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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Over time, it becomes evident that the decisions made in boardrooms carry their greatest weight in those very moments. It would take years to understand it fully, but these moments were not isolated. They were the foundation for something I would later try to give voice to.

The Day the Ordinary Disappeared

In January 1975, I was traveling through Paris on my way to the United States. What should have been a routine journey became something else entirely. Terrorists fired two RPG shells at our plane. They missed us but struck a Yugoslav Airlines JAT aircraft on the tarmac nearby.

The randomness of it all was almost impossible to process. One moment, you are a traveler moving through the world, the next, you are told to hug the floor of the aircraft, confronted with how easily that world can be altered or taken away. I did not have the language for it then; however, I carried the feeling forward. Life is not guaranteed. It is a gift given to us to deploy.

In 1978, I was leading the first Think Tank Peace Mission to Egypt and Israel. There were no direct flights between the two countries. From Cairo, we flew to Cyprus, then to Tel Aviv.

An Air Cyprus flight had landed just before ours. It was overtaken by terrorists. An Egyptian Entebbe-like rescue was attempted. It failed. When we landed hours later, the aftermath was still there — the remains of the Egyptian military C-130 sat on the tarmac, destroyed and covered. It reinforces the adage, “that timing is everything.”

You do not process it fully in the moment. You carry it. An appreciation for what lies beyond our control. A respect for those who act with purpose, regardless of outcome. An understanding that we plan for the future, yet we live in the moment.

Years later, during my military service as a paratrooper and combat medic, that lesson was no longer abstract. It was immediate, urgent and often unfolding before me. I served six frontline combat tours in Lebanon, in places where the noise of conflict was constant and the margin between survival and loss was measured in inches.

I tended to friends and foes under fire. In those moments, there was no room for theory. Care was not a matter of courage or a concept; it was an instinctive action. Communication was not a strategy; it was survival. A word, a look, a clear instruction could steady someone, guide them and save them.

Where Care Is Action, Not Theory

War has a way of stripping away everything except what matters most. You see clearly how dependent we are on one another. You understand that courage is not the absence of fear; it is the determination to act despite it. You learn that presence, simply being there for another person in their most vulnerable moment, is one of the most powerful forms of care.

I thought I understood risk. I thought I had come to terms with uncertainty. Then life reminded me again.

On a flight to visit my parents in the United States, the Tower Air jet I was on caught fire over the Atlantic. Two engines on the left side were burning. We needed to find a place to land quickly or hit the ocean. There is a particular kind of silence that fills a plane in that moment. It is not panic. It is something deeper, more introspective. You feel time stretch. You think about the people you love. You consider what has mattered and what has not.

As we made our emergency landing in Gander, Canada, I remember not relief first, but reflection. Once again, life had placed me in a moment where its fragility was undeniable.

These experiences did not turn me away from the world. They pulled me closer to it. They shaped how I see people, how I listen and how I respond. They taught me that every interaction carries weight, that every conversation can matter more than we realize.

In recent years, I have traveled to Ukraine annually before and during COVID and now during the war, supporting friends and spending time in a small community facing circumstances most of us can only imagine from afar. There, I saw the same truths I had encountered earlier in life. Community becomes everything. Information becomes lifeblood. People look to one another not only for physical support, but for clarity, reassurance and meaning. Even in the darkest conditions, communication is not secondary to care. It is part of care.

Most in the business world know me through my work at FINN Partners as a health communicator, through my writing, speaking and advocacy as a champion of health innovation and a more human-centered health system. They see my professional journey. What they do not always see is the foundation beneath it. Decades of lived experience that have reinforced, time and again, that life is precious, that it can change in an instant and that how we show up for one another in those moments defines us.

At FINN Partners, I have found a community of colleagues who reflect these same values. There is an understanding that our work carries responsibility, and that we are capable of more when we challenge ourselves to rise to it. It is a culture that encourages each of us to think beyond the immediate and contribute to something more enduring.

That understanding became even more personal through my family. My wife and I have walked alongside our child as she navigates the complexities of a rare disease. There are highs and there are lows. There are moments of hope and moments of uncertainty. In those experiences, I have seen health care from another vantage point, not as a cohesive system, but as a series of human interactions that can either comfort or compound the challenge.

When you are a parent in those moments, you listen differently. You look for clarity in every word. You hold on to empathy when it is offered and you feel its absence when it is not. You come to appreciate that communication in health is not an accessory. It is essential. It shapes understanding, trust and the ability to move forward.

The Human Thread Through Every Moment

All of these experiences converge into a single, enduring belief. Communication is not separate from care. It is how care travels along its continuum. There are moments when that truth reveals itself outside the settings we expect.

On a transatlantic flight in 2001, turbulence turned severe. At one point, a call came over the intercom: “Are there any doctors aboard?” No one responded. Minutes later, the request broadened to “any health professionals.”

My wife looked at me and quietly suggested I press the call button.

I was escorted to a passenger, pale and wrapped in a blanket. He had lost and regained consciousness. I introduced myself warmly and began with simple questions to assess his awareness. His name. The President of the United States. The day we had taken off. He answered each one without hesitation. His vitals were stable.

I explained that I was not a physician, but a former military EMT. Given the turbulence and the length of the flight, dehydration and stress were likely contributors. I reassured him and suggested that he follow up with his physician upon landing and, if he needed me, not to hesitate to hit his call button.

As I returned to my seat, a man two rows behind called out, “I’m a neurologist. I would have handled that exactly as you did.”

It was meant as an affirmation. I received it that way. Yet it lingers differently. In that moment, the instinct to act had been replaced by the comfort of waiting. The systems we build, even when grounded in expertise, can condition us to hesitate when action is needed most.

In moments like these, care is not a title or a credential. It is the willingness to engage, communicate, and act.

Across the health ecosystem and in responsible business settings, success is often measured by growth, scale and financial performance. These are necessary markers of progress. They enable innovation, access and reach. However, there is a deeper measure that often goes unspoken. When we understand our role within the continuum of care and recognize the connection between balance-sheet decisions made in boardrooms and people’s experiences felt at the bedside, our work takes on greater meaning. It moves beyond what can be counted to what ultimately counts.

Over time, I came to understand that moments are not separate. They are connected. Each one revealing, in its own way, what happens when people are seen, heard and cared for, and what happens when they are not.

That understanding became Healing the Sick Care System: Why People Matter.

A life of observing, listening, engaging and caring was the kindling. The moments themselves were the spark. Together, they revealed a simple truth: when we lose sight of people, the system falters. When we honor them, it begins to heal.

That truth asks something of us.

It is not simply about words. It is about presence. It is about accountability. It is about the choice to act when action is needed. This is how humanity shows up in systems, and how those systems, in turn, earn the trust of the people they are meant to serve.

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Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

The post AI Will Not Fix Health Care – Leadership Might appeared first on Medika Life.

That tension became unmistakable during the recent EcoHealth dialogue convened by The Galien Foundation. The gathering brought together innovators addressing climate and environment needs, not-for-profit organizations mobilizing global youth action and corporate-enabling technologies strengthening responsible business.

Their work spans ocean-wave power, grassroots environmental leadership, women-led solar entrepreneurship, next-generation water treatment, industrial cooling, soil restoration platforms and algae mitigation technologies. Their perspectives may differ, but their commitment to science is united. However, each voiced the same underlying truth: climate tech, like medicine, advances only when society answers the defining question of our era –Who pays?

A Planet Under Stress and a Market Slow to Respond

Climate instability is not a distant worry; it is a daily force shaping people and planetary health. When lakes collapse due to toxic blooms, communities lose access to drinking water, fisheries and tourism. When drought tightens its grip, agricultural regions face diminished yields and economic pressure. When wildfire smoke drifts across borders, respiratory health deteriorates even hundreds of miles away. Stability in water, air and soil is inseparable from human wellbeing, and climate innovators working in these areas, such as BlueGreen Water Technologies, which restores threatened lakes, operate on the very front line of prevention.

Yet companies like BlueGreen often face a steep path to investment because their work benefits everyone but belongs to no single customer. A restored lake sustains tourism, agriculture, local economies, ecological health and community wellbeing. However, responsibility is spread across municipalities, counties, state agencies, and the Federal government and national ministries, all of which manage immediate crises that overshadow the slow, devastating progression of environmental decline.

The same challenge confronts innovators such as Solar Sister, which expands access to clean, safe solar energy for communities without reliable power, and the POP Movement, which mobilizes youth populations to drive local environmental action. Their impact is generational, and their value is immeasurable, yet their funding often relies on philanthropy or public grants, mechanisms that rarely match the scale of the problems they address.

Even climate technologies designed for industrial operations face the challenge of being essential but not urgent in public budgets. Infinite Cooling, for example, captures water evaporating from power-plant cooling towers, reclaiming resources that would otherwise be lost to the atmosphere. It offers a response to the costs of water as an essential business resource. Yet, because these benefits impact industries – from pharmaceutical companies to power plants – rather than county governments, adoption is championed by supply chain and corporate financial stewards. 

A similar story emerges from companies like Greenore, which is building biological solutions to regenerate soil systems. Healthy soil underpins food security, agricultural productivity and community resilience. It is as essential to global health as any medicine. However, soil restoration often lacks a corporate customer and competes with established agricultural practices and stretched public budgets.

Corporate Imperative: Climate Tech Cannot Wait

Compare these funding obstacles with the experiences of corporate-oriented climate tech innovators whose solutions support operations, reduce costs, or address regulatory pressures. Eco Wave Power illustrates the point with clarity. Its technology harnesses ocean waves to produce clean electricity, transforming coastal infrastructure into renewable-energy assets. For ports, industrial campuses, and commercial centers along coastlines, this is not only an environmental benefit but also an energy security strategy and an additional revenue source.  The value is concrete, the payer is clear. Operations leaders can place it within a capital plan.

The contrast is evident in how global companies behave. Cloud providers racing to meet AI demand are committing billions to renewable power purchases, as their data centers cannot operate without stable, cost-controlled energy. Manufacturing companies often sign long-term agreements for clean electricity because energy risk poses a significant threat to their production output and profitability. Logistics and e-commerce giants invest heavily in biodegradable packaging because regulations are tightening, and sustainable materials avoid reputational damage and secure supply chains. These forms of climate innovation do not wait for budget approvals across 10 public agencies. They fit within the clearly defined corporate operating model.

Two Speeds, One Planet

The result is a two-speed climate economy. The technologies that support business continuity scale quickly; in contrast, the technologies that protect the environmental foundations of life struggle to secure investment despite their importance.

The Galien Foundation EcoHealth dialogue highlighted the precarious nature of this imbalance. BlueGreen restores waterways before they collapse. Solar Sister brings clean energy into homes before households turn to harmful alternatives. Greenore regenerates soil before agricultural regions face collapse. POP Movement ensures communities are engaged before consequences become irreversible. However, without clear lines of accountability, these organizations perpetuate the existential paradox of the Myth of Sisyphus, who is constantly pushing the rock uphill only to see it roll down again and again.  The problem is real.  The solution is proven. The funding environment is challenging.

Meanwhile, corporate-oriented climate tech companies are racing to meet demand because their value proposition directly connects to corporate cost, efficiency, or continuity. Eco Wave Power and Infinite Cooling demonstrate how quickly solutions advance when they operate within a budget line rather than under a public-funding process.

The Answer That Determines the Future

The question, then, is not whether climate innovation exists; rather, it is whether it is effective. It is without question. The polemic is whether society is prepared to fund climate innovations that protect human survival with the same urgency as those that safeguard business operations.

Municipalities, counties, and state agencies are tasked with safeguarding water, soil and air; yet, public funding cycles often prioritize immediate crises over slow-burning threats. Tourism boards rely on restored lakes and healthy ecosystems, yet rarely have the budget authority to invest early. Agricultural departments rely on resilient soil, yet their funding models prioritize short-term yields over long-term regeneration. Responsibility is diffused across institutions, so that no one bears the full responsibility to allocate resources.

This is where climate tech faces its greatest challenge and where corporate and public leadership must step forward. Preventive climate action needs its equivalent to the payer system that supports access to health care. Blended finance, climate resilience bonds, public–private partnerships and impact investment models can help fill the gap. Policy can make restoration and resilience non-negotiable long before crises mature. Communication can transform the invisible and delayed into the immediate and owned.

The innovators showcased in the Galien Foundation EcoHealth dialogue offer a roadmap. Their work illustrates that climate technologies are not abstract “science fiction” climate solutions; they are the infrastructure of human continuity. They restore the systems that allow communities to thrive, and they ensure the global economy has the stable environmental foundations it requires.

The future of climate tech equity will be defined by whether society chooses to treat environmental health with the same seriousness as business operational resilience. Without an answer to who pays, one side of the climate tech industry will continue sprinting while the other waits for the world to catch up.  

The post The Climate Tech Paradox: Innovation Surges, But Who Pays? appeared first on Medika Life.

We are a species enamored with technology. I count myself among the early adopters who eagerly embraced gadgets promising to reshape how we live and heal. I owned an Apple Newton decades before smartphones became second nature. I strapped on a Fitbit long before step-counting became a cultural norm. I’ve carried an AliveCor device to track my heart rhythm because I care about heart health.

I’m fascinated by AI and impressed by the capabilities of ChatGPT and other large language models. But through all of it, a pattern emerges: while we sprint forward with healthtech innovation, we often overlook the element that gives health its soul—human empathy.

Hollywood’s visions of the future play out that tension. Films like iRobot and Outside the Wire imagine a world where advanced technology surrounds humans. Yet even in these hyper-connected futures, the human spirit, judgment and emotional resonance triumph over computation. Tech may win the movie battle, but empathy wins humanity’s greatest challenges.

So, the question becomes: in our real love affair with technology, how do we ensure that it doesn’t diminish our humanity but enhances it?

Digital Health’s Expanding Horizon

Health delivery is at a crossroads. On one hand, we are real-time participants in a sea-changing Cambrian explosion of innovation—AI models reading radiology scans, wearable devices transmitting real-time health data from patient to provider, voice assistants aiding mental health, AI tools “scraping” data from EMRs and predictive algorithms alerting physicians to early signs of disease. On the other hand, the average patient still struggles to schedule an appointment, reach a care coordinator, navigate their health insurance coverage or feel heard during a clinical encounter.

Digital health evolves faster than we can emotionally and ethically integrate into the care journey. As Hal Wolf, CEO of global NGO HIMSS, said in a 2025 HIMSS Europe keynote, “Old Organization + New Technology = Expensive Old Organization.” The implication is clear: technology alone is not transformation. It is a tool that is only as effective as the system and the people who apply it with purpose.

The Role of Emotion in Healing

John Nosta, a leading innovation theorist and founder of NostaLab, explores health decisions’ emotional and cognitive complexity in his essay Unraveling the Human Mind. He highlights the importance of understanding emotions like joy, guilt, and envy in AI development, reminding us that behavior is not driven solely by data but by deeply human emotional processes. This insight is critical: health behaviors—whether quitting smoking, taking medication, or attending a follow-up appointment—are deeply influenced by fear, hope, anxiety, trust and love.

AI can surface patterns, but it cannot feel. It can summarize a patient’s medical history, but cannot detect the tremble in their voice when they say, “I’m scared.” We must not confuse intelligence with insight or information with understanding.

Empathy as a Design Principle

For health tech to reach its most significant potential, we must design systems that understand and adapt to the human condition. That means:

Unfortunately, many current implementations miss the mark. A chatbot that dismisses a patient’s concerns with canned responses doesn’t reflect innovation—it reflects embedded institutional indifference. An app that tracks glucose but fails to create bridges to a community of support doesn’t reflect progress—it reflects a missed opportunity for connection and adherence.

The Power of AI and LLMs

Tom Lawry, former National Director for AI in Health & Life Sciences at Microsoft, captures this idea succinctly: “AI can automate highly repetitive activities and augment activities that are more highly varied and require a higher level of problem solving.” This duality reflects the essence of digital health transformation—technology that supports human expertise, not replaces it.

When used with intention, large language models like ChatGPT can democratize information, translate medical jargon, and surface unseen risks. They are powerful co-pilots in care. But they lack the capacity for compassion and their accuracy rests solely with the people designing the system.

I’ve often said that communication is the currency of care. That truth remains. The best outcomes emerge not from algorithms alone but from conversations between doctors and patients, health systems and communities, policymakers and people. Technology can facilitate these conversations, but it cannot replace them.

Leadership Needed

This is a call to the architects of our health future: More than “Don’t forget the patient, the caregiver, or the person behind the data,” people must be at the center of the action. We must remember that we are always people, only sometimes patients. Therefore, we need leaders—inside industry, government, academia and advocacy—who understand that digital transformation must be human-centered.

At the intersection of health and technology, we need humility. We need to listen more, build thoughtfully and measure success not just by the speed of a process or precision of a model, but by the quality of life it helps sustain and save.

Reimagining the Health-Tech Love Story

Our fascination with technology isn’t wrong. It’s natural. Much like the cinematic heroes in iRobot and Outside the Wire, we are drawn to machines’ capabilities but are also reminded that their greatest strengths emerge when paired with human insight. In those stories, it is not the tech that ultimately saves the day but the human instinct for empathy, ethics and engagement.

Progress is essential. But as we reimagine the health ecosystem, we must write a new love story—one where technology and empathy are co-stars, not competitors. One where innovation is in service of intimacy, and the promise of AI is fulfilled not in clinical outputs, but in human outcomes.

As someone who has watched health tech innovation unfold through the decades—from Apple Newton to neural networks—I remain hopeful. But hope, like health, is a human trait. It can’t be programmed. It must be lived.

Let’s remember that healing is not just about fixing bodies. It’s about touching hearts. Let’s code for that.

The post Human First: Reclaiming Empathy in Our Love Affair with Health Tech appeared first on Medika Life.

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.

Q&A

Gil Bashe: You called Converge\OIST the “very first gathering.” Why did Okinawa feel like the right birthplace?

Yasushi Yamamoto: Yes, this is the very first gathering, and we named it Converge\OIST because I’m a big fan of ‘OIST’—the context of the birth, this location, these people. It was the right place and people, a great gathering, and a pleasure to meet old friends in such a beautiful, inspiring place.

Gil Bashe: Your career bridges Tokyo boardrooms and Jerusalem start-ups. Where did your obsession with “convergence” begin?

Yasushi Yamamoto: Innovation cannot be done in an isolated form; it should be done in collaboration with various fields. Professionals with beautiful résumés in Tokyo surround me, but many lack a broader vision. They are so good at something particular, yet it’s a pity they’re busy in silos. I saw the lack of collaboration and started my business, raising money from Japanese corporations for Israeli start-ups. That contrast—dinosaurs with big systems but little ‘challenging spirit’ versus entrepreneurs who ‘run and fix’—motivated me to build synergy between powerful pieces.

Gil Bashe: Modern medicine seems to multiply silos every year. How do you see convergence breaking that pattern?

Yasushi Yamamoto: Medicine has become hyper-specialized. We have gastroenterologists who only look at the upper esophagus or the colon, cardiologists in electrophysiology, and neurologists focused on one nerve pathway. They perfect an art, but they have blinders. Convergence is breaking down those walls.

Gil Bashe: Inviting violinists and AI ethicists to the same podium can feel radical. How did people react when you pitched this mix?

Yasushi Yamamoto: People would never believe me if I hadn’t done serious work in the previous decade. Thanks to that track record, we built trust. Gathering in Okinawa sounded out of context for many professionals, but it wasn’t curiosity but trust that made them come.

Gil Bashe: Every July, you disappear into Kyoto’s 1,200-year-old Gion festival to play the traditional Japanese flute. What does a month of music teach a CEO?

Yasushi Yamamoto: Back home, I’m participating and serving. When I set up my company, I realized it would never be greater than this festival. The experience makes me humble. I received a baton from previous generations and must pass it on to the next. After that month, I ask, ‘Two generations later, how will young people judge the work I’m doing now?’

Gil Bashe: You’ve spoken of building on three “wheels”: science, art, and philosophy. Where is Corundum on that journey?

Yasushi Yamamoto: We started in hardcore science and investment, then gradually expanded to art—like Leonardo da Vinci, artist and scientist in one person. In the coming three to five years, I will put the vehicle of philosophy on top. Combining great minds and spirit, we can create something AI alone cannot deliver.

Gil Bashe: What tangible outcomes do you want from Converge?

Yasushi Yamamoto: First, I want to support OIST, an institution I love. We held the first event there; followed by South by Southwest London. I want more gatherings in multiple locations, bringing talented people with good hearts.

Gil Bashe: You’ve set up subsidiaries for neuroscience, virtual mixed human-data AI, and the microbiome. Why those intersections?

Yasushi Yamamoto: Think of the gut–brain axis. Discovery comes from interaction: AI power, system biology, and the microbiome. Add the element of art to inspire other curious, intelligent people, and the community expands.

Gil Bashe: Food as medicine used to be folk wisdom; you’re turning it into data science. How?

Yasushi Yamamoto: We invested in a project from the Weizmann Institute—the deepest phenotype cohort, hundreds of people over 20 years with genes, metabolites, behavior, nutrition. We link ancient wisdom to ultra-modern science by layering AI on that dataset. We are converging the past, the future, and current ways of life.

Gil Bashe: Philosophy sounds noble, but ventures need cash. How do you square capital with conscience?

Yasushi Yamamoto: I strongly believe in setting vision on a solid philosophical idea, but also in the power of capital. Our job is to propose a hypothesis, bring capital, deploy people, and prove the hypothesis with action. So, we’re raising our next venture fund while creating the Corundum Convergence Institute, a U.S. 501(c)(3), as an alternative financing model to advance science.

CLOSING THOUGHTS

Yamamoto-san reframes the entrepreneur’s impossible triangle—mission, money, and meaning—into an orchestral score. Science provides the bass line, art supplies melody, philosophy sets tempo, and well-deployed capital funds the concert hall. As Converge expands from Okinawa to London and beyond, its founder is betting that harmony, not hierarchy, will unlock the next era of precision health.

The takeaway is disarmingly simple for the rest of us: when great minds tune their instruments to work in harmony, the walls separating our disciplines start to fall—and patients everywhere will hear the music of life-sustaining innovation.

According to Gil Granot Mayer, Executive Vice President, Technology Development & Innovation at OIST:

“In just two days, we managed to connect people from different disciplines and geographies, immersing them in the OIST spirit and Okinawa’s culture. From understanding the value of the long tail to different approaches to improving life through the Human Phenotype Project, or the understanding of a new aging mechanism associated with cell membrane damage. I hope that these new connections and cutting-edge talks will spark new collaborations and great results.”

The post Blending Renaissance Thinking and Collaborative Power to Address Global Health Challenges appeared first on Medika Life.

—Gil Bashe, “Global Marketing Strategies” and “Emotion: The New Brand Integrator,” Pharmaceutical Executive, 2000

Twenty-five years ago, in a series of articles for Pharmaceutical Executive that may have seemed radical at the time, I argued that successful marketing wasn’t built on features but feelings. Back then, people were skeptical. “Emotion?” they responded. “We’re here to sell solutions.” Yet, as I revisit that article from the perspective of today’s fractured health landscape, I realize just how prescient that core message was.

In 2000, I wrote, “A brand must reflect the soul of the company. It must reflect its leadership and people’s beliefs, philosophies, and practices.” That truth remains, but in today’s health sector—beset by cost crises, consumer distrust, and system complexity—the soul of the brand must go even deeper. It must speak to human experience. It must unite the head (facts), heart (feelings), and gut (intuition) to unite the five pillars of the care community: patients, payers, product innovators, policymakers and providers.

The brands that do this don’t just survive, they lead.

The Head: Anchor in Truth, Lead with Clarity

We live in an era of data deluge. The health industry is drowning in numbers, from EMR systems to clinical trial dashboards. Yet many brands still mistake data for direction.

Yes, the head—facts—matter. Health is a science of logic, science, and proof. But it is also an art.

In my original article, I noted that the “hallmark of a strong brand is clarity—a clear promise, consistently delivered.” In health, clarity is more than a brand virtue; it’s a money and mission obligation. Patients need clarity in order to make life-altering choices. Providers need clarity in order to correctly apply new technologies and administer novel treatments. Payers need clarity so that they may judge value and outcomes.

A brand that leads with the head communicates what it does and why it matters. The science, the evidence, the safety profile: these aspects of health products are essential. But they are not enough.

I wrote in 2000, “Even the most successful product will not remain so without continuous reaffirmation of its value and identity.” It’s still true today, but that reaffirmation must be human, not simply clinical.

The Heart: Where Value Becomes Meaning

A quarter-century ago, I argued that “emotional connection” was key to global brand success. In 2024, I echoed that idea, stating that empathy is a strategic imperative, not a “soft skill.” Writing in Medika Life, I asserted that “Empathy—the ability to sense and connect to another’s experience—has clinical consequences.”

Health isn’t delivered in abstracts. It’s experienced in human moments: a nurse’s tone of voice, the wait time for an appointment, a doctor’s bedside manner. Patients remember how they felt, not what was said.

The same is true of brand impressions. A health brand’s heart is measured by its humanity: how it listens, responds, and affirms the lived experience. Consider the rise of narrative medicine, patient-centered care design, or trauma-informed policy. These are not trends. They are a return to what medicine truly is: a human endeavor.

In 2000, I wrote that “People buy brands because they trust them and because those brands represent a relationship.” It’s never been more true. That relationship must be emotional. If we don’t move hearts, we will never move health.

The Gut: The Compass for Courage and Change

If the head is what we know, and the heart is what we feel, then the gut is what we sense. It’s instinct informed by experience. It’s the courage to take a stand when the data is inconclusive. It’s also the discipline to say no when a decision doesn’t align with the brand’s soul.

In 2000, I observed that “Global brands are built not just on strategy, but on intuition—on understanding the culture and values of the people they serve.” That same intuition now guides how we engage health audiences. Do we sense distrust? Fear? Exhaustion? Our gut tells us when a message is too technical, dense or transactional to resonate. It urges us to simplify and re-center on the human.

Great leaders trust their gut because it helps them detect the intangibles: tone, timing and truth. In brand leadership, that same sense keeps us authentic.

Reuniting the Quintet: Patients, Payers, Product Innovators, Policymakers and Providers

Today’s health ecosystem is fractured along functional lines. Patients seek access, providers seek time, and payers seek value. Too often, they work in silos, leaving innovation and empathy at the margins.

But brands can be bridges. When built with head, heart, and gut, they become platforms for unity.

I wrote in 2000 that the “challenge is to ensure that everyone in the organization consistently communicates the brand through behavior, not just brochures.” That principle is now essential in aligning care delivery. Health brands must operate across disciplines, sectors, and even continents, but always with a singular message: we see, hear, and serve you.

Whether you’re a Medicaid insurer, a diagnostics company, or a telehealth platform, your brand is a promise. And that promise must connect the person in the exam room with the person writing the policy.

The ROI of Human Experience

In 2025, health leaders face dual pressures: cut costs and elevate care. This seems like a paradox, but it’s not. Investing in human experience is not a detour from efficiency; it’s the gateway to it.

Empathy reduces readmissions, clear communication improves medication adherence, and trusted brands drive engagement. When we center on people, we improve systems.

Put simply, mission and money must align. One cannot exist without the other in sustainable health ecosystems.

Final Thought: A New Brand Equation

As I wrote in Global Marketing Strategies 25 years ago, “A brand is the product of what people feel, not just what they see.” That message, once contrarian, is now the compass.

The future of health brands is in the hands of those willing to embrace complexity with clarity, wield emotion with discipline, and make instinct an asset, not a liability. In short, the best brands will speak to the head with intelligence, the heart with empathy, and the gut with courage.

In an age when trust is currency and gaining attention means cutting through the information jungle, this is not just good branding. It’s savvy mission-centered business leadership.

The post Beyond Data: Why Human Decisions Are Shaped by Facts, Feelings—and the Fire Within appeared first on Medika Life.

Real-world evidence (RWE) captures health data from outside controlled clinical trials, such as wearable devices, patient forums, electronic health records, and symptom tracking apps. It reflects the daily realities of individuals managing chronic conditions, navigating medications, and seeking answers when the system fails to provide clarity.

As Yael Elish, one of the founders of the mega crowdsourcing transportation platform WAZE and now CEO and Founder of the patient RWE platform StuffThatWorks, has said, “Patients will never have a seat at the table until their data is considered part of the navigational guide for new medicines, devices, and indications of use.”

She’s right, but perhaps we must push ourselves to a new level of acknowledging why we are pursuing new approaches to care. This is not just about offering patients a seat at the allegorical table. It’s about acknowledging that the table of discovery and development exists for their benefit. The science, systems, and research all exist to serve their needs. Their voices and their lived experiences must inform the path forward.

A Silent Majority Speaks: The Data We Ignore

A recent survey conducted by StuffThatWorks, which gathered insights from more than 15,000 patients across 145 medical conditions, revealed a staggering disconnect. 92% of patients expressed a willingness to participate in clinical trials. Yet, 84% said they had never been asked, never invited to the table of health innovation.

This isn’t a communications oversight—it’s a justice issue. The system is failing those it claims to serve. Patients are ready to contribute. Some are out of options and want concrete action steps. They are waiting to be seen, heard, and engaged. Their personal care and experiential data they offer doesn’t just enrich our understanding—it recalibrates it.

“The Pharma Industry’s efforts to include patient voices have been a step in the right direction; however, they remain on a very small scale today,” notes Caroline Redeker, chief strategy officer at Advanced Clinical, a leading clinical research organization. “Interacting with a minimal number of patients without scale does not accurately represent the full patient population with the condition.  Using available static data (claims, EMR) detached from the patient covers the ‘what’ of patients – how many, where, with claims or treatments of a condition, and mostly in the US.”  

Redeker adds: “The more important factors in trial design include the ‘why,’ including most bothersome symptoms, effectiveness of treatments, regional differences, comorbidity considerations, and other valuable information to accurately design the right trial.  The ‘why’ information can only come directly from the patients.  The future will bring patients and their organized data to the table and include patient insights from all over the globe.”

Real-World Realities: Lessons from Ehlers-Danlos Syndrome

Consider Ehlers-Danlos Syndrome (EDS), a connective tissue disorder with 13 recognized subtypes, ranging from the more common hypermobile form to the rare, life-threatening vascular subtype. Each type carries a distinct constellation of symptoms and risks. Some people face chronic dislocations and mobility challenges, while others live with the daily fear of spontaneous organ rupture.

A single disease name masks and encompasses a spectrum of experiences. Standard clinical trial models often can’t keep up with that complexity. But thousands of patients with EDS are documenting their treatment responses, flares, and management strategies across RWE platforms such as StuffThatWorks—building a picture of this disease that’s as varied, inclusive and individualized as the people living with it.

“The StuffThatWorks survey expands the usual definition of real-world data to include this novel direct patient-inputted database,” says George Sands, MD, FAAN, FAHA, FACC, a former senior director at Pfizer and noted advocate for collaborating with patient communities  “Patients and patient advocacy groups want to partner with sponsors, whether pharma or academia, and to be fully represented in research endeavors. This is very different from sponsors looking to recruit and retain research participants more fully. This is about collaborating.” 

Dr. Sands adds, “Additionally, it is important to have all different patient populations, including people of color, so their data is included in the trials and can be used for clinical care. Otherwise, clinicians have to extrapolate based on those included in the trials; it doesn’t always work exactly.” 

That kind of data, born of day-to-day life, isn’t just complementary to clinical trials. It completes a fuller picture essential for developing effective treatments. Without it, we risk designing therapies that help in theory but fail in practice.

From Inside-Out to Outside-In

Much of medicine’s traditional approach to research is inside-out, starting with what companies aim to prove and working outward through the trial process. That’s not inherently wrong—it is how rigorous science is structured—but that is not enough.

We must also embrace outside-in realities as part of the innovation journey, where patients’ lived experiences inform what we explore, how we measure success, and who we prioritize.

Craig Lipset, founder of Clinical Innovation Partners, and co-chair of the unifying NGO/research industry Decentralized Trials & Research Alliance, and former Head of Clinical Innovation at Pfizer, has long championed a rebalancing of power in clinical trials.

“To earn and maintain trust, research participation must become a symbiotic relationship powered by a bidirectional flow of data and learnings, says Lipset. As patients share insight into study design, bring real-world data into trials, and share experience that shapes new endpoints, sponsors must commit to ensuring that study data and results are flowing back.”

That vision is becoming increasingly possible. With technologies that enable decentralized trials, remote monitoring, and electronic patient-reported outcomes, we can now collect real-world data at scale. However, that only matters if we respect what the data tells us and are willing to let patient experiences guide the research agenda. We give lip service to the importance of patient voice in biopharma research and marketing, but do we see them as “customers” of innovation?

The Science of Listening

Real-world evidence is the patient’s language—a narrative built not in labs but in lived experience. It’s not peripheral to science—it is science when equity is the goal. When patients share their stories, whether on online platforms, through wearables, or in digital symptom trackers, they are offering far more than an anecdote. They are offering insight. They contribute to the science of what works, for whom, and under what conditions.

And when we listen, we don’t just become better researchers. We become better healers.

We stand at a pivotal moment. Innovation can either deepen the divide between patients and science or bridge it. Real-world evidence is the support structure for that bridge. It offers us the opportunity to democratize discovery and reimagine research collectively.

Reflects StuffThatWorks Elish, “The current methods of involving patients in the clinical and health spaces are doomed to failure.  Patients’ voice will be heard ONLY when turned into organized data, at scale.  Because no one can argue or ignore organized data at scale. Once that’s accomplished, the integration of patient voice will proliferate and become easily accessible to everyone in multiple forms, including a simple AI-based chat question.  We are doing that at StuffThatWorks; it’s core to our mission and vision.” 

Ultimately, the patient wants to be invited to the table—but not as a token guest. Without them, there is no table worth setting. The patient is not a peripheral player in this system—they are the system’s purpose. They are not guests in the system. They may not be the core customer of the health system, a universe that centers around its economic viability. Still, in the world of health system kinetics, where we examine the relationship and priority of the sector pieces, they are the reason we unite to invent solutions to confront disease, despair and death.  And the future of medicine depends on how well we recognize that truth.

The future of medicine hinges on one choice—do we continue treating patients as passive subjects, or as co-creators of the care that might save their lives? That’s not philosophy. That’s leadership.

Interested in this topic – here is more to explore:

Aman Gupta: Patient Inclusivity: The Missing Piece In Pharma’s Global Innovation Strategy

The Need for High Quality, Reliable Information that is Data-Driven – A Conversation with Yael Elish

Gil Bashe: Real-World Evidence Unlocks Consumer Voice to Improve Care

The post How Real-World Evidence Proves the Power of Patient Engagement appeared first on Medika Life.

Security, Migration, and Budget Pressures

A confluence of political and fiscal forces is accelerating this shift. Europe’s strategic priorities have evolved, driven by concerns ranging from war and migration to domestic economic strains. Many governments feel pressure to divert funds toward defence and security amid Russia’s war in Ukraine and other threats. In Britain, for example, leaders explicitly tied an aid rollback to military needs: Prime Minister Keir Starmer vowed to boost defence spending to 2.5% of GDP and fund it by cutting the aid budget from 0.5% to 0.3% of national income. “National security must always come first,” Starmer said, framing the cut as a painful necessity in a “dangerous new era”.

Curbing immigration is another powerful motivator. Italy’s Prime Minister Giorgia Meloni, elected on a hard-right platform, has bluntly rejected the notion of altruistic aid in favour of deals that keep migrants from leaving Africa. “What needs to be done in Africa is not charity,” she declares. “What needs to be done in Africa is to build cooperation and serious strategic relationships as equals, not predators”. For Rome, that means investing in African infrastructure and economies (dubbed the “Mattei Plan”) to create jobs in migrants’ home countries — and securing Italian energy and business interests — rather than simply writing checks. Other governments in Europe’s north echo this tougher line: the Netherlands’ new ruling coalition plans to trim aid by 2029 while “prioritising the interests of the Netherlands,” shifting funds toward domestic migration control and trade promotion.

At the same time, budget constraints and surging nationalist politics have made foreign aid a prime target for cuts. The populist refrain of “charity begins at home” has grown louder amid economic uncertainty, pandemic debts, and inflation. Even in France — historically a champion of development aid — the government quietly backtracked on a legally enshrined promise to reach the U.N.’s 0.7% aid spending target by 2025. Facing pressure to reduce deficits, President Emmanuel Macron’s administration postponed the 0.7% goal to 2030 and slashed next year’s aid budget by over one-third. A €742 million reduction in 2024 was followed by plans for a further 37% cut (more than €2 billion) in 2025. Such steep cuts, unprecedented in modern French policy, were justified as tough choices in a tight fiscal environment — though critics called it a betrayal of France’s global commitments. Likewise, aid has been swept up in a broader fiscal odyssey in Germany. After a constitutional court ruling forced Berlin to reallocate spending, the development ministry’s 2024 budget was pared down by about 8% (roughly €940 million) compared to the previous year. Germany’s humanitarian relief budget also dropped about 10%. These reductions make it unlikely Germany will maintain its recent 0.7% GNI aid level.

European Aid Budgets in Retreat

The result of these pressures is a marked pullback in many European aid budgets — a trend that spans both EU member states and neighbours like the UK and Switzerland. Recent moves include:

• United Kingdom — Once a leader in aid, the UK has reversed course. It first lowered its long-held 0.7% of GNI aid commitment to 0.5% in 2021 and now plans to sink to just 0.3% by 2027 to free up billions for defence. Aid groups warn this will drag UK assistance to its lowest share of national income in generations, a “short-sighted and appalling move” that will “undoubtedly risk lives,” according to UNICEF.
• France — After years of incremental increases, France is making an abrupt U-turn. The 2025 budget envisions a 35% cut in official development assistance, delaying ambitions to scale up programmes. Lawmakers in Paris concede domestic needs and security priorities are eclipsing foreign aid — a stark change for the world’s fourth-largest donor, which in 2023 still spent €13.9 billion (0.48% of GNI) on development.
• Germany — The eurozone’s largest economy is trimming aid amid belt-tightening. Germany’s 2024 federal budget reduced core development spending to €11.2 billion, about 7–8% lower than in 2023. Humanitarian and crisis aid saw even sharper declines. Further cuts are on the table for 2025 as Berlin prioritises energy price relief and defence.
• Belgium — A new coalition government in Brussels has agreed to cut development cooperation funding by 25% over five years. Belgium’s aid agency has sounded the alarm, with Enabel CEO Jean Van Wetter warning that “disinvestment in international cooperation is a poor decision in our interconnected world” and will undermine Belgium’s global influence.
• Switzerland — Historically, Switzerland has been a steady donor, but the Swiss government is also scaling back. It approved a CHF 110 million reduction in its aid budget and plans to shut down or hand off development programmes in at least three countries (Albania, Bangladesh, and Zambia) by 2028. Swiss officials argue that resources must be focused on fewer priorities as part of a wider cost-cutting drive.
• Italy — Italy’s aid budget has not seen dramatic cuts, but its focus has pivoted under Meloni’s leadership. Rome is redirecting funds toward projects that dovetail with Italy’s geopolitical agenda — chiefly stemming migration. Italian ministers talk of “investment, not charity” and have struck deals, for instance, to finance development projects in Tunisia in exchange for cooperation on keeping migrants from crossing the Mediterranean.

Notably, this contraction is Europe-wide. A recent review tallied seven European donor governments announcing major aid reductions or reallocations in the past year. The collective EU aid effort is sliding: EU institutions and member states gave 0.51% of GNI as aid in 2023, down from 0.56% the year before. In a mid-2024 budget review, the EU reallocated €2 billion of its external aid fund into migration and refugee support — effectively a 7.5% pro-rata cut to other development programmes. As one analyst bluntly summed up, “The door is just closing on aid everywhere we look.”

From Grants to “Blended” Finance

Beyond budget cuts, Europe is fundamentally changing how it delivers whatever aid remains. Rather than simply funding government budgets or health clinics in poor countries, European donors are channelling money into financial instruments — loans, equity stakes, guarantees — that attract co-investors and, ideally, pay for themselves. The buzzword is “blended finance,” which means using a small amount of public or aid money to unlock a larger pool of private capital for development projects. In theory, everyone wins: poor countries get more investment than aid alone could provide, while investors (including European development banks) get risk cushioned by public funds.

All across Europe, aid agencies have been refashioned as mini-development banks. The UK’s famous aid department has been folded into the Foreign Office, and its once grant-focused bilateral programmes are diminished. Instead, Britain is leaning on British International Investment — a government-owned DFI (development finance institution) — to finance projects from renewable energy in India to tech start-ups in Africa, expecting modest returns. France’s Agence Française de Développement (AFD) has likewise expanded its lending, often via its private-sector arm Proparco, under what President Macron calls a “policy of results” approach. “The ambition of this strategic plan is [for AFD] to become a platform for development policy,” Rémy Rioux has said, describing AFD’s evolution beyond traditional aid. AFD now provides billions in low-interest loans for infrastructure and climate programmes, blending French funds with multilateral and private money.

Germany’s KfW Development Bank and its investment subsidiary DEG follow a similar model, financing everything from solar parks to microfinance institutions in developing markets. Even smaller donors have set up investment vehicles — Switzerland’s SIFEM fund, for instance, takes equity stakes in emerging market SMEs. Increasingly, European aid is less about writing checks than structuring deals. As Rioux explains, “Development financing is undergoing a major transformation… Investment has another advantage: it’s built for the long term. It creates lasting partnerships, allows us to track tangible impacts, and demonstrates returns… far more effective and convincing than traditional public aid”. In his view, and that of many peers, mobilising “sustainable resources” through investment is the only way to meet 21st-century challenges as government grants stagnate.

Critically, Europe’s new approach isn’t just about altruism — it’s about mutual gain. Donor governments are so unabashed that they expect strategic payoffs. “International cooperation is not just an act of global solidarity,” says Enabel chief Jean Van Wetter, whose Belgian agency increasingly ties aid to domestic interests. “It is a strategic investment that will bring numerous benefits to Belgium, its businesses and its citizens… By encouraging stability, growth and sustainability in partner countries, Belgium strengthens its own security, economy and international reputation”. This “good for them, good for us” philosophy now permeates European development strategy. Nowhere is it clearer than the European Union’s flagship Global Gateway initiative — a €300 billion plan unveiled in 2021 to fund infrastructure in Africa, Asia, and Latin America. Billed as Europe’s answer to China’s Belt and Road, Global Gateway explicitly seeks “mutually beneficial partnerships” that serve development needs and boost the EU’s strategic autonomy. Projects range from African internet connectivity (benefiting EU telecom firms) to renewable energy grids that could one day supply Europe. “We are moving away from traditional development to mutually beneficial partnerships,” the EU’s development commissioner’s office said, underscoring that the old donor-recipient dynamic is being replaced with joint ventures.

Ripple Effects on Poor Countries

Europe’s pivot has profound implications for countries on the receiving end. In the short term, budget cuts are already being felt in vulnerable communities. Programmes that tackle poverty and disease — but yield no financial return — face an uncertain future. Global health initiatives, in particular, are reeling. Several of Europe’s biggest aid donors have been mainstays of funding for vaccines, HIV treatment, and health systems in Africa. Now, those budgets are shrinking just as need remains high. “Some of Europe’s biggest global health funders are slashing their aid budgets, which health groups fear could spell catastrophe for countries reliant on foreign cash to combat malaria, HIV, tuberculosis,” reports Euronews. Because Europeans are turning inward, health programmes that saved millions of lives may lose support. In 2023, about 10% of European ODA went to global health. Still, going forward, that share must compete with climate projects and private-sector loans for a “shrinking pot of money”. “Many lives are at stake,” warns Dr Michael Charles, head of a major anti-malaria partnership, describing the situation as “quite dire” in countries where donor-backed health services are now at risk.

Lower-income countries could also struggle to attract the kind of private investment Europe is now favouring. The pivot to loans and equity tends to favour middle-income states or commercially viable ventures — where investors see a reasonable chance of returns. Poorer nations, or social sectors like basic education, may be left behind because they offer little profit. Aid advocates note that European funds are flowing increasingly to regions of strategic interest (for example, North Africa for migration control or Ukraine, which alone absorbed nearly €19 billion of EU institutions’ aid in 2023 (Donor Profile: EUI)). Meanwhile, the share going to the least-developed countries has been “trending downward since 2017”. If this continues, the world’s poorest countries may face a double blow: less grant money and limited access to investment capital. Those who do take on more loans could risk new debt burdens down the line. “We have to ensure no one is left behind as we shift to finance and investment,” cautions one development official, noting that purely market-driven aid could bypass fragile states that need help most.

On the other hand, some developing nation leaders welcome the rhetoric of partnership over patronage. African governments have long bristled at the demeaning connotations of “aid” and have called for “trade, not aid” for decades. They see opportunity in Europe’s investment pivot — if it delivers real infrastructure and business growth. In their view, being treated as an investment destination, not a charity case, is a step toward equality. However, they also emphasise that partnerships must be genuine. At a recent EU-Africa forum, several African presidents pointedly rejected mere “EU charity”, saying Europe should address structural imbalances in trade and invest in African value chains rather than offer handouts as a way to buy political favours. In practice, the jury is still out on whether Europe’s new model will benefit developing nations or mainly serve Europe’s interests.

A New Hybrid Model — End of Aid as We Know It?

Is this the end of traditional aid? In many respects, yes. Europe’s development assistance is becoming inseparable from its economic and geopolitical strategy. Whereas 20th-century aid often aimed to foster development for its own sake — rooted in post-colonial moral duty or Cold War diplomacy — 21st-century aid from Europe is increasingly transactional. Grants with no strings attached give way to loans, equity investments, and deals tied to policy conditions (migration management, economic reforms, climate goals). The old model of wealthy nations simply donating money is fading. “The old model of public development aid is disappearing and must be replaced by sustainable and inclusive investment,” says AFD’s Rémy Rioux, who argues that virtually all stakeholders now “agree that we need to rethink the model”. European officials often bristle at the word “aid” altogether. They prefer terms like “cooperation,” “partnership,” and “investment.”

Yet this is not so much an end as an evolution into a hybrid model. Europe isn’t abandoning poorer countries; it is just engaging on different terms. In place of one-way charity, it envisions joint ventures — what one Belgian policy paper calls “reciprocity-based development”. Even as budgets tighten, Europe is leveraging other tools to stay involved abroad: development banks, venture funds, risk guarantees, and diplomatic agreements linking aid to trade. In effect, official development assistance is blended with foreign and commercial policies. It’s no coincidence that the UK merged its aid agency into its diplomatic service or that the EU’s development projects now fall under a strategy explicitly tied to European industrial and security interests. As the European Council concluded its next budget, the goal is to “ensure the [aid] budget advances the EU’s strategic priorities, which are increasingly shaped by domestic interests such as competitiveness, access to raw materials, migration, and security.” This signals a permanent change in mindset.

Whether this new approach can deliver positive results for developing nations remains an open question. Optimists argue that by making development cooperation more about business and mutual gain, Europe will sustain political support and unlock larger pools of money than stagnant aid budgets could. They point to initiatives like Global Gateway and say that if Europe invests smartly in emerging economies, it can help build sustainable industries (from African solar farms to Southeast Asian supply chains) that benefit everyone. Sceptics, however, worry that something fundamental is lost when self-interest justifies aid. There are fears that vital but unprofitable work — fighting extreme poverty, tackling malnutrition, bolstering primary healthcare — will fall by the wayside. They note that global pandemics or climate change require outright grants and global solidarity, not investments that expect a financial return.

European officials insist they are not retreating from global development, just modernising their approach. “France is not stepping back from its international role,” Rioux insists, citing Europe’s $150 billion collective development contribution — roughly three times the U.S. level. But he and others acknowledge the need to “build a more resilient and efficient model” that can withstand domestic political winds. That model increasingly blurs the line between aid and business. It treats poorer countries less as beneficiaries and more as partners — or, in some cases, markets. The Wall Street Journal once dubbed this trend “aid as investment”, and today it’s an apt description of Europe’s new paradigm. Traditional aid may not be entirely dead, but it has undeniably been subsumed into a broader strategy of strategic partnerships.

As Europe resets its development playbook, the world is watching to see if this grand experiment produces genuine development — or if “mutual benefit” mostly benefits the donor. For millions in Africa, Asia, and beyond who have depended on European aid, the hope is that this new era will bring a different rhetoric and tangible progress. If Europe’s investments can drive growth and stability in poorer nations while satisfying European taxpayers, it could herald a new global development model for the 21st century. If not, retreating from traditional aid could leave a void that other powers — or crises — will fill. The only certainty is that Europe’s role in international development is changing profoundly, in real-time, trading in the old charity mindset for something more hard-nosed and, it believes, sustainable for the long haul.

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