This is the second in a series of interviews conducted by Kirsten Gallagher, managing editor of HMPI, with leading health management faculty. Forest S. Kim, PhD, MBA, MHA, FACHE, is a Clinical Associate Professor in the Department of Economics in the Department of Economics, Hankamer School of Business at Baylor University in Waco, Texas. He serves as the Executive Director of the Robbins Institute for Health Policy & Leadership and Co-Director of the Robbins Healthcare MBA.

He previously served as Program Director of the University of the Incarnate Word Graduate Program in Health Administration in San Antonio, Texas. Dr. Kim is an Army veteran who served 22 years on active duty as a health care administrator and educator. His Army career culminated as Program Director of the Army-Baylor University Program in Health and Business Administration. He is a Fellow in the American College of Healthcare Executives (ACHE) and Past Board Chair of the Commission on the Accreditation of Health Management Education (CAHME).

Dr. Kim’s research interests include federal and private sector health program evaluation, graduate educational outcomes, and competency development and assessment.

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You could have pursued multiple pathways during your 22-year U.S. Army career. Why did you decide to focus on healthcare administration and education?

As many in the profession, I stumbled into healthcare administration. I was pre-medicine at UCLA. But due to a variety of factors, I struggled in my science classes. Though I applied to medical school, I didn’t get accepted to any of the schools that I had applied to. Thankfully, I had completed the Reserve Office Training Corps (ROTC) and had a commission in the U.S. Army waiting upon graduation. Since the Medical Corps was out as a branch where I could be assigned, my advisors and I settled upon the branch that was most closely related to medicine, the Medical Service Corps. It wasn’t until my second assignment at Madigan Army Medical Center that I realized I could serve patients by serving those who treat them. It was during administrative rounds that realized I could meaningfully contribute to the healing profession by providing a safe and effective environment of care.

My desire to serve as a health management educator came when I was a student in the Army-Baylor University Graduate Program in Health Administration. At this point, I had developed a true love of learning and enjoyed being in the academic setting. When I found out that the Army had a pathway for Medical Service Corps officers to obtain their PhD and then serve as a professor at the Army-Baylor program, I jumped at the opportunity. Again, I realized that I could serve patients by educating the next generation of healthcare leaders.

Before your retirement as a Lt. Col., you led the Army-Baylor University Graduate Program in Health and Business Administration in San Antonio that focuses on preparing service members for leadership roles in the federal healthcare sector. What makes this program unique, and what could other health management programs learn from it?

The Army-Baylor is unique in that its mission is to educate the next generation of federal healthcare executives. In its over 75-year history, the program has trained countless numbers of officers who have served in the Army, Navy, Airforce, Coast Guard, and Veterans Administration. Another unique attribute about the program is that the professors are all former military administrators, so there is a strong practitioner focus. Also, apart from the civilian professors who serve as the program’s continuity, military professors have limited tenure. This creates continual turnover of faculty. Though there are certainly some downsides, a major upside is that the program is constantly evolving with new faculty providing fresh perspective and bringing cutting-edge research into the program. Last, though the program historically trained healthcare administrators, during my tenure, we launched an executive clinical leadership (ECL) track allowing military clinicians to earn their health management degree in one-year.

Regarding what other health management programs can learn from the Army-Baylor program, a couple of things come to mind. First, the constant evolving nature of the program could be a lesson. There could be a tendency of programs to have the mentality of “if it’s not broken, don’t fix it”. With the healthcare landscape changing so rapidly, programs should be nimble – able to shift with changing educational needs. The second is for programs that aren’t involved in educating clinicians to explore ways to do so. Today’s healthcare environment demands that clinicians have a greater understanding of the business of medicine. I believe health management programs are best positioned to do this.

Following your retirement from the military, you opted for academia over the federal or private health care sector. Why?

Teaching has been a long-time interest of mine which was affirmed while serving as a professor at the Army-Baylor Program. Developing the next generation of healthcare leaders is very fulfilling work to me. Academia also affords me to opportunity to learn continuously.  Sometimes, I have to pinch myself that I get paid to learn.

You have held numerous leadership roles. Currently, you are the executive director of Baylor’s Robbins Institute for Health Policy and Leadership and serve as chair of Board Chair of the Commission on the Accreditation of Health Management Education (CAHME). How has your military experience informed your leadership style?

Some of the key leadership attributes that my military experience taught me were mutual respect, attention to detail, and follow-through. These qualities are required for successful mission accomplishment especially in environments, such as the military, where the work requires interdependence and the stakes are high. In these regards, the healthcare environment is unlike the military where work performance is highly interdependent and could mean the difference between life or death for those being served.

Are there specific takeaways from your health administration experience in the miliary that you’re incorporating in the classroom at Baylor’s Hankamer School of Business?

A key lesson is the need for adaptive leadership. Many people believe that military leaders bark orders all day. Though there are certainly situations that call for a directive leadership style (“Charge that hill!”), trust and respect from followers in the military context are earned from the same leadership qualities (e.g., integrity, compassion, sound judgement, empowerment, and humility) that are valued in the civilian setting. Because environments and follower attributes vary widely, an effective leader must have the ability to adapt his or her leadership style to the current context. As a student of leadership, I’ve identified four leadership styles that all leaders should be proficient in: directive, servant, visionary, and reflective. I’ve shared this model and corresponding leadership lessons with my students at Baylor.

What can the healthcare industry learn from U.S. military healthcare that could benefit clinical operations and health care delivery as a whole?

One lesson I share with my students is the Military Health System’s (MHS) use of the Quadruple Aim as a strategic management framework. The MHS took the Triple Aim developed by Don Berwick and the Institute of Healthcare Improvement and added a fourth aim: increased readiness. Since the military’s mission is to fight and win the nation’s wars, it makes sense that the MHS would place increasing the readiness of servicemembers at the center of its strategic framework. In this case, readiness would be servicemembers’ ability to deploy into a combat environment and comprises their physical, emotional, and spiritual fitness. Fitness is measured through things like being up to date with annual physical and dental exams, being current on vaccines, and passing physical fitness tests.

The Military Health System promotes wholistic well-being through several programs. One such program is the Performance Triad that emphasizes the importance of sleep, activity, and nutrition.

I think there’s a case to be made for readiness being similarly important for the civilian sector as the military. With rising obesity rates, growing absenteeism and presenteeism, and a high percentage of working-age individuals with chronic conditions, health concerns are affecting the readiness of our workforce.

So, a key lesson from the military health system would be the adoption of greater wellness and prevention programs and a shift from a more reactive to proactive approach to healthcare.

What do you view as some of the biggest challenges facing U.S. healthcare, and how can health management education programs best help address them?

The biggest challenges facing U.S. healthcare are well known: rising costs, limited access, and lagging outcomes. I think health management education programs can best help address these issues by first developing principled leaders with the personal and interpersonal skills needed to thrive in healthcare’s complex and taxing environment. Qualities such as integrity, resilience, and empathy are essential. Next, students need to understand the forces shaping healthcare like reimbursement models, government regulations, and insurance. Last, healthcare’s biggest challenges require new solutions; thus, developing innovative and critical thinking skills is essential. Programs should provide opportunities for students to envision and design new delivery and payment models and test their models against established theory.

What would you advise students who are contemplating the possible pursuit of a health care-focused MBA?

I believe students pursuing a career in the health profession – whether clinical or administrative – are seeking to help and serve others. However, in order to maximize their impact, students need to have an understanding of the business-side of medicine and develop the skills to navigate its complexities. A health care-focused MBA provides these tools which can be applied to traditional healthcare settings such as hospitals and health systems, consulting firms, and physician practices. In addition, a healthcare MBA has application to emerging sectors such as technology start-ups, value-based care organizations, private equity-backed groups, and insurance plans. Earning a health care-focused MBA would expose a student to healthcare’s diverse ecosystem and could help launch the student into a deeply fulfilling career.

Contact Forest Kim: Forest_Kim@baylor.edu

 

 

 

Cleave Therapeutics: Taking a Risk on Oncology Drug Discovery

(Harvard Business School Case 323-045, January 2023; 24 pages)

Authors: Regina Herzlinger and Brian Walker, Harvard Business School

Abstract: What should a successful executive (HBS Baker Scholar) assess as her next move as the CEO of a firm with a promising and yet uncertain new drug? Amy Burroughs’ mandate to successfully commercialize Cleave Therapeutics’ drug for a cancer with no current successful therapy was on track but faced an unclear future. Overseeing the human trials of a refined second-generation drug candidate, Amy had led the company back from the “valley of death” after Cleave’s initial offering resulted in off-target toxicity. Still, after completing multiple dose escalation cohorts, Cleave’s scientists told Amy that they could not draw any definitive conclusions about the benefits of the drug. Amy and her team knew the importance of speed and capital in the high-risk business of oncology drug development where success often takes more resources and time than expected and competitors lurk. Nearing the close of a five-year investment window, should the thinly staffed Cleave 2.0 continue to recruit patients and clear dosing cohorts at a rapid rate, or should Amy prioritize funding and partnership discussions?

Download the case here. For inquiries, contact Regina Herzlinger: rherzlinger@hbs.edu

Christopher R. Idelson, ClearCam; Maansi Srinivasan, ClearCam; Austin Fagerberg, McGovern Medical School, The University of Texas, ClearCam; John M. Uecker, Dell Medical School, The University of Texas at Austin and ClearCam; Douglas G. Stoakley, ClearCam; Marian Yvette Williams-Brown, Dell Medical School, The University of Texas at Austin; Christopher G. Rylander, Department of Mechanical Engineering, The University of Texas at Austin

Contact: cidelson@utexas.edu

Competing Interest Statement: Existing competing interests between the authors and the subject matter do not appear to be present.

Acknowledgements & Funding Sources: The National Science Foundation Innovation Corps Program, Award Number 1844732 September 5^th 2018- August 31^st 2019

The authors confirm contribution to the paper as follows:

• Study Design and Conceptualization: The study was conceived and designed by Christopher R. Idelson, PhD, John M. Uecker, MD, Christopher G. Rylander, PhD, Marian Yvette Williams-Brown, MD, and Douglas G. Stoakley, BS.
• Data Collection: Data gathering/interviews were conducted by Christopher R. Idelson, PhD, John M. Uecker, MD, Christopher G. Rylander, PhD, and Douglas G. Stoakley, BS.
• Analysis and Interpretation:  Data was analyzed and interpreted by Marian Yvette Williams-Brown, MD; Christopher G. Rylander, PhD; Christopher R. Idelson, PhD; John M. Uecker, MD; Douglas G. Stoakley, BS; and Maansi Srinivasan, BS.
• Drafting of Manuscript: The initial draft of the manuscript was written by Christopher R. Idelson, PhD. and further contributions were made by Maansi Srinivasan, BS and Austin Fagerberg, BS. All authors reviewed and approved the final manuscript.

Each author contributed significantly to the research and manuscript preparation, ensuring the integrity and accuracy of the work.

Introduction

Medical devices are pivotal in healthcare, especially in surgery, where advancements in medical technology (MedTech) are crucial [1-3]. Surgical innovations, like the Intuitive DaVinci robotic system, have transformed procedures by enhancing precision, reducing invasiveness, and improving patient outcomes [4]. As healthcare evolves, understanding the MedTech ecosystem is essential for developing new technologies.

Commercializing innovative technologies is a complex process requiring significant time and resources [5-6]. Bringing products to market involves navigating regulatory approvals, quality assurance, distribution networks, and partnerships with healthcare providers [7]. The MedTech ecosystem presents unique challenges due to its diverse customer base, which includes a diverse group of stakeholders, of which primary segments typically include patients, providers, and payers [6-11]. This dynamic environment complicates market entry for both large enterprises and smaller businesses.

Effective customer needs analysis (CNA) is vital to ensure product design is meaningful and viable for the wide range of stakeholders in today’s medical marketplace [12-15]. Previous research has emphasized the accurate identification of customer needs through various techniques [16-21]. Stanford University’s Department of Management Science and Engineering has developed a robust “customer discovery” (CD) curriculum, recognized as the standard for the National Science Foundation (NSF) Innovation Corps (I-Corps) program since 2011 [16, 22-24]. The CD approach focuses on formulating and testing hypotheses to tackle commercialization challenges [28], acknowledging the significance of different stakeholders or “customer segments” within the ecosystem, including end-users, decisionmakers, payers, and influencers. Engaging with these stakeholders helps product teams understand the importance of, and value attributed to, specific needs [29]. Value propositions (VPs), which align with customer needs across various segments, are crucial in the CD process as they are the key link between technology features that create value propositions for customer needs [13, 24, 30-32].

The impact of organizations and policies governing the MedTech ecosystem is significant. Regulatory bodies, intellectual property protection, financial reimbursement, product development and manufacturing – these and many other aspects hold sway over the successful adoption of products and services, sometimes playing life-giver and life-taker for these technologies [33-38]. Regulatory standards and approvals, quality standards, and reimbursement mechanisms are centered around ensuring patient safety, but they can also create barriers to innovation. Regulatory bodies like the U.S. Food and Drug Administration (FDA) set important-yet-stringent requirements to ensure safety and efficacy, which may hinder innovation. Healthcare policies regarding reimbursement and funding significantly impact the adoption and scalability of new technologies. Favorable policies encourage adoption, while restrictive ones limit market penetration. Public policy influences the MedTech ecosystem through incentives like grants and tax credits, which foster early-stage research and development. However, high startup failure rates may indicate these incentives could be insufficient for many of these companies to overcome the “valley of death” in translating concepts into viable products.

Successful innovation requires understanding the interplay between policy and market dynamics. Integrating policy considerations into early-stage customer discovery and needs analysis can help innovators navigate regulations and align value propositions with incentives for successful commercial advancement. However, this can be distracting during early development stages when the focus is on proving concepts, raising capital, and achieving regulatory milestones. Given commercialization challenges, a structured approach to gathering customer requirements and understanding market influences is necessary. This study explores the practical implementation of CNA and the relevance of CD, focusing on stakeholders, policy impact, and other factors affecting surgical technology development and adoption within the MedTech ecosystem, and offering a roadmap for bringing innovative surgical devices to market [39-44].

Methods

More than 100 interviews were conducted with professionals in the healthcare ecosystem across the US, focusing on the laparoscopic operating room (OR) to aid in the understanding and development of a broad CNA for this growing surgical field. The interviews were geographically spread across the country, with a significant concentration in Texas.

Figure 1: (A) Customer segments interviewed (B) Areas of clinical expertise interviewed regarding Minimally Invasive Surgery (MIS) surgeons.

A total of 112 live, spoken interviews were completed with various experts in the clinical domain (e.g., those in the OR during surgery) (n = 32), healthcare supply chain domain (n = 21), channel/partner domain (n = 32), industrial/sales/medical device executive domain (n = 22), and the regulatory domain (n = 5) (Fig 1A). Among these, 99 were face-to-face and 13 via phone. Of the 32 interviews from the clinical domain, 31 were with surgeons experienced in laparoscopic surgery (of varying disciplines). Within the clinical customer segment of laparoscopic surgeons, various areas of clinical expertise were interviewed including obstetrics and gynecology (OBGYN), gastrointestinal (GI), general, thoracoscopic, oncologic, and trauma (Fig 1B). These laparoscopic surgeons provided meaningful insights into everyday issues and workflow in their respective institutions. Laparoscopic surgeon interviews were only counted as such if they averaged >3 laparoscopic cases/week. Sex, age, and degrees/levels of experience were not otherwise tracked. Similar to the scientific method and as employed by the I-Corps curriculum, interviews were structured to test specific hypotheses regarding customer needs in the context of the ecosystem and workflow in the healthcare field.

Interviews began with general introductions. Participants were informed that the goal of the interview was to understand their daily routine and pain points. That is to say, the only aspect interviewees were made aware of was that the team was participating in the NSF I-Corps program and wished to speak to experts in their respective fields to understand problems that they faced as well as general ecosystem dynamics. This allowed for an unbiased launch into broad interview topics. Professionals from various customer segments, including Surgeons in Minimally Invasive Surgery (MIS), Hospital Administrators, Sales, and Distribution entities, contributed to a comprehensive understanding of the roles and responsibilities driving the adoption of surgical technologies. Interview questions were prepared specific to each customer segment, included in Appendix A. Questions were structured to allow for open-ended responses, and interviewers allowed the interviewees to drive the conversation in a natural manner, so as not to impart any bias in the interview. All interviewees primarily operated in the United States, so results focus on the U.S. healthcare ecosystem.

After completing interviews and assessing the relevant raw data, secondary research was performed to assess what policies and/or policymakers existed as related to the impacted areas that interviewees discussed.

Results

Results from interviews proved extremely fruitful in providing both high-level and detailed accounts of stakeholder requirements, perspectives, and preferences when considering medical devices in laparoscopic surgery. Though numerous challenges exist in the operating room, our results demonstrate that launching and commercializing a product in this domain follows a multistep process with distinct requirements at each phase.

Interview results supported the notion that truly understanding pain points of end users, decisionmakers, and payers is the first domino to address in the innovation process to ensure that the value propositions for innovations validate a “product-market fit.” For example, our findings highlight the specific clinical needs of laparoscopic surgeons. Out of 31 clinicians interviewed, 100% identified laparoscope lens debris obstructing vision (i.e. fog/condensation, blood, or fat tissue/residue on the laparoscope lens) as a problem in the OR, with 18 mentioning this issue unprompted and the remaining 13 confirming it when specifically asked. FDA and other regulatory bodies further influence the trajectory of medical devices from an early development stage in the innovation and commercialization process, a key stakeholder ultimately providing the first “go/no-go gate” for potential clinical adoption in the U.S. Results also revealed a specialized distribution network for laparoscopic devices, which differs significantly from conventional hospital supply chains due to technical specialization and higher equipment costs. These distribution challenges are further complicated by adoption pathways that, while primarily surgeon-driven from the user needs perspective, must navigate multi-layered approval processes involving OR managers, evaluation committees, and potentially GPO negotiations before new technology can reach the operating room. Further results showcased the relevance, importance, and influence that intellectual property, manufacturing, and customer contracts may have on successful market adoption. Interview results were further supplemented by secondary research in these various domains regarding applicable policies and standards to extrapolate and elaborate on details relative to interview data.

In light of these findings, the raw interview data can be clustered into two broad themes: (i) Standards and Regulations – the essential regulatory compliance and quality standards that establish the minimum viable product for legal market entry; and (ii) Product Development & Commercialization Considerations – the subsequent development factors in MedTech affecting clinical adoption and commercial viability.

Standards and Regulations

All stakeholders operating within or closely connected to regulatory processes, comprising FDA regulatory consultants, liaisons, and executives in channel/partner and industrial/sales domains (n=35), unanimously emphasized that adherence to FDA authorization and compliance underpins the safe and legal introduction of medical devices in the U.S. healthcare system. The FDA plays a central role, setting stringent standards for safety and efficacy through the established classification system  (Class I, II, III), the Medical Device Reporting (MDR) regulation, and the Quality System Regulation (QSR) [45-48, 52-54].

Interviewees stressed that obtaining the FDA’s “stamp of approval” for clinical use involves navigating the FDA’s classification system (Figure 2). The FDA’s classification system (Class I, II, III) is based on device risk, requiring different levels of regulatory review and approval (registration/listing, clearance, granting, or approval) [52-54]:

• Class I Devices: These require FDA Registration/Listing.
• Class II Devices: These typically require Clearance through the 510(k) process using an established predicate device [53-54]. De Novo devices, also often Class II, require FDA Granting for novel low- to moderate-risk devices.
• Class III Devices: These need FDA Approval following the most rigorous evaluation available [52].

Figure 2: Common issues experienced during laparoscopic surgeries

Procurement specialists and hospital policies engage with FDA-authorized products, often excluding non-authorized products (outside of Class I designation) except possibly under extremely special circumstances. While interviewees acknowledged that FDA classification principles generally apply uniformly, they described specialty-specific variations within laparoscopic surgery. For example, most laparoscopic accessories (e.g., trocars, graspers, insufflation tubing) were identified as Class II due to their moderate risk profile, whereas certain OBGYN laparoscopic tools were mentioned as potentially qualifying for Class I if they present minimal risk. Specialty-specific classifications can shape development timelines, influence market entry strategies, and require hospitals to procure devices that match each specialty’s intended use.

Interviewees reinforced the importance of meeting FDA standards not only for initial market entry, but also for sustaining trust and clinical adoption over the long term. Under the MDR regulation, the Manufacturer and User Facility Device Experience (MAUDE) database collects adverse event reports with information on medical devices and patient demographics. MAUDE serves as a primary database for post-market surveillance of medical devices for the monitoring of device performance and safety. It further enables companies and regulatory bodies to make informed decisions based on reported adverse events and malfunctions. Additionally, the Quality System Regulation (QSR) mandates comprehensive documentation and manufacturing controls to ensure product integrity throughout the device’s lifecycle. It is critical to note that at the time of this manuscript’s writing and review, the QSR is transitioning to the Quality Management System Regulation (QMSR) in an effort to better align the current good manufacturing practice (cGMP) requirements with ISO 13485:2016 (details below) – the international consensus standard for a medical device quality management system (QMS) [46-48]. Beyond the FDA, other organizations play crucial roles in shaping regulations for MedTech. These include, but are not limited to:

• ISO Standards/Certifications: Regulatory consultants (n=5) emphasized that establishing a QMS aligned with ISO 13485 is not required, but could be very beneficial for FDA clearance during audits. ISO 13485 enforces standards for medical device QMS, focusing on product reliability and safety [45-48]. It is typically expected/required for a number of ex-U.S. markets. ISO 9001 (quality management) and ISO 14971 (risk management) further ensure high-quality production and mitigate potential hazards [65-66].
• The Sunshine Act: Managed and overseen by the Centers for Medicare & Medicaid Services (CMS), this law requires transparency in financial relationships between medical device manufacturers and healthcare providers, potentially impacting pathways for successful product promotion and acceptance [50].
• Anti-Kickback Statutes (HHS Office of the Inspector General and Department of Justice): These laws prevent financial relationships from inappropriately influencing medical decisions, encompassing both hospital procurement and distribution [51].
• The American Society for Testing and Materials (ASTM) and the International Electrotechnical Commission (IEC): These organizations establish guidelines for medical device standards, encompassing electrical safety and device specifications [67-70].

Product Development & Commercialization Considerations

In interviews with 31 laparoscopic surgeons, the primary challenges in the operating room (OR) were lens debris, equipment malfunctions, and workflow issues (Figure 3). All surgeons identified obscured vision from lens debris – such as fog, blood, or tissue residue – as a primary issue. Other concerns included inadequate equipment, team coordination challenges, and scheduling delays, though these were mentioned less frequently (Appendix Table B). Interviews with supply chain and OEM representatives revealed additional design criteria considerations primarily related to overhead burden and equipment compatibility. For instance, the number of parts or stock keeping units (SKUs) requiring management and compatibility with existing equipment could influence perceived value. Re-sterilization practices also emerged as a key consideration, with some hospitals valuing reusable instruments, while others favored disposables to avoid reprocessing logistics and overhead costs. This preference appears product-specific, secondarily influenced by hospital policies.

Figure 3: Map of healthcare ecosystem relative to new device workflow within hospital system.

Interview data focused on commercialization efforts primarily focused on market launch, access, and adoption. The concern that lens debris presents a barrier to surgical tool use and robotic system adoption was echoed by nine representatives from large surgical Original Equipment Manufacturers (OEMs) (OEM 1, n=3 | OEM 2, n=2 | OEM 3, n=4).

Hospitals, prioritizing economic benefits, often require third-party reimbursement (HCPCS and CPT codes [55-57]) for new devices. This CMS/AMA-overseen process (~2-3 years) involves stringent FDA labeling and manufacturing standards [58]. Reimbursement policies (Medicare, Medicaid, private insurance) greatly influence a technology’s commercial viability [59-60], with hospitals often relying on reimbursements to justify investments. Companies frequently accept reduced initial margins for long-term profitability. Hospitals may purchase low-cost, high-benefit products without reimbursement. A strategic approach considers the trade-offs between pursuing reimbursement (lengthy process) and accepting lower margins initially, and this may include different/parallel approaches depending on the product and resources. Developing a low-cost product, comparable to standard supplies, can expedite sales and adoption by circumventing the reimbursement process.

Thirty-two interviews with supply chain, sales, and medical device operations experts revealed a complex distribution network for laparoscopic surgical devices (Figures 4 and 5). While superficially similar to the conventional hospital supply chain (e.g., sourcing, procurement, logistics), the laparoscopic supply chain is characterized by specialization across clinical (12 specialties) and technical domains (robotics, advanced imaging, instrumentation, etc.) [71-74]. This specialization impacts users, administrators, and procurement pathways due to varying expertise and technical needs. Additionally, high equipment costs increase hospital cost sensitivity, while surgeon influence appears to be more prominent than in conventional procurement.

Products reach hospitals through major distributors, Prime Vendor direct sales, or GPO negotiations. GPOs exert significant influence, with hospitals utilizing Open (multiple organizations) or Closed (specific systems) GPOs, impacting product approval times (six to 24 months). Companies, especially with new products, often pursue both direct sales and GPO negotiations to expedite market entry, despite resource inefficiencies. Local contracts within a GPO’s network can sometimes shorten approval timelines. Open GPOs offer broader market access but longer approvals, while Closed GPOs may expedite approvals but limit market reach. Local/regional value analysis committees (VACs) add further complexity, with their evaluations (influenced by CMS regulations, hospital policies, and the Sunshine Act [49-50]) affecting adoption feasibility and speed. Companies must adapt to these dynamics, balancing revenue optimization with risk-reward calculations. This entire network operates under strict FDA regulations and anti-kickback statutes (DOJ [51]) intended to ensure ethical practices.

Seventeen executives – 10 from the healthcare supply chain and 7 from industrial/sales/medical device sectors – confirmed that surgeons are the primary drivers of medical device adoption in the OR, with OR nurses and technicians also playing influential roles (Figure 6). These personnel engage with medical device sales representatives within hospitals, at conferences, trade shows, and through digital media. Requests to purchase or trial new devices typically go through the OR manager, the initial “gatekeeper,” before being reviewed by local representatives and then local or national evaluation committees. For initial demo/trial approval, three major criteria must typically be met: product need, cost-effectiveness, and alignment with current medical standards. If the product is contracted with the hospital or an affiliated GPO, the process may often be streamlined. Otherwise, evaluation timelines range from one to six months for local groups to six to 24 months for national groups, influenced by factors like hospital/system type, product type, cost, and impact/need. Status as a “Prime Vendor” (e.g., Medtronic, Johnson & Johnson, Stryker) versus a smaller company also affects adoption speed, often due to the influence of existing contracts. The evaluation process often involves a clinical champion presenting to an evaluation committee comprising medical, technical, and business professionals. Their decision, based on clinical and economic impact, is followed by a trial period to collect evaluation data, after which a final decision is made. Some evaluations may involve free product trials, though some institutions, such as military/government-funded ones, may not allow for free trials and require product purchase.

Figure 4: Broad-strokes sample map of the many paths to bring a medical product to market, with channels and partners included with customer segments. Map primarily helps illustrate extremely convoluted distribution system and level of considerations/entities relative to navigating this space.

Figure 5: Potential routes for small-to-medium sized companies attempting to enter medical device space within healthcare ecosystem. Note that this figure is not all-encompassing of the many healthcare industry entities, but instead just a representation with core example groups.

Figure 6:  Medical Device Classification and FDA Approval Pathways

Intellectual Property (IP) protection is critical in the MedTech industry. Companies employ extensive patent strategies that vary depending on business goals and resources, incorporating nuances for patent filing dependent on depth and breadth of claims and descriptions, as well as geographic coverage potentially further influenced by market sizes and dynamics. The types of protections may also influence strategies. For example, while many companies rely on patents for protection, trade secrets may be the better course of action, since a patent eventually must be publicly disclosed. Companies engage with the United States Patent and Trademark Office (USPTO) and the World Intellectual Property Organization (WIPO) [61-63]. These organizations help ensure that IP rights are legally upheld. In the U.S., the Federal Trade Commission (FTC) and the Department of Justice (DOJ) enforce antitrust and competition laws to prevent monopolistic practices, which may be particularly relevant during mergers and acquisitions involving smaller companies [64]. IP concerns also arise during hospital product approvals, especially when clinician champions have a stake in a particular product (conflicts of interest). Internal hospital policies play a crucial role in managing these conflicts, with some institutions prohibiting the use of products with potential conflicts, while others allow them with proper disclosure and alternative clinician/personnel involvement. These various organizational frameworks collectively influence legal protection, market competition, and ethical considerations in the commercialization of MedTech innovations.

Discussion

This study illuminates the intricate landscape of the U.S. MedTech ecosystem, particularly for laparoscopic surgical innovations. Our findings, drawn from over 100 interviews with diverse stakeholders, reveal critical challenges and opportunities spanning standards and regulations, product development, and commercialization. These challenges disproportionately burden smaller companies, hindering their ability to bring innovative solutions to market and potentially limiting patient access to improved surgical technologies.

Surgeons consistently cited lens debris and obstructed views as major procedural issues. However, analysis of standards and regulations revealed a notable absence of policies specifically targeting these issues. This gap represents opportunities for stakeholders to collaborate on developing and implementing standards that directly address such critical clinical concerns. While excessive regulation can stifle innovation, establishing objective standards for commonly encountered issues, offers a significant opportunity to improve patient care while concurrently reducing economic burden on hospitals and the broader healthcare system. Moreover, the potential variations in regulatory requirements across laparoscopic specialties introduce added complexity.  In this context, guidelines must provide sufficient clarity to address critical challenges – such as lens debris and obstructed views – yet remain flexible enough to foster the timely introduction of novel technologies that meet evolving clinical demands.

The optimal source for these standards, potentially a combination of guidance from leading clinical societies coupled with government incentives, merits further investigation. Furthermore, uncertainty about reporting requirements and time/resource constraints are significant factors contributing to underreporting in the MAUDE database. These issues, combined with the underutilization of the system, create substantial challenges for effective post-market surveillance of medical devices [75-79]. Smaller companies often rely heavily on a feedback loop of clinical use data for product improvement, regulatory compliance, and scaling newer technologies, while larger companies with product lines leveraging years of use cases have a large amount of historical use data to feed into their design feedback loop. While it may seem intuitive to attribute fewer or delayed submissions of MAUDE reports from smaller manufacturers to limited staffing, the reality is that underreporting and delays reflect broader, systemic deficiencies in reporting practices across all manufacturers. Underreporting encompasses not only the scarcity of submitted reports, but also the frequent submission of incomplete or insufficient clinical information. Beyond underreporting itself, delayed reporting, mainly from manufacturers who file 97% of MAUDE reports (some taking up to 80 days), points to deeper problems with database usage and standardized reporting beyond just company size or representation [80].

Comprehensive post-market surveillance data is essential for smaller companies to demonstrate adherence to applicable standards (e.g., QMSR, ISO 13485), further strengthening their QMS. Developing more robust reporting systems and addressing OR inefficiencies will not only enhance innovation opportunities and commercial success of large and (especially) smaller companies, but also broadly improve the quality of patient care.

Investigation of product development factors highlights significant hurdles faced by smaller companies seeking to introduce novel MIS technologies. Although the FDA’s rigorous regulatory pathways are essential for ensuring device safety and efficacy, they often pose substantial financial and logistical challenges for smaller entities that have limited resources. This disparity may create an uneven playing field, placing smaller companies at a distinct disadvantage and potentially hindering groundbreaking innovations. Compounding this issue are the complexities of IP protection. Market adoption can expose smaller companies to IP vulnerabilities, including potential legal challenges or infringement claims by competitors. Moreover, larger competitors often possess the financial means and legal expertise to circumvent or leverage existing patents, effectively exploiting the vulnerabilities of emerging innovators. By contrast, smaller entities are forced to divert limited capital and attention toward safeguarding their IP, potentially delaying product development and undermining their ability to effectively compete at a global, and potentially even national, scale.

The rigorous standards required for manufacturing and design processes, including FDA’s QMSR, ISO 13485, ISO 9001, ISO 14971, along with guidelines such as those seen in ASTM and IEC, further exacerbate the challenges for bringing innovations to market. These standards, while crucial for ensuring traceability of products, designs, and materials, confirm product safety, but demand significant resources in terms of funding and personnel. The necessity of these standards underscores the importance of regulatory approval and patient safety, but simultaneously reinforces the impact of barriers faced by smaller companies in achieving market adoption where personnel headcount is lean and bandwidth is already spread thin. Resources, such as fractional headcount (i.e., part time consultants/contractors/advisors) and specialty software tools, might enable greater management and efficiency for accessing and maintaining such standards which could provide notable value to smaller companies to be more competitive.

Interview results shed light on FDA authorization/compliance as a critical, non-negotiable milestone for introducing medical devices into clinical settings. However, for many smaller MedTech ventures, the most daunting challenge is actually centered on commercialization [81]. While MedTech regulatory standards and commercialization efforts are both complex, standards are readily accessible, change very little (mostly), and are broadly applicable from one company to the next. However, commercialization efforts require navigation of a more nebulous and amorphous arena with variables such as technology, people, patients, hospital site locations and policies, hospital systems, and timing – all yielding a seemingly ever-changing landscape requiring constant adaptation. While there is of course some level of structure, the equation for successful clinical adoption of a technology does shift quite often, but is easier to manage once a company already has a foot in the door. This is why the current system favors large OEMs/Prime Vendors who leverage market dominance and established relationships to fend off competition.

Associated complex distribution channels create substantial barriers to entry for smaller innovators. This limited market access, coupled with lengthy hospital evaluations (exacerbated by GPO influence), hinders smaller companies from gaining traction, securing funding, and reaching patients. Furthermore, the complex reimbursement process (HCPCS and CPT codes [82]) adds another hurdle, directly impacting financial viability and overall success or failure of early-stage companies. Standardized, transparent evaluation criteria are essential for a level playing field. A nationally standardized systematic product evaluation, while potentially similar to health technology assessments (HTAs) (considering clinical effectiveness, safety, and cost-effectiveness), would likely need to differ in scope and implementation. HTAs comprehensively evaluate a technology’s healthcare system impact, including societal and ethical considerations [76]. Conversely, a standardized evaluation might focus more narrowly on product-specific features, performance, and value within a hospital or health system. While less comprehensive, such standardized criteria could empower smaller manufacturers by providing an objective framework to demonstrate value, facilitate comparisons, and streamline purchasing decisions.

Current market dynamics necessitate a deep understanding of all stakeholder interests, encompassing the full cycle of care, financial considerations, and the broader MedTech ecosystem [30]. This understanding is crucial for identifying advocates within the system, anticipating potential resistance, and identifying opportunities to streamline processes for cost-effective resource allocation. While not inherently advantageous or disadvantageous, the comparatively higher levels of process ownership and flexibility observed in military/government hospitals are noteworthy. By strategically focusing on key roles and influencing factors within the procurement process (i.e., communication and negotiation), companies can improve their chances of overcoming these complex barriers.

Conclusion

This study reveals the significant hurdles in translating MIS medtech innovations in the U.S. market. Findings, based on extensive stakeholder interviews, expose critical challenges across standards and regulations, product development, and commercialization, which, due to their frequently limited resources, disproportionately impact smaller companies and potentially limit patient access to innovative technologies. Furthermore, findings highlight how current policies often exacerbate these challenges, creating a systemic bias that favors established players and stifles disruptive innovation.

A primary concern is the disconnect between surgeons’ clinical needs and the existing regulatory framework. While surgeons prioritize solutions for visualization challenges, current policies lack specific guidance on these crucial issues. This gap necessitates policies that better reflect real-world OR needs and directly address surgeon priorities. Moreover, the underutilization of the MAUDE database for adverse event reporting limits access to valuable data, especially crucial for smaller companies who rely on this information for iterative product development and navigating regulatory compliance. The costs/complexities associated with robust MAUDE reporting create an additional burden for smaller companies, making policy changes to simplify and incentivize reporting even more critical. Improving MAUDE reporting, especially the impact on small companies, should be a focus of policy changes.

The path from product development to commercialization is fraught with obstacles heavily influenced by policy. The current FDA approval process, while vital for safety, places heavy financial and logistical burdens on smaller companies, often stressing or even exceeding their limited budgets and personnel. A similar burden is also seen in the lengthy and complex reimbursement process, which further disadvantages resource-constrained startups. For these small companies, navigating reimbursement and demonstrating clinical and economic viability during protracted regulatory processes is extremely taxing on limited runway budgets. Moreover, current policies governing distribution channels and hospital evaluations often favor established entities with existing contracts/relationships, creating an uneven playing field for smaller innovators. As well, the nebulous and dynamic nature of the commercial and stakeholder engagement disproportionately impacts the ability of smaller companies to drive clinical adoption and achieve commercial success. This systemic bias limits market access for smaller companies, hindering competition and potentially delaying, or even preventing, the availability of beneficial patient technologies.

Beyond these direct policy implications, other product development, manufacturing, and legal considerations further disadvantage smaller companies. Limited resources, coupled with the need to operate leanly, and further impacted by existing policies such as FDA, MDR, and QMSR that set high bars for design, testing, quality control, and manufacturing, make navigating these complexities even more difficult. This high bar often exceeds the financial, operational, and logistical resources that small companies have access to. This can create vulnerabilities that larger, more established companies can exploit, reinforcing the urgent need for policies that promote a more equitable environment for competition and innovation.

To foster a more innovation-friendly MedTech ecosystem, policies must adapt to support emerging companies:

• Prioritize clinical needs: Policies should address unmet surgical needs (e.g., visualization challenges), enhancing the standard of care and opportunities for innovation.
• Streamline regulatory and reimbursement processes: Streamlined regulatory and reimbursement pathways for smaller companies are crucial to reduce financial and logistical barriers, accelerating patient access to innovative surgical tools.
• Democratize market access: Implement standardized, transparent evaluation criteria and possibly restructure distribution channels to mitigate bias, promote competition, and better enable smaller companies to reach the market. Adoption of standardized policies will further level the playing field by encouraging a more steady and structured sector regarding clinical and commercial adoption opportunities.
• Improve adverse event reporting mechanisms: Improved reporting mechanisms are essential for collecting comprehensive data, improving device safety, and informing both product development and regulatory policies. This is especially critical for smaller companies who rely on this information for product development and market entry.
• Foster collaboration and transparency: Enhanced communication among stakeholders (policymakers, regulatory bodies, hospitals, manufacturers, and clinicians) is crucial for identifying systemic barriers, sharing best practices, and promoting successful integration of innovations.

By addressing these issues through targeted policy changes, a more balanced and dynamic MedTech ecosystem might better improve patient safety and foster development and adoption of innovative surgical technologies, leading to better outcomes for all.

 

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Appendix A

Interview Questions – Rubric/Guideline: Minimally invasive surgeons who use laparoscopes (including residents, fellows, medical students)

• How many surgeries do you perform each year?
• Do you have a specialty?
• What types of surgeries do you perform more often than others?
• How many of these surgeries do you perform each year?
• How long have you been performing surgeries that use a laparoscope?
• What are the most common and frustrating/annoying problems you experience on a daily basis, throughout all the laparoscopic procedures you perform?
• Are there current methods, practices, or devices that try and solve these problems?
• Could you list them?
• How well do they work?
• Could you provide a range on how much they cost?
• Which issues are more important than others? Which issue(s) annoy you the most?
• What issue occurs the most often?
• How often does that one occur?
• What are some general trends you notice with the field of laparoscopic surgery? Regarding preparation, execution of surgery, overall process, and equipment?
• What items/aspects of your job are most important in terms of job performance, both from your personal opinion and on a professional level with how surgeons are evaluated?
• How do you find out about new medical products? What makes you decide to try a new product? What’s the process of getting a new product into a hospital for you to try?

If they do not talk much about the scope getting dirty, we then dive into the topic….

• Other surgeons have mentioned that their lens becomes obstructed by debris in the body – it doesn’t sound like that is much of a problem for you at all. Does that even happen for you?
• How often does this happen in a typical surgery?
• Does it happen more often in some surgeries compared to others?
• Is losing visual during even a concern for you? Why/why not?
• On a scale of 1-10 (with 10 being high importance), how serious is this concern? Why did you choose that number?
• Can you describe the current solutions to this problem that you use most?
• Are there any other solutions you are aware of that you don’t use, or don’t use as often?
• Could you imagine in your head the steps you go through to clean the scope, and roughly how long it takes?
• On a scale of 1-10 (with 10 being high importance), how important is it to be able to quickly and effectively clean the laparoscope and re-obtain visual? Why?
• Are there any other issues you have with current laparoscopes or laparoscopic technologies?
• Are there any other clinicians, hospital administrators, purchasing agents, or even patients who you could put us in contact with?

Medical Device Channel Experts

• What type of instruments do you typically sell?
• What’s your average process to try approaching new hospitals/customers?
• What do you find is the most effective strategy?
• Do you think/know of any ways
• What “selling points” do you typically see work best when selling products to new customers? Is it usually financial benefits, patient safety, physician preferences? Something else?
• Who do you really need on your side to get your product into a hospital?

Supply chain agents (i.e. purchasers, coders, etc)

• How much does a typical minimally invasive surgical procedure cost?
• Could you provide a cost range and possibly break it down into major sectors/focuses (e. laparoscopic, robotic, VATS, etc)?
• What major instruments or packages are purchased for every procedure that uses a laparoscope?
• How do you decide what instruments you purchase? Are these purchasing decisions made by you, or someone else?
• What factors influence whoever makes the decisions regarding what to purchase?
• On average, how much does each instrument or package or device cost the hospital?
• If that information is proprietary, then could you offer a maximum amount the hospital would be willing to pay for such instruments or packages or devices?
• Or perhaps an estimate/range for what the industry standard cost might be for such instruments or packages or devices?
• What and who are the major factors and/or decision makers that encourage you make purchases of certain items?
• What about certain items over others when they perform a similar task?
• Could you try and weight the importance of these factors in the decision making process?
• Are there any other clinicians, hospital administrators, purchasing agents, or even patients who you could put us in contact with?

Hospital directors

• What and who are the major factors and/or decision makers that encourage you make purchases of certain items?
• What about certain items over others when they perform a similar task?
• Could you try and weight the importance of these factors in the decision making process?
• What are important metrics that matter most for your job performance?
• Are considerations such as readmission rates a big deal for you, in particular? What about occurrences of surgical complications or post-surgical site infections? Are those important for you, or for the surgeons?
• Are there any other clinicians, hospital administrators, purchasing agents, or even patients who you could put us in contact with?

Reimbursement experts

• What and who are the major factors and/or decision makers that encourage you make purchases of certain items?
• What about certain items over others when they perform a similar task?
• Could you try and weight the importance of these factors in the decision making process?
• What are the most important factors that you decide how much you will reimburse for certain types of surgeries
• Are there certain factors regarding surgeries that help you save money, either immediately or later on down the road?
• Is length of time of the surgery something you care about?
• Why/why not?
• How much do you care about it on a scale of 1-10 (with 10 being “I care a lot”)?
• Do you reimburse for the amount of time spent under anesthesia?
• Is a shorter surgery likely to save you money?
• Is overall cost of the surgery something you care about?
• Why/why not?
• How much do you care about it on a scale of 1-10 (with 10 being “I care a lot”)?
• Are there any other clinicians, hospital administrators, purchasing agents, or even patients who you could put us in contact with?

 

• Surgical patients
• What are the most important factors that you think about when you decide whether or not to get surgery?
• Is length of time of the surgery something you care about?
• Why/why not?
• How much do you care about it on a scale of 1-10 (with 10 being “I care a lot”)?
• Is cost of the surgery something you care about?
• Why/why not?
• How much do you care about it on a scale of 1-10 (with 10 being “I care a lot”)?
• Are there any other clinicians, hospital administrators, purchasing agents, or even patients who you could put us in contact with?

Appendix B

Tables of Results by Domain

Table B.1: Clinical Domain – Key Laparoscopic Surgery Issues Identified from Surgeon Interviews

Issue	Description	Number of Surgeons	Priority
Lens Debris	Obstacles to clear visibility caused by debris (e.g. fat, blood, condensation) on the laparoscopic lens.	31	High
Equipment Issues	Problems related to defective or incorrect surgical equipment.	6	Medium
OR Team Workflow	Delays and inefficiencies in the OR due to team coordination.	4	Medium
Other	Wasted time and delays due to OR scheduling. Challenges with inexperienced or out-of-sync surgical team members.	2	Low

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

David Scheinker, Stanford University School of Engineering, Clinical Excellence Research Center, Stanford University School of Medicine; and Matt Hollingsworth, Recombinate Health

Contact: dscheink@stanford.edu

The invention of the electric motor had relatively little immediate impact on productivity. Initially, companies swapped one large steam engine for one large electric engine. Productivity increased only after factories redesigned the production process in ways made possible by each machine being powered by its own electric motor.(1,2) As Google continues to announce large language models (LLMs) that extend their lead over physicians in a growing set of clinical tasks,(3,4) hospitals are investing in the modern day equivalent of installing light bulbs in a factory powered by one large steam engine, the Electronic Medical Record (EMR). To unlock the potential of AI to improve quality and reduce costs will require the development of a new AI-EMR. Here, we lay out the potential benefits by drawing on examples from Amazon, Apple, and Google and a roadmap for a partnership with the appropriate capital and expertise to do so.

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Figure: A hospital figuratively powered by a steam engine EMR – one large, outdated system on which all the machinery within relies.

Potential Benefits

Large language models (LLMs) have drastically improved their ability to interpret simple medical text, aid physician decision-making, and tackle a variety of administrative tasks. Hospitals are racing to adopt LLMs as: digital scribes, a replacement for Google for medical questions, administrative assistants, and to draft everything from responses to patient messages to prior authorization requests (3–6). The initial results are promising, but the cost, quality, and efficiency of these use cases is fundamentally handicapped by their dependence on the hospital’s EMR steam engine.

An AI-first EMR would open the door to complementary optimization technology ubiquitous in other industries; facilitate the creation of a learning health system; improve quality and reliability; automate the low-value patient and provider tasks that consume the majority of time people spend interacting with the healthcare system; and lower rather than raise costs. Amazon demonstrates the potential efficiency allowed by AI and complementary technology. The Apple and Android ecosystems illustrate the potential of secure, connected data sharing. All of these companies improve productivity rapidly by building randomization into the consumer experience to learn from their own data.

In order to generate value, general purpose technologies such as LLMs require complementary infrastructure.(1,7,8) Mathematical optimization or mathematical programming is a central pillar of this complementary infrastructure in practically all large efficient industries. From early uses to solve logistics problems for the U.S. Air Force during World War II and to design pharmaceuticals in the 1960s, mathematical optimization is now widely used by retailers like Amazon and Walmart; all major airlines; electrical grids; financial investment firms; and even The National Football League.(9–11) To generate value from AI-based forecasts of consumer demand, retailers like Amazon and Walmart use mathematical optimization to create efficient shipping schedules for millions of packages per day that minimize delivery times and fuel costs.(12) To generate value from AI-based forecasts of weather patterns and potential delays, major airlines use mathematical optimization to assign planes to routes and crews to planes while minimizing fuel costs and respecting myriad practical and regulatory constraints.(13) Such optimization is part of the reason that airline productivity and costs have improved over the last 30 years while hospital productivity and costs have not.(14,15)

To create a schedule that respects myriad rules such as each team playing every team in their division twice and not flying across the country for consecutive games, the NFL uses mathematical optimization to create a draft that humans revise.(16) There have been hundreds of papers demonstrating how mathematical optimization can complement AI to improve surgical scheduling, nurse staffing, patient clinic appointments, and service-to-unit assignments, but, because modern EMRs do not support mathematical optimization, there have been very few implementations.(17–20) A new AI-EMR would include this century-old complementary technology.

An AI-EMR could deploy functionality well-established by technology firms to inject randomization into the standard of care to generate evidence that helps the U.S. healthcare system to attain the long-sought goal of becoming a learning system.(21) For decades, technology firms have generated improvements from evidence collected by injecting randomization and experimentation into their technology and user experience.(22,23) They have leapfrogged healthcare, where experimentation and standards of evidence go back almost 3,000 years to King Nebuchadnezzar of Babylon. (24) Most clinical trials remain expensive, time consuming, and restricted to small populations with limited potential to generalize.(25) Healthcare has taken promising steps in this area, such as the inclusion of pseudo-randomization into the standard of care for algorithm-enabled remote patient monitoring of youth with diabetes, but as individual engineering efforts outside the EMR.(26) Progress would improve rapidly with an EMR engineered to allow randomization and experimentation as part of the standard of care.

Integrating all relevant information about a patient, to replace current context-free messages, would drastically improve quality and reliability. Recently developed model context protocol (MCP) technology connects LLMs to other software and databases in a standardized way that improves efficiency and reduces hallucinations. (27,28) Only with access to data can LLMs help nurses, physicians, or patients answer common questions like, “Which medications and doses were prescribed?” or “How much out-of-pocket spending will this entail?” or “How long was the ICU stay?” Only with access to data can a digital scribe ensure that it heard a physician in a noisy room say “hydroxyzine” not “hydralazine” or “.2 mg” not “2 mg.” On your phone, applications that auto-fill your passwords, measure your pulse, or let you talk to an LLM are possible only with secure, reliable connections to the FaceID, camera, and microphone. 

Designing administrative and clinical workflows based on the current capabilities of LLMs, and projections about their improvement, would allow the elimination, rather than reduction, of numerous low-value tasks that drive burnout and reduce the time physicians and nurses have with their patients. Entire categories of non-clinical work currently done by highly trained people could be eliminated. Nurses could stop scheduling routine appointments and focus on patients with complex needs. Physicians could save hours each day and eliminate pajama time by not documenting billing codes. Experienced nurses could review and approve AI-generated schedules or nurse-patient assignments rather than dedicate half their time to creating these themselves. Analysts could dedicate time to generating insights about opportunities to improve care or cut costs rather than putting together static dashboards. Apple and Android helped third-party travel applications improve convenience, choice, and competition by letting people compare the costs of rides to the airport, hotel stays, and plane tickets. The operating system makes this possible by providing the applications with location information, saved payment information, and a Bluetooth connection for the wireless hotel room key.

Perhaps the most pragmatic reasons are lower costs and better security. Current AI productivity improvements raise costs because current EMR deployments are customized to each hospital at the wrong level, e.g., to each hospital’s preferences and needs at the “system level.” Every application is tested by every hospital’s security and stability teams, then it is customized and installed in an expensive 6- to 18-month-long process, and is often incompatible with user preferences. Each Apple and Google phone is customized to the user’s preferences and needs at the “user level.” Every app is tested by Google and Apple security and stability teams once, each user installs it with a click and customizes it to their preferences. The resulting process is more efficient and more secure. The fragmented nature of healthcare systems makes them a frequent target of large, costly attacks. It took dedicated teams of experts employed by the FBI months to break into an iPhone they had in their possession. CAN WE SWITCH THIS LAST SENTENCE WITH THE PREVIOUS ONE FOR CLARITY AND CONTEXT? 

Table: Potential benefits and uses of AI-EMR technology with examples from other industries

Potential benefits of AI-EMR	Additional technology built into EMR	Examples from other industries	Potential hospital uses
Improve procedure, physician, nurse, and staff scheduling	Mathematical optimization to complement generative AI for scheduling and logistics	Amazon, Walmart, airlines, and the NFL use optimization to schedule staff, supplies, and logistics	Schedule surgical procedures, nurse-patient assignments, and other aspects of operations using patient characteristics combined with physician and nurse experience and preferences
Generate evidence for a learning system	Pseudo-randomization and experimentation as part of the standard of care	Amazon, Apple, Google, and other technology firms generate data by integrating randomization and experimentation into the user experience	Enable virtual randomized clinical trials as part of the standard of care by, for example, allowing randomization in digital health interventions such as remote patient monitoring
Improve LLM clinical decision-support	Connections, such as Model Context Protocols, between LLMs and patient history and data	LLMs properly integrated into the programming infrastructure significantly improve the speed and efficiency of software development	LLMs provide more accurate, relevant answers to care providers by drawing on patient history and data
Fully automate tasks carried out by LLMs	Connections, such as Model Context Protocols, between LLMs and EMR functionality	Broad adoption of full-service chatbots for low-risk, low-value tasks such as scheduling or providing financial information	LLMs schedule non-urgent appointments and answer simple patient questions
Lower adoption and maintenance costs	A secure system of permissions for data sharing and functionality between the EMR and new applications.	Apple and Google application environments reduce the initial and ongoing cost to validate new applications and integrate them into the operating system	Low adoption and integration costs for direct access to a large ecosystem of applications such as clinical risk prediction, scheduling, or financial tools

A Practical Roadmap

Modern patient care is too complex for a group of motivated innovators to build something in their garage. A company or partnership with expertise and capital, such as Amazon and Google, would have to buy a small hospital system; embed clinical, technical, financial, and operational experts in the care teams; and develop the new hospital AI-EMR from the ground up. There would be significant challenges. Fortunately, the resources necessary to overcome them are available, and the rewards of doing so are vast.

The market is already moving in this direction. Amazon has made several forays into the space, first through an ultimately unsuccessful partnership with other large companies to create a new firm, the creation of its own online pharmacy, and most recently with the $4 billion dollar acquisition of primary care provider One Medical.(19–21) However, these efforts are not yet targeted at hospitals nor the creation of a new EMR. Oracle, a large technology company, purchased Cerner, the second largest EMR after Epic, in 2021 for $28 billion and is working to transform it into an AI-powered EMR.(22,23) Unfortunately, that effort has run into the problems common to modernizing legacy technology. Venture capitalists (VC) have committed to the viability of a similar opportunity. A VC group recently finalized a $485 million deal to acquire an 8,000-person integrated healthcare delivery system with the goal of using technology to improve its efficiency.(24,25)

Purchasing a small hospital system would be a relatively small, low-risk investment for a partnership like Amazon and Google. Google recently acquired 1,800- person cyber security firm Wiz for $32 billion, approximately 100 times their $350 million annual revenue. In contrast, hospitals are typically acquired for several hundred million to one billion dollars, approximately one to three times their annual revenue and a small fraction of what Google spends on electricity each year. Purchasing a hospital system and building an EMR from the ground up may also be safer than purchasing an EMR and inheriting the problems Oracle is attempting to overcome.

Although the costs of transitioning to a new EMR are high, they could be defrayed by the massive short-term savings made possible by the AI technology Google has just developed and Amazon’s expertise with efficient operations.(3,4,12) An AI-EMR would embed or reduce the cost of the functionality on which hospitals currently spend hundreds of billions of dollars each year. Because modern EMR‘s do not offer any kind of meaningful support for operations, practically every large hospital pays for systems to visualize their data such as Tableau, to schedule their employee such as Kronos, to manage their budgets such as EPSI, to deploy AI point solutions such as scribes, etc. In addition to the licensing fees, hospitals employ massive workforces dedicated entirely to supporting and maintaining the systems. The costs of the systems are high and the user experience poor, because companies compete based on their ability to sell to and integrate with hospitals, not on the quality of their technology. In contrast, markets with low barriers to entry such as Apple and Google app ecosystems see fierce competition to improve quality, user experience, and reduce cost. Finally, the value provided by these systems is limited by the difficulty, sometimes impossibility, of data sharing data. Nurse scheduling, the top operational expense of most hospitals, would be more efficient if it were powered by mathematical optimization integrated with data on: patient census, scheduled patient admissions, and detailed clinical data such as the specific needs of patients for whom only certain licensed nurses are appropriate.(26–28)

Modern patient care is too complex for a group of motivated innovators to build something in their garage. A company or partnership with expertise and capital, such as Amazon and Google, would have to buy a small hospital system; embed clinical, technical, financial, and operational experts in the care teams; and develop the new hospital AI-EMR from the ground up. There would be significant challenges. Fortunately, the resources necessary to overcome them are available, and the rewards of doing so are vast. 

The market is already moving in this direction. Amazon has made several forays into the space, first through an ultimately unsuccessful partnership with other large companies to create a new firm, the creation of its own online pharmacy, and most recently with the $4 billion dollar acquisition of primary care provider One Medical.(29–31) However, these efforts are not yet targeted at hospitals nor the creation of a new EMR. Oracle, a large technology company, purchased Cerner, the second largest EMR after Epic, in 2021 for $28 billion and is working to transform it into an AI-powered EMR.(32,33) Unfortunately, that effort has run into the problems common to modernizing legacy technology. Venture Capitalists (VC) have committed to the viability of a similar opportunity. A VC group recently finalized a $485 million deal to acquire an 8,000 person integrated healthcare delivery system with the goal of using technology to improve its efficiency.(34,35) As the  

Purchasing a small hospital system would be a relatively small, low-risk investment for a partnership like Amazon & Google. Google just acquired an 1,800 person cyber security firm Wiz for $32 billion, approximately 100 times their $350 million annual revenue. In contrast, hospitals are typically acquired for several hundred million to one billion dollars, approximately 1-3 times their annual revenue and a small fraction of what Google spends on electricity each year. Purchasing a hospital system and building an EMR from the ground up may also be safer than purchasing an EMR and inheriting the problems Oracle is attempting to overcome. 

Although the costs of transitioning to a new EMR are high, they could be defrayed by the massive short-term savings made possible by the AI technology that Google has developed and by Amazon’s expertise with efficient operations.(3,4,12) An AI-EMR would embed or reduce the cost of the functionality on which hospitals currently spend hundreds of billions of dollars each year. Because modern EMRs do not offer any kind of meaningful support for operations, practically every large hospital pays for systems to visualize their data (e.g., Tableau), to schedule their employee (e.g., Kronos), to manage their budgets (e.g., EPSI), to deploy AI point solutions (e.g., scribes), etc. In addition to the licensing fees, hospitals employ massive workforces dedicated entirely to supporting and maintaining the systems. The costs of the systems are high and the user experience poor, because companies compete based on their ability to sell to and integrate with hospitals, not on the quality of their technology. In contrast, markets with low barriers to entry such as Apple and Google app ecosystems see fierce competition to improve quality, user experience, and reduce cost. Finally, the value provided by these systems is limited by the difficulty, sometimes impossibility, of data sharing data. Nurse scheduling, the top operational expense of most hospitals, would be more efficient if it were powered by mathematical optimization integrated with data on: patient census, scheduled patient admissions, and detailed clinical data such as the specific needs of patients for whom only certain licensed nurses are appropriate.(36–38)

Challenges

The primary risks of the proposed approach would be the clinical complexity of deploying a new EMR during clinical practice, the short-term declines in quality associated with the adoption of a new EMR, and the risk data blocking and legal action by incumbent EMRs.(39–41) Fortunately, the adoption of a certified EMR by practically every hospital in the United States has generated a vast literature of the common challenges and strategies to overcome them, as well as numerous specialists with experience in multiple EMR deployments. The appropriate partnership could have, or could acquire, the expertise to meet these challenges. Amazon has a multi-decade record of successfully acquiring and improving the efficiency of low-tech, labor-intensive companies. Google’s forays into AI have yielded Nobel-prize winning improvements in medical technology.(42,43) Both companies have world-class legal teams with decades of experience in litigating intellectual property.

Conclusion

Those of us working in healthcare have long lamented that Epic is like democracy: it’s the worst system of operations, except all the others that have been tried. In an era of breathtaking technological transformation, EMRs remain the expensive, harmful steam engines of an era bygone. The risks of radical transformation are great. Recent advances in AI make the promise greater still.

 

References

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3. McDuff D, Schaekermann M, Tu T, Palepu A, Wang A, Garrison J, et al. Towards accurate differential diagnosis with large language models. Nature. 2025 Apr 9;1–7.

4. Tu T, Schaekermann M, Palepu A, Saab K, Freyberg J, Tanno R, et al. Towards conversational diagnostic artificial intelligence. Nature. 2025 Apr 9;1–9.

5. Tierney AA, Gayre G, Hoberman B, Mattern B, Ballesca M, Wilson Hannay SB, et al. Ambient Artificial Intelligence Scribes: Learnings after 1 Year and over 2.5 Million Uses. Catal Non-Issue Content. 2025 Mar 31;6(2):CAT.25.0040.

6. Garcia P, Ma SP, Shah S, Smith M, Jeong Y, Devon-Sand A, et al. Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages. JAMA Netw Open. 2024 Mar 20;7(3):e243201.

7. Brynjolfsson E. The productivity paradox of information technology. Commun ACM. 1993 Dec 1;36(12):66–77.

8. Reddy A, Scheinker D. The Case For Mathematical Optimization In Health Care: Building A Strong Foundation For Artificial Intelligence. [cited 2025 Apr 11]; Available from: https://www.healthaffairs.org/do/10.1377/forefront.20201110.585462/full/

9. INFORMS. INFORMS. [cited 2025 Apr 11]. Optimization/Mathematical Programming. Available from: https://www.informs.org/Explore/History-of-O.R.-Excellence/O.R.-Methodologies/Optimization-Mathematical-Programming

10. Fonner DE, Buck JR, Banker GS. Mathematical Optimization Techniques in Drug Product Design and Process Analysis. J Pharm Sci. 1970 Nov 1;59(11):1587–96.

11. Introduction to Operations Research [Internet]. [cited 2025 Apr 11]. Available from: https://www.mheducation.com/highered/product/Introduction-to-Operations-Research-Hillier.html

12. Chiles CR, Dau MT. An analysis of current supply chain best practices in the retail industry with case studies of Wal-Mart and Amazon.com [Internet] [Thesis]. Massachusetts Institute of Technology; 2005 [cited 2025 Apr 11]. Available from: https://dspace.mit.edu/handle/1721.1/33314

13. Yu G. Operations Research in the Airline Industry. Springer Science & Business Media; 2012. 492 p.

14. National Football League Scheduling [Internet]. Gurobi Optimization. [cited 2025 Apr 11]. Available from: https://www.gurobi.com/case_studies/national-football-league-scheduling/

15. Zenteno AC, Carnes T, Levi R, Daily BJ, Dunn PF. Systematic OR Block Allocation at a Large Academic Medical Center: Comprehensive Review on a Data-driven Surgical Scheduling Strategy. Ann Surg. 2016 Dec;264(6):973.

16. Fairley M, Scheinker D, Brandeau ML. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manag Sci. 2019 Dec;22(4):756–67.

17. Al Amin M, Baldacci R, Kayvanfar V. A comprehensive review on operating room scheduling and optimization. Oper Res. 2024 Dec 6;25(1):3.

18. Shi Y, Mahdian S, Blanchet J, Glynn P, Shin AY, Scheinker D. Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press. Health Care Manag Sci. 2023 Dec;26(4):692–718.

19. Why Haven Healthcare Failed. Harvard Business Review [Internet]. [cited 2025 Apr 11]; Available from: https://hbr.org/2021/01/why-haven-healthcare-failed

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22. Oracle Health [Internet]. [cited 2025 Apr 11]. Available from: https://www.oracle.com/health/

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25. Landi H. General Catalyst’s HATCo to buy Summa Health for $485M [Internet]. 2024 [cited 2025 Apr 11]. Available from: https://www.fiercehealthcare.com/health-tech/general-catalysts-hatco-moves-forward-485m-summa-health-deal

26. Abdalkareem ZA, Amir A, Al-Betar MA, Ekhan P, Hammouri AI. Healthcare scheduling in optimization context: a review. Health Technol. 2021 May 1;11(3):445–69.

27. Jafari H, Salmasi N. Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. J Ind Eng Int. 2015 Sep 1;11(3):439–58.

28. Svirsko AC, Norman BA, Rausch D, Woodring J. Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process. J Emerg Nurs. 2019 Jul 1;45(4):425–32.

29. Lin SC, Jha AK, Adler-Milstein J. Electronic Health Records Associated With Lower Hospital Mortality After Systems Have Time To Mature. Health Aff (Millwood). 2018 Jul;37(7):1128–35.

30. Particle v. Epic Antitrust Lawsuit [Internet]. [cited 2025 Apr 11]. Available from: https://www.particlehealth.com/blog/epic-systems-stranglehold-on-u-s-medical-records-harms-patient-care-lawsuit

31. Janett RS, Yeracaris PP. Electronic Medical Records in the American Health System: challenges and lessons learned. Ciênc Saúde Coletiva. 2020 Apr 6;25:1293–304.

32. List of mergers and acquisitions by Amazon. In: Wikipedia [Internet]. 2025 [cited 2025 Apr 11]. Available from: https://en.wikipedia.org/w/index.php?title=List_of_mergers_and_acquisitions_by_Amazon&oldid=1275556573

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Aarthi Palaniappan, Leonard Davis Institute of Health Economics, University of Pennsylvania, and Herricks High School, New York, and Mark V. Pauly, Leonard Davis Institute of Health Economics and Department of Health Care Management, University of Pennsylvania

Contact: pauly@wharton.upenn.edu

Introduction

Price transparency legislation has emerged as a critical tool in shaping the landscape of healthcare coverage; it has had bipartisan support based on the idea that better information about transaction prices between healthcare providers and insurers/consumers can, in some way, increase affordability and accessibility. On one hand, supporters and users of high-deductible health plans (HDHPs) view price transparency as a crucial tool for consumers to shop for lower prices. On the other hand, consumer advocates view price transparency as a step toward greater fairness and influence over both price and quality.
Historically, there has been poor information available to insurers or consumers on the price that medical providers will require. Often the price is not known until after the service is rendered, and often the same service supplied by the same provider will have different prices for different buyers. This opacity is thought to be the reason why medical prices are “high” (since shopping for a lower price is difficult) and why sometimes consumers or insurers are surprised by prices higher than what they might have expected to pay.

The market for medical care is the antithesis of a typical commodity market in which prices are posted in advance and are uniform across buyers. The hope for price transparency is to have a buyer who knows the price or range of prices for a “shoppable” medical service that can determine the lowest price possible (presumably the lowest price the seller is willing to accept) and have an idea of what price would be charged by one seller compared to another(1). While knowing prices alone cannot guarantee a low-priced transaction, ignorance about prices can make it impossible.

The relationship of legislation to the prevalence of HDHPs is of particular interest, given the growing importance of HDHPs in the healthcare market and the argument that the consumer will have a strong incentive to search for lower priced sellers for a service whose cost will be under the deductible. For someone with insurance that has little or no cost sharing, there is no financial incentive to search for lower prices or better financial deals. But for those with high deductibles, there is a strong incentive, albeit one, the argument goes, that will work best if the consumer knows the range of prices in the local market for a given service. This study aims to investigate whether voters and their representatives are more likely to have passed price transparency legislation in states where more privately insured have insurance with higher deductibles. Consumers with low-deductible health insurance plans will not benefit from price transparency, but those who have chosen HDHPs require price information to make the best decisions. Previous literature has found that price transparency can lead to significant consumer savings for shoppable services if these services’ costs are below HDHP thresholds, indicating potential financial benefits for consumers from the reinforcement of price transparency in healthcare affordability. (2,3,4,5,6)

Building on the rationale that price transparency promotes competition, reduces information asymmetry, and empowers consumers, especially those with high-deductible plans, this research explores the potential implications of HDHP market shares for the passage of price transparency laws. The study looks at data from 2007, when the first state law was enacted, to 2022, when federal laws were passed and implemented in all states, to show how t

Federal Price Transparency Activities

The Affordable Care Act (ACA), or Obamacare, contained a provision requiring hospitals to post maximum list prices for a range of services (similar to the maximum room rates that were once required to be posted on motel doors). A rare example of effective bipartisan cooperation occurred in the early years of the Trump administration when Congress reached agreement on the need for further federal rules requiring hospitals to publicly disclose at least their list prices and eventually, the full set of negotiated prices from all payers. This process was slow; even after an executive order was signed by President Trump, final rules did not take effect until January 2021 and compliance by hospitals has been very slow, up to the present. Regulation has since been gradually extended from hospitals to health plans and, in some cases, net out-of-pocket payments (a combination of price and insurance provision information).

State Price Transparency Legislation

The time pattern of price transparency legislation can be divided into three phases. In the first (“pioneer”) phase extends from 2007, when New Hampshire first initiated legislation, through 2018, by which time a small number of states (12) had passed legislation. The evidence on effectiveness of these varying state plans was mixed(7), but bipartisan sentiment grew among states; impatient with implementation of modest federal legislation, six states passed state laws in the second phase, 2018 through 2020. From 2020 until the end of 2022, an additional nine states enacted complementary legislation. After compliance with stronger federal rules had taken effect nationwide, there was little additional state legislative action and the focus moved to federal efforts. We therefore show a comparison between states that passed price transparency laws in each of these three phased time periods. We relate passage in each time interval to the contemporaneous level proportion of group insurance taking the form of high- deductible health plans by state. We show the cumulative growth of state laws related to the HDHP proportion. We also discuss other possible influences on state legislation.

The Political Economy of Price Transparency

Opinions differ on both the desirability and the effectiveness of high deductibles on medical care spending and price shopping. HDHPs twinned with tax- shielded medical spending accounts of various types have for decades been a staple of Republican or market-oriented programs to contain healthcare costs. We hypothesize that voters have exogenously different preferences for this approach to health insurance and healthcare financing compared to other approaches such as managed care, which do not entail price shopping by insureds. Holding health risk constant, HDHPs will be favored by consumers willing to accept more financial risk, seeking to avoid managed care insurance restrictions on use, and eager to choose their own providers. The idea that HDHPs are “consumer-directed” health plans, where individuals decide what care from different hospitals or doctors is worth it rather than ceding that choice to an insurance administrator, is central to the ideology of HDHPs. It is also true that high-deductible plans are generally not attractive to people who are already at above-average risk of using healthcare. However, since the levels and distribution of health risk vary little across states, this is unlikely to affect the demand for HDHPs.

Our hypothesis is that in states with higher-than-average demand for HDHPs, voters will successfully push for price transparency legislation that facilitates rational decision-making under a high deductible. It is important to note that this hypothesis does not require people with HDHPs to actually engage in more price shopping than those with lower-deductible plans; it only requires that they believe they will and seek information to support that belief.

Method

Data on Plan Deductibles and Characteristics

The percentage of private-sector employees enrolled in high-deductible health insurance plans, per state, was collected from the State Health Access Data Assistance Center(8). Because the development and implementation of legislation takes time, we use three year averages centered on the year in question. Data on state passage of price transparency laws over the same period was obtained from the National Academy of State Health Policy Health Systems Tracker(9). The data on proportion of group insured workers with high deductible-plans comes from the same source

Measurement of State Legislative Actions

The dataset analyzed in Tables 1 and 2 used cutoffs to December 2018, December 2020, and December 2022. In addition, Tables 1 and 2 made use of the Annual Prescription Drug Price Transparency Tracker.(9,10, 11)

Statistical Analysis

By looking at data from the three periods described above, we tested the hypothesis that higher HDHP participation is associated with the higher likelihood of passage of state price transparency legislation. We compared average HDHP penetration per year in the 11 states that passed laws in the first period up to December 2018 with penetration in states that did not pass laws. In the second period, we compared, among the remaining 39 states, those that passed legislation during the period from December 2018 to December 2020 (calendar years 2019 and 2020) with those that did not. For the final two-year period, in which federal price transparency regulation became effective nationwide, we make a similar comparison.

Results

In Table 1, during the period before 2018, the percentage of group insured workers with HDHPs in “pioneer” states that passed price transparency legislation, was significantly higher compared to states that did not. While we cannot attribute causation to the HDHP proportion and while some states might have passed laws in any case, the results for the initial period are consistent with the hypothesis that an approximate 10% increase in private HDHP penetration was associated with 26% of the states passing legislation.

The first row of Table 1 shows that HDHP penetration was significantly higher in the “pioneer” states that passed laws compared to those that did not, with a statistical significance of 0.02. The next row shows the same qualitative difference in the second period with a significance of 0.09. In the last, less relevant period, there was still the same association of passage with high HDHP penetration, but now with a lower level of statistical significance. The importance of HDHP purchase thus was highest in the initial period but made less difference over time.

The last two rows of this table show the cumulative results for the two time periods (up to 2020 and up to 2022) with similar patterns, though with less statistical precision.

Discussion

These results suggest that there was bottom-up support for price transparency from consumer voters, who preferred the kinds of health insurance plans for which such legislation would make the largest difference. The complementary nature of high-cost sharing and better information to make choices using one’s own funds, is striking. Of course, supporting shoppable services is not the only reason to favor price transparency laws; they also help journalists to expose the complexities of the U.S. healthcare system and may assist some employers in determining which of their competitors are securing better deals from hospitals and doctors—so that they can try to secure them, too. In any case, further progress in improving pricing and reducing price discrimination in healthcare and health insurance can rely on citizens who prefer high-deductible plans for support.

Limitations

The associations shown here are not necessarily causal. The relationship between citizen preferences and collective actions in majoritarian representative states is obviously far from precise; there are many influences on political actions. The power of any study of state actions is limited by the maximum sample size of 51. It is possible that the spread of HDHPs was assisted by past or pending state price transparency legislation. In addition, the data on passage of legislation is only a rough measure of effective action, since both the timing of legislation and the timing of implementation can vary.

Conclusion

This analysis provides evidence that in states with higher levels of HDHPs, states were more likely to adopt price transparency laws before 2018. Actual evidence on how consumers with such coverage might use information mandated by law is scarce. More detailed information on what, if anything, consumers, employers, or insurers do with the additional information from such laws, still remains to be developed.

 

References

1. Pauly, V., & Burns, L. R. (2020). When is medical care price transparency a good thing (and when isn’t it)? Transforming Health Care, 19, 75-97. https://doi.org/10.1108/S1474-823120200000019009\
2. Emanuel, E. J., & Diana, A. (2021). Considering the future of price transparency initiatives – Information alone is not sufficient. The Journal of the American Medical Association, 4, Article e2137566. https://doi.org/10.1001/jamanetworkopen.2021.37566
3. Han, A., Lee, K., & Park, J. (2022). The impact of price transparency and competition on hospital costs: A research on all-payer claims databases. BMC Health Services Research, 22. https://doi.org/10.1186/s12913-022-08711-x
4. Parente, S. T. (2023). Estimating the impact of new health price transparency policies. Inquiry: The Journal of Health Care Organization, Provision, and Financing, 60. https://doi.org/10.1177/00469580231155988
5. Pollack, H. A. (2022). Necessity for and limitations of price transparency in American health care. American Medical Association Journal of Ethics, 24, Article e1069-1074. https://doi.org/10.1001/amajethics.2022.1069
6. Zhang, X., Haviland, A. M., Mehrotra, A., & Huckfeldt, P. J. (2017). Does enrollment in high-deductible health plans encourage price shopping. Health Services Research, 53. https://doi.org/10.1111/1475-6773.12784
7. Pauly, M.V., Rao, K., & Futoron, D. (2021). Light under a bushel: Medical price transparency regulation and low-priced seller behavior. Health Management, Policy and Innovation, 6.  https://hmpi.org/2021/01/12/light-under-a-bushel-medical-price-transparency-regulation-and-low-priced-seller-behavior/
8. State Health Compare. (n.d.). Percent of private-sector employees enrolled in high-deductible health insurance plans by total. State Health Compare. https://statehealthcompare.shadac.org/table/172/percent-of-privatesector-employees- enrolled-in-highdeductible-health-insurance-plans-by-total
9. National Academy for State Health Policy. (n.d.). Prescription drug pricing transparency law comparison chart. National Academy for State Health Policy.https://nashp.org/state-tracker/prescription-drug-pricing-transparency-law-comparison-ch art/
10. National Academy for State Health Policy. “Transparency Law Comparison Chart.” NASHP, https://nashp.org/wp-content/uploads/2018/04/Transparency-Law-Comparison-Chart-fina pdf.
11. “2024 State Legislation to Lower Prescription Drug Costs – NASHP.” National Academy for State Health Policy, 3 September 2024,https://nashp.org/state-tracker/2024-state-legislation-to-lower-pharmaceutical-costs/

 

 

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

Contact: sullmann@bus.miami.edu

The Business of Health Care: Post-Election

What lies ahead for the nation’s biggest economic sector was the focus of the University of Miami’s 14th annual Business of Health Care conference, hosted by the Miami Herbert Business School’s Center for Health Management and Policy. Since the conference was held three days after the Presidential inauguration in January, it was appropriate to have this year’s theme be “Post-Election.” Nearly 900 individuals registered for the conference, which was held on the Coral Gables campus, and live-streamed globally.

Given the depth of the subject matter and the level of expertise of the participants, the conference featured two panels of healthcare organizational leaders, who discussed the incoming administration’s policies and priorities, as well as other timely issues, such as artificial intelligence (AI), home healthcare, and pharmaceutical innovation.

The first panel was moderated by Patrick J. Geraghty, president and CEO, GuideWell and Florida Blue, and focused on “The Election Impact on U.S. Health Care.” The four panelists were Virginia A. Caine, M.D., president, National Medical Association (NMA); Halee Fischer-Wright, M.D., president and CEO, Medical Group Management Association (MGMA); Jennifer Mensik Kennedy, president, American Nurses Association (ANA); and Bruce A, Scott, M.D., president, American Medical Association (AMA).

Geraghty kicked off the discussion by asking the panelists about their hopes and fears with the new administration. They agreed that access to care and affordability of services and medications are critical policy issues for patients, providers, and payers. “Good medicine begins with access to care,” said Scott. “Now, we have an opportunity to educate new legislators in the states and nationally, and hopefully find common ground to improve access to care.”

Caine agreed, noting that ensuring access and affordability remain top priorities for individuals and families in underserved communities. “We are very concerned about patients in states that did not expand their Medicaid programs, as well as safeguarding states that did go ahead with Medicaid expansion so they don’t lose that status,” she said.

Fischer-Wright with the MGMA said her hope is that a capped Medicaid model would lead to better preventive care and population management, while her biggest fear is denial of access to care by limiting services or shifting costs to patients. “We need to have thoughtful legislators weighing the pros and cons of policies.”

Kennedy said the election results may change how providers approach better care. “Clinicians and nurses know what regulations are barriers, and we can have thoughtful conversations about them,” she said. “We are also concerned about the ability of all our patients, whether here legally or not, to be able to get care. It is also important for nurses to represent their communities. We need more diversity in nursing, rather than being a white female profession.”

As for what policy initiatives to recommend to the administration about improving health care, the AMA’s Scott pointed to increasing physician reimbursements, noting that the Centers for Medicare & Medicaid Services (CMS) has cut reimbursements for five years in a row. “Right now, there is a serious shortage of physicians and 80 percent of rural counties have no specialty care,” Scott said. “It takes 10 years to train a new physician, so we have to fix the reimbursement issue now. If physicians close their practices, patients won’t be able to get healthcare.” He added that a move to value-based care, while encouraging Americans to be healthier, can help address overall cost and access issues.

Caine, with the National Medical Association, raised several other points regarding the delivery of healthcare. “We need to integrate data among hospital systems, such as radiology and lab results,” she said. “That would bring tremendous cost savings with greater connectivity of patient data.”

Another problem hindering access to care is the high rate of denial for Black Americans’ insurance claims, compared with other demographic groups, Caine said. “We need to ask what is the expertise of administrators who make denials, and why there is such a disparity based on race.” Noting that there are good and bad actors in healthcare, Scott called for greater transparency by insurance plans, such as their rates of denial and prior authorization delays.

The conversation turned to the Affordable Care Act (ACA) also known as “Obamacare.” The MGMA’s Fischer-Wright said abolition of the ACA has become a political rallying cry for the new administration. “I don’t think people understand how much the ACA benefits American families,” she said. “If the ACA goes, issues of equality, equity, and accessibility will be eradicated quickly, and I haven’t heard any solutions to replace the act.”

Caine looked at the ACA’s tax credit provisions, which are critical in helping middle- and lower-income Americans afford health insurance. “We need to educate policymakers and the public, so they understand how many families utilize the ACA,” she said. “That means taking an aggressive and effective approach to communicating the benefits of the ACA.” Scott suggested emphasizing the importance of the ACA’s provisions that support wellness and preventive care. “We need to reach out to the fiscal hawks in Congress to understand there are cost savings from Americans having access to preventive or physician care versus going to a hospital. Along with a tremendous difference in the cost of care, this is a better way of taking care of people.”

After discussing policies, the panelists commented on how AI could change the future of healthcare. From a nursing standpoint, Kennedy said AI could draw on data points to identify which patients to prioritize and decrease mortality. “But there needs to be a nurse clinician in the process, rather than relying on an ‘AI nurse,’” she said. “We also need to be sure that there is no discrimination in the models used to train AI applications.”

Scott said AI should stand for “augmented intelligence” to emphasize the human role. “The more complex the AI intervention, the earlier the provider needs to be involved,” he said, “AI won’t replace the nurse or physician, but can reduce administrative and documentation burdens, giving clinicians more bedside time with their patients.” Caine said augmented intelligence can advance the delivery of personalized medicine for all, such as helping physicians identify a special chemotherapy regimen based on a cancer patient’s genetics, or test hundreds of drugs for effectiveness against a specific cancer.

“As a physician, my hope is that AI will support patient diagnosis and treatment, while reducing physician burnout,” said Fisher-Wright. “That means being able to delegate some tasks to augmented intelligence systems.”

Concluding the discussion, Florida Blue’s Geraghty commented, “The human element in health care is not going away. Instead, we need to use these tools to do a better job for our patients!”

Navigating in 2025 and Beyond

Geraghty also moderated the second conference session on “The Election Impact of Health Care: Navigating in 2025 and Beyond.” The four panelists were C. Ann Jordan, J.D., president and CEO, Healthcare Financial Management Association (HFMA); Steven Landers, M.D., CEO, National Alliance for Care at Home; Mike Tuffin, president and CEO, America’s Health Insurance Plans (AHIP); and Stephen J. Ubl, president and CEO, Pharmaceutical Research and Manufacturers of America (PhRMA).

Noting that healthcare is the largest segment of the U.S. economy, Geraghty asked the panelists if the current high-cost system is sustainable, and what changes they might recommend. Jordan said 94 percent of the HFMA’s 130,000 members believe the current system is not sustainable. “We need to cooperate and find solutions, such as a greater focus on preventing chronic illness. This is a bright spot for AI and other aspects of health innovation.”

Tuffin agreed, saying, “Our system is not sustainable for families, employers or taxpayers. We need to get to a transparent system where all stakeholders are aligned on the patient.”

The nation’s aging population provides opportunities for value creation, such as giving more post-acute and end-of-life care in the home setting, said Landers, citing the example of the late President Carter who spent many months in hospice care before his death at age 100. Older people want to stay at home, if possible, where the cost is less and the setting is comfortable and familiar, he added, but a support system needs to be in place.

To trim costs, payers and providers should focus on prevention, Ubl said, adding that there are promising new vaccines for RSV, pneumonia and other infectious diseases. Innovative therapies may also help to reduce the toll of chronic disease, which drives about 80 percent of health care costs. “We spend $200 billion a year on obesity and metabolic diseases,” he added. “GLP-1 (glucagon-like peptide-1) medications will help address the long-term costs of that care.”

Tuffin credited U.S. companies for innovations like GLP-1 drugs, which could help a large segment of the population. “However, we need to tackle the root causes, such as lifestyle and nutrition. These therapies are one tool in the toolbox.” Geraghty noted that the strong consumer demand for these drugs with unclear long-term data makes it hard to set policy.

Working with the New Administration

Geraghty asked the panelists for their thoughts about working with the new administration. “Their focus on eliminating costs makes me nervous,” said Jordan, with the HFMA. “I worry about where the cuts will occur, whom they will impact, and the outcome for underserved populations,” she said. “However, the new administration may also be considering alternative models for delivering care, and that may offer some grounds for optimism.”

Ubl said there is plenty of room to find common ground to strengthen U.S. businesses. “On the chronic disease front, we are all in favor of a comprehensive approach,” he said. PhRMA is also focused on reforming the pharmacy business manager (PBM) system where third-party companies serve health plans and insurance companies – a topic President Trump has talked about. “We want to delink the way PBMs are paid on the list price of the drug, which discourages them from lower-cost medications,” Ubl said.

Another area for potential reform is the Inflation Reduction Act’s “pill penalty,” where small molecule drugs have only seven years on the market before price controls begin, compared with 11 years for large molecule drugs and biologicals. There is no reason for this difference, which leads to more research on large molecules that can be less efficient in addressing some patient conditions, Ubl said.

Asked about price controls for medications, Ubl said he is not in favor of that approach, particularly for companies that operate on a global basis. “U.S. patients now receive about 85 percent of approved new medications,” he said. “Setting the price reduces access for patients.”

AHIP’s Tuffin added that Medicaid needs to be on a sustainable trajectory for working families and communities across the United States. “Disruptions would impact the entire system, so we are looking for stability and affordability. As for pharmaceuticals, we don’t think administered pricing is best, but that’s something that can be negotiated. We want market incentives to keep great American companies innovating and bringing new medications to market.”

Regarding AI, Jordan said, “We are just at the beginning phase of AI. AI will hit so many areas of healthcare, from treatment to administration. Our association looks at the revenue cycle and claims processes, and if payers and providers work together, AI can play a substantial role in making things more efficient.”

For the pharmaceutical industry, AI can accelerate the drug discovery and development process, Ubl said. “Running clinical trials is the most time-consuming part of the process, and AI can help by matching patients with specific trials.”

Tuffin said AI is the key to tackling the administrative drag on the healthcare system, while also supporting clinicians. Because it is difficult for physicians to keep up with the high volume of research in their fields while managing patients, AI can provide support by delivering a summary of relevant knowledge, he said.

Landers, with the National Alliance for Care at Home, indicated that given significant waste and fraud in the home health sector, technology can help identify fraudulent or criminal activity, while helping with back-office processes and workforce logistics.” Then, Geraghty summed up the panelists’ thoughts on AI, saying, “There are huge opportunities here, and human beings are part of it.”

In the weeks since the Business of Health Care conference, the administration has already made significant changes to this vital economic sector. It will be important to follow these changes in the months ahead and their impact on the U.S. healthcare sector.

 

Stacy Wood, Poole College of Management and The Consumer Innovation Collaborative, North Carolina State University; Phoebe Crosthwaite, Stanford School of Medicine; Kevin Schulman, Stanford School of Medicine and The Graduate School of Business, Stanford University 

Contact: kevin.schulman@stanford.edu

Key Words: Prevention, Primary Care, AI/ML, Financing Models

Background

The financing model for the U.S. healthcare system was largely codified with the establishment of the Medicare program in 1965. In it, health insurance would pay for services provided by physicians and hospitals, equating to the covered benefits of a contemporary Blue Cross/Blue Shield health insurance plan. Health insurance was not linked to services needed by beneficiaries, nor to health outcomes of individuals or populations. In many ways, by staying with this original design, health insurance models have become decoupled from (or even orthogonal to) our access to everyday healthcare services.

The discussion around AI in healthcare generally revolves around technology, and less frequently involves discussion of the business model needed to deploy the technology at scale. While many might consider a 60-year-old payment model to be ready for reassessment under any circumstances (the structure of how Medicare would pay for treatment was developed at roughly the same time that color television was first launched commercially in the United States), the emergence of digital healthcare services and artificial intelligence (AI) technologies make it urgent to reconsider this legacy model. At issue is how to organize the market and pay for the digital services that this technology can bring to patient care. This issue is made more challenging given that the use cases for AI are still evolving. To achieve cost and quality benefits from technology, we need technology innovation and business model innovation that develop in cooperation¹ and that are not siloed from physician fee schedules². Given the explosion of interest in digital technology in healthcare, what is remarkable is how little thought is going into business model innovation needed to deploy this technology.

One opportunity to consider as a novel market structure are subscription models. In general, subscription models involve a pre-payment to a subscription provider in exchange for easy access to a platform of goods and services for a set period. The subscription provider serves to organize the market for consumers. Borrowing from marketplace success in consumer goods, the idea of using subscription-style services in healthcare has recently been touted as a radical, but promising, route to improving healthcare access. Subscription-based healthcare includes formats like direct primary care (DPC), membership in fitness or health centers, and direct-to-consumer (DTC) platforms that offer set health services to members (e.g., Hims³). In contrast to capitation payment models which can restrict access to services, subscription models are designed to enhance ease of access to services for consumers by incentivizing providers to be proactive with regular care.

Research on healthcare subscription models has demonstrated promise in several populations, including patients with hepatitis C, obesity, cardiovascular disease, and those who are candidates for gene therapy (see Table 1 for a review of the current literature exploring subscription healthcare innovations). This idea is gaining increasing awareness; a recent industry study reported that 60% of surveyed employers were willing to contribute money to a monthly subscription care service and add it to their health plan⁴.

Table 1: Existing Literature on Subscription Models in Healthcare

Author (year)	Premise
Glover et al., 202328	Subscription model payment system for innovation in drugs (e.g. NHS subscription model pilot for antibiotics; fixed payments for antibiotic access)
Trusheim et al., 201829	The “Netflix model” for new treatments for HCV infections
Hampson et al., 202330	Spreading payments over time. Increased certainty in the healthcare space as manufacturers receive fixed fee for supply and payers know what they are paying for/know for how long they will have access to that service.
Kirubakaran et al., 202331	Subscription based model for remote health monitoring (mobile app/interface for a medical kit that allows for remote patient visits)
Dutta et al., 202032	Subscription model/community financing for primary care in rural areas includes family-based membership, free consultations, medicine and lab discounts
Hohmeier et al., 202333	Membership Pharmacy Model: partnership between pharmacy and employer, without the use of PBM (example: Good Shepherd Pharmacy (GSP) nonprofit community pharmacy – charges monthly membership fees, sells all prescriptions at acquisition cost)

To date, advocates of subscription-styled innovations primarily urge the healthcare community to think of the operational model (and success) of Netflix⁵. Yet, by invoking Netflix in particular, discussions have been constrained to a limited view of subscription models which, unfortunately, undermines a full discussion of the value they can bring. In everyday life, subscription models go well beyond streaming services. Until we better understand the full range of different subscription types, we can’t assess their creative potential as a novel healthcare solution. While Netflix might be the best-known exemplar of the broader category, it may not be the type (or the only type) that best suits healthcare innovation. Thus, we describe an expanded framework of subscription models as they are commonly conceptualized in marketing research and offer examples of how they are currently used in- and outside of healthcare. What is clear from this review is that subscription models can provide workable models to increase access, engagement, and price transparency in medical care.

A Typology of Subscription Models

While the business world has made much of the recent boom in subscription models and its impact on consumers⁶, the concept is a long-established market model, and streaming platforms were not the pioneers in this space. Many of the earliest lending libraries in the 17^th and 18^th centuries were not public goods but were private subscription-based services and accessible only to members. Sepia images of paperboys and milkmen show the prevalence of 19^th century subscription-based businesses. Many of us are familiar with common 20^th century subscription models like gyms, public transport passes, and season tickets.

There are many different types of subscriptions, and, at a first pass, they can be divided into flat-fee and variable-fee formats. We define flat-fee subscriptions as those that offer either limited or unlimited services for a pre-determined and pre-paid cost per period. The individual may consume (or forego) the offerings available during the period. Variable-fee formats (or access-only subscription models) are essentially two-part tariffs, where payment confers access to services which are then consumed and paid for per use, often at a discounted price (e.g., an independent pharmacy that charges an annual membership fee and then sells drugs at their cost). In this review, we focus on flat-fee formats. Flat-fee formats, in general, increase price-transparency and reduce uncertainty because buyers can know and account for their future expenditures; preference for a flat-fee over variable-fees, even when the flat-fee is more expensive, is known as flat-fee bias⁷.

There are a surprising number of flat-fee subscription types (see Figure 1). Critically, they differ in a) the access model for goods and services (unlimited/limited), b) the primary benefit to the consumer/patient, c) the cost of the product/service, and d) the frequency of use of the product/service. Each category has some common consumer value (e.g., price transparency) that cascades to each category member. Additionally, each subscription type also has unique consumer value they offer.

Figure 1: Cascading Value in an Expanded Typology of Subscription Models

Figure 1 shows a framework of subscription model types and the common and unique value they provide to firms and consumers. Value descriptions are shown in yellow boxes; lower order branches have unique value and share the values of higher order branches.

 

For unlimited offerings, one can group subscription models into “unlimited use” and “unlimited platform” categories. Unlimited use refers to subscriptions where consumers receive unlimited “free” access for a set period to a product, service, or digital good (e.g., gym or museum membership, all-you-can-eat buffet, apps like Noom⁸ or Slack⁹). Here, the offerings are known beforehand and are stable; for example, the fitness machines at the gym generally don’t change. In these situations, the subscription provider has control or ownership of the offerings so that their availability and quality is reasonably static. Unlimited platforms offer the consumer unlimited access for a set period to a curated set of products, services, or digital goods on a common platform (Netflix, Hulu+¹⁰, the New York Times Digital Edition). In this case, the offerings are more dynamic, being either curated or obtained from other producers. The content may change noticeably from period to period. Unlimited Platforms like Netflix are undeniably attractive to consumers. Platforms also have tools to help patients identify the highest value content for their needs. These “recommender” algorithms could be based on patient input, physician input, or a combination of these two perspectives. In addition, “recommender” algorithms could promote novel content or services based on patient characteristics or diagnoses.

Research provides insight into how this model benefits consumers where the difference between consumer perceptions of “free” versus a small cost (such as the price of a movie ticket) can have significant effects on attitudes and behaviors¹¹.  Pre-payment for a period of unlimited usage creates a sunk cost against which consumers can view incremental usage as free and subsequently increase consumption¹² .Additionally, the consumer benefits of unlimited supply are that they reduce planning-burden for those who are uncertain about their usage frequency¹³, stimulate consumption through a motivation to “get your money’s worth,”¹⁴ create a precommitment to usage of products and services that consumers believe they should consume (e.g., gym memberships), offer a perception of abundance that increases satisfaction¹⁵, and help build consumption habits through frequency of usage.

From a supplier perspective, unlimited use models are really scale models that spread the fixed cost of the platform (gym equipment, Netflix content, New York Times reporters) over a large subscriber base. They work best when the marginal cost of additional usage is relatively low cost (for example, the cost of serving an additional subscriber on the digital New York Times platform is essentially zero).

Limited use models are subscription models that offer a defined set of products and services on a periodic basis. These models differ from the platform models in that there is a non-zero cost to each marginal user (thus, the pricing model needs to account for the fixed costs of the platform and the cost of serving each user). Here, we describe four types of limited use subscription models: replenishment, curation, collection, and concierge subscriptions.

Replenishment subscriptions are those that provide consumers with regular delivery of the same high-frequency use products, services, or digital goods (e.g., Dollar Shave Club¹⁶, Amazon Subscribe & Save). Subscription providers may either produce the goods they deliver to consumers or may acquire them from manufacturers. The key consumer benefits that replenishment subscriptions provide are convenience, alleviating planning burden, and protection against shortages; the key supplier benefit is certainty in product demand and revenue. Consumers often over-estimate demand to avoid shortage¹⁷ but then also hold responsibility for the storage or discard of excess items. In some cases, having an excess of a product may independently trigger use (or even increased use) of the product¹⁸. Replenishment models may work for products/services at different price points and are best when consumption is both regular and frequent (for example, using this model for generic hypertensive medications).

Curation subscriptions offer consumers regular “gift-like” delivery of a novel or surprise set of products, services, or digital goods selected (often by experts) to match subscribers’ preferences (e.g., Birch Box¹⁹, Coffee of the Month, Stitch Fix²⁰). In this way, curations allow consumers to explore product/service categories where they may be interested novices, enabling them to benefit from the expertise of others, and to interject self-care or “self-gifting” into their life^21,22. Here, the novelty of what is received is important and so regular repetition (such as in replenishment) is negatively perceived. Because of the cultural norms of gifting, consumers may be inclined to perceive offerings more positively and to feel more satisfaction in what is received. Curations show a high degree of variability in the cost of products/services and in the frequency of their use. Successful curations fit consumers’ identity (i.e., “I am the sort of person who ___”) and may be time-limited as part of a larger goal (i.e., “This is my tool to learn about ___ or to get ___ under control”). For providers, offering different or rotating curations to consumer segments allows for smoothing demand (i.e., the firm does not have to provide 100% of consumers with the same “box” at the same time, but can design curations to fit supply patterns).

Collection subscriptions provide members with access to a shared set of products whose availability depends on other subscribers’ use (e.g., a public library, Community Toolshed programs, Rent the Runway²³, Inspirato²⁴). Thus, a community member may have easy access to some products but must wait for those in high demand. While libraries are a longstanding example, the academic business literature has been intrigued by the growth of these types of subscriptions because, for many product categories like clothing, they represent a dramatic shift from ownership to a sharing economy. Consumer interest in sharing a collection of expensive products like cars or very intimate products like clothes, rather than have the benefits and costs of ownership, has increased significantly. Here, individuals trade off access for cost-sharing, so these subscriptions are predominantly for high-cost, infrequent-use goods (e.g., ballgown, garden tiller, vacation home) where preferences may shift by occasion (e.g., a 4-wheel drive for a mountain trip and a sedan for a highway trip). Thus, collection subscriptions are very different from models that focus on relatively low-cost, frequent-usage goods like unlimited or replenishment models.

Concierge subscriptions offer consumers access to a shared set of services whose availability depends on other subscribers’ use; in this way, they are like a collection of (often human) service providers (e.g., Harper Concierge²⁵, Duke Signature Care²⁶). Subscribers benefit from committing a priori to the program for goods or services, such as a monthly carwash service for those who feel best in a clean car. They expect faster-than-normal access to services and may value this access as a status symbol, a time-saver, or both²⁷. In practice, concierge medical subscriptions are often variable-fee models as they have a combination of unlimited and fee services; for example, medical concierge groups may offer unlimited calls, office visits, and simple tests, but charge separately for more complicated scans or treatments. In either case, the availability of the service provider is dependent on shared demand among subscribers, and so demand may outpace supply, especially at peak utilization seasons (flu season, for example). The success of concierge subscription models relies on access in practice and a balance of free and fee-based offerings. At this ideal position, the consumer feels that they are operating in a state of relative abundance, while the provider retains a profitable service model.

Figure 2: Descriptions and Examples of Flat-Fee Subscription Models

Figure 2 highlights the distinctions between different flat-fee models and emphasizes that providers can use one or a subset of these models (a “mix and match” strategy) based on the fit with care delivery in specific areas and the existing provider fee schedule.

 

Together, unlimited-use and limited-use subscription models have significant potential for healthcare adaptation, and several of these models are already deployed in the healthcare market today. They also offer significant opportunities for development of novel business models, especially when coupled with the emerging AI technologies. Table 2 outlines the six types of subscriptions we have described and how they could be used to build novel offerings in the healthcare market. Importantly, the diversity of subscription models prompts a more concrete and creative exploration of possible clinical uses based specifically on a) the type of service (e.g., human or digital), b) the benefit to the consumer (e.g., reduce planning burden or pre-commit to ideals), c) the benefit to the clinician (e.g., improved performance on population health metrics), and d) the potential for a sustainable business model. Payers that adopt subscription models can choose one, or a sub-set of subscription types in a “mix and match” strategy, based on unique patient populations or system goals.

Table 2: An Expanded View of Subscription Models and their Scope for Healthcare

Subscription type	Benefit to patient	Current examples in healthcare	Potential healthcare use
Unlimited use	o   Increase price transparency o   Reduce uncertainty in future expenses o   Don’t need to predict usage frequency o   Don’t need to conserve or skimp on usage o   Increase consumption/usage to get money’s worth	o   Gym/fitness memberships o   Digital health apps, such as Headspace34 and Calm35 o   Digital therapeutics, such as Big Health36 Akili37 o   24/7 clinical help lines o   Some digital services in direct primary care (e.g. One Medical38, Parsley Health39, Curative40	o   Smart device (Amazon Echo Show) with a therapy-specific skill to increase patient adherence o   Digital health coach to navigate services for key events (puberty, pregnancy, post-diagnosis, menopause) o   Skin care AI app that allows patient to upload photos of moles.
Unlimited platform	o   Increase price transparency o   Reduce uncertainty in future expenses o   Don’t need to predict usage frequency o   Don’t need to conserve or skimp on usage o   Increase consumption/usage to get money’s worth o   Dynamic or rotating products (novelty) o   Wide array of products offered (variety-seeking)	o   Online platform that has access to variety of services (e.g. Hinge Health41) o   Online platform that has access to a variety of information and content (MyChart by Epic42]	o   Smart device (Amazon Echo Show) with multiple therapy-specific skills for different outcomes o   Subscription to access a range of digital resources (sleep apps, mental health apps, nutrition apps) for general health or specific conditions o   Platform with offering of various AI algorithms to serve patients holistically o   Healthcare platform for patient visit coordination with prep and aftercare
Replenishment	o   Convenience o   Efficiency (reduces planning/time burden) o   Decreases risk of shortage o   Regularizes consumption (prevents over- or underconsumption) o   Avoids storage issues by minimizing surplus	o  Online pharmacies that provide monthly access to/delivery of prescription medications (e.g. RxPass43 Nurx44)	o  Monthly delivery of prescription medications/vitamins o  Program that delivers products covered by health insurance (sanitary products, sunscreen) o  Regular delivery of treatments that have a regular but unusual schedule so that the patient uses/does the treatment as soon as they receive it (e.g., a patch that needs to be changed every 21 days)
Curation	o  Novelty; element of ‘surprise’ o  Discovery — provides patient with opportunity to try a range of products o  Good for things where the patient wants to learn from an expert o  Gifting or self-gifting framing creates more positive attitudes toward the sender and the things sent. o  Builds trust and engagement when the curation fits the patient’s self-identity	o Health/wellness boxes (e.g. Therabox45, Curology46)	o   Monthly box with “freebies” that include tokens for medical services/products based on patient demographics o  Curations of digital app trials that spark interest in longer-term use (e.g., trial of Noom47, sleep apps). o  Curations for patients with chronic conditions (for example, A1C level tests/CGM for people with diabetes. o  Curations of screening tests or services to catch chronic disease early. o  Curations to re-engage patients who have “fallen off the wagon”
Collection	o Price transparency o Improves cost control o Decreased likelihood of skimping on needs or rationing of equipment o Don’t take on costs of ownership (repair, storage, discard) o Increases access to high-cost, infrequent-use goods	o   Medical Loan Closet of Henderson County48 o   H.E.L.P (Health Equipment Loan Program) of Durham County49 o   Concierge level rental program (e.g. Game Ready50)	o Access to rent durable medical equipment for members/residents o Short-term subscription to recovery equipment for specific procedure (e.g., ACL tear; Cardiac rehab) o Collection of athletic gear to encourage inactive patients to try different activities
Concierge	o  Peace of mind o  Increased speed of access o  A sense of status or prestige o  Increased perceptions of personalized service o  Convenience o  Lowered planning burden o  Self-identity as someone who prioritizes health	o   Direct Primary Care (DPC) o   Concierge medicine (e.g. Duke Signature Care51)	o Expanded use of DPC to provide specialty services for high-risk patients (e.g., COPD, skin cancer, mental health) o Integration of human provider and digital services to increase coverage

Application to AI

The revolution in AI is causing many sectors of the economy to pause and reconsider core business process and models. This pause asks the essential question of why we are doing what we are doing and might there be a better way to accomplish our goals. In this paper, we have identified a set of business models that can provide direct benefit to patients by providing a novel approach to service delivery for healthcare.

It is relatively easy to conceptualize unlimited platform subscription models as a means of organizing a set of digital services for patients. Platforms could acquire and curate algorithms to provide a complete set of offerings across populations. Platforms can be based in existing service delivery structures at the health system or payer level or could be based on new models of service delivery established in parallel with existing health insurance products or funded within or outside traditional health insurance plans. Platforms could also be coupled with other types of subscription models. Further, platforms could offer customized content views for different population groups stratified by age, race, gender, clinical condition, or language. This could provide a new pathway to help address health disparities by improving access to services and by building trust through new models of care.

What is also clear from this review is that the current payment models in healthcare are not designed to support value creation for consumers from direct applications of digital technology. For example, one can conceptualize a digital platform to support basic primary care and preventive services using a subscription framework (with access to technology and clinical services), but the current payment model supports only in-person physician visits (not even associated services like e-mail messaging).

Ultimately, the time is now for national healthcare stakeholders and established healthcare providers to think deeply about how we should organize the market for emerging services, especially digital services. Developing a novel market and allocating a dedicated stream of funding to that market is one means to spur development of technology that meets patients where they are throughout their care journey. Subscription models are a powerful means to consider as part of this discussion.

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