patient preferences and contributing to the overall cost burden cannot be ignored. DTCA-DTCP can influence coverage decisions by employers who are struggling to address the cost of this new explosion of interest in these expensive products.⁸

Suggested Changes

For manufacturer DTCA-DTCP, it is crucial to develop specific guidelines that align with pharmacovigilance and the FDA adverse events monitoring system. This entails fortifying regulatory frameworks to ensure that advertisements with such a mass appeal offer precise, comprehensive, and easily understandable information regarding medication benefits and potential side effects, which the new rule from FDA in November 2023 partly addresses,⁹ pending widescale implementation. Addressing misconceptions about drug safety and the mixed evidence on these drugs, including the ‘boomerang effect’ for weight regain, requires balanced, evidence-based marketing. Furthermore, the inclusion of pricing information in these advertisements, with full transparency for discounted price eligibility with drug coupons and co-pay/co-insurance assistance, can inform individuals with crucial knowledge about the economic implications of the medications being promoted.

Independent research and assessment are crucial components of a robust regulatory framework. Encouraging and funding independent randomized trials and retrospective matched-cohort studies can produce the unbiased information required to help assess the impact of the DTCA-DTCP model. These studies would use administrative claims databases on effectiveness, along with patient behaviors such as adherence and impact on quality of life, for individuals using medicines such as Zepbound through DTCP, compared to those individuals obtaining access through conventional provider prescription. This research could build on past studies from the 2000s on anti-depressants.¹⁰

Given the influence of social media and the rising demand for AOMs, it is crucial to monitor DTCA-DTCP ads on these platforms. Collaboration with social media companies is essential to curb misinformation. International cooperation, particularly with countries like New Zealand, the only other nation to allow DTCA, is vital for sharing best practices and strategies in regulating prescription medication ads globally.

The rise of DTCA-DTCP with the discovery and diffusion of AOMs amid the global increase in non-communicable diseases ,presents a nuanced and significant challenge for the healthcare industry. Eli Lilly aims to secure an early-mover advantage and create inelastic consumers to leverage pricing power as competition grows. But, from a regulatory perspective, are Lilly product advertisements statements of manufacturers or of independent telehealth providers? The answer to this question will guide our ability to understand how to regulate the messages received by consumers.

Balancing accurate information, patient safety, and healthcare affordability requires collaborative efforts from regulators, healthcare professionals, and pharmaceutical companies to ensure informed patient decisions.

Conflict of Interest

Dr. Ray has no conflicts of interest to disclose.

Dr. Chatterjee has no conflicts of interest to disclose.

Funding Information: None

References

1. Donohue J. A history of drug advertising: the evolving roles of consumers and consumer protection. Milbank Q. 2006;84(4):659-99. doi: 10.1111/j.1468-0009.2006.00464.x.
2. Rome BN. Direct-to-Consumer Drug Company Pharmacies. JAMA. 2024;331(12):1003-1004. doi: 10.1001/jama.2024.2911.
3. BBC (2024). Global alert issued over fake Ozempic drugs – WHO. Retrieved on November 08, 2024 from: https://www.bbc.com/news/articles/cn00dkw9479o
4. Food and Drug Administration (2023). Direct-to-Consumer Prescription Drug Advertisements: Presentation of the Major Statement in a Clear, Conspicuous, and Neutral Manner in Advertisements in Television and Radio Format. Retrieved on November 08, 2024 from: https://www.federalregister.gov/documents/2023/11/21/2023-25428/direct-to-consumer-prescription-drug-advertisements-presentation-of-the-major-statement-in-a-clear
5. Federal Trade Commission. Advertising and Marketing. Retrieved on November 08, 2024 from: https://www.ftc.gov/business-guidance/advertising-marketing
6. The Association of the British Pharmaceutical Industry (2023). Novo Nordisk has been suspended as a member of the Association of the British Pharmaceutical Industry (ABPI) for two years due to serious breaches of the ABPI Code of Practice. Retrieved on November 08, 2024 from: https://www.abpi.org.uk/media/news/2023/march/novo-nordisk-is-suspended-from-abpi-membership/
7. Khokhar B, Weathers S, Joseph Mattingly T II. Direct-to-consumer advertising and the advancement of the quadruple aim: A narrative review. J Am Coll Clin Pharm. 2022; 5(4): 459-466. doi:10.1002/jac5.1599
8. Fortune (2024). North Carolina drops coverage for Wegovy and Ozempic, with implications for anti-obesity drug market projected to hit $100B by 2030. Retrieved on November 08, 2024 from: https://fortune.com/2024/01/27/north-carolina-drops-coverage-for-wegovy-and-ozempic-weight-loss-drugs/
9. Federal Register (2023). Direct-to-Consumer Prescription Drug Advertisements: Presentation of the Major Statement in a Clear, Conspicuous, and Neutral Manner in Advertisements in Television and Radio Format. Retrieved on November 08, 2024 from: https://www.federalregister.gov/documents/2023/11/21/2023-25428/direct-to-consumer-prescription-drug-advertisements-presentation-of-the-major-statement-in-a-clear
10. Kravitz RL, Epstein RM, Feldman MD, et al. Influence of patients’ requests for direct-to-consumer advertised antidepressants: a randomized controlled trial. JAMA. 2005;293(16):1995-2002. doi: 10.1001/jama.293.16.1995.

 

Sneha S. Jain*, Stanford University School of Medicine, Morgan Cheatham*, Bessemer Venture Partners, Michael A. Pfeffer, Stanford University School of Medicine, Linda Hoff, Stanford Health, Nigam H. Shah, Stanford University School of Medicine

*These authors contributed equally to the work as co-first authors

Contact: snehashahjain@stanford.edu

Introduction and Overvie

In 1995, Charlie Munger said “Show me the incentives, and I’ll show you the outcome.” Thirty years later, this holds true for artificial intelligence (AI) in healthcare. Current approaches to evaluating, regulating, and paying for AI in healthcare incentivizes use of AI in a manner that is likely to increase total cost of care. The problem stems from disconnected regulation and reimbursement approval decisions, anemic health information technology (IT) budgets, and complex revenue structures across stakeholders necessitating creative business models to subsidize adoption of AI tools. Often these business models are crafted independently of, and without, the necessary workflow redesign to capture the full potential of AI¹.

Three federal agencies, the Food and Drug Administration (FDA), the Office of the National Coordinator for Health IT (ONC), and the Centers for Medicare & Medicaid Services (CMS), regulate AI tools in healthcare. These tools are regulated as software as a medical device (SaMD)², practice-management software under Health Data, Technology, and Interoperability Certification Program (HTI-1)³, and the Clinical Laboratory Improvement Amendments (CLIA). There is also software as a treatment modality, or digital therapeutics (DTx)⁴, which are subject to safety and efficacy evaluations. Additionally, other software and tech-enabled services, such as revenue cycle management, are procured directly by healthcare entities outside of regulatory purview and are only subject to enforcement by the Federal Trade Commission (FTC) or the Office of Civil Rights (OCR).

This patchwork of regulatory frameworks leads to the inconsistent evaluation of the clinical impact of AI tools. For example, Wu et al., found that most FDA-cleared medical AI devices were evaluated pre-clearance through retrospective studies, with many lacking reported number of evaluation sites or sample sizes⁵. El Fassi et al. found that almost half of authorized tools were not clinically validated and were not even trained on real patient data, concluding that FDA authorization is not a marker of clinical effectiveness⁶. In general, it is widely accepted that robust AI testing and validation infrastructure in medicine is lacking⁷ and our regulatory regimens need to be updated.

Regulatory approvals examine if AI tools “work”, but not whether they create “value” in the form of better quality of care for patients relative to the cost, which is often considered in procurement and reimbursement decisions. They also do not take into consideration how an AI tool will fit into existing or new workflows. This decoupling between regulatory approval and reimbursement requires users of AI tools – especially those tools that are used to render medical care – to figure out how to pay for the cost incurred by using a tool based on the value obtained.

Lobig et al. recommend that reimbursement for an AI tool, if separate from the cost of the underlying imaging study, should be decided based on evidence of improved societal outcomes ⁸.  However, for regulated tools, the assignment of value to a reimbursed AI tool is artisanal at best ⁹. Payment rates differ significantly between private vs public payers. For example, Wu et al. found that reimbursement for CPT code 92229 for diabetic retinopathy is approximately 2.8 times higher for private patients than for CMS patients ⁹. There is little consistency in how reimbursement for AI tools used for medical care is valued compared to the non-AI alternatives. For example, reimbursement for AI-based interpretation of breast ultrasound is comparable to a traditional breast ultrasound, whereas the reimbursement for AI-powered cardiac CT for atherosclerosis is two to three times the out-of-pocket cost of such a study^9,10. While mechanisms exist to facilitate reimbursement during the nascent stages of technology adoption, such as New Technology Add-On Payment (NTAP) ¹¹ and Transitional Coverage for Emerging Technologies (TCET), these solutions are unlikely to fully accommodate the rapid growth of AI-based tools in healthcare ¹².

For non-regulated technology, adoption depends on market forces to identify high-value solutions, and incumbent vendor platforms in facilitating their use, which may vary by care setting and reimbursement (i.e., urban vs. rural, fee-for service vs. value-based payment).

Therefore, as Davenport and Glaser note, despite abundant research and startups, very few AI tools have been adopted by healthcare organizations ¹³. They attribute this to factors such as  regulatory approval, reimbursement, return on investment, integration challenges, workforce education, the need for changing workflows, and ethical considerations, and conclude that new organizational roles and structures will be necessary to successfully adopt these technologies. Many of these challenges stem from needing to pay the hidden deployment costs of AI tools ¹⁴.

To address these challenges, Adler-Milstein et al. emphasize the need to couple the creation of equitable tools, their integration into care workflows, and training of health care providers with strong regulatory oversight and financial incentives for adoption in a way that benefits patients ¹⁵.

Whether an AI tool is regulated or not, the developers (and users) currently have to conform to existing payment methods for technology or medical care. Thus, paying for technology ends up being a net-new cost to health IT budgets. For example, a per-user license for ambient scribe technology is not a form of direct ‘medical care’, and hence brings no new revenue to a provider. Existing ways to pay for the tools as ‘medical care’ – while having the potential to bring new revenue – is fraught with value judgments and is still a net-new cost to the payers. As a result, adoption of AI tools remains low compared to the hype around them. For example, Wu et al. find that even though the number of devices cleared under the FDA’s SaMD exceeds over 500, only two of them – for assessing coronary artery disease and for diagnosing diabetic retinopathy – had over 10,000 CPT claims reimbursed in a four-year period ⁹.

The lack of suitable payment models for health AI tools has led to prioritizing solutions that offer financial over clinical benefits ¹⁶. In addition, AI developers face high costs driven by compute, data, and large enterprise healthcare sales teams. However, IT budgets are not large enough to sustain the payback assumptions made when investing in the creation of AI tools¹⁷. The total health IT spend in the US is approximately $46 billion, and approximately 10% of this spend is captured by leading electronic health record vendors ^16,18 which does not include the hardware and people needed to run them. The remaining budget includes software (both clinical and business systems), medical devices, imaging equipment, hardware and networking components, cybersecurity, and salaries for IT personnel, leaving little room to pay startups or incumbents creating AI tools. This results in immense pressure to find non-IT budgetary spend (such as re-allocating salaries and professional services) and for alignment with the way medical care is paid for. This tension has prompted a reevaluation of traditional business models—the overarching strategies companies use to create, deliver, and capture value from their solutions such as software or services—and pricing models, which describe the specific mechanisms by which vendors monetize their solutions, such as recurring subscription fees, pay per use, or contingency-based pricing ¹⁹.

Given these constraints, health AI vendors are developing pricing strategies that align with existing payment paradigms for technology or for medical care. These strategies attempt to cover the high upfront costs of AI implementation, the long-term additive costs associated with ‘augment the human’ design paradigms, and the uncoupling of technology users from who pays. The challenge becomes clear when we cross-tabulate the pricing strategies (usage- or performance-based) with the payment paradigm (for technology or medical care), as shown in the table below.

Table. Pricing Strategies for Health AI Tools. Examples of technology and medical AI tools that use either usage-based or performance-based pricing strategies.

	Technology	Medical
Usage-based	E.g. Ambient scribe	E.g. AI-based screening for diabetic retinopathy
Performance-based	E.g. AI-powered coding and clinical documentation integrity	E.g. Algorithm guided post-acute management

Usage-based pricing charges customers based on volume of utilization of an AI tool. The payment can be a direct payment by the customer (e.g., for a per-user license for ambient scribe) or via reimbursement (e.g., CPT code 92229 for AI-based screening for diabetic retinopathy). The first adds net-new cost to the IT budget while the second generates new revenue for providers but increases costs for payers and carries the risk of overuse via unnecessary screenings. When AI tools, like ambient scribes, do not generate revenue directly, costs are justified by indirect benefits, such as reduced physician burnout and potentially lower turnover, or downstream benefits such as better documentation for billing. However, in other instances, the expectation is that costs will be covered by having users of the technology see more patients in the time saved. If time is saved, physicians must decide whether to add a patient to their schedule or to keep their normal case load in hopes of providing better care²⁰. In some cases, patients bear the cost via out-of-pocket fees, such as with AI-enhanced mammography interpretations ²¹. Usage-based pricing can create conflicting incentives, with vendors promoting increased utilization to boost revenue while their customers may limit usage arbitrarily to control costs.

Performance-based pricing distributes financial risk between AI developers and healthcare providers or payers, with payments tied to measurable outcomes (not necessarily clinical outcomes). Risk-sharing arrangements range from a base annual fee plus a share of the financial savings to payment solely from created financial savings. For example, an AI-augmented screening program to detect worsening heart failure (HF) may allow for early intervention by the care provider, reducing readmissions²². There can be a base fee for access to the software with some percentage of additional revenue to the AI vendor generated by reducing a hospital’s readmission rates, and therefore decreasing the associated Medicare reimbursement penalty. Similarly, an AI-powered coding and clinical documentation integrity system can analyze clinical documentation to suggest appropriate diagnostic codes such as Risk Adjustment Factor (RAF) coding and increase compliance by identifying clinical documentation that may be insufficient to support accurate coding for billing. There can be a base fee for access to the software plus some percentage of additional revenue to the AI vendor generated from the improved coding accuracy.

Performance-based pricing has limitations. A vendor may be incentivized to assign codes that reflect higher illness severity than clinically justified, a problem called ‘upcoding’ that is often reported with tools used by payers offering Medicare Advantage plans²³. For example, FDA-cleared screening tools were allegedly used to add diagnosis codes to a patient record, even when no further care was rendered ²⁴. Performance-based pricing, when used to pay for AI-tools that decide on access to post-acute care based on patient needs²⁵, may override clinicians’ judgment and deny care for seniors to generate ‘cost savings’ in Medicare Advantage plans²⁶. A recent ProPublica investigation reported on the use of an algorithm backed by AI, which can be adjusted to lead to higher denials with the promise of saving $3 for every $1 spent on its use²⁷.

A Path Forward

The core issues with health AI tools stem from their inadequate evaluation and their decoupled regulatory and reimbursement approval criteria. These challenges are compounded by how technology and medical care are currently paid for. The resulting pricing arrangements reflect the machinations currently necessary to get paid for the use of AI in healthcare either via IT budgets or aligning with either fee-for-service or value-based care paradigms. However, these pricing paradigms reveal a concerning trend: AI can increase the total cost of care without improving healthcare quality, or worse, lead to care denials and possible care disparities in the quest to create “financial savings”. To navigate this situation, we make the following suggestions:

Conduct assessments to specify and verify benefits

Regardless of whether an AI tool is under FDA, ONC, or CMS regulation, before adoption, it is necessary to ensure that the use of the tool improves healthcare quality, either through more efficient operations, improved patient experience, or enhanced patient outcomes^28,29. We need robust estimates of benefits prior to deployment, and then verification of that benefit after deployment, in order to ensure that the use of AI tools improves overall value. For example, healthcare systems can put in place local evaluation regimens to ensure that the use of AI tools is fair, useful, reliable, and monetarily sustainable ²⁸. An upfront ethics evaluation to assess for unintended consequences is critical to avoid some of the situations detailed in the examples above³⁰. Additionally, impact assessments should examine financial sustainability for addressing the disconnect between regulation and current reimbursement for clinical AI tools ³¹. In situations where there is no short-term financial benefit – typically defined as return on investment in one year or less – there may be intangible benefits such as improved provider wellness³² or better long term patient outcomes. If estimated, these can form the foundation for advocating for the adoption of certain AI products. At the minimum, an upfront evaluation of fairness, usefulness, reliability, and monetary sustainability can prevent organizational waste in the form of pilotitis – where hundreds of pilot projects happen and none convert to a broad implementation ^33,34. Finally, given that hundreds of health AI tools have been approved on the basis of limited clinical data³⁵, there is a related urgent need to institute ongoing evaluation of health AI tools that are already in use³⁶.

Create consensus on mechanisms of transparent evaluation

Given that value from the use of an AI tool is notoriously difficult to define, and is an interplay of a tool’s performance with the care workflows in which it is used, it is necessary to evaluate both³⁷. The AI tools (or the underlying models) should be subject to certain manufacturing constraints – as is already the case with FDA-regulated AI tools. For those AI tools that are currently not regulated, consensus best practices are needed around the creation, testing, and reporting of AI tools^38,39. Given that best practices are typically offered in the form of checklists and reporting guides, adherence to them remains challenging⁴⁰. The necessary next step is to facilitate the routine use of these desiderata (as well as verification of a vendor’s adherence to them), which can initially be done via a nationwide network of assurance labs⁴¹ and can gradually be transitioned into assurance software that is widely shared for self-service use. For example, Epic Systems has already taken the first step in this direction, with two academic groups contributing code to the software^42,43. Finally, the construct of the tool in the context of its workflow can only be evaluated in the local setting, for which we need to create consensus assurance guidelines ⁴⁴, shared open-source software ⁴⁵ , as well as communities of practice (such as Health AI Partnership⁴⁶ and RAISE ⁴⁷) to develop implementation best practices and centers that can evaluate clinical effectiveness ⁴⁸. The creation of common, accepted practices can ensure the evaluation process of AI is as cost-efficient as possible.

Align AI implementation with appropriate business models

Strategic alignment of AI implementation with the right business model is crucial for cost-effectiveness and value creation in healthcare. The concept of modality-business model-market fit ⁴⁹, how the choice of the form AI takes and the business model to support it, determines the value potential of the resulting solution. By selecting appropriate modalities — such as AI-enabled software, copilots, diagnostic or therapeutic tools — and aligning them with appropriate business models, stakeholders can generate value without unnecessarily inflating costs. For example, AI copilots (ambient scribes being an example) integrated into existing workflows and funded by current budgets, can enhance efficiency without requiring significant infrastructural changes. Hospitals that already allocate resources for human scribes can reallocate that budget to IT for a transition to AI alternatives, improving consistency and quality of clinical documentation while operating within established budgets. An AI-agent conducting a post-discharge follow-up workflow⁵⁰, or an agent performing medication titration (such as a voice agent managing insulin dosage using data from a continuous glucose monitor⁵¹), can off-load work from burnt out and overworked nurse care managers, allowing reallocation of time to other tasks. Other modalities, such as the AI-augmented screening tool for heart failure⁵², may require the creation of new workflows in a value-based care setting, so that avoidance of later complications is prioritized in a population health setting.

The value created from AI in healthcare will depend on how well we balance technology innovation, the incentives created by the complex payment structures in healthcare, the payback expectations of those investing in technology creation, the business models adopted by AI vendors, measurable clinical benefit, and associated healthcare costs. We must balance appropriate oversight with flexible infrastructure that continues to support innovation. Current business models of AI tools that impact medical care are square pegs in the two round holes by which medical care is paid for – i.e. fee-for-service and value-based payment methods. For pure technology plays, the available IT budgets might be missing a zero or two. To bridge this gap, we need focused efforts to connect high quality evaluation of benefits, business model choice, regulation, and reimbursement for promising, high-value emerging technologies in order to finally achieve the promise of health IT lowering the cost of healthcare.

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Ernest G Ludy, Founding CEO, Medstat Systems; Senior Advisor, Clinical Excellence Research Center, Stanford University

Contact: ernie@egloffice.com

Introduction

U.S. healthcare inflation is an economic black hole. It consumes our individual, business, and government income faster than we can earn it, and it erodes our personal, corporate, and national net worth faster than we can build it.

Since 1980, national health spending has consistently grown two to three times faster than GDP, expanding from 9% to almost 20% of our economy.  Similarly, health insurance premiums have continued to increase four to five times faster than wages, and two to three times faster than general inflation or the Consumer Price Index (CPI).

Health insurance premium inflation is posing an existential threat to the industry itself, increasingly destabilizing its own financial foundation — businesses and workers who pay premiums and taxes to finance employer- and government-sponsored health insurance.

The situation is worsened by steadily increasing coinsurance, copayments, deductibles, and out-of-pocket expenses for consumers. Poor patient care, waste, and clinical errors further erode value. Health insurance is becoming an absurdity, leaving most Americans in an untenable paradox — insured but not protected.

Solving healthcare inflation and stabilizing insurance premiums has been a decades-long and frustrating challenge, especially for the buy-side of insured healthcare. With few exceptions, most strategies launched by business and government, or advanced by policymakers and advisors on their behalf, have proven to be more tried than true, yielding little in the way of sustainable cost reduction or premium stability.

One exception was a business model innovation strategy pioneered by Medstat Systems and first adopted by its Fortune business customers. The strategy was designed to empower corporate sponsors of employee health insurance plans with a new “optimal care” business model and tech-enabled execution platform. The goal was to safely and sustainably reduce and stabilize premium inflation. The broader strategic intent was to leverage the buying power of large employers to directly challenge the insurance industry’s cost-plus business model.

It worked.

Strong customer results confirmed the impact on slowing premium growth, with the best performers achieving near-zero inflation (Figure 1). Customer retention and operating results also validated the strategy’s commercial viability. The new optimal care business model had won the day by reversing inflation and turning cost-plus insurance economics upside-down.

Figure 1: Medstat customers versus U.S. health benefit spending trend: 2005-2010; Medstat Systems business profile and operating results

The purpose of this paper is to explore how business model innovation can serve as a national strategy for reversing healthcare inflation and stabilizing insurance premiums over the next decade. The paper will cover three areas:

• First, the unique market structure and value chain economics that define the competitive landscape for building new business models in U.S. healthcare.
• Second, the optimal care business model including the paradigm for its design, and the engineering and intelligence technology platform for its execution.
• Third, the challenge of widespread business model adoption and industry transformation.

The larger intention here is to spark the imagination of innovators and entrepreneurs, from within and outside healthcare, and drive business model innovation as a disruptive strategy leading to longer term industry transformation.

Market Structure and Value Chain Economics

Healthcare inflation is systemic. It’s the outcome of an economic structure that creates it, a complex system of relationships that comprise the value chain for insured healthcare. Understanding how this value chain creates unintended consequences and drives excess inflation is fundamental for designing new business models to reverse it.

Health insurance generally works like this (Figure 2): Sponsors pay premiums to Insurers to finance health benefits for Members. Members use benefits to consume services from Providers. Providers in turn bill Insurers for costs. Insurers reimburse Providers, add administrative fees on top of reimbursements, and bill premiums to Sponsors. A four-party system, two supply chains and a cost-plus business model — a perfect storm for premium inflation!

Figure 2: U.S. insured healthcare value chain economics

 

Several unintended and inflationary consequences occur from this structure. First, a four-party system neutralizes demand-supply economics, weakening competitive market forces for improving quality, cost, or price.

Second, the two supply chains of insurance and care delivery obscure end-to-end supply chain visibility. Sponsors fly blind as they struggle to use modern purchasing practices to track quality and costs to improve performance and stabilize premiums.

Third, the industry’s cost-plus business model is inherently inflationary. Providers earn more if they do more. Insurers earn more if they spend more. Both drive premium inflation at sponsor expense. In this conflicted position, insurers and providers have more incentives to increase costs rather than reduce them.

Weak market forces, poor supply chain visibility, and a cost-plus business model with few incentives for reducing costs are the structural (and dysfunctional) consequences of our healthcare system that drive inflation. Reversing or neutralizing their inflationary impact defines the agenda for reframing the problem and designing new business models to solve it.

Business Model Innovation Strategy

A business model is the way a business or industry creates value for its stakeholders. Business model innovation is the process of connecting new technologies and exponential mindsets to generate new business models that drive exponential stakeholder value, often leading to market disruption, incumbent adoption, and industry transformation.

The optimal care business model was developed to disrupt the conventional cost-plus business model of U.S. health insurance. It was generated by connecting emerging intelligence technologies with a new optimal care paradigm for reducing healthcare inflation and stabilizing insurance premiums. The following discussion lays out the paradigm, business model, engineering practice and enabling technology platform.

Optimal Care Paradigm

First principles thinking and exploring different mindsets for solving intractable problems often lead to breakthrough solutions. A new paradigm emerges, causing a shift in consciousness and perception, suggesting new ways to reframe a problem so it can be solved. This was the case in discovering the Optimal Care paradigm for designing a new business model to stabilize premium inflation in health insurance.

Imagine asking an engineer and an actuary for their best thinking on how to reduce costs. What first principles would they use to solve the problem?

The industrial engineer responds that in her world of product manufacturing, the best way to reduce costs is to improve quality. She explains that by improving systems, processes, and supply chains to eliminate defects, you can avoid the unnecessary costs of do-over work and scrap.

The actuary counters that in her world of estimating the cost of underwriting losses, the best way to reduce costs is to reduce risk. She continues that by eliminating avoidable risk, you can avoid the costs of unnecessary underwriting losses.

Is there any common ground here? Is there a way to blend the two professional mindsets into a new “actuarial engineering” paradigm for solving premium inflation? Bingo!

The “actuarial engineer” would reason that in her world of insured healthcare, the way to reduce costs is to improve quality to reduce the risk of poor care. Improving quality by improving systems, processes, and supply chains to reduce patient risk for poor care, avoids the costs of unnecessary medical underwriting losses and leads to reduced premiums.

This quality-first principle is the foundation of “optimal care”. Combining risk mitigation and quality improvement drives best care-best cost healthcare and a new quality dividend for stabilizing premium inflation. Capturing this dividend is the endgame for business model innovation in U.S. health insurance — and optimal care is the north star.

Understanding the structure of patient risk for suboptimal care is critical to capturing the quality dividend. (Figure 3). In the context of value chain economics, the risk of suboptimal care (poor outcomes, excess costs, high error rates) is a function of system risk (insurance coverage, reimbursement, care delivery, and patient health), in the context of social risk (social, economic, demographic, and geographic determinants). In other words, patient risk for suboptimal care is the outcome of system and social risk drivers. Eliminating system risks while neutralizing social risks, leads to avoiding unnecessary underwriting losses and generating a quality dividend.

Figure 3: Three-tiered risk model for understanding patient risk for suboptimal care

To bring the potential economic value of the quality dividend into sharper focus, suboptimal care generally accounts for 30% of care and up to 50% total medical spend. In a five-year analysis of one million Medicare patients between 2005 and 2010, the worst 10% of care accounted for 50% of medical spend (Figure 4). Targeting the worst 10% of the nation’s suboptimal care could reasonably deliver enormous quality-based returns, especially as we approach $6 to $7 trillion in annual national health spending by 2030.

Figure 4: Annual Suboptimal care spend for one million Medicare members: 2009-2013

Achieving zero inflation healthcare is not hard to imagine given this amount of quality waste and excess spending. But capturing this quality dividend depends on a deploying a new business model with new incentives to drive new value. Without it there is little chance of avoiding the massive spending from suboptimal care or the accelerating insurance premiums to fund it.

Optimal Care Business Model

The optimal care business model is a quality-based margin growth, health insurance business model (Figure 5). It’s designed to drive the best care at the best cost for each insured member by eliminating avoidable risk and unnecessary underwriting losses caused by suboptimal care. Reducing suboptimal care reduces medical spend and generates new margins to reduce premium growth, add benefits, and fund provider incentives.

Figure 5: Optimal care business model for U.S. insured healthcare

The combined effect of insuring and delivering optimal care reverses the inflationary flywheel of conventional health insurance, captures the quality dividend, and stabilizes premium growth for individuals, business and government.  If adopted by industry innovators, the premium advantage would position them to disrupt their markets and grow member enrollment, revenue, earnings, and share at their competitors’ expense. But competing based on optimal care is not likely to be championed by innovators from inside the health insurance industry.

Why? The century-old sacred cow: the cost-plus business model.

A closer look at how premiums are calculated reveals the stark difference between cost-plus and optimal care strategies for creating value, and it clarifies the disincentives for industry incumbents to lower premiums.

In general, health insurance premiums are calculated based on the expected claims experience of a given insured population, plus a regulated administrative services fee based on paid medical claims, capped at 15% to 20% of total premium (Figure 6).

Figure 6: Calculating health insurance premiums

Underlying the paid claims experience are the two drivers of payments. First, expected risk, or the probability of adverse health conditions or events. Second, expected loss, or the portion of expected risk that materializes requiring medical services. Both risk and loss can be managed and, if managed well, can reduce the total premium.

But therein lies the rub. Reducing risk and loss lowers expected payments. Lower payments reduce administrative fees and lead to lower income for insurers. Under the cost-plus business model insurers are conflicted. There is little, if any, incentive to lower the expense base (paid medical claims) for calculating service fees and premiums. Period.

In contrast, under the optimal care business model, insurers would take a longer view. Improving quality to lower costs (and administrative fees) drives margin growth. Spreading new margins across the value chain to sponsors, members, and providers to lower premiums, improve benefits, and incentivize quality, creates a best care-best cost market position. This in turn leads to growth in member enrollment, revenues, earnings, and market share.

The optimal care business model represents disruptive innovation at its best. A quality-based, margin growth, win-win business model for growing share and market leadership in markets that are value-hungry, ripe for disruption, and up for grabs.

Actuarial Engineering

Actuarial engineering is the risk management practice for executing the optimal care business model. It drives new margins for stabilizing premium inflation by improving quality to eliminate the avoidable risk and unnecessary underwriting losses from suboptimal care. As a practice it cultivates the defining core competency for innovation and creating new stakeholder value.

The actuarial engineering practice follows a four-step process for eliminating suboptimal care (Figure 7).

• Model and measure risk
• Target high impact cohorts
• Determine system and social risk drivers
• Design & refine mitigation strategies

Figure 7: Actuarial engineering practice for optimal care

The Member Risk Signature, or MRS, is the foundation of actuarial engineering. Member-centric, risk-focused, and care-based, MRS embodies both the model and metrics for estimating an individual member’s risk for suboptimal care.

MRS is built on the three-tiered risk model that frames care risk as a function of system risk in the context of social risk (Figure 3).  That means that the probability of clinical transactions becoming suboptimal (poor outcomes, excessive costs, and high error rates) depends on system drivers (insurance coverage, reimbursement, care delivery, and patient health) and social determinants (social, economic, demographic, and geographic) which directly and contextually influence the quality and cost of care.

Using this multi-factor risk model, MRS accumulates deep knowledge of each insured member over time, incorporating over 100 risk variables from more than 30 sources of data. Unique MRS intelligence is developed for each member, which can be thought of as a DNA-like signature or “code” for signaling a person’s current and future risk for suboptimal care.

Applying MRS across a total insured population identifies the suboptimal care segment. This segment can be further classified into 12 cohorts based on their primary and secondary risk drivers. By further examining each cohort’s suboptimal care spend, high impact cohorts can be targeted for mitigation strategies.

By diving deeper into each cohort’s primary and secondary risk drivers, customized risk mitigation strategies can be designed, combined, tested and scaled. Tailoring coverage, cultivating health, improving care delivery, or aligning reimbursement: these are strategies aimed squarely at eliminating the risk and losses of suboptimal care, and they complete the actuarial engineering cycle by improving quality to reduce costs and capturing new margins to stabilize premium inflation.

Intelligence Technology

Intelligence technology platforms combine advanced computing and information technologies on a single platform and are designed to achieve three objectives. First, to automate and accelerate intelligence cycles for decision-making and performance improvement. Second, to stockpile learnings from recurring intelligence cycles to build higher order knowledge. Third, to link knowledge to workflow, fast and at scale, to reduce cycle time from intelligence to operations, and to narrow the gap between what we know and what we do (Figure 8).

Figure 8: Intelligence technology platform for optimal care

Intelligence cycles are data-model-workflow cycles which enable measurement, analysis and execution. Five questions reveal the structure of a complete cycle: What’s going on? What does it mean? What should be done? Was it done well? Did it work?

Answers create intelligence. Recurring cycles refine intelligence and stockpile knowledge to improve performance. And single-platform architecture, integrating data-model-workflow cycles, drives intelligence fast and at scale.

In the context of optimal care business model execution, the intelligence platform is the enabling technology that performs the heavy lift of actuarial engineering: detecting and modeling risk using MRS-like models; refining and targeting customized risk mitigation strategies based on system and social risk drivers; and modeling margin growth and designing incentives for quality improvement to further stabilize premium inflation. This is the work of intelligence technology — automated intelligence cycles imbedded into a single platform that learns fast, scales quickly, and, in a word, is indispensable.

Indispensable in two ways. First, without an intelligence technology platform, meaningful initiatives for driving optimal care fall victim to the daunting complexity and scale of the data acquisition and curation work that drives analytics and workflow. Inertia prevails with no momentum for reversing the inflationary flywheel.

Second, with such a platform, insured healthcare becomes AI-ready, countering inertia with exponential speed, and making it easy to imagine a powerful Actuarial Engineering AI-assistant for accelerating optimal care and stabilizing premiums — a highly probable development for achieving escape velocity in the very near future.

Optimal Care as A National Strategy  

The central thesis of this paper is that business model innovation can serve as a national strategy for reversing healthcare inflation and stabilizing insurance premiums for individuals, business and government. The underlying reasoning goes as follows:

• First, healthcare inflation is systemic and the outcome of a four-party economic structure which neutralizes demand-supply economics, weakens competitive market forces, obscures supply chain visibility, and tolerates the industry’s inflationary cost-plus, fixed margin business model.
• Second, stabilizing premium inflation requires strengthening market forces, and empowering sponsors with an alternative paradigm, engineering practice, and intelligence platform. The goal is to clarify supply chain performance and build new business models that grow margins and offset premium increases.
• Third, the optimal care business model is a tech-enabled, win-win business model which empowers sponsors to drive quality-based margins by eliminating suboptimal care, and then to deploy those margins to stabilize premiums, add benefits, and incentivize providers.

Adopting the optimal care business model as a national strategy would leverage the immense buying power of business and government to stabilize premium inflation by reducing suboptimal care. The intention would be to empower markets to drive value and then let markets work — no new legislation or regulation, while aligning the healthcare economy with the national economy and the general inflation rate.

We have the intelligence technology and engineering know-how to build and execute a new healthcare business model and stabilize premium inflation at CPI levels and below. We have the purchasing power of large corporate sponsors and the ability to consolidate smaller business sponsors to create scale. State and Federal government could play an even larger purchasing role, insuring more than half the nation through Medicare, Medicaid, and its employee health plans.

But the question remains: Over the next decade will we have the moral imagination and compassionate resolve to end high inflation healthcare and the hardship it causes for so many?

High inflation healthcare is a choice. Will we choose to stabilize it or not?