This white paper was researched and written with the assistance of Claude Sonnet, an AI system developed by Anthropic. AI assistance was used to accelerate literature retrieval, improve the quality of writing, and support editing and formatting. The intellectual framework, argument structure, source selection, and all substantive claims reflect the author’s own thinking and direction. All citations have been identified and verified by the author. The author assumes full responsibility for the accuracy and integrity of all content presented in this paper.

Executive Summary

Artificial intelligence is arriving in American healthcare at scale. Health systems are investing in AI-powered diagnostics, clinical decision support, predictive analytics, and administrative automation. The promise is real. So is the risk. Machine learning models learn from data. In healthcare, that data is generated by the systems deploying the AI. And if those organizations have not been designed to produce clean, reliable, clinically meaningful data, then the AI built on top of them will automate and amplify the dysfunction already present in the system, not correct it.

This is the argument this paper makes. It is not primarily an argument about technology. It is an argument about organizational design.

The concept of the Learning Health System, formally defined by the Institute of Medicine in 2007, describes a system in which knowledge generation is so deeply embedded in the delivery of care that improvement becomes continuous and self-reinforcing rather than episodic and externally driven. Nearly two decades after that definition was published, widespread adoption remains limited. The gap is not one of awareness. It is one of operationalization. And in an era of AI-driven healthcare, the cost of that gap is no longer just missed improvement opportunities. It is corrupted training data, biased models, and clinical decisions shaped by intelligence that learned the wrong things from a system that was never designed to learn at all.

This paper examines why the Learning Health System has not been built at scale, using the organizational thinking design framework of Vision, Mission, Capacity, and Learning developed by Drs. Derek and Laura Cabrera, and the wicked problem literature in strategic management. It identifies three conditions most visible in clinical, policy, and public discourse as illustrations of the organizational design problem: physician burnout, electronic health record burden, and payer interference through prior authorization. These three are not presented as an exhaustive explanation. They are presented as a coherent causal chain that leads directly to the data quality crisis sitting underneath every AI deployment in American healthcare today.

The paper concludes not with a prescriptive framework but with an invitation to think differently about how health systems are designed, led, and held accountable, before the next wave of AI investment locks in the mistakes of the current one.

I: A Conversation That Sparked a Question

American healthcare is in the middle of an AI gold rush. Health systems, technology companies, and investors are moving fast, betting that machine learning, predictive analytics, and AI-powered clinical tools will transform how care is delivered and how outcomes are measured. The enthusiasm is understandable. The technology is genuinely powerful. But a question is not being asked loudly enough: what kind of system is this AI learning from?

In early 2026, Gil Bashe, Chair of Global Health and Purpose at FINN Partners, published Healing the Sick Care System: Why People Matter, arguing that American healthcare is not failing because it lacks innovation, investment, or talented people.[2] It is failing because it has lost sight of the people it exists to serve. That argument sparked a different but related question for the author: what kind of system do we actually have?

We call them healthcare systems. We build teaching hospitals. We invest in teaching rounds and residency programs and the careful, structured transmission of clinical knowledge from one generation to the next. Teaching is a word we use with confidence and pride in medicine. But when do we talk about the system itself learning? Not individuals acquiring competency, but the institution changing what it does based on what it discovers. Teaching and learning are not the same thing, and that distinction, hiding in plain sight, may be one of the most consequential unexplored ideas in American healthcare today, especially at a moment when AI is being asked to learn from systems that were never designed to learn themselves.

This question led to an examination of a concept that has existed in formal academic and policy literature since 2007 but has not entered the broader conversation about healthcare reform in any meaningful way: the Learning Health System.

II: What Is a Learning Health System, and Why Has It Not Been Built?

Understanding why AI in healthcare is sitting on a compromised foundation requires understanding what a Learning Health System actually is, and why one has never been fully built. The Learning Health System is not simply a framework for improving data quality. It is the only organizational model in which clean, clinically meaningful data is a natural and continuous byproduct of how care is delivered. Every other approach to the data quality problem in healthcare AI is essentially trying to fix the output without changing the system that produces it. The Learning Health System changes the system. That is why it matters now, and that is why AI in healthcare makes it urgent.

The term Learning Health System entered the formal vocabulary of American medicine in 2007 when the Institute of Medicine convened a roundtable on value and science-driven health care. The definition it produced has held up well: a Learning Health System is one in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.[1] Knowledge generation in this vision is not adjacent to practice. It is not a research department down the hall or a quality improvement initiative launched when funding permits. It is embedded in practice itself, and it leads to continual, self-reinforcing improvement in which care creates evidence and evidence improves care.

Nearly two decades later, widespread adoption remains limited. Not because the concept has been ignored. It has attracted sustained attention from the National Academy of Medicine, federal agencies including Agency for Healthcare Research and Quality (AHRQ) and Patient-Centered Outcomes Research Institute (PCORI), major academic health centers, and research networks such as National Patient-Centered Clinical Research Network (PCORnet) and the NIH’s National COVID Cohort Collaborative. What has proven difficult is operationalization at scale: figuring out what a genuine commitment to learning actually means in terms of changed practice, realigned infrastructure, new staffing, revised policy, and real shifts in organizational culture. The IOM’s deliberately broad definition, intended to maximize applicability, had an unintended consequence. It left every institution to solve the operationalization problem largely on its own, without a shared language for the organizational design work that learning at scale actually requires.[16]

The cycle the Learning Health System literature describes is straightforward in concept. Knowledge is identified and synthesized to address clinical challenges through evidence reviews and clinical practice guidelines. That knowledge gets applied in care delivery through clinical decision support and care pathways. Care delivery generates data, captured in patient registries and EHRs, assessed for performance, and fed back into the knowledge generation process. The loop closes. Patients are at the center throughout, not as passive recipients of decisions made elsewhere, but as active contributors to the knowledge the system generates.[11]

It is also worth being clear about what a Learning Health System is not. It is not a teaching hospital. A teaching hospital organizes itself to transfer knowledge from experienced clinicians to trainees. Knowledge flows in one direction, and the institution learns incidentally if at all. A Learning Health System organizes itself to change based on what it discovers in the course of delivering care. The institution itself is the learner. American medicine has invested heavily in building teaching capacity. The investment in learning capacity, the organizational infrastructure that allows a health system to discover, synthesize, and act on what its own practice is telling it, has been far more limited and far less systematic.

The concept operates at two levels that are easy to conflate. At the macro level, it describes what American healthcare as a sector could become. At the micro level, it is an organizational design challenge that has to be solved institution by institution through specific decisions about how care is delivered, how data is captured, how knowledge is synthesized, and how evidence actually changes what clinicians do on any given day. The macro vision only becomes real through micro organizational choices. The research literature suggests those choices have not yet been made in ways that support learning at meaningful scale.

III: A Wicked Problem and a Strategic Dilemma

Before examining why the Learning Health System has been so difficult to build, it is worth being precise about the nature of the problem itself. Not all hard problems are the same kind of hard. Some are difficult because resources are insufficient. Some are difficult because the right solution has not yet been found. The failure to operationalize the Learning Health System at scale is neither of these. It is something more structurally challenging, and naming it correctly matters because the type of problem determines what kind of thinking is adequate to address it.

In strategic management and organizational theory, a distinction is drawn between problems that are complicated and problems that are wicked. A complicated problem, however technically demanding, has a definable solution. Building an aircraft is complicated. The right answer exists, the variables can be enumerated, and expertise applied systematically will eventually produce the result. A wicked problem is different in kind, not just in degree. The concept was introduced by Rittel and Webber in their foundational 1973 paper “Dilemmas in a General Theory of Planning,”[5] which argued that problems of social policy cannot be solved using scientific-engineering approaches because they lack a clear problem definition and involve stakeholders with genuinely differing and legitimate perspectives. Wicked problems are not merely unsolved. They resist definitive formulation. Every attempt to solve them reveals new dimensions of the problem. Solutions cannot be tested in advance and cannot be undone cleanly once implemented. There is no single right answer, and the people working on the problem do not agree on what success would look like.

The challenge of building a Learning Health System is a wicked problem in precisely this sense. It is not a technology problem, though technology is implicated. It is not a regulatory problem, though regulation shapes the environment. It is not a funding problem, though funding matters. It is a problem that cuts across all of these domains simultaneously, involves stakeholders whose legitimate interests are in genuine tension with one another, and resists any solution that addresses only one of its dimensions. Researchers working in this space have noted that strategy scholars who attempt to address wicked problems using conventional approaches tend to build causal models that seek to optimize organizational success, an approach that ironically divorces the analysis from the very complexity that makes the problem wicked in the first place.[6]

Within this wicked problem, however, there is a more specific structure worth naming. The Learning Health System presents what might be called a strategic dilemma: a situation in which legitimate goods are in genuine tension with each other, and in which choosing to prioritize one value necessarily creates pressure on another. Patient safety and the imperatives of research require different things from a consent framework. The need for standardization conflicts with the need for clinical judgment. The value of data utility for population-level learning conflicts with individual privacy rights. The urgency of improvement conflicts with the rigor that improvement based on evidence requires. These are not tensions that can be dissolved by finding a smarter solution. They are structural features of the problem that any serious approach must hold in view simultaneously rather than resolving prematurely in favor of one side.

This distinction between a wicked problem and a strategic dilemma is not merely academic. It has direct implications for how we think about leadership and organizational design in this space. Wicked problems cannot be assigned to a committee and solved on a timeline. They require what the Cabreras would describe as thinking design rather than framework imposition: the cultivation of a quality of thinking in leaders and institutions that is capable of holding complexity, adapting continuously, and learning from the system rather than simply managing it. The Learning Health System is not waiting for the right policy. It is waiting for a different quality of organizational thinking. And that is a problem that systems thinking, properly understood, is specifically designed to address.

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

Understanding why the Learning Health System has been so difficult to operationalize requires more than a catalogue of obstacles. It requires a way of thinking about organizations that is adequate to their actual nature. Most health systems have been designed and managed as if they were complicated machines: hierarchical, controllable, and optimizable through the right combination of process improvement, technology, and incentive alignment. The persistent failure of that approach to produce genuine organizational learning suggests that the underlying model of what a health system is may itself be the problem.

Drs. Derek and Laura Cabrera at Cabrera Research Lab have spent decades developing and empirically grounding a different model. Their work, elaborated in Flock Not Clock and in an extensive body of peer-reviewed research,[3] begins from a foundational premise: all organizations, regardless of their formal structure, are complex adaptive systems. A complex adaptive system, or CAS, is composed of autonomous agents whose individual behaviors interact to produce collective, emergent outcomes that cannot be predicted or controlled by managing the agents individually.[13] The agents are not cogs in a machine executing instructions from above. They are people making decisions, moment by moment, in response to the conditions and incentives around them. The organization does not produce its outcomes by command. It produces them by emergence, as the aggregate result of countless individual decisions made at every level of the system every day.

This changes how we think about organizational design. If a health system is a complex adaptive system, then the question of how to build a learning culture inside it is not primarily a question of policy, technology, or incentive structure, though all of these matter at the capacity level. It is a question of what conditions and orientations the autonomous agents in the system are operating under, and whether those conditions make learning a natural emergent outcome of their daily work or an additional burden layered on top of everything else they are already asked to do.

The Cabreras developed a thinking design structure called VMCL, standing for Vision, Mission, Capacity, and Learning, to help leaders understand and shape the four functions that any organization must perform in order to move purposefully toward its goals.[4] VMCL is not a framework to be implemented as a checklist or adopted as a rebranding exercise. It is a thinking design lens, a way of seeing clearly what an organization is actually doing across its four essential functions, and whether those functions are genuinely aligned with each other and with the organization’s deepest purpose. The value is in the quality of thinking it cultivates in leaders, not in the mechanical application of its categories. Of the organizational design frameworks the author has encountered across three decades of operational leadership, the Cabrera VMCL structure is the most useful for making visible what is actually happening inside a complex organization and why.

Vision is a destination, not an action. It is a picture of a specific future state, clear enough to be genuinely directional and distant enough to be genuinely aspirational. Vision is not a description of what the organization does or how it operates. It is the answer to the question: if everything this organization is trying to accomplish were fully realized, what would the world look like? Most organizational vision statements fail this test entirely. They are the product of committee processes in which boards, executives, communications professionals, and legal reviewers each add words until the original impulse toward meaning has been buried under qualifications and compromises. The result is statements that are long, passive, and forgettable, that could belong to any organization and therefore belong to none, and that no frontline worker could honestly say lives in their hearts and minds while doing their job. Genuine vision is short enough to remember, true enough to feel, and clear enough to orient behavior without requiring a footnote.

Mission is the mechanism by which vision becomes real. In the VMCL structure, mission is not a values statement or a description of organizational purpose. Mission is the simple rules: the small number of repeatable, measurable actions that, when enacted consistently by autonomous agents throughout the organization, produce movement toward the vision as an emergent outcome.[12] The Cabreras draw on complex adaptive systems science to make a counterintuitive but empirically grounded argument: large-scale coordinated behavior in complex systems does not require elaborate instructions or top-down control. It requires simple rules, followed by many agents, repeatedly. Consider the wave at a stadium. No policy memo was issued. No training was conducted. The behavior that ripples across tens of thousands of people in a single coordinated arc emerges from a small number of simple rules enacted by each individual: watch your neighbor, rise when they rise, sit when they sit, raise your hands. The wave is not managed into existence. It emerges. Mission, properly conceived, functions the same way inside organizations. If the simple rules of mission are well designed, genuinely understood, and authentically shared, coordinated movement toward vision emerges from the collective behavior of autonomous agents without requiring command and control of every decision. The parallel failure mode matters equally: if mission consists of a lengthy statement written for external audiences rather than a small number of actionable rules that people can actually carry in their heads, then the organization’s agents have nothing simple to enact, and the coordinated movement that vision requires cannot emerge.

Capacity is the infrastructure, systems, tools, skills, and resources that enable the mission to be carried out. It is what the organization has built, or inherited, or been forced to adopt, to allow its agents to do the work that produces the vision. Capacity includes technology, physical infrastructure, trained personnel, financial resources, data systems, and organizational structures. The critical insight in the VMCL framework is that capacity must be aligned with mission. Capacity built for a different mission, however large, sophisticated, or expensive, does not support the mission it was not designed to serve. It actively competes with it, consuming the time, attention, and energy of the autonomous agents who are supposed to be carrying out the simple rules that produce the vision. The question of whether a health system has the capacity to be a Learning Health System is therefore not simply a question of whether it has electronic health records, data analytics capabilities, or quality improvement staff. It is a question of whether those investments were designed and are being used in service of a learning mission, or whether they were designed for other purposes entirely and are now being asked to serve a mission they were never built to support.

Learning is the function that makes the other three adaptive rather than static. In the VMCL framework, learning is the organization’s capacity to gather honest feedback from its own behavior and from its environment, assess that feedback against its vision and mission, and actually change what it is doing as a result.[4] In the specific context of the Learning Health System, this has a precise meaning that goes beyond general organizational learning or individual professional development. Learning in the LHS sense is the cycle of gathering clinical and operational data generated within the health system itself, subjecting it to rigorous analysis, producing knowledge about what is actually working for actual patients in this actual system, and feeding that knowledge back into changed clinical practice in ways that improve patient outcomes. The unit of learning is the system. The measure of learning is not the number of insights generated or reports published. It is whether practice changes and whether patients do better as a result. Quality dashboards that nobody acts on, annual reports that circulate among administrators without altering clinical behavior, and research findings that never make it from the journal to the bedside are all symptoms of an organization that has the appearance of learning without the substance of it.

These four functions are not sequential steps. They are simultaneous and mutually dependent. Vision without mission produces inspiring rhetoric that changes nothing. Mission without vision produces activity without direction. Capacity without aligned mission and vision produces expensive infrastructure that serves the wrong ends. And Learning without the other three produces insight that has no home in the organization’s structure and no pathway to changing behavior. The question the VMCL lens asks of any health system is not whether these four functions exist in some form, because they all do in every organization. The question is whether they are genuinely aligned with each other, whether they are all oriented toward the same destination, and whether that destination is honestly about learning and patient outcomes or about something else dressed in that language.

V: Three Conditions Hostile to Learning

The VMCL lens developed by the Cabreras does not merely describe what a well-functioning organization looks like. It also provides a diagnostic structure for understanding where and why organizational function breaks down. When a complex adaptive system is failing to move toward its vision, the failure can almost always be located in one or more of the four functions: the vision is unclear or not genuinely shared, the mission lacks simple rules that agents can actually carry and enact, the capacity is misaligned with the mission, or the learning function is absent, performative, or structurally disconnected from the decisions that govern practice.

Applied to the challenge of building Learning Health Systems in the United States, this diagnostic structure surfaces something important. The barriers most frequently discussed in clinical, policy, and public discourse cluster with particular intensity around the Capacity and Learning functions. Three conditions in particular have emerged with enough consistency across enough professional, policy, and clinical circles to warrant focused examination here. They are not presented as the only barriers. The published literature names others, including interoperability failures, governance gaps, funding misalignment, and cultural resistance to change.[15] They are presented because each is vivid, well-documented, and together they do something more important than illustrate three separate problems. They form a causal chain.

That chain runs as follows. Electronic health record systems were designed for billing, documentation, and regulatory compliance rather than for clinical care or learning. They impose structural friction on the daily work of every physician in the country. Payer interference through prior authorization requirements compounds that friction, consuming hours of clinical time every week, systematically overriding clinical judgment, and producing a persistent experience of professional constraint that no amount of individual resilience can fully absorb. Together these two systemic forces create the organizational conditions that produce physician burnout at scale. Burnout is not an independent variable sitting alongside EHR burden and payer interference. It is the human output of a system that has been designed at the capacity level for the wrong mission. And a system whose agents are burned out cannot learn, because learning requires the cognitive availability, the reflective capacity, and the institutional trust that survival mode structurally forecloses.

This is what the Cabreras mean when they say that the system is what the system does. If the system consistently produces burned-out physicians, demoralized care teams, and a clinical workforce increasingly oriented toward self-preservation rather than adaptive engagement, that is not a failure of individual character or professional commitment. It is the system performing as it was designed to perform, optimizing for throughput, administrative control, and reimbursement rather than for learning and patient outcomes. Understanding the three conditions in sequence, rather than as a parallel list, is essential to understanding why the organizational design problem is as deep as it is.

Electronic Health Records: Capacity Built for the Wrong Mission, Sitting on the Right Data

The widespread adoption of electronic health records in the United States was accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009 [23]. As of 2021, 96 percent of nonfederal acute-care hospitals and 78 percent of office-based physicians used an EHR, making these systems integral to routine clinical practice.[10] On its face, this represents exactly the kind of data infrastructure that a Learning Health System requires. A system that captures clinical data at scale, across encounters, patients, and populations, is precisely what the knowledge generation and data functions of the LHS cycle depend on. In this narrow sense, American healthcare has already built something the Learning Health System needs. The data is there. Decades of patient encounters, clinical decisions, treatment courses, and outcomes are sitting in these systems at a scale that would have been unimaginable to the architects of the NAM’s 2007 vision.

The problem is not the existence of the data. The problem is everything surrounding it.

EHRs were not primarily designed for learning. They were designed for billing, documentation, and regulatory compliance. The gap between the data infrastructure a learning mission requires and the data infrastructure that exists is not a gap in hardware or software capability. It is a gap in design intent, and that gap has consequences that run in two directions simultaneously. The first is the burden the systems impose on the clinicians who must feed them. A recent scoping review published in the Journal of Evaluation in Clinical Practice found that clinicians now spend an estimated one-third to one-half of their working day interacting with EHR systems, translating to over $140 billion in lost care capacity annually.[10] The same review found that clinicians frequently experience significant workflow disruptions caused by poorly designed interfaces, leading to task-switching, excessive screen navigation, and fragmented critical information that necessitates workarounds and increases the risk of documentation errors. Research published in JAMA found that physicians spend approximately 36.2 minutes documenting in the EHR for every 30-minute office visit [24], meaning the administrative burden of capturing an encounter now routinely exceeds the clinical time of the encounter itself.

The second consequence is less frequently discussed but equally important for the Learning Health System argument. The data that EHRs generate is not clean learning data. It is documentation data, structured around billing codes, shaped by prior authorization requirements, and produced through documentation processes that clinicians have adapted, often through workarounds, to minimize burden rather than to maximize clinical accuracy. The result is a paradox at the heart of the LHS challenge: American healthcare is sitting on an extraordinary volume of clinical data that a learning system would need, and simultaneously that data is less useful for learning than its volume suggests, because the processes that generated it were optimized for reimbursement rather than for clinical fidelity.

Mining that data for genuine learning insights would require significant investment in data science, informatics, and clinical expertise working in close collaboration. It would require clinicians who have the time, the cognitive availability, and the institutional support to participate in that work. It would require organizations that have aligned their capacity with a learning mission rather than a billing mission. And it would require a workforce that has not been burned out by the very systems that are generating the data in the first place. The EHR is not an obstacle to the Learning Health System in spite of the data it holds. It is an obstacle in part because of the conditions it has created around that data. The data exists. The capacity to act on it does not, because the system has consumed that capacity in the process of generating the data.

In VMCL terms this is a Capacity problem of a specific and frustrating kind. The investment has been made. The infrastructure is in place. But it was built for the wrong mission, and the friction it generates spills directly into the clinical encounter itself, into the relationship between physician and patient, and into the professional experience of every clinician who ends the day staring at a screen long after the last patient has gone home.

Payer Interference: External Rules Overriding Internal Mission

If EHR burden creates structural friction in the tools physicians use, payer interference through prior authorization creates structural friction in the decisions physicians are permitted to make. Together they constitute a double compression of clinical capacity that is difficult to fully appreciate from outside the daily experience of practicing medicine in the United States today.

The American Medical Association conducts an annual nationwide survey of 1,000 practicing physicians on the burden of prior authorization. The 2024 findings are both consistent with prior years and striking in their severity.[9] Physicians reported completing an average of 39 prior authorization requests per physician per week, consuming an average of 13 hours of physician and staff time. Ninety-three percent of physicians reported that prior authorization delays access to necessary care. Eighty-nine percent reported that it contributes to burnout. Ninety-four percent said it has a negative impact on patient clinical outcomes. More than one in four reported that prior authorization caused a serious adverse event for a patient in their care. Seventy-eight percent reported that it often or sometimes results in patients abandoning a recommended course of treatment entirely. Forty percent of practices have hired staff whose exclusive function is managing prior authorization requests.

In the language of complex adaptive systems, prior authorization represents external agents, payers and insurers, injecting rules into the system that redirect the behavior of internal agents, physicians and care teams, away from what their clinical training, judgment, and the available evidence would support, and toward what the external agent will reimburse. The internal simple rules of the care delivery mission are being overridden at the point of care by administrative requirements that serve a different set of goals entirely. This is not a marginal disruption. At 39 prior authorization requests per physician per week, it is a structural feature of the environment in which clinical work now happens.

The implications for the Learning Health System extend beyond the administrative burden. The LHS cycle depends on clinical practice generating data that reflects actual clinical judgment applied to actual patient needs. When a substantial proportion of clinical decisions are being shaped not by evidence and judgment but by prior authorization requirements, the data that clinical practice generates no longer cleanly reflects what works. It reflects what gets approved. The knowledge that a learning system could generate from that data is therefore systematically biased before it is ever analyzed. The learning loop is not merely slowed by payer interference. In important respects it is compromised at the source.

And when a physician has spent 13 hours in a week on prior authorization paperwork, on top of the hours already consumed by EHR documentation, the cumulative weight of that friction does not remain a professional inconvenience. It becomes a clinical emergency of a different kind entirely. It becomes burnout.

Physician Burnout: The Human Output of a Broken System

Physician burnout is not the beginning of the problem. It is the end of a chain that starts with organizational design decisions made far from the bedside. It is what happens when the agents of a complex adaptive system are placed inside a capacity structure so misaligned with the mission of care that adaptive engagement becomes unsustainable. The EHR consumes time and cognitive energy. Prior authorization consumes professional agency and clinical judgment. Together they produce a working environment in which the question a physician must increasingly ask is not what does this patient need but what will I be permitted to do, and how long will the paperwork take.

The data on physician burnout in the United States is not ambiguous. According to the Dr. Lorna Breen Heroes’ Foundation, 76 percent of healthcare workers reported burnout in 2020, and during the COVID-19 pandemic 69 percent of physicians experienced depression, with 13 percent reporting thoughts of suicide.[7] Physicians in the United States are more likely to die by suicide than physicians in other nations. The Physicians Foundation’s 2022 Survey of America’s Physicians found that burnout rates remain at 62 percent, significantly higher than the pre-pandemic figure of 40 percent in 2018, with no meaningful improvement in the intervening years.[8] Nearly 400 physicians die by suicide annually in the United States, a figure the research literature connects directly to stigma, fear of licensing repercussions, and untreated depression in a profession that has historically treated the need for mental health support as a professional liability.[7]

The Dr. Lorna Breen Heroes’ Foundation, established by the family of an emergency physician who died by suicide in April 2020 after treating patients during the early COVID-19 surge, has been explicit about the systemic nature of the problem. Individual support alone, the foundation states, does not address the causes of burnout. The underlying processes and systems within healthcare operations must be confronted.[7] That is a systems thinking argument made in plain language by people who lived the consequences. It points directly at the Capacity layer of the VMCL structure and asks why the system was designed this way and whether the people responsible for that design have fully reckoned with what it produces.

For the Learning Health System, burnout represents the final compression of capacity. Learning requires clinicians who can observe, reflect, contribute to knowledge generation, and adapt their practice in response to what the evidence is telling them. It requires agents who are present, engaged, and operating with enough cognitive and professional reserve to participate in something beyond the immediate transaction of care. Burnout forecloses that participation systematically, across specialties, settings, and the full arc of a clinical career. A system that is burning out its physicians at the rate American healthcare currently does is not a system that can learn. It is a system that is consuming its own capacity to improve.

The three conditions examined in this section are not a complete explanation of why Learning Health Systems have been so difficult to build. But they are a coherent one. They describe a system that has built the wrong capacity, allowed that capacity to be further distorted by external rule-making, and in doing so created the organizational conditions that make the human beings at the center of care less and less able to participate in the continuous learning that better care requires. The system is, in the most precise sense, doing exactly what it was designed to do. The question this paper is asking is whether it could be designed to do something different.

VI: Thinking Design, Not Framework Prescription

If the argument of this paper has been constructed carefully, the reader has arrived here with a specific kind of discomfort. The problem is real, well-documented, and serious. The VMCL lens has provided a coherent way of seeing why the Learning Health System has not been built at scale. The three conditions examined in Section V have illustrated, in concrete and citable terms, how the capacity layer of American healthcare has been so comprehensively misaligned with a learning mission that the human beings at the center of care are being systematically consumed by the friction of a system that was designed for other ends. The natural next question is: so what do we do about it?

This section is going to resist the impulse to answer that question with a prescription. That resistance is not evasion. It is the most honest and useful response available, and the reasons for it are worth stating plainly.

The wicked problem literature is clear that conventional problem-solving approaches are structurally inadequate to problems of the kind this paper has been examining. The Learning Health System is not waiting for the right policy intervention or the right technology platform or the right reimbursement model, though all of these matter and deserve serious attention. It is waiting for a different quality of organizational thinking in the people and institutions responsible for designing, leading, and reforming American healthcare.

The Cabreras make a distinction that is useful here. They differentiate between organizations that impose frameworks and organizations that develop genuine thinking capacity, the internal ability to see clearly, reason carefully, and adapt continuously in response to what the system is actually doing.[3] A framework can be adopted without changing the underlying quality of thought. A new software platform can be installed without changing the organizational culture that will use it. A new policy can be passed without changing the incentive structures that will determine whether it is followed in spirit or circumvented in practice. What cannot be faked, and what the Learning Health System actually requires, is the organizational capacity to ask honest questions about what the system is producing, to follow the answers wherever they lead, and to change course based on what is discovered.

Before any of that can happen, the system must be mapped. Not fixed. Not optimized. Mapped. This is a critical distinction. The problems do not precede the mapping. They emerge from it. A system cannot be improved by agents who cannot see it clearly, and seeing it clearly requires a specific and disciplined quality of thinking. The Cabreras offer exactly that through a cognitive framework called DSRP, standing for Distinctions, Systems, Relationships, and Perspectives.[19][21] DSRP describes four universal patterns of thinking that, when applied deliberately, allow a leader or organization to see a system as it actually is rather than as habit, assumption, or organizational mythology would have it appear. To understand what the system does, you must first understand what the system is. DSRP is the toolkit for that work.

Before reaching for solutions, the Cabreras ask leaders at every level to sit with a set of honest diagnostic questions:

Does your organization have a vision that is genuinely and specifically about the future it is trying to create, stated clearly enough that every person in the system, from the bedside nurse to the chief executive, could carry it in their hearts and minds while doing their job on any given day? Or does it have a statement written for a board presentation, long, passive, and laden with qualifications, that could belong to any organization and therefore belongs to none?

Does your organization have a mission in the specific sense of simple rules, repeatable actions that autonomous agents at every level of the system can enact without a manual, that would make learning a natural outgrowth of daily clinical practice? Or does it have a strategic plan, full of initiatives and objectives and key results, that bears no relationship to what a nurse or a physician or a data analyst actually does on a Tuesday morning?

Has your organization built capacity that is aligned with a learning mission, or has it built capacity for billing, documentation, and regulatory compliance and then asked that infrastructure to support learning as a secondary function while simultaneously burning out the people who are supposed to use it?

And does your organization have genuine learning mechanisms, honest feedback that actually changes clinical practice, that actually improves patient outcomes, that actually closes the loop between what the system discovers and what the system does? Or does it have quality dashboards and compliance reports and annual reviews that circulate among administrators without ever altering what happens in an exam room?

These are diagnostic questions, not rhetorical ones. They are the questions that thinking design asks of any organization that claims the Learning Health System as an aspiration. They are uncomfortable because for most health systems, across most of these dimensions, the honest answer is not encouraging. And they are important precisely because the discomfort they produce, if it is held rather than resolved prematurely, is the beginning of genuine organizational learning.

The four DSRP patterns work as follows.

Distinctions are the act of identifying what something is and what it is not, drawing a boundary between a thing and everything that is not that thing. In the context of the Learning Health System, making clear distinctions means being honest about what a learning system actually is, and separating it clearly from what merely resembles it. A teaching hospital is not a learning health system. A quality dashboard is not a learning mechanism. An EHR is not a learning infrastructure simply because it generates data. Without the discipline of making clean distinctions, organizations substitute the appearance of learning for the substance of it and never notice the difference.

Systems, in the DSRP sense, is the recognition that any phenomenon of interest is simultaneously a part of larger wholes and a whole composed of smaller parts, and that understanding it requires attending to both levels at once.[20] In the healthcare context, physician burnout is a part of a larger system of capacity failures, and it is itself a whole composed of contributing conditions including EHR burden, prior authorization load, professional isolation, and the erosion of clinical agency. Understanding both the part and the whole simultaneously is what prevents the mistake of treating burnout as an individual problem rather than a systemic one.

Relationships are the causal and dynamic connections between elements of a system, the action and reaction that link one condition to another and produce the emergent outcomes the system generates.[20] The causal chain this paper has traced, from EHR misdesign through payer interference to burnout to the collapse of learning capacity, is a relationships argument. These three conditions are not parallel and independent. They are sequentially and causally connected, and intervening in one without attending to the others will produce incomplete and temporary relief at best.

Perspectives are the recognition that every observation of a system is made from a point of view, and that changing the perspective from which a system is examined reveals different features, different problems, and different possibilities.[20] The Learning Health System has been examined primarily from the perspectives of bioethicists, health policy scholars, and informatics researchers. Those are valuable perspectives. But they are not the perspective of the burned-out emergency physician at the end of a 13-hour shift, or the primary care doctor who spent two of those hours on prior authorization paperwork, or the patient whose recommended treatment was abandoned because the approval process took too long. Bringing multiple genuine perspectives into the analysis is not a concession to inclusivity. It is an epistemic requirement for seeing the system accurately.

Together these four patterns constitute the cognitive foundation for systems mapping, the act of making the system visible in a form that allows its parts, relationships, boundaries, and embedded perspectives to be examined honestly and collectively.[17] Making the system visible before reaching for a solution is not a preliminary step on the way to the real work. It is the real work.[17][18] This paper is, in one sense, a partial map of a system. It does not resolve the wicked problem of the Learning Health System. It attempts to make that problem more visible, more precisely named, and more honestly held, in the conviction that a system cannot be improved by agents who cannot see it clearly.

VII: Building the Ecosystem

This paper has traced a specific arc. It began with a conversation, with the recognition that a system described as healthcare has organized itself primarily around sick care, and that a system capable of learning from its own practice toward the goal of genuine health remains largely unbuilt. It named that gap as a wicked problem, structurally resistant to the kinds of solutions that work on complicated problems. It introduced a thinking design lens, VMCL, that reveals where and why the organizational design of American healthcare has been misaligned with a learning mission. It examined three conditions, EHR burden, payer interference, and physician burnout, not as a comprehensive catalogue of everything wrong but as a coherent illustration of a system doing exactly what it was designed to do, which is the wrong thing. And it argued that before solutions can be designed, the system must be mapped, using the cognitive tools of Distinctions, Systems, Relationships, and Perspectives, so that what is actually happening can be seen clearly by the people responsible for changing it.

What comes next is not a conclusion in the conventional sense, because wicked problems do not conclude. They develop. They yield to sustained, cross-disciplinary, honest engagement over time, or they do not yield at all. And that engagement, to be genuine, cannot be organized as a committee or delegated to a working group. It has to function as an ecosystem.

An ecosystem, in the organizational sense, is not simply a collection of stakeholders. It is a community of interdependent actors whose collective behavior produces outcomes that no single actor could generate alone, and whose health depends on the health of every part. The Learning Health System cannot be built by clinicians alone, or technologists alone, or policymakers alone, or systems thinkers alone, because each of those communities has a partial view of the system, and partial views applied with confidence have contributed to the problem as much as to any solution. What the Learning Health System requires is an ecosystem response, one in which diverse and genuinely interdependent actors develop a shared sense of responsibility for the knowledge the system is capable of generating and for the patients whose outcomes depend on whether that knowledge is actually used.

Several conditions define what a functional ecosystem for this work looks like.

Patients must be active contributors, not symbolic participants. The Stanford course materials that informed this paper make a point worth stating directly: in the Learning Health System, every patient is also a research participant, and their data represent an opportunity to learn.[11] The ethical framework developed by Ruth Faden, Nancy Kass, and their colleagues[25] argues that patients have not only rights but obligations within a learning health system, specifically an obligation to contribute to the knowledge that the system generates for their benefit and for the benefit of others, particularly when the risk to them is minimal. Designing health systems that honor that relationship, rather than treating patients as subjects to be protected from the learning process, is one of the most important organizational design challenges the field faces.

Health system leaders must be willing to ask honest questions about what their organizations are actually producing. The wicked problem of the Learning Health System will not be solved by a consultant engagement, a technology platform, or a strategic planning cycle. It will be addressed, partially and incrementally, by leaders who are willing to hold the discomfort of answers that do not reflect well on past choices and design differently in response to what they discover. That requires vision that is genuinely about learning and patient outcomes. It requires mission in the form of simple rules that every agent in the organization can carry and enact. It requires capacity built and aligned for the right purpose. And it requires learning mechanisms that are honest, structural, and actually connected to changed practice.

The ecosystem must also have a convening architecture. Calling for cross-disciplinary engagement on a wicked problem is easy. Designing the conditions under which that engagement can actually happen is considerably harder. In June 2020, the author designed and led SparkJam 2020, a statewide initiative convened through The Rocket Factory in partnership with Activation Capital, the VCU da Vinci Center for Innovation, and other Virginia-based organizations.[22] The initiative brought together entrepreneurs, technology visionaries, business strategists, and community leaders to collaborate in real time on solutions to challenges facing small businesses during the pandemic. The methodology that made it work rested on a specific structural logic: a small group of influential leaders set the agenda, identified the most consequential problems, and recruited a broader population of participants whose direct knowledge and diverse perspectives were needed to work those problems in depth. Structured sessions generated insights that no individual perspective could have produced alone. The broader group returned its work to the leadership tier for synthesis and prioritization, and working groups carried specific initiatives forward. That architecture, a credible leadership tier, broad and diverse participation, structured synthesis, and sustained working group commitment, is precisely what ecosystem convening for the Learning Health System requires.

This paper is itself a beginning and not an answer. It is a partial map of a system far larger and more complex than any single document can represent. What it hopes to contribute is a quality of framing adequate to the problem’s actual complexity. The ecosystem that the Learning Health System requires is waiting to be convened. The methodology exists. The will to build it is what remains to be found.

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

The organizational design argument this paper has been making has urgent implications that extend beyond health system walls and into the ambitions of every health technology company, AI developer, and investor currently betting that data-driven tools will transform American healthcare. The case for cross-disciplinary convening made in Section VII is not merely about improving care delivery. It is also about creating the organizational conditions under which technology can actually function as promised. Because the technology being deployed into American healthcare today is only as trustworthy as the data it learns from. And that data was produced by the system this paper has been describing.

Any health technology company seeking to leverage healthcare data to improve patient outcomes must first understand and reckon with what is happening upstream of that data. The organizational conditions under which data is generated determine what that data actually contains. This is not a theoretical concern. It is an engineering one, with direct consequences for patient safety.

Machine learning models learn from the data they are given. They do not evaluate the conditions under which that data was produced. They do not know whether the physician who entered a clinical note was on hour eleven of a shift, copying and pasting from a prior visit to manage an impossible documentation burden, or making a fully considered clinical judgment after a thorough examination. They do not know whether a treatment decision reflected the best available evidence or the path of least resistance through a prior authorization process. They do not know whether a diagnostic code was selected because it most accurately described the patient’s condition or because it was the code most likely to be reimbursed. The model sees the data. It cannot see the system that produced it. That is the job of the humans who build and deploy these tools. And it is a job that is not yet being done with sufficient rigor or honesty in the current wave of enthusiasm for AI in healthcare.

A well-known illustration in machine learning circles, included in the Stanford AI for Healthcare coursework that is part of this author’s ongoing study,[31] captures the failure mode precisely. During the Cold War, the US military hired computer scientists to develop a model that could identify Russian tanks in photographs. The model performed perfectly on the test set. In a live field test it failed completely, performing worse than random guessing. The reason: Russian tank photographs had been taken in winter conditions and American tank photographs in summer conditions. The model had not learned to identify tanks. It had learned to identify weather. It was, in the precise technical sense, a weather classifier dressed as a tank detector.[31]

The same failure mode has been documented in clinical settings. A machine learning model developed to detect pneumonia from chest X-rays outperformed human radiologists in controlled testing. In a small clinical deployment it failed. The model had learned to use the L marker, a physical positioning marker visible in the X-ray images, as a signal to distinguish between the two hospital systems in its training data. One hospital had a one percent prevalence of pneumonia. The other had a 34 percent prevalence. The model did not need to read the X-ray clinically. It learned to read the marker institutionally, and used that artifact rather than any clinical feature to predict pneumonia.[31] It was not learning medicine. It was learning to tell the hospitals apart.

These failures share a common structure. In each case the model learned the wrong signal because the training data encoded something other than the clinical reality the model was supposed to capture. The model was not broken. The data was. And the data was compromised not by random noise but by systematic, directional bias baked into the conditions under which it was produced. This is precisely what the three conditions examined in Section V create for any AI or machine learning system trained on American healthcare data at scale.

It is worth noting that the organizational conditions examined in this paper represent one category of the data bias problem in healthcare AI, and not the only one. The research literature identifies additional sources of bias that compound what has been described here, including the dynamic nature of medical practice over time, which causes historical EHR data to accumulate outdated correlations and effectively expire as a reliable training source as clinical practices evolve, and the demographic non-representativeness of many health system datasets, in which race, ethnicity, gender, and socioeconomic status are inconsistently captured or reported across studies, raising serious questions about whether AI models trained on such data can perform equitably across the full diversity of patients they will ultimately serve.[31]

When 90 percent of clinicians report using copy-paste functionality to manage documentation burden, and when by one estimate 50 percent of the text in a given clinical note is duplicated from prior notes,[27][28][29] the clinical notes that constitute training data for natural language processing models are not accurate records of clinical reasoning. They are records of documentation behavior under pressure. When prior authorization requirements shape which treatments are administered and which are abandoned, the treatment decisions that feed outcome models do not reflect clinical judgment applied to patient need. They reflect what the payer approved. When burned-out physicians experiencing cognitive fatigue make more documentation errors, a connection the research literature supports directly,[30] the signal in the data degrades in direct proportion to the degradation of the workforce producing it.

The research on EHR data quality confirms that these are not marginal concerns. A systematized review published in 2025 examining EHR data quality in critical care settings found that missing data rates exceeded 80 percent for some variables, that EHR-related medication errors comprised 34 percent of all medication errors in ICUs with one-third having life-threatening potential, and that copy-paste prevalence reached 82 percent in residents’ progress notes.[26] The same review found direct and measurable consequences for machine learning: sepsis detection models that achieved strong performance in internal validation dropped significantly in external validation under real-world conditions, a degradation the authors attributed directly to data quality issues pervasive in the underlying EHR data.[26]

The Stanford coursework poses the right question directly: the issue is not whether the data exists. Medical data now doubles every eight to twelve months and there is more of it than ever before. The better question is whether that data is actually usable for the intended purpose.[31] In the current organizational state of American healthcare, the honest answer is not exactly.

This does not mean AI has no role in healthcare. It means the role AI can play is constrained and shaped by the organizational conditions that produced the data it learns from. A 2025 perspective published in npj Health Systems argues precisely this point, noting that while the LHS ecosystem has been well described and its potential widely endorsed, operationalizing the LHS in the era of artificial intelligence requires deliberate attention to data governance, workforce development, and institutional design, the same organizational prerequisites this paper has been examining.[14] The organizational design work this paper has been describing, building genuine Learning Health Systems with aligned vision, mission, capacity, and learning functions, is not merely a clinical improvement agenda. It is the prerequisite for trustworthy AI deployment in healthcare. A health system that has not addressed the upstream conditions producing biased data cannot deploy AI safely or effectively. It will automate the distortions already present in its data and present the result as intelligence. Health technology companies that build on that foundation without looking upstream are not just taking a technical risk. They are taking a patient safety risk. And they are building businesses on data they do not fully understand.

IX: Strategic Implications — The Cost of Not Learning

This paper has operated at two levels simultaneously, and it is worth naming that distinction clearly before drawing it to a close. At the macro level, the Learning Health System is a vision for what American healthcare as a sector could become: a system in which knowledge generation is so embedded in the delivery of care that improvement becomes continuous, self-reinforcing, and oriented genuinely toward the people the system exists to serve. At the micro level, it is an organizational design challenge that must be addressed institution by institution, health system by health system, through specific and deliberate choices about vision, mission, capacity, and learning. The wicked problem lives at the macro level. The work of addressing it happens at the micro level. And the cost of not doing that work accumulates at both levels simultaneously, in individual clinical encounters that produce biased data, in technology deployments built on compromised foundations, in physicians who leave the profession, and in patients who do not receive the care the system was capable of providing if it had been designed to learn.

Gil Bashe argued that American healthcare is not failing for lack of innovation, investment, or talent. It is failing because it has lost sight of the people it exists to serve.[2] This paper has tried to show that losing sight of people and losing the organizational capacity to learn are not two separate failures. They are the same failure, expressed differently depending on where you are standing in the system. The burned-out physician who copies and pastes a clinical note at the end of an impossible shift has not lost sight of their patients. The system that created those conditions has. The EHR that generates data optimized for billing rather than clinical fidelity has not lost sight of patients. The design decisions that produced it have. The AI model that learns the wrong signal from compromised training data has not failed its patients. The upstream conditions that corrupted the data before it ever reached the model have.

The cost of not learning is not abstract. It is clinical. It is financial. It is technological. And it is human. At the macro level it is a sector that has spent nearly two decades describing a vision of continuous learning and improvement while building the organizational conditions that make that vision structurally unreachable. At the micro level it is every health system that has adopted the label of a Learning Health System without asking honestly whether its vision is felt, its mission is enacted, its capacity is aligned, and its learning loops actually close. The gap between those two things, between what is said and what is designed, is where patients fall through.

This paper has not proposed a solution. It has drawn a map. The map shows a system doing exactly what it was designed to do, which is the wrong thing, and it names the organizational thinking, the VMCL lens, the DSRP cognitive tools, the systems mapping discipline, that would allow leaders at every level to see that clearly and begin designing differently. It has also named what is at stake for those who choose not to look. For health system leaders the cost of not learning is an organization that optimizes toward the wrong destination and calls it excellence. For policymakers the cost is interventions that address symptoms without touching causes. For health technology companies the cost is products built on data they do not understand, deployed into systems they have not mapped, producing outcomes they cannot fully explain or defend. And for patients the cost is a system that was capable of learning how to serve them better and chose, through a thousand organizational design decisions made without that possibility in mind, not to.

The Learning Health System is not an idea whose time has not yet come. It is an idea whose organizational prerequisites have not yet been built. Building them is the work. It is hard, sustained, cross-disciplinary, and uncomfortable. It requires the kind of thinking this paper has been describing: honest, structural, willing to see the system as it is rather than as its mission statements describe it. It requires leaders at the macro level of American healthcare policy and at the micro level of every individual health system who are willing to ask whether they are designing for learning or designing for something else and calling it learning.

The conversation is open. The map is incomplete. The cost of not continuing it is borne by patients. That is reason enough to begin.

Citations

[1] Olsen, L.A., Aisner, D., and McGinnis, J.M., editors. Institute of Medicine (US) Roundtable on Evidence-Based Medicine. The Learning Healthcare System: Workshop Summary. Washington, DC: National Academies Press, 2007. PMID: 21452449. DOI: 10.17226/11903. Available at: https://pubmed.ncbi.nlm.nih.gov/21452449/ and https://www.ncbi.nlm.nih.gov/books/NBK53494/

[2] Bashe, Gil. Healing the Sick Care System: Why People Matter. Thought Leader Press, February 1, 2026. https://www.amazon.com/Healing-Sick-Care-System-People/dp/1613431805

[3] Cabrera, Derek and Laura Cabrera. Flock Not Clock: Design, Align, and Lead to Achieve Your Vision. Plectica LLC, 2018. ISBN: 978-1948486019. https://www.amazon.com/FLOCK-NOT-CLOCK-DESIGN-ACHIEVE-ebook/dp/B07DFPWTDS

[4] Cabrera Research Lab. VMCL Overview. Cabrera Research Lab Blog. https://www.cabreralab.science/blog/categories/vmcl

[5] Rittel, Horst W.J. and Melvin M. Webber. “Dilemmas in a General Theory of Planning.” Policy Sciences, vol. 4, 1973, pp. 155-169.

[6] Grewatsch, Sylvia, Steve Kennedy, and Pratima Bansal. “Tackling Wicked Problems in Strategic Management with Systems Thinking.” Strategic Organization, 2023. https://journals.sagepub.com/doi/10.1177/14761270211038635

[7] Dr. Lorna Breen Heroes’ Foundation. “Burnout.” https://drlornabreen.org/burnout/

[8] The Physicians Foundation. “2022 Survey of America’s Physicians.” https://physiciansfoundation.org/press-releases/npsa-day-2022/

[9] American Medical Association. “2024 AMA Prior Authorization Physician Survey.” https://www.ama-assn.org/system/files/prior-authorization-survey.pdf

[10] “Usability Challenges in Electronic Health Records: Impact on Documentation Burden and Clinical Workflow: A Scoping Review.” Journal of Evaluation in Clinical Practice, 2025. https://onlinelibrary.wiley.com/doi/full/10.1111/jep.70189

[11] Stanford University School of Medicine. Course materials on Learning Health Systems and research ethics. Materials on file with author.

[12] Cabrera Research Lab. “Simple Rules.” Cabrera Research Lab Glossary. https://help.cabreraresearch.org/simple-rules

[13] Cabrera Research Lab. “Complex Adaptive System (CAS).” Cabrera Research Lab Glossary. https://help.cabreraresearch.org/complex-adaptive-system-cas

[14] Steel, Peter A.D., Gabriel Wardi, Robert A. Harrington, and Christopher A. Longhurst et al. “Learning health system strategies in the AI era.” npj Health Systems, vol. 2, article 21, June 17, 2025. https://www.nature.com/articles/s44401-025-00029-0

[15] Tenenbaum, J.D. et al. “Accelerating a learning public health system: Opportunities, obstacles, and a call to action.” Learning Health Systems, 2024. https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10449

[16] “Implementing the learning health system paradigm within academic health centers.” Learning Health Systems, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10797573/

[17] Cabrera, D., Cabrera, L. “Why You Should Map: The Science Behind Visual Mapping.” White paper. Cabrera Research Lab, New York, 2018. https://www.researchgate.net/publication/349868707_Why_You_Should_Map_the_science_behind_visual_mapping

[18] Cabrera, L. and Cabrera, D. “Adaptive Leadership for Agile Organizations.” In Cabrera, D., Cabrera, L. and Midgley, G. (Eds.), Routledge Handbook of Systems Thinking. Routledge, London, UK, 2021. Draft preprint on file with author.

[19] Cabrera, Derek. “Distinctions, Systems, Relationships, and Perspectives (DSRP): A Theory of Thinking and of Things.” Evaluation and Program Planning, vol. 31, no. 3, 2008, pp. 311-317. https://pubmed.ncbi.nlm.nih.gov/18554716/

[20] Cabrera, Derek and Laura Cabrera. “DSRP Theory: A Primer.” Systems, vol. 10, no. 2, 2022. https://www.mdpi.com/2079-8954/10/2/26

[21] Cabrera Research Lab. “The Four Simple Rules of Systems Thinking: The Distinction Rule.” Cabrera Research Lab Blog, cabreralab.science. Available at: https://www.cabreralab.science/post/the-four-simple-rules-of-systems-thinking-the-distinction-rule

[22] The Rocket Factory. “The Rocket Factory Presents SparkJam 2020 to Benefit the Virginia 30 Day Fund.” PR.com, June 2020. https://www.pr.com/press-release/814285

[23] U.S. Department of Health and Human Services. “HITECH Act Enforcement Interim Final Rule.” Health Information Technology for Economic and Clinical Health Act, enacted as part of the American Recovery and Reinvestment Act of 2009, Public Law 111-5. Available at: https://www.hhs.gov/hipaa/for-professionals/special-topics/hitech-act-enforcement-interim-final-rule/index.html

[24] Rotenstein, L.S. et al. “System-Level Factors and Time Spent on Electronic Health Records by Primary Care Physicians.” JAMA Network Open, 2023. PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC10665969/

[25] Faden, Ruth R., Nancy E. Kass, Steven N. Goodman, Peter Pronovost, Sean Tunis, and Tom L. Beauchamp. “An Ethics Framework for a Learning Health Care System: A Departure from Traditional Research Ethics and Clinical Ethics.” Hastings Center Report, Special Issue, January-February 2013, pp. S16-S27. DOI: 10.1002/hast.134. PubMed PMID: 23315888. Available at: https://pubmed.ncbi.nlm.nih.gov/23315888/

[26] “Discovery of data quality issues in electronic health records: profound consequences for critical care medicine applications — a systematized review.” PMC, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12784561/

[27] Tsou, A.Y. et al. “Safe Practices for Copy and Paste in the EHR: Systematic Review, Recommendations, and Novel Model for Health IT Collaboration.” Applied Clinical Informatics, 2017. https://pubmed.ncbi.nlm.nih.gov/28830856/

[28] Urology Times. “Why is copying and pasting in the EHR such a problem?” February 2026. https://www.urologytimes.com/view/why-is-copying-and-pasting-in-the-ehr-such-a-problem-

[29] AMA Journal of Ethics. “How to Teach Good EHR Documentation and Deflate Bloated Chart Notes.” November 2025. https://journalofethics.ama-assn.org/article/how-teach-good-ehr-documentation-and-deflate-bloated-chart-notes/2025-11

[30] “Burnout Related to Electronic Health Record Use in Primary Care.” PMC, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10134123/ [31] Stanford University School of Medicine. Course materials: Fundamentals of Machine Learning for Healthcare. Lecture transcripts on data bias, the Russian tank problem, clinical machine learning applications, medical data shelf life, and demographic representativeness in EHR-based AI research. Part of the AI for

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Unfortunately, the system has frequently failed to live up to that ideal. I learned that from having a conversation with Matthew Zachary, CEO of We the Patients, a patient advocacy organization. In a recent LinkedIn post, in fact, Mr. Zachary wrote this:

Prior auth takes longer than the time between scan and surgery. Drugs get denied because someone flipped a spreadsheet cell from green to red. Surprise bills show up because the hospital was in network but the anesthesiologist’s LLC was not.

None of this is a glitch. This is the product working exactly as designed. Built to maximize profit and gaslight us into believing WE are the problem for not “navigating” it better.

This is horribly unfortunate, and Matthew Zachary is harnessing his anger at this reality to make a difference through his activism.

There are indeed a lot of problems with the current healthcare system, and fixing them will take a lot of time, effort, and work. And there is one thing we physicians can do to immediately help make things better for the patient: make an intentional effort to overwhelm our patients with compassion.

No one wants to be sick. No one wants to willingly engage with the healthcare system. And when they do, it is because there is a threat to their life and limb.

With the way the current insurance system is set up, it is not uncommon that patients face myriad barriers and pain points to get the care they need. I myself have experienced these barriers and pain points with my own healthcare and that of my family. It can add more stress to an already extremely stressful situation.

How can we help mitigate this stress? Overwhelm our patients with kindness and compassion. Give them a big smile; hold their hand; tell them that we will do everything to help them feel better.

And if they will not get better; if our patient is going to die, then we need to do everything in our power to ensure a death with dignity, comfort, and ease.

Aren’t we supposed to be doing this all along? Most definitely. And, as is the norm of the human condition, we tend to forget amid the drudgery of the day in and day out of working in healthcare and dealing with the very same barriers to care and pain points with which our patients are also dealing.

That’s why I’m so grateful for Matthew Zachary. He has the courage to share his incredible story of illness and recovery and his anger at the system that did not help promote his healing to try and do something about it.

And his story, and his activism, was a potent reminder for me to do all that I can to make my patients in the ICU as comfortable as possible, to make them feel as good as a critically ill person can possibly feel. Will I fail at times? Yes. But I pray that, for the rest of my career, I will keep trying to overwhelm my patients and kindness and compassion. That’s what I can do to help the healthcare system today.

Listen to the entire conversation: https://www.healthcaremusings.com/we-the-patients-are-really-pissed-off-my-conversation-with-matthew-zachary/

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We usually think about outdoor air quality, but sometimes the air in our kitchens is even more dangerous. Did you ever think that home cooking could be dangerous for you? Not in terms of spills or burns, but the air you breathe?

Understanding the Problem

People in the UK spend about 90% of their time indoors, but most air pollution rules only focus on outdoor air. This is a serious problem because many things we do at home, especially cooking, create airborne pollutants that can harm our health.

The INGENIOUS project at the University of York is studying what happens to indoor air quality when we cook. Their research examines homes where many families experience poor air quality both indoors and outdoors.

What’s in the Air When You Cook?

Cooking releases several types of pollutants into your home’s air. The main ones are fine particulate matter (PM2.5), nitrogen dioxide, volatile organic compounds, and formaldehyde. Did you ever think you would be breathing formaldehyde in your home as a result of how you cooked?

PM2.5 is especially concerning. These tiny particles can travel deep into your lungs and even get into your bloodstream. One study found that pan-frying chicken produced PM2.5 levels of 92.9 micrograms per cubic meter. The outdoor air quality standard for PM2.5 is 50 micrograms per cubic meter, so some cooking methods can more than double that amount in your kitchen.

Gas stoves create another issue. They release nitrogen dioxide (NO2), which can irritate your lungs and is linked to asthma and other breathing problems. If you don’t use a range hood, cooking with gas can add 25% to 33% more nitrogen dioxide to your indoor air in summer, and even more in winter. In four out of ten homes studied, gas burners released enough nitrogen dioxide to go over the health standards set for outdoor air.

Who’s Most at Risk?

Young children, older adults, and people with asthma or heart and lung diseases are especially at risk.

The numbers are worrying for children with asthma. A 2006 study found that pollution from gas stoves more than doubles the chances of wheezing and shortness of breath for kids with asthma who live in apartments. Another study showed that children with asthma who are exposed to higher nitrogen dioxide levels use their rescue inhalers 14% more often.

Some communities are affected more than others. In Washington State, Black people are exposed to PM2.5 levels that are over 1.3 times higher than White people, and Asian people face levels 1.5 times higher. American Indian and Alaska Native adults have the highest asthma rates at 18%. And there are cultural factors at work here, as well as the type of cooking you do indoors and the airflow in your home.

The Long-Term Health Impact

Being exposed to PM2.5 for a long time raises the risk of early death for people with heart or lung disease. It is also linked to chronic heart and lung problems, effects on brain health, and pregnancy issues.

Around the world, household air pollution causes 6.7 million early deaths each year. The main health problems are stroke, heart disease, chronic obstructive pulmonary disease (COPD), and lung cancer.

Use Your Range Hood Every Time You Cook

This is the most important step you can take. A range hood that works well and vents air outside can remove 50% to 70% of pollutants if you use it correctly. But studies show that people use their range hoods only 36% of the time in houses and 28% in apartments.

If your range hood only recirculates air back into the kitchen instead of venting it outside, you should open windows or use another exhaust fan.

Cook on Your Back Burners

Range hoods work best when you use the back burners because they are more fully covered by the hood. Cooking on a single back burner with the hood on low speed usually captures 50% to 70% of the pollutants.

Open Windows and Doors

If you do not have a range hood, opening windows or doors can help. One study found that opening both the front and back doors for ventilation creates strong airflow that can remove over 95% of cooking pollutants in just 10 minutes.

Consider Switching to Electric

All cooking creates some pollution, but gas stoves cause extra problems by releasing nitrogen dioxide, carbon monoxide, and benzene. A recent Stanford study found that switching to electric stoves could lower nitrogen dioxide exposure by over 50% across the country. One of the problems, of course, is that electricity or cooking with electricity is more expensive than using gas.

Choose Your Cooking Methods Wisely

Pan-frying and stir-frying at high temperatures make much more pollution than boiling, steaming, or using an air fryer. When you can, choose cooking methods that use lower temperatures.

Why This Matters Now

About half of the people surveyed did not know that cooking creates unhealthy air pollutants. But after learning about the health risks, 64% said they would think about using their ventilation devices more often.

People are spending more time at home. In 2021, Americans spent about 62% of their waking hours at home, up from 50% in 2019. With more people cooking at home, kitchen ventilation is more important.

Newer homes are built to be more energy-efficient, so there is less air exchange with the outdoors. Without good ventilation, pollutants can get trapped inside and build up to harmful levels. In homes with poor ventilation, indoor smoke can have fine particle levels 100 times higher than what is considered safe.

The Bottom Line

Cooking is a normal part of daily life, but it shouldn’t harm your health. By learning what pollutants are released when you cook and taking simple steps to ventilate your kitchen, you can protect yourself and your family.

The research is clear: using a range hood every time you cook, opening windows for airflow, cooking on back burners, and thinking about cleaner cooking technologies can really help. These are not complicated or expensive changes. They are simple habits that can greatly improve the air quality in your home and your health as well.

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He is a second-generation physician who prefers to be called by his first name, honoring his father, “the original Dr. Grossmann.”  In his own right, he’s a trailblazer at the nexus of surgical care and innovation. Born in Caracas, Venezuela and carrying forward his family’s medical legacy, he completed his surgical residency in Ann Arbor, Michigan, before establishing his practice in New England, serving as a general, trauma, advanced laparoscopic, and robotic surgeon at Portsmouth Regional Hospital in New Hampshire and Eastern Maine Medical Center.

Rafael is frequently linked to his groundbreaking use of Google Glass during surgery. But to define him by that singular innovation is to miss the deeper force driving his work: an unwavering belief that technology must serve—not supplant—the doctor–patient relationship. In recent interviews and longstanding contributions across digital health platforms, Rafael shares an increasingly urgent message: humility and empathy are not soft skills of the past—they are foundational elements of the future.

Proximity Over Performance
Rafael’s approach to technology is both deliberate and human-centered. He integrates AI, extended reality, and telehealth into care environments with one goal: to foster proximity between healer and patient. Whether bringing loved ones into ICU rooms through virtual tools, using augmented reality to teach medical trainees, or deploying wearables to enhance surgical insight, his purpose is consistent: technology must deepen the human connection.

“If the technology doesn’t enhance the connection between physician and patient,” Dr. Grossmann notes, “it has no role in care.”

That conviction reflects a broader truth in modern medicine: innovation must be guided by intention. The impact of a new tool is not measured by its complexity, but by its capacity to sharpen listening, expand compassion, and build trust. In this view, humility is not an abstract virtue—it is a clinical competency.

Humility as a Clinical Skill
While empathy is increasingly recognized as a measurable component of quality care, humility remains underappreciated. Yet humility—the ability to acknowledge limits, listen fully, and elevate the patient’s needs—may be one of the most critical skills a clinician can develop.

Rafael challenges medical education to do more than train for outcomes; he calls for cultivating presence. In trauma settings and academic halls alike, he models humility not as passivity, but as active, intentional leadership. It takes courage, he says, to be honest with patients—not just about diagnoses, but about uncertainty.

“The best medicine,” he reflects, “comes from presence, not only performance.” In high-tech environments where algorithms analyze and recommend, the clinician’s humility may be the most human—and healing—intervention available.

Empathy, Elevated by Innovation
To Rafael, empathy and innovation are not opposites. When used wisely, technology can extend—not replace—the clinician’s presence. Telemedicine platforms become conduits for comfort. Immersive simulations train for compassion. Data becomes dialogue when interpreted with care.

This mindset is especially important now. Patients today may have unprecedented access to information, yet they often feel unseen. In an age of instant answers, the experience of being truly heard remains rare. Rafael reminds health-sector leaders and policymakers that no system—however advanced—can succeed if it forgets the people it was designed to serve.

Clinicians stand at a crossroads as health delivery accelerates toward predictive analytics and AI-driven decisions. Technology offers an undeniable opportunity: greater access, improved accuracy, and better outcomes. But these advances must be matched by a return to the timeless principles of great medicine—empathy, humility, and presence.

Rafael’s work represents a rare blend of innovation and introspection. His willingness to explore the boundaries of digital medicine is matched by a steadfast insistence that patients remain at the center. The future of care, he contends, won’t be defined by who uses the most sophisticated technology, but by who uses it to deepen human connection.

Rafael is not focused on being remembered for the tools he introduced. He hopes to be known for something quieter: helping patients and clinicians feel seen, heard, and supported.

In an era when health systems are rethinking priorities, medical schools are reassessing competencies, and companies are racing to redefine care delivery, the voices of clinicians like Rafael’s matter more than ever. Humility, after all, is not the opposite of expertise—it is its most authentic expression.

The post “Humility” Is Cutting-Edge Medicine: What a Physician Innovator Teaches Us About Patient-Centered Care appeared first on Medika Life.

It’s a brilliant question — practical, personal, and rooted in lived experience.

We’ve all been there: eating blueberries, sipping matcha, nibbling dark chocolate, hoping it’ll offset the chaos of our lives.

We’re told that foods like blueberries are miracle cures — that if we just eat clean enough, we can outrun stress.

But here’s what I’ve seen in practice:

You can’t eat your way out of chaos.

→ Why Everyone’s Brain Feels Broken Right Now — And What I Tell My Patients

Still, food matters. Deeply.

Let’s unpack what antioxidant-rich foods can do for a burned-out brain — and where their power ends.

(P.S. That “Let food be thy medicine” quote? Not really Hippocrates.)

What Are High-ORAC Foods, Anyway?

ORAC, short for Oxygen Radical Absorbance Capacity measures how well a food can neutralize free radicals (unstable molecules that damage cells, accelerate aging, and promote inflammation).

Some of the highest-ORAC foods include:

These are some of the most evidence-based foods that fight burnout by countering oxidative stress and inflammation.

Consuming these foods regularly can make your body more efficient at extinguishing the “metabolic fires” triggered by stress, poor sleep, and inflammation.

So yes, these foods help.

But they’re not enough.

Stress, Sleep, and the Limits of Diet

Take a real-world example:

A 49-year-old entrepreneur came to me burned out.

She exercised.

Ate mostly plants. Drank matcha. Took magnesium.

Still exhausted. Irritable. Foggy.

Why?

She was sleeping five hours a night, answering emails at midnight, skipping meals, and never pausing.

Her nervous system was locked in a state of fight-or-flight.

And even the most antioxidant-rich foods won’t restore the parasympathetic state we need to digest, repair, and think clearly.

Nutrition supports healing, but it doesn’t initiate it when the system is overloaded.

Curious how patients actually recover from burnout? My ebook, What Dying Patients Taught Me About Living, shares what I’ve seen firsthand.
👉 [Get your copy here]

What Antioxidants Can Do

So what can antioxidants do?

A nutrient-dense, antioxidant-rich diet can help:

• Lower CRP (a marker of inflammation)
• Improve cognition under stress.
• Stabilize mood via the gut-brain axis.
• Protect mitochondria from oxidative stress.
• Support neurogenesis (yes, new brain cell growth)

These are some of the most powerful antioxidant benefits for the brain — and they’re magnified when paired with rest and rhythm.

One 2023 study found that a Mediterranean-style, antioxidant-rich diet was linked to a lower risk of depression.

Related: 10 Tiny Habits That Quiet Your Mind — Without Meditating

Another study showed that people who consumed more polyphenol-rich foods had better memory scores, regardless of their sleep quality.

The takeaway? Antioxidants can buffer the damage. But they can’t reset the machine.

What They Can’t Do

Let me be direct:

No number of blueberries can fix:

• Poor sleep hygiene
• Work addiction
• Emotional suppression
• Constant digital overload

Clean eating can quietly backfire — especially when it becomes a way to control life instead of nourish it.

Food is a foundation, not a fix.

What Actually Works (In Real Life)

Here’s what I tell patients when they’re doing all the “right” things — but still feel off:

1. Anchor meals to rhythm, not mood

Eat at consistent times daily. This stabilizes your gut clock and supports digestion.

2. Start the day with color

Aim for 3+ natural colors before noon: blueberries, spinach, turmeric, red pepper.

3. Pair food with ritual

Eat away from screens. Use real dishes. Go outside if you can. This activates your parasympathetic system.

4. Don’t supplement stress away

Magnesium, ashwagandha, resveratrol — all useful. But only after the basics are covered: sleep, movement, light, and breath.

5. Get morning light every day

Even 10 minutes of sunlight in the first two hours after waking can reset your circadian rhythm, improve sleep, and reduce stress reactivity.

Rhythm Over Rescue

Here’s the core truth:

Health isn’t about rescue. It’s about rhythm.

We chase the perfect food, supplement, or hack to undo imbalance.

But the body doesn’t crave intensity.

It craves consistency.

Yes, antioxidant-rich foods help.

But when food is paired with consistent rest, movement, morning light, connection, and meaning?

That’s when transformation happens.

A Series for the Questions That Matter

Reader questions shape how I practice medicine — and how I write.

If this one resonates, know this:

You’re not alone. Many of you are doing the right things, just in the wrong context.

You can eat perfectly and still feel off.

When food becomes a companion to healing, not a crutch, that’s when the real magic begins.

The food is just the beginning.

Healing comes when your life makes space for rest.

Download my recent ebook:
My latest ebook: What Dying Patients Taught Me About Living
👉 Grab your copy here.

Read next:
→ 25 Tiny Habits That Strengthen Mental Health
→ The Silent Fire: How Chronic Inflammation Fuels Aging — and 4 Ways to Cool It Down
→ 10 Tiny Habits That Recharge You, Without Quitting Your Job

Author bio: Michael Hunter, MD, is a cancer physician, over-60 competitive bodybuilder, and bestselling wellness writer. His latest ebook is available here.

Illustration generated using ChatGPT’s image tools.

The post Can Blueberries Save You From Burnout? appeared first on Medika Life.

The launch of Roferon-A, alpha interferon marked a watershed moment in medicine: the first biotherapeutic to treat cancer, targeting the rare disease hairy-cell leukemia. I remember the packed press conference at The Pierre Hotel in New York City. Thought leaders like Dr. Jerome Groopman inspired awe. Headlines followed. The world paid attention.

That was decades ago. Since then, the biotech sector has evolved from fragile start-up spirit into a multibillion-dollar force. In the eyes of many, what was once miraculous has become mundane. And yet, the science has only grown more awe-inspiring. So why don’t we talk about it that way anymore?

Have we become numb to the very progress that extends and saves lives?

Biotechnology is arguably one of humanity’s most transformative achievements. From precision cancer immunotherapies to gene editing tools like CRISPR, we’ve leapt across medical milestones that were once the stuff of science fiction. CAR-T cell therapy rewrites the body’s immune system; mRNA platforms taught us how to respond to pandemics in real time; and personalized medicine now tailors treatments to the uniqueness of our DNA.

Despite these triumphs, we now face a paradox: the more frequently we succeed, the less exceptional it seems. Biotech, in its reliability, risks becoming background noise.

The danger here isn’t just perception—political, economic, and moral. When we stop being amazed, we stop advocating. And advocacy is essential, because science doesn’t fund itself.

The Birth of a Movement: BIO’s Role in Advancing Innovation

In the early days of this field, the promise of biotech required more than scientific breakthroughs—it demanded an organized, united voice to advocate for science, policy, funding, and public trust. That’s when the Biotechnology (Industry) Innovation Organization (BIO) emerged, uniting a fledgling industry around a shared mission: to promote innovation and ensure that the fruits of biotech reach the people who need them most.

What began as a coalition of pioneers has evolved into one of the most influential global voices for biotechnology. BIO has helped shape legislation, fostered partnerships, supported startups, and advanced equity in access and clinical trials. It has been a tireless advocate for the idea that science serves people—and that innovation without access is innovation incomplete.

As we reflect on biotech’s journey—from niche science to essential public health engine—BIO’s efforts to engage policymakers, educate the public, and convene global stakeholders at events like the annual BIO International Convention, BIO2025 have played a defining role. It’s a reminder that scientific progress is never just about the petri dish. It’s about ecosystems—coalitions of scientists, communicators, investors, and institutions aligned toward a common good.

The Threat of Institutional Apathy

Innovation doesn’t flourish in a vacuum. It requires funding, partnerships, regulatory foresight, and yes, public interest. Today, with DOGE pinching national budgets and partisanship clouding consensus, Federal funding for research is under threat. The National Institutes of Health (NIH), the world’s largest public funder of biomedical research, faces increasingly skeptical eyes and plummeting appropriations.

In parallel, biotech investors—once exuberant—have become cautious. Valuations are down. IPO windows are narrow. Even promising start-ups are forced to downsize or shutter. This isn’t just an economic cycle. It’s a societal test.

If we stop investing in innovation, diseases that could have been conquered will remain entrenched. Rare conditions will stay rare because they’re unprofitable. And the promise of personalized, preventive care will fade back into abstraction. Let’s take stock.

We’ve made incredible strides in HIV, hepatitis C, certain leukemias, and now we see glimpses of progress in previously unyielding diseases like ALS and pancreatic cancer. In some cases, such as HIV, biologics have helped turn some diseases into manageable conditions. Patients who once faced death sentences now live long, productive lives.

But so much work remains. Alzheimer’s disease continues to challenge us. Autoimmune conditions like lupus and Crohn’s demand better solutions. Pediatric rare diseases—often overlooked—leave families desperate for options. And mental health, despite its growing visibility, remains underfunded and underexplored from a biotherapeutic standpoint.

We can’t stop now. The urgency is not over.

Science Needs Storytellers

One of the most potent forces in advancing biotherapeutics isn’t just the lab bench—it’s the lens through which the public sees that bench. This is where communicators come in.

Media, public relations professionals, and advocacy leaders are not passive observers. We are active players in this ecosystem. When we frame scientific progress as human progress, we drive interest, funding, and talent into the field. When we tell stories that connect molecules to people, we give science a face—and a heartbeat.

In the early days of Roferon-A, calls from a young PR pro would turn out a full-room press conference, launching a wave of national interest. Today, the media landscape is fragmented. Clicks compete with credibility, and sensationalism wins over substance.

That only means our responsibility has grown. We must elevate the authentic voices of scientists, patients and advocates. We must cover biotech stories not just as business news, but as human stories, because they convey the struggle and potential.

Bench to Bedside is a Human Endeavor

Behind every molecular breakthrough is a researcher who missed birthdays to run experiments, a trial participant who volunteered without knowing the outcome, and a caregiver hoping that science can offer one more chance. We cannot allow their efforts to be invisible.

Let us remember that biotherapeutics are not just lab products—they are the embodiment of human hope and courage. Each FDA approval to market is a victory for a company and a patient.

And yet, even as we acknowledge this, we must grapple with another complexity: equity.

Not all communities have equal access to these innovations. Biologics are expensive. Insurance structures are slow to adapt, sometimes even resisting. Global disparities persist. If we believe in the power of biotech, we must also commit to making it accessible, advocating for affordability, inclusive clinical trials and compassionate pricing strategies.

Reclaiming the Wonder

So, where do we go from here?

We start by reawakening awe. As communicators, we must use our platforms to remind the world that biotech is not just another industry—it is a movement, a mission.

We must protect the budgets that sustain research, defend the credibility of science against misinformation, and inspire young minds to enter STEM fields not just for jobs but for the opportunity to change lives.

It starts with how we talk. Let’s use language that evokes possibility. Let’s tell stories that illuminate the patient journey. Let’s spotlight scientists with the same reverence we show to athletes or entertainers.

Biotech Without Borders

The original promise of biotechnology was to break boundaries between disciplines, possibilities, and life and death. That promise is still alive, but it needs guardians.

Now more than ever, biotech needs communicators, policymakers, and citizens who care.

I remember the days when biotech press conferences made front pages. Maybe we’ll never go back to that exact moment. But we can choose to go forward—together—into a future where science is again seen not just as data, but as destiny.

Let’s reclaim the wonder. Let’s continue to give scientists a voice, patients hope, start-up enterprises resources, and policymakers direction.

Because what’s at stake is not just the next miracle drug.  What’s at stake is our collective belief that we can still do miraculous things.

The post Biotech Without Borders: Reclaiming the Wonder of Science in a Distracted Age appeared first on Medika Life.

When I asked him what sparked joy — my now-standard question for new consults — he didn’t hesitate.

“Travel,” he said, his eyes lighting up. “Dozens of countries. I love learning how people live, eat, think.”

Then he paused. “But if I had to choose just one?”

He leaned forward, almost conspiratorially.

“Japan. Lived there over 25 years.”

I perked up. “Hontō ni?”

“Eh? Hontō hontō!” he beamed.

And just like that, the oncology suite turned into an izakaya.

We chatted in Japanese for several minutes — I, a Black man from the Pacific Northwest with a Japanese wife and a daughter who had attended college in Kyoto; he, a white man with a surprising Tokyo accent and stories that could have filled a dozen ryokans.

The nurses outside the curtain must’ve been baffled.

There we were: two middle-aged men, dressed like surgical extras, speaking rapid-fire Japanese about onsen, natto, and konbini snacks.

In radiation oncology, these are the moments you don’t forget.

The Kind of Joy You Can’t Buy

When our conversation drifted back to English, we kept circling the same theme: experience.

He told me about sleeping in the Sinai desert under a blanket of stars.

About sipping strong coffee in Addis Ababa.

About riding motorcycles through Southeast Asia before Google Maps existed.

What he didn’t talk about were things.

No fancy watches.

No new Teslas.

No gadgets.

Just the texture of moments lived.

And it hit me: the joy that lit up his face wasn’t the kind you get from opening a box.

It was the kind you earn by stepping into the unfamiliar. The kind that asks something of you — and gives back more than it takes.

The Science of Why It Feels So Good

We tend to think happiness is about comfort.

But psychologists like Dr. Laurie Santos (of Yale’s wildly popular Science of Well-Being class) suggest that the happiest people spend less on stuff and more on experiences.

Why?

Because of experiences:

• Give us stories we can retell
• Bring us into contact with others
• They are often tied to personal growth
• Don’t lose their sparkle the way objects do

A new phone gets old fast. But your first tuk-tuk ride in Bangkok? That stays with you.

There’s even a term for the trap we fall into with material things: hedonic adaptation.

The idea is that we quickly get used to new pleasures.

The house, the car, the clothes — they stop thrilling us.

But experiences?

They stay vivid.

I wrote about a similar theme in 10 Tiny Habits That Make You Healthier, Calmer, and Harder to Kill — the idea that intentional living creates lasting joy, not just fleeting dopamine hits.

My Favorite Journeys

Some of my favorite travel memories come from places that required a little more effort than, say, Paris or London.

• Egypt: navigating the frenetic, poetic chaos of Cairo traffic, then standing in stillness before the pyramids.
• Turkey: sipping tea in the shadow of the Blue Mosque, hearing the call to prayer echo across centuries.
• Japan: of course — always Japan — with its contradictions, its grace, its reverence for detail.
• Indonesia: maybe my favorite of all, where time moves differently and kindness is a national trait.

These places didn’t just offer a change of scenery. They offered a shift in me — the way I saw others, the way I understood culture, the way I experienced time.

What Travel (and Cancer) Teaches Us

My patient and I shared one more truth that day: that illness, like travel, strips you down to what matters.

It makes you see the world in a different light.

It humbles you.

And if you let it, it can open you.

Sometimes I think the best journeys aren’t measured in miles, but in mindset.

You don’t have to get on a plane.

You just have to notice something new.

Final Thoughts

That day in the exam room, two men with nothing in common on paper laughed like old friends, because we shared a language — and not just Japanese.

We shared curiosity.

And in that moment, amid machines and masks and schedules, we were both simply human.

Let the numbers tell the story.

• Free Download: “Debunked: 7 Health ‘Facts’ That Are Quietly Hurting You — Grab It Here.”
• Liked this story? Read “25 Ways to Reduce Your Cancer Risk” or “What Dying Men Confessed When No One Was Listening.”

Author bio: Michael Hunter, MD, is a cancer doctor, travel junkie, and collector of patient wisdom. His new ebook, What Dying Patients Taught Me About Living, is available here.

P.S. If this story resonated with you, follow me for weekly insights from the clinic and beyond.

The post Clinic Notes: I Didn’t Expect to Speak Japanese Today appeared first on Medika Life.

Sometimes it’s a look.

A line.

A moment that lands deeper than diagnosis.

This brief essay is a collection of those moments.

Brief.

Unexpected.

And always real.

“The Healing Power of Touch: A Patient’s Insight”

This week, a patient shared a poignant realization that emerged after years of emotional distance from his wife.

They had grown apart, but recently discovered a shared need: the simple, profound act of touch.

He reflected on how a gentle hug or a reassuring hand on the shoulder seemed to bridge the emotional gap between them.

“I think we’re wired for this,” he mused, referencing hormones like oxytocin that respond to physical affection.

His insight aligns with scientific findings.

Oxytocin, often referred to as the “love hormone,” plays a crucial role in social bonding and emotional connection.

Studies have shown that affectionate touch can increase oxytocin levels, reduce stress, and foster feelings of trust and closeness.

In fact, research indicates that even brief moments of affectionate touch can lead to measurable increases in oxytocin, a hormone that contributes to an improved mood and reduced anxiety.

This finding underscores the biological underpinnings of our need for physical connection.

My patient’s experience serves as a reminder that sometimes, healing in relationships doesn’t require grand gestures — just a touch of understanding, quite literally.

For more reflections on connection at the edge of life, read my essay: What Dying Men Confessed When No One Was Listening.

“The Prostitute’s Pasta”

In oncology, gratitude comes in many forms — thank-you notes, quiet nods, even tears.

But sometimes, it arrives as a steaming pan of pasta.

One of our patients, an older Italian gentleman with a twinkle in his eye and impeccable taste, has taken to feeding the staff.

Not metaphorically — literally.

Lasagna, tiramisu, and even delicate cannoli are dusted with sugar like freshly fallen snow.

Today, he arrived bearing a new dish. “Pasta Puttanesca!” he announced proudly. “You know — the prostitute’s pasta.”

A pause.

Then laughter. Nurses chuckled. My medical assistant nearly dropped her stethoscope.

He winked. “They say it was made quickly, between clients.”

I’ll leave the etymology to linguists.

But I can tell you this: the olives were briny, the sauce was bold, and the gratitude was unmistakable.

This event was something else entirely in a world often defined by scans and side effects.

A recipe for connection.

Served al dente.

Note: For patient privacy, I have modified some details.

Here are my previous Clinic Notes essays:

1. Clinic Notes 5/18/2025
2. Clinic Notes 6/26/2025

Want more stories like these — plus the science behind living longer and better? I’ve distilled the most powerful lessons from oncology, aging research, and patient wisdom into my new ebook: Extending Life and Healthspan.

Practical, evidence-based, and full of humanity.

The post Clinic Notes: What My Patients Said This Week appeared first on Medika Life.

When skilled medical care is guided by empathy, it becomes something greater. It rises beyond competency. It becomes a connection. It becomes care.

We celebrate survival and measurable outcomes — and we should, however, carry with us what people carry with them long after the diagnosis, such as what was done and how we were treated in the process. It is when a physician takes the time to explain the next steps clearly. It’s the kind gesture from a nurse, the smile behind the mask, the call that came just to check-in. These human elements are not extras. Communication is always part of care.

The most enduring part of the health journey is whether people survive and how they are treated while they try.

People Always, Sometimes Patients

People navigating illness rarely rave about systems. They remember moments — how a physician sat and listened, the hand on the shoulder, the honest, unhurried explanation. They speak of kindness, clarity, and dignity. Empathy is not an accessory to care — it is the essence of care. The data backs that up.

A Medscape report confirmed that patients with chronic pain experienced significant improvement when treated by physicians who exhibited empathy. In Patient Education and Counseling studies, empathy led to better communication, treatment adherence, and clinical outcomes. Harvard Medical School research shows that when patients feel heard and understood, they’re more likely to engage in their care and experience improved results. This is more than perception — it’s physiology. Stress levels drop, trust increases, and healing accelerates.

Empathy isn’t a “soft skill.” It’s a clinical tool that improves outcomes and humanizes healthcare. In How Doctors Think, Jerome Groopman, MD, reminds us that data does not replace presence: “Statistics cannot substitute for the human being before you; statistics embody averages, not individuals.” Every patient has a backstory — a unique path to the clinic door — and when physicians listen, they gain context and insight.

In 1993, the late Arnold P. Gold, MD of Columbia University, reimagined the start of a medical student’s journey by introducing the “White Coat Ceremony” — a symbol of professionalism and compassion in care. His vision was simple but profound: remind future physicians from day one that medicine is about people, not just pathology.

The ceremony expanded to nursing and other health professions under the leadership of Richard I. Levin, MD, Professor Emeritus of Medicine, Department of Medicine at NYU Langone Health and former President and CEO of The Arnold P. Gold Foundation. “We mourn for him,” Dr. Levin said of Dr. Gold, “but as we celebrate his unique life, we can all carry his legacy forward in love.” The White Coat Ceremony, born from one physician’s belief in the power of human connection, can be a learning experience shaping the heart of health professionals.

The Clinicians Who Lead with Heart

This approach is not hypothetical. It’s exemplified by physicians whose names are synonymous with excellence and empathy. These are outstanding physicians I have encountered through the years. They and their office or department teams collectively demonstrate that knowledge and empathic caring create memorable medical experiences.

Empathy is not just a soft skill in healing — it’s a clinical art. It allows a physician to step into the world of their patient and grasp their experience emotionally, intellectually, and behaviorally. However, empathy goes further: it means communicating that understanding, validating the patient’s concerns, and transforming the exchange into therapeutic action. Empathy becomes the bridge between the healer and those seeking to be healed — where listening becomes treatment, and understanding becomes care.

This fusion of clinical excellence and genuine empathy distinguishes exceptional physicians in healthcare delivery. These physicians prove that it is possible to be both an outstanding clinical physician with a heart and conscience:

A distinguished urologist in New Jersey, Joseph V. DiTrolio, MD, who only recently announced his retirement from clinical practice, seamlessly integrated innovative techniques with a patient-first philosophy for years. His approachable demeanor — insisting patients call him “Joe” — fosters a comforting environment where individuals feel genuinely valued. Beyond his clinical practice, Dr. DiTrolio has contributed significantly to urology through product development and holds several patents. His commitment to education is evident in his Clinical Professor of Surgery role at New Jersey Medical School.

Serving as President of Mount Sinai Fuster Heart Hospital and Physician-in-Chief of The Mount Sinai Hospital, Valentin Fuster, MD, embodies the integration of empathy and leadership. His holistic approach to cardiovascular health — encompassing research, patient care, public health policy and education — has made a global impact. Dr. Fuster’s development of a cardiovascular “polypill” reflects his dedication to accessible patient care, reducing cardiovascular mortality by 33 percent among heart attack survivors. His contributions have been recognized with numerous accolades, including the World Heart Federation Lifetime Achievement Award. He makes himself 100 percent available, whether focusing on a patient in the ER awaiting treatment, rebuilding Haiti’s health system, focusing on the needs of communities in Spain, or leading a global professional association. His heart is in everything he engages.

As Chair of Radiation Oncology at Cooperman Barnabas Medical Center, Alison Grann, MD, exemplifies the harmonious blend of clinical precision and emotional intelligence. Recognizing the emotional weight of specific diagnoses, she ensures that every patient interaction — from reception to treatment — is infused with warmth and respect. Her leadership fosters an environment where patients feel seen and heard, reinforcing their trust in their care. Dr. Grann’s commitment extends beyond patient care; she actively engages in research and holds a Clinical Assistant Professorship at Rutgers Cancer Institute of New Jersey.

At Weill Cornell Medicine, Allyson J. Ocean, MD, stands out as a leading gastrointestinal oncologist and patient advocate. Her compassionate approach clarifies the uncertainties of oncology, deeply resonating with patients. As a co-founder of “Let’s Win Pancreatic Cancer,” she heeded the counsel of a communication leader – one of her patients – the late Anne Glauber – and turned the pancreatic cancer journey into a broader mission to extend others’ lives. Dr. Ocean’s dedication to patient-centered care is further demonstrated through her roles at The Jay Monahan Center for Gastrointestinal Health and her active involvement in clinical research and education.

At NYU Langone, Lawrence (Larry) Phillips, MD, delivers exceptional cardiovascular care rooted in empathy and connection. In a specialty where urgency often overtakes interaction, he slows the pace — making listening his first intervention. Patients consistently highlight how seen and heard they feel, a testament to his people-first approach. Board-certified in Internal Medicine and Cardiovascular Disease, Dr. Phillips also serves at NYC Health + Hospitals/Bellevue and is a passionate advocate with the American Heart Association, leading grassroots efforts to expand CPR training and community heart health education.

As Chief Medical Officer of WebMD, John Whyte, MD, MPH, bridges the worlds of clinical practice, public health, and consumer education — delivering trusted health information with empathy and clarity. He sees patients weekly, grounding his clinical leadership in real-world care. Dr. Whyte’s books, including Take Control of Your Cancer Risk and Is This Normal?, reflect his commitment to guiding people through their health journeys with candor and compassion. A former leader at the Food and Drug Administration and the Centers for Medicare & Medicaid Services, he brings a public health lens to his work, ensuring each message empowers and every patient feels seen.

These physicians exemplify how empathy and medical expertise transform patient care and cement community admiration. They do more than heal — they restore faith in the system and dignity to the patient’s journey.

Stop Calling Us Beneficiaries — We’re Paying Customers

Empathy must extend beyond the individual clinician and become a defining trait of our health system, from how we write policies to how we answer phones.

Too often, people in need of care face frustration instead of compassion. Insurance denials, confusing bills, impersonal communications — these compound the stress of illness. But forward-thinking organizations are challenging that norm.

In the health industry, we often claim that the patient is at the center of care and that naïve Band-Aid should be ripped off. The murder of Brian Thompson is a tragic and terrifying wake-up call to the US health insurance industry. However, the headlines have receded into the background, and with the passing news cycle, so has the urgency to evolve the care coverage system.

The public reaction to Thompson’s death gives us an essential opportunity to recognize that we have lost the trust of many of the people the sector seeks to serve. The trauma of this terrible moment can be addressed in one of two ways — avoidance or engagement. This event must catalyze health companies to reassess their approach to patient care and public trust.

Profitability and service to the patient aren’t two separate outcomes. Investing in interventional care and providing that care are also inextricably intertwined. The industry should consider several key recommendations. Some companies do what is needed to treat “beneficiaries” and “members” more like paying and valued customers.

MetLife and Guardian Life have partnered with Empathy, a platform designed to support beneficiaries through bereavement. These companies understand that losing a loved one is not just a claims process; it’s a human-customer-like — experience. They’ve committed to making that moment one of guidance, not bureaucracy.

Operationally, insurance teams must rethink how they respond to prior authorization requests. What if instead of a hard “No,” a representative said, “Let’s see what we can do together”? That’s not just good service. It lowers people’s tension and is good medicine. When insurance becomes a partner, not a process, people feel seen — not shuffled.

In the broader system, patient navigation — first championed by a physician who became an authority on race, poverty, and cancer — Harold Freeman, MD — shows that outcomes improve when people have support navigating the maze of care. Especially in underserved communities, navigators ensure that empathy is not dependent on privilege. It becomes a universal right.

Designing for Dignity

Empathy isn’t just something we express; it’s something we can design. We can build it into:

• The architecture of clinics that create calm, not chaos
• The approach we take in helping patients complete registration
• The way we train AI and LLMs to converse with respect
• The tone of insurance letters that minimize legalese and empathize with care
• The workflows that give nurses and doctors more time to connect

We must think of empathy not as reactive but as proactive. It must be embedded in systems, supported by policy, and measured by efficiency and experience. Empathy differentiates between treating a condition, focusing on a body part, and healing a person.

The System’s Soul

At its best, medicine is more than what we do — it is about how we do it. Empathy does not appear on a chart and is not coded into billing. But its absence is always felt, and its presence potentially transforms the entire care experience.

Empathy binds us back to our purpose, whether we are physicians in a clinic, nurses in an ICU, call center agents helping a customer navigate issues with a denied claim, or patient navigators walking with someone through a new diagnosis.

As we move into the future of AI, automation, digital health, and remote care, our most vital technology remains human connection. Our most critical clinical protocol is compassion.

Empathy is not a soft idea. It is medicine’s soul in action.

The post Empathy and Health Excellence — The Superpower of Care Delivery appeared first on Medika Life.

“First 100 days,” said Means, a former consultant to Big Pharma who uses the social platform X to focus attention on chronic disease. “What should be done to reform the FDA?”

The question was more than rhetorical. Means is among a cadre of health business leaders and nonmainstream doctors who are influencing President Donald Trump’s focus on health policy.

Trump’s return to the White House has given Means and others in this space significant clout in shaping the nascent health policies of the new administration and its federal agencies. It’s also giving newfound momentum to “Make America Healthy Again,” or MAHA, a controversial movement that challenges prevailing thinking on public health and chronic disease.

Its followers couch their ideals in phrases like “health freedom” and “true health.” Their stated causes are as diverse as revamping certain agricultural subsidies, firing National Institutes of Health employees, rethinking childhood vaccination schedules, and banning marketing of ultra-processed foods to children on TV.

Public health leaders say the emerging Trump administration’s interest in elevating the sometimes unorthodox concepts could be catastrophic, eroding decades of scientific progress while spurring a rise in preventable disease. They worry the administration’s support could weaken trust in public health agencies.

Georges Benjamin, executive director of the American Public Health Association, said he welcomes broad intellectual scientific discussion but is concerned that Trump will parrot untested and unproven public health ideas he hears as if they are fact.

Experience has shown that people with unproven ideas will have his ear and his “very large bully pulpit,” he said. “Because he’s president, people will believe he won’t say things that aren’t true. This president, he will.”

But those in the MAHA camp have a very different take. They say they have been maligned as dangerous for questioning the status quo. The election has given them an enormous opportunity to shape politics and policies, and they say they won’t undermine public health. Instead, they say, they will restore trust in federal health agencies that lost public support during the pandemic.

“It may be a brilliant strategy by the right,” said Peter McCullough, a cardiologist who has come under fire for saying covid-19 vaccines are unsafe. He was describing some of the election-season messaging that mainstreamed their perspectives. “The right was saying we care about medical and environmental issues. The left was pursuing abortion rights and a negative campaign on Trump. But everyone should care about health. Health should be apolitical.”

The movement is largely anti-regulatory and anti-big government, whether concerning raw milk or drug approvals, although implementing changes would require more regulation. Many of its concepts cross over to include ideas that have also been championed by some on the far left.

Robert F. Kennedy Jr., an anti-vaccine activist Trump has nominated to run the Department of Health and Human Services, has called for firing hundreds of people at the National Institutes of Health, removing fluoride from water, boosting federal support for psychedelic therapy, and loosening restrictions on raw milk, consumption of which can expose consumers to foodborne illness. Its sale has prompted federal raids on farms for not complying with food safety regulations.

Means has called for top-down changes at the U.S. Department of Agriculture, which he says has been co-opted by the food industry.

Though he himself is not trained in science or medicine, he has said people had almost no chance of dying of covid-19 if they were “metabolically healthy,” referring to eating, sleeping, exercise, and stress management habits, and has said that about 85% of deaths and health care costs in the U.S. are tied to preventable foodborne metabolic conditions.

A co-founder of Truemed, a company that helps consumers use pretax savings and reimbursement programs on supplements, sleep aids, and exercise equipment, Means says he has had conversations behind closed doors with dozens of members of Congress. He said he also helped bring RFK Jr. and Trump together. RFK Jr. endorsed Trump in August after ending his independent presidential campaign.

“I had this vision for a year, actually. It sounds very woo-woo, but I was in a sweat tent with him in Austin at a campaign event six months before, and I just had this strong vision of him standing with Trump,” Means said recently on the Joe Rogan Experience podcast.

The former self-described never-Trumper said that, after Trump’s first assassination attempt, he felt it was a powerful moment. Means called RFK Jr. and worked with conservative political commentator Tucker Carlson to connect him to the former president. Trump and RFK Jr. then had weeks of conversations about topics such as child obesity and causes of infertility, Means said.

“I really felt, and he felt, like this could be a realignment of American politics,” Means said.

He is joined in the effort by his sister, Casey Means, a Stanford University-trained doctor and co-author with her brother of “Good Energy,” a book about improving metabolic health. The duo has blamed Big Pharma and the agriculture industry for increasing rates of obesity, depression, and chronic health conditions in the country. They have also raised questions about vaccines.

“Yeah, I bet that one vaccine probably isn’t causing autism, but what about the 20 that they are getting before 18 months,” Casey Means said in the Joe Rogan podcast episode with her brother.

The movement, which challenges what its adherents call “the cult of science,” gained significant traction during the pandemic, fueled by a backlash against vaccine and mask mandates that flourished during the Biden administration. Many of its supporters say they gained followers who believed they had been misled on the effectiveness of covid-19 vaccines.

In July 2022, Deborah Birx, covid-19 response coordinator in Trump’s first administration, said on Fox News that “we overplayed the vaccines,” although she noted that they do work.

Anthony Fauci, who advised Trump during the pandemic, in December 2020 called the vaccines a game changer that could diminish covid-19 the way the polio vaccine did for that disease.

Eventually, though, it became evident that the shots don’t necessarily prevent transmission and the effectiveness of the booster wanes with time, which some conservatives say led to disillusionment that has driven interest in the health freedom movement.

Federal health officials say the rollout of the covid vaccine was a turning point in the pandemic and that the shots lessen the severity of the disease by teaching the immune system to recognize and fight the virus that causes it.

Postelection, some Trump allies such as Elon Musk have called for Fauci to be prosecuted. Fauci declined to comment.

Joe Grogan, a former director of the White House’s Domestic Policy Council and assistant to Trump, said conservatives have been trying to articulate why government control of health care is troublesome.

“Two things have happened. The government went totally overboard and lied about many things during covid and showed no compassion about people’s needs outside of covid,” he said. “RFK Jr. came along and articulated very simply that government control of health care can’t be trusted, and we’re spending money, and it isn’t making anyone healthier. In some instances, it may be making people sicker.”

The MAHA movement capitalizes on many of the nonconventional health concepts that have been darlings of the left, such as promoting organic foods and food as medicine. But in an environment of polarized politics, the growing prominence of leaders who challenge what they call the cult of science could lead to more public confusion and division, some health analysts say.

Jeffrey Singer, a surgeon and senior fellow at the Cato Institute, a libertarian public policy research group, said in a statement that he agrees with RFK Jr.’s focus on reevaluating the public health system. But he said it comes with risks.

“I am concerned that many of RFK Jr.’s claims about vaccine safety, environmental toxins, and food additives lack evidence, have stoked public fears, and contributed to a decline in childhood vaccination rates,” he said.

Measles vaccination among kindergartners in the U.S. dropped to 92.7% in the 2023-24 school year from 95.2% in the 2019-20 school year, according to the Centers for Disease Control and Prevention. The agency said that has left about 280,000 kindergartners at risk.

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF—an independent source of health policy research, polling, and journalism. Learn more about KFF.

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