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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Artificial Intelligence (AI) is rapidly reshaping public health — from enhancing disease surveillance and diagnostics to easing workforce burdens — but it also raises complex risks and ethical questions. In Europe and globally, public health leaders are grappling with how best to harness AI’s revolutionary potential while managing its pitfalls. After decades of experience, many recognise that AI is not a magic fix for health challenges; its value depends on thoughtful integration into health systems. This article provides an in-depth review of the current relationship between AI and public health. It examines the opportunities it offers, real-world innovations already underway, practical implementation challenges, and the risks and governance frameworks that must guide responsible use. All discussions equally consider European contexts (including emerging EU regulations) and broader global health perspectives.

TL;DR Summary

• AI’s growing role in health: Artificial intelligence is increasingly used to augment public health efforts — from automating administrative tasks to advanced disease surveillance and diagnostics — offering new ways to improve efficiency and reach.
• Tangible benefits observed: Early deployments show promising results. AI tools have reduced clinicians’ paperwork burden, flagged outbreaks days before traditional systems, and enhanced diagnosis in low-resource settings (e.g. catching 15% more TB cases via X-ray analysis).
• Innovations across sectors: NGOs, governments, and companies are all investing in AI for health. For example, PATH and others use AI in field programmes, the NHS has dozens of AI pilots improving care delivery, and pharma companies leverage AI to speed up drug and vaccine development.
• Practical hurdles remain: Successful implementation requires robust data infrastructure, interoperability, and high-quality data. Many health systems must modernise IT systems and address data silos and quality issues before AI can perform optimally.
• Human factors are critical: Integrating AI into workflows and gaining staff acceptance are significant challenges. Training health workers, providing explainable outputs, and maintaining human oversight are essential to building trust in AI-assisted care.
• Key risks to manage: AI in public health brings serious risks — privacy breaches, algorithmic bias harming disadvantaged groups, opaque “black box” decisions undermining trust, and AI-generated misinformation spreading false health advice. Over-reliance on AI without safeguards can also be dangerous.
• Ethics and governance frameworks: Clear principles and regulations are emerging to guide responsible AI use. WHO’s six ethical principles (e.g. transparency, equity, accountability) set value-based guardrails, while the EU’s AI Act will enforce strict requirements on high-risk health AI (mandating transparency, risk management, and human oversight).
• Collaboration and capacity-building: Effectively advancing AI in public health will require interdisciplinary collaboration (health experts with technologists), investment in workforce AI literacy, and inclusive approaches that involve LMICs and marginalised groups so benefits are shared widely.
• Continuous evaluation and adaptation: To ensure AI delivers on its promise, public health authorities must continually monitor outcomes, audit algorithms for bias or errors, and be ready to adjust or suspend systems if problems arise. Adaptive governance and ongoing community feedback are vital for safe, effective AI integration.
• Seizing the opportunity responsibly: When guided by ethical principles and strong oversight, AI can greatly strengthen public health, easing workforce burdens, expanding outreach, and providing data-driven insights. The next few years are crucial for implementing the policies, education, and trust-building measures that will allow AI to be a force for health equity and innovation rather than a source of new disparities or dangers.

Opportunities: Transforming Public Health with AI

AI is being deployed to alleviate several longstanding public health challenges. One significant opportunity is reducing clinician burnout and workforce shortages by automating routine tasks. For example, a 2024 survey found that 57% of physicians believe automating administrative burdens is the top opportunity for AI to ease workloads amid staff shortages. Machine learning systems can transcribe medical notes, pull up patient records, and handle scheduling or prescription refills — freeing clinicians to spend more time on patient care. Many doctors see such automation as a key to improving work efficiency and reducing stress, suggesting AI could help mitigate the healthcare burnout epidemic.

AI also offers powerful tools for disease surveillance and epidemic intelligence. Algorithms can continuously scan vast data sources — news reports, social media, travel data — to spot early signs of outbreaks far faster than traditional methods. Notably, the HealthMap and BlueDot platforms (which use natural language processing and machine learning) flagged the COVID-19 outbreak days before official alerts. By sifting through informal signals and anomalies, AI-driven systems can provide precious early warnings of emerging health threats. BlueDot’s AI surveillance tools have dramatically sped up outbreak detection, reducing manual scanning time by nearly 90% in some cases. Such early alerts enable public health agencies to mobilise quicker responses and potentially contain outbreaks before they spread.

Another area of opportunity is improving diagnostics and clinical decision support, especially in resource-constrained settings. AI image recognition has shown great promise in interpreting medical images like X-rays and retinal scans. For example, AI-based chest X-ray tools for tuberculosis (TB) are being used to help screen patients in low-resource areas that lack radiologists. A recent programme in India led by PATH found that an AI tool (qXR) boosted TB case detection by ~15.8% — identifying cases that human readers missed. Many countries are now utilising AI-assisted chest X-ray screening for TB, which can lead to earlier diagnosis and treatment in underserved communities. Beyond imaging, AI-powered diagnostic apps and chatbots can guide patients through symptom checks or flag high-risk cases for follow-up, expanding access to essential healthcare advice where clinicians are scarce.

Crucially, AI is also being enlisted to address climate-related health threats and environmental impacts on health. Public health researchers increasingly pair AI with climate data to predict disease patterns under changing environmental conditions. For instance, machine learning models can correlate weather patterns (temperature, rainfall) and even animal health data with disease outbreaks to anticipate risks in specific locations. By analysing such data, AI-driven predictive analytics can serve as early warning systems — forecasting surges in vector-borne diseases like malaria following heavy rains or heat-related illness during extreme heatwaves. This capability is ever more critical as climate change intensifies health hazards. AI can help public health officials prepare for climate-sensitive disease outbreaks, allocate resources proactively, and develop adaptation strategies to protect vulnerable populations.

Real-world Applications and Innovations

AI in public health is not just theoretical — numerous real-world initiatives by NGOs, governments, and private companies have already demonstrated its potential. Global health nonprofits and international agencies have been early adopters of AI to support their missions. For example, the Bill & Melinda Gates Foundation has invested heavily in AI-driven global health projects. In 2023, it awarded grants to nearly 50 pilot projects exploring AI solutions for health and development challenges — these range from AI-augmented diagnostic tools to data systems for disease surveillance in low-income settings.

One Gates-backed innovation is AI-assisted ultrasound: in 2020, a $44 million grant was given to develop an AI-guided portable ultrasound to improve lung disease diagnosis in low-resource countries (e.g. detecting pneumonia). Likewise, PATH and other NGOs are integrating AI into field programmes — as seen in the TB screening project, where an AI tool significantly increased case finding while illuminating practical deployment hurdles. These efforts by NGOs underscore AI’s promise to close gaps in healthcare access and quality for underserved populations.

Governments and public health agencies are also launching AI initiatives. In Europe, national health systems pilot AI to improve services and efficiency. For instance, the UK’s National Health Service (NHS) created an NHS AI Lab to fund and evaluate AI innovations in care delivery. By 2025, the NHS had over 80 AI projects live, targeting everything from optimising nurse rostering and predicting hospital bed occupancy to speeding up radiology workflows.

One NHS program provided £100+ million in awards to develop AI for earlier cancer detection, resource management, and patient safety improvements. The NHS AI Lab’s “Skunkworks” team has run short-term projects that yielded practical tools — e.g. an algorithm to streamline the placement of nurses across wards and a natural language processing engine to search health records more efficiently. Meanwhile, European public health agencies are leveraging AI for epidemiology; the European Centre for Disease Prevention and Control (ECDC) has incorporated systems like BlueDot’s AI to enhance epidemic intelligence, including monitoring outbreaks during events such as the 2020 Olympics. These government-led efforts illustrate growing public sector commitment to deploying AI for health system strengthening and emergency preparedness.

The private sector, particularly in healthcare and pharmaceuticals, is likewise driving innovation at the intersection of AI and public health. Pharmaceutical companies now routinely use AI in drug discovery and development. For example, Novartis recently struck a wide-ranging partnership (worth up to $1 billion) to use a generative AI platform for designing new protein-based therapies — aiming to accelerate the search for novel disease treatments. GSK has also embraced AI to speed up R&D: its CEO noted that AI modelling helped cut two years off an RSV vaccine trial by predicting where outbreaks would occur and optimising trial site selection. This led to the faster development of the world’s first RSV vaccine, an essential public health breakthrough.

Beyond pharma, medical technology firms are integrating AI into devices, from smart wearables that flag irregular heart rhythms to imaging systems where AI assists in analysing scans for early signs of cancer. Startups and tech companies are introducing AI-driven health apps and chatbots (such as symptom checkers and mental health conversational agents), which some health services in Europe are trialling for patient triage and support. These real-world examples underscore that AI is already deeply enmeshed in the health ecosystem — from global disease surveillance networks to hospital wards and R&D labs — delivering innovations that could improve population health outcomes.

Practicalities and Implementation Challenges

While the potential is immense, implementing AI in public health is a pragmatic challenge. Infrastructure and data interoperability are foundational hurdles. Effective AI requires robust digital infrastructure — high-quality data streams, electronic health records, and cloud computing capacity — which many health systems lack, especially in low-resource settings. Data needed for public health AI often reside in silos or incompatible formats across hospitals, labs, and agencies. Poor interoperability means AI tools struggle to aggregate and interpret information from disparate sources. Bridging these gaps will require significant investment in health information systems, common data standards, and connectivity. Encouragingly, current AI technology can assist in standardising and mapping messy health datasets to make them more usable. Nonetheless, without reliable infrastructure and data-sharing frameworks, even the best AI algorithms cannot deliver consistent results across a public health network.

A related challenge is data quality and representativeness. AI models are only as good as the data they learn from, and health data can be incomplete, biased, or unrepresentative of specific populations. Studies highlight issues like variability in how data are recorded, large amounts of unstructured text, missing information, and coverage bias (e.g. most training data coming from high-income populations).

These factors can undermine an AI system’s accuracy and value to end users. Developing good AI for health requires carefully cleaning and curating data to reflect clinical reality. For instance, algorithms trained only on European hospital data may perform poorly in rural African communities. Implementers must thus invest effort in data preparation and continuously monitor model outputs for anomalies. Establishing metadata standards, common terminologies, and data quality metrics can facilitate better AI development. Additionally, clarity on data ownership and governance is needed: questions about who “owns” health data (patients, providers, governments?) affect how data can be integrated for AI. Resolving these issues through policies and trust frameworks is key to unlocking data for public health AI while respecting privacy and rights.

Another practical consideration is integrating AI tools into healthcare workflows and gaining workforce acceptance. Introducing AI decision-support systems or automation in clinics requires adapting processes and training staff. Health workers may be understandably cautious — some lack familiarity with AI, worry about accuracy, or fear being displaced. Clear protocols are needed if an AI system’s recommendation conflicts with clinical judgment. Early experience shows that human-AI collaboration works best when AI is framed as an assistive tool rather than a professional replacement. Building trust among the workforce involves providing explainable outputs and demonstrating reliability in pilot phases. It also means training clinicians in basic AI concepts and ensuring they feel confident interpreting AI outputs.

Successful deployments (like the PATH TB screening program) emphasise that significant workflow integration and training efforts are required. In that program, implementers had to solve issues of installing the software in clinics, securing internet connectivity for the AI, and ensuring staff could effectively use the AI results within their screening workflow. Without such groundwork, even a high-performing algorithm might sit on the shelf unused. Thus, the human element is crucial: public health organisations must engage and educate their workforce, adjusting roles and processes so that AI enhances rather than disrupts care delivery. Over time, as clinicians see AI reducing drudgery (e.g. auto-filling forms) and improving outcomes, their acceptance tends to grow. Indeed, physician enthusiasm for health AI has been rising year-on-year. Patience and iterative refinement are needed to blend AI smoothly into the complex fabric of health systems.

Risks and Concerns of AI in Public Health

Despite the optimism, it is vital to acknowledge the risks and potential harms associated with AI in public health. Data privacy and security tops the list of concerns. AI systems often require large datasets of patient information, raising the stakes for protecting sensitive personal health data. Any breach or misuse of such data can erode public trust and violate individuals’ rights. There is also the risk of “function creep”, where data collected for health purposes might be used in other ways (for example, a COVID-19 contact tracing app’s data later being used for law enforcement — a scenario that drew criticism in some countries). Moreover, complex AI models could inadvertently leak private details — for instance, a model might be reverse-engineered to reveal records it was trained on. Ensuring robust cybersecurity and strict data governance is therefore paramount. Many call for comprehensive privacy safeguards and compliance with regulations like Europe’s GDPR whenever AI handles health data. Techniques such as anonymisation or synthetic data can help, but they are not foolproof (even de-identified data can sometimes be unidentified).

The bottom line: without public confidence that AI will maintain confidentiality and data security, its benefits will be lost. Public health agencies must be transparent about what data are used and how to obtain informed consent where appropriate and implement state-of-the-art security measures to prevent breaches. Privacy isn’t just a legal box to tick — it’s fundamental to preserving the trust on which public health interventions depend.

Another significant risk is algorithmic bias and the exacerbation of health inequalities. AI systems can unintentionally perpetuate or even worsen disparities if their design is not carefully managed. This was starkly illustrated by a widely used healthcare risk algorithm in the United States that was found to be racially biased. The algorithm helped determine access to extra care programs and used healthcare cost as a proxy for need. This choice systematically underestimated the needs of Black patients (who often had lower healthcare expenditures due to access barriers). As a result, many high-risk Black patients were less likely to be flagged for additional care, denying them the resources they needed. This example shows how bias in data or design can translate into inequitable outcomes: the AI effectively discriminates against a vulnerable group. Similar issues could arise in public health if an AI model is trained on predominantly male patients under-detect conditions in women or if disease surveillance AI better covers wealthier communities with more data. AI could widen gaps if not addressed, with marginalised populations benefiting the least or even being harmed.

Equity must be a central design principle to counter this: datasets should be diverse and inclusive, algorithms should be tested for bias, and bias mitigation strategies (like reweighing data or algorithmic fairness adjustments) should be applied. The WHO explicitly highlights inclusiveness and equity as core ethical principles for AI, ensuring that AI tools work for all segments of society regardless of race, gender, income, or other characteristics. Ultimately, careful governance and auditing of AI systems are needed to avoid encoding systemic biases into digital form and instead use AI to reduce health inequities (for example, by targeting interventions to underserved areas).

A further concern is the lack of transparency (“black box” issue) and its impact on trust and safety. Many AI models, especially deep learning networks, operate as complex black boxes — they do not explain their reasoning in human-understandable terms. In healthcare, this opacity is problematic. Clinicians and public health decision-makers are wary of acting based on a recommendation they don’t understand, particularly if an AI’s advice contradicts intuition or standard practice. Unexplainable AI can also undermine accountability: if an AI makes a harmful mistake, it may be unclear why it happened or who is responsible. This lack of transparency feeds directly into trust issues among professionals and the public. If people perceive AI as a mysterious, untrustworthy “magic wand” imposed on health decisions, they may reject its use. There have been cautionary tales: an AI system deployed in hospitals to predict which COVID-19 patients would need ICU care was later found to underperform because it hadn’t been adequately validated. Clinicians grew sceptical of its risk scores.

To prevent such scenarios, experts call for explainable and interpretable AI in health — algorithms that can provide reasons for their predictions or use transparent, logical rules where possible. At a minimum, users should have access to information about how an AI was developed and its known limitations. Regulatory frameworks like the EU AI Act are likely to mandate a degree of transparency for high-risk AI (including many medical applications) precisely to bolster trust and enable oversight. Building more explainability into AI models remains a technical challenge, but one that is essential for aligning with the principles of transparency and accountability in healthcare.

In the age of ChatGPT and generative AI, misinformation and “AI hallucinations” have emerged as new public health risks. Advanced chatbots can produce remarkably human-like answers to questions — but they do not guarantee factual accuracy. They can hallucinate false information, confidently output incorrect medical advice, nonexistent statistics, or even fake health news. The potential for harm is considerable if the public uses such tools for health information. There is concern that AI chatbots could magnify the health misinformation problem exponentially — for instance, by generating convincing anti-vaccine narratives or spurious cures, which then spread on social media.

In recent years, public health agencies have struggled to combat misinformation (for example, false claims about vaccines or COVID-19 treatments that undermine uptake). The rise of AI-driven content generators and deepfakes only fuels this fire. Misinformation undermines public trust and can lead people to reject proven interventions in favour of dangerous alternatives. Tackling this will require new strategies — such as watermarking AI-generated content, strengthening content moderation, and improving digital health literacy so the public can better discern credible information. On the flip side, public health communicators might also leverage AI to fight misinformation (for example, using AI to detect false rumours early or personalise accurate health messages). Regardless, the advent of easy, AI-generated disinformation is a serious risk factor that the global health community cannot ignore.

Finally, there is the risk of over-reliance and systemic dependency on AI. If health systems come to depend on AI for critical functions without adequate safeguards, any failures in the technology could have severe consequences. For example, an AI model might perform well in normal conditions but fail to generalise during an unexpected scenario. If everyone has come to rely on its output, they may miss the warning signs until too late. Moreover, heavy reliance on automation might erode human skills over time (a phenomenon observed in other industries). In healthcare, this raises concerns about “deskilling” — clinicians might lose practice in specific tasks (like reading x-rays or making complex diagnoses) if those are always handled by AI, leaving them less prepared to step in when needed.

Over-reliance can also dull vigilance: users might stop double-checking results if an algorithm usually works well so that an undetected error could propagate. The key is to maintain a human-in-the-loop approach: AI should support, not replace, human expertise. Mechanisms for human review of AI outputs and fallback plans in case of system outages are essential.

Additionally, performing regular audits and updates of AI models can prevent performance from degrading unnoticed. In summary, while AI can increase efficiency, public health systems must guard against blindly relying on algorithms. A balanced approach that values human judgment and institutional memory, alongside AI’s computational power, will be safest in the long run.

Ethical and Regulatory Frameworks

Addressing the above risks requires robust ethical guidelines and regulatory oversight for AI in health. Globally, there is growing consensus on core ethical principles that should govern AI development and use in public health. The World Health Organization’s landmark 2021 report laid out six guiding principles for ethical AI in health: (1) Protect human autonomy — humans should remain in control of health decisions, with informed consent and respect for privacy; (2) Promote human well-being and safety — AI must be safe, effective, and designed to improve health outcomes; (3) Ensure transparency, explainability and intelligibility — stakeholders should have sufficient information about how AI systems work and decisions should be traceable; (4) Foster responsibility and accountability — developers and users are accountable for AI behaviour, and mechanisms for redress must exist; (5) Ensure inclusiveness and equity — AI should benefit all groups, enhancing fairness and not amplifying disparities; and (6) Promote AI that is responsive and sustainable — meaning AI should be adaptable, monitored, and designed for long-term societal benefit.

These principles, while high-level, provide a value framework to guide everything from design choices (e.g. using diverse training data to ensure equity) to deployment (e.g. always keeping a human in the loop to protect autonomy). Public health organisations are increasingly adopting such ethical frameworks. For instance, the WHO urges that AI deployments be accompanied by community engagement, training for health workers, and continuous evaluation to ensure technologies remain aligned with the public interest. The ethos is straightforward: AI must be people-centred and uphold human rights. Ethics committees or advisory boards can help oversee AI projects, reviewing them for compliance with these principles before they scale up.

On the regulatory front, governments are now moving to establish formal rules for AI in healthcare. The European Union’s AI Act is a pioneering example of comprehensive regulation. Passed in 2024, the EU AI Act takes a risk-based approach, classifying AI systems by risk level and imposing requirements accordingly. Health-related AI is generally deemed “high-risk” under this law, given its potential impact on people’s lives and rights. High-risk AI systems (including most AI used for medical diagnostics, decision support, or resource allocation in health) will face strict obligations. These include rigorous standards for transparency, risk management, and human oversight. For instance, developers of a clinical AI tool must implement a quality management system, ensure their model is trained on appropriate data, and provide documentation detailing the AI’s function and limitations. They must also conduct risk assessments and put in place human oversight measures to prevent automation bias. Notably, the EU AI Act doesn’t just apply to creators of AI — it also holds deployers (such as hospitals or public health agencies) accountable for the safe use of AI.

Health providers must monitor AI system performance, keep logs, and retain ultimate responsibility for decisions (clinicians must have the authority to override AI recommendations if needed). These provisions aim to ensure that human accountability and patient safety remain paramount even as AI becomes embedded in care delivery. Additionally, the Act has a broad reach: any AI system impacting people in Europe must comply, even if developed elsewhere. This could set an effective global benchmark as companies worldwide adjust their practices to meet the EU’s requirements.

Other jurisdictions are also crafting guidelines. The United States, through the FDA, has been evolving its regulatory approach for AI/ML-based medical devices, focusing on premarket evaluation and the idea of “continuously learning” algorithms needing ongoing monitoring. International bodies like the WHO have issued guidance and urged governance innovation, suggesting that governments update regulations to cover AI, establish certification processes, and possibly create registries of approved AI health products. We also see emerging governance models such as algorithmic impact assessments (to evaluate a health AI system’s potential societal impact before deployment) and independent reviewers’ bias audits. In some health systems, procurement of AI now requires meeting ethical checklists or obtaining approval from institutional review boards, similar to new medical interventions.

These steps are part of building a “responsible innovation” culture around AI, encouraging experimentation and advancement, but within guardrails that protect individuals and communities. Multi-stakeholder collaboration is key here — regulators, technologists, health professionals, and patient representatives need to work together to define safe and effective AI in practice and update those definitions as the technology evolves. As one example, the NHS AI Lab in the UK partnered with regulators to create a sandbox for AI developers, guiding them on navigating regulatory pathways and using synthetic data for testing. Such efforts show that with thoughtful governance, innovation and safety can advance hand in hand.

Future Directions and Recommendations

To fully realise AI’s promise in public health while minimising its downsides, several changes and strategic efforts are needed going forward:

• Investing in data and digital infrastructure: Health systems, especially in low- and middle-income countries, need support to build the data foundations for AI. This means digitising health records, improving data quality, and ensuring platform interoperability. Governments and global donors should prioritise funding for health information systems and broadband connectivity as part of public health capacity building. Better data infrastructure not only enables AI — it strengthens health systems overall. Innovative approaches like federated learning (where AI models train on distributed data without moving it) could be scaled to allow resource-constrained regions to benefit from AI insights without breaching privacy. The goal is to create a world where data flows securely and efficiently to wherever it can improve health outcomes.
• Strengthening workforce capacity and AI literacy: As AI becomes a standard tool, public health and healthcare workers must be equipped to use and oversee it. Training programmes are needed to raise AI literacy among the health workforce, including understanding AI’s capabilities and limitations. This may involve updating medical and public health curricula to cover data science basics. Additionally, new specialist roles (such as clinical AI safety officers or epidemiologists with AI expertise) could be developed to bridge the gap between tech and health domains. Frontline staff should be engaged in co-designing AI solutions so that tools are user-friendly and address actual pain points. When health workers understand and trust AI, they can become champions for its adoption and serve as critical watchdogs who notice when something isn’t right. Fostering a culture of continuous human oversight and feedback will ensure that AI remains a servant to health professionals, not a black box dictator.
• Ensuring inclusivity and equity in AI advancement: The global health community must actively work to prevent a digital divide in AI. Much cutting-edge AI development is concentrated in wealthier countries and tech companies. Deliberate efforts are needed to include researchers and perspectives from low- and middle-income countries in AI design so that solutions address diverse needs. This could consist of research funding earmarked for LMIC-led AI projects, technology transfer programs, and south-south collaboration on AI for health. Moreover, data from underrepresented populations should be collected (with consent and protection) to improve algorithms’ relevance in those settings. By democratising AI knowledge and resources, we can avoid a scenario where only certain countries or communities benefit from AI while others are left behind or subject to unchecked harm. Equity considerations should also extend to gender, age, and other demographics — for instance, ensuring women and minority groups are included in AI development teams and that tools serve users of different languages and literacy levels. An inclusive approach will make AI tools fairer and enlarge the talent pool working on creative AI solutions for entrenched public health challenges.
• Fostering collaboration between public health and technology sectors: Effective AI in public health sits at the intersection of epidemiology, medicine, data science, and engineering. No single sector can do it alone. We need stronger partnerships: governments linking with academia and tech firms, NGOs working with startups, and international agencies convening multi-sector consortia for global health AI initiatives. Such collaboration can accelerate innovation and ensure that public health priorities guide technological development (and vice versa, that technologists are aware of on-the-ground needs). For example, a partnership between a national health ministry and AI researchers might focus on building an early warning system for malaria outbreaks, combining epidemiological expertise with cutting-edge modelling. A pharmaceutical company could also collaborate with global health organisations to use AI in vaccine R&D for diseases of poverty. These cross-sector collaborations should be underpinned by fair agreements (e.g. around data sharing or intellectual property) so that all parties benefit and trust is maintained. The complexity of health + AI demands breaking down silos. International forums and networks can play a role here, enabling countries to share best practices and lessons learned (e.g. how one country successfully regulated an AI symptom-checker or how another trained health workers on AI). Since pathogens do not respect borders, a collaborative global approach to AI-enhanced public health security is in everyone’s interest.
• Adaptive governance and continuous evaluation: As AI tools roll out, it is critical to monitor their real-world impact and be ready to adjust course. Public health authorities should implement mechanisms to continuously evaluate AI interventions — collecting data on their accuracy, outcomes, and any unintended effects. Are the predictions helping improve disease control? Is a triage algorithm safely directing patients to the right level of care? This requires establishing key performance indicators and perhaps creating independent evaluation units. When problems are identified (such as an AI starting to drift in accuracy due to changes in data), there should be processes to update or pull back the tool until fixes are in place. Regulation must also remain adaptive; rigid rules could stifle innovation or become outdated as technology advances. One idea is regulatory sandboxes where new AI solutions can be tested under supervision, allowing regulators to learn and guidelines to evolve. Governance models should be proactive yet flexible, emphasising learning and iteration. Importantly, communities and civil society should have a voice in evaluating AI in public health — their feedback on whether these tools are culturally acceptable, understandable, and improving services is invaluable. Responsible AI is not a one-time certification but an ongoing commitment to quality and ethics throughout the technology’s lifecycle.

Looking ahead, it is clear that AI will play an expanding role in public health — whether in combating the next pandemic, extending healthcare to remote villages via smart apps, or analysing big data to pinpoint disease drivers. The revolution is already underway, but its trajectory depends on our current choices. With enlightened leadership, adequate safeguards, and inclusive collaboration, AI could usher in significant public health gains — from more efficient health systems to healthier communities worldwide. However, if we ignore the risks — allowing unchecked use, widening inequities, or losing the human touch in care — the potential benefits could unravel, and public trust could be irrevocably lost. The coming years are thus pivotal. Armed with decades of hard-won experience, public health professionals have a key role in steering this journey. By insisting on evidence, equity, transparency, and community engagement, they can ensure that the AI revolution in health truly becomes a boon and not a threat. The opportunity is immense, but so is the responsibility to guide AI’s integration into public health thoughtfully and ethically.

The post AI in Public Health: Revolution, Risk and Opportunity appeared first on Medika Life.

You probably know what I am talking about — those meetings where it seems as though a limited number of people want to hear their voice.

For many of us, with non-productive meetings comes stress and fatigue. Productivity can take a hit, too. You will not be surprised to learn that one survey reported 83 percent of the meetings on their calendars were unproductive.

Professionals in the United States rated meetings as the leading office productivity killer.

Today, I want to look more broadly than unproductive meetings. Let’s examine work burnout, including a new study showing how it can change your brain.

Job burnout — Do you have it?

To determine whether you may be burning out, ask yourself these questions:

• Have you become cynical in the workplace?
• Do you dread going to work and have challenges getting going?
• Are you impatient or irritable?
• Are you non-productive because you lack energy?
• Is it challenging for you to concentrate?
• Do achievements provide no satisfaction to you?
• Are you disillusioned about your job?
• Are you using alcohol, drugs, or food to feel better or not feel?
• Have you had negative changes to your sleep?
• Do you have physical ails such as stomach problems or headaches?

Did you answer yes to any? If so, you may be suffering from job burnout. Please consider talking to a healthcare provider or a mental health professional because these symptoms can also be related to health conditions such as depression.

Job burnout — Stress can change your brain

Chronic stress can contribute to physical and psychological problems. But does our brain change structurally? Researchers recently provided insight into what happens to the brain when under stress.

U.T. Health San Antonio researchers discovered memory loss and brain shrinkage occurring before stress symptoms emerged.

Study author Sudha Seshadri, M.D. explains that the brain’s matter can thin in the prefrontal cortex. This brain region enables appropriate behavior and insights into ourselves and others.

We also use the prefrontal cortex for complex decision-making and abstract reasoning.

These structural changes can translate to a compromise in our ability to pay attention and retain memories. We have more challenges learning and have a higher chance of making mistakes.

There is more: Burnout and related stress can make our “fight or flight” center (the amygdala) more prominent. This part of our brain can generate emotions such as fear, with an increase in its size potentially causing us to see the world as harmful (when it is not).

Mice studies hint that we may be able to reverse these brain changes. In addition, a 2018 human study showed that the amygdala size could be reduced and the prefrontal cortex changes brought back to pre-stress levels.

I want to keep the negative brain changes from occurring in the first place. The locus of control — feeling we are in charge — can go a long way to preventing negative brain changes from occurring.

Job burnout — An action plan

Let’s look at some ways you may fight burnout. First, discuss your concerns with your supervisor. Perhaps you can change expectations or re-set goals.

Getting physical activity is a crucial element to dodging work-related burnout. I am also trying to incorporate stress reducers such as meditation and yoga. I did tai chi during my Shito-Ryu karate days. Do you practice some form of mindfulness?

I would be remiss if I did not mention sleep, as it can be critical to protecting your health. Finally, reach out; get support from co-workers, friends, or loved ones. While I have not had burnout issues, I am glad that my workplace has an employee assistance program.

Don’t let a demanding job undermine your psychological and physical well-being. If you feel you need help, please get it, whether through an employee assistance program, a therapist, a psychologist, or your primary care doctor.

The post Did You Know Burning Out Changes Your Brain? appeared first on Medika Life.

But for me, I have one outstanding memory that I shall never forget. No, it’s not of a major loss or a historical event like the mythic Christmas truce during one of our too-many world wars. It’s a memory of sadness and staff who were too poorly trained to know what they were doing.

When I was a psychology intern, patients who had become long-term residents of state psychiatric hospitals could expect a few things at Christmas, if not family coming to visit. An employee on one of the wards was tasked with ordering gifts for them from a man who had managed to receive the contract.

In years gone by, probably before many of us were born, they would have been the guys selling from the trunk of their cars or small vans that wended through the hinterlands in the US to sell inexpensive gifts of clothing for scattered families living in a rural area. It was an era before malls or shopping centers and with “dry goods” stores many miles away. Now they sold to institutions, but the merchandise was still low quality, cheap and forgettable.
I was introduced to this practice as an intern at a huge psychiatric hospital, now shut forever, where my curiosity pushed me to ask what a supervisor was doing.

“I’m ordering Christmas gifts for the patients,” she responded.
“What kind of gifts,” I asked.

“The same things we have to choose from every year; sweaters, pajamas, hats, scarfs, or gloves,” was her annoyed reply.

The budget she was given for her 60 patients wasn’t anything but meager, but in her heart, she knew it was the one gift these patients would ever be getting from someone, too bad it was like the lackluster food trays they used every day.

OK, the patients were getting a gift at least and there would be some semblance of their participation in a holiday all of us looked forward to each year. And there would be ward decorations to further the attempt at holiday cheer. But there was one thing that stands out over the small gifts: the decorations on the unit.

Staff at the hospital did their jobs, but training in too many things was missing. I can’t, however, bring myself to think anyone needs training in poison control.

Ersatz fireplaces were in each day room on each unit’s ward. I don’t believe they were more than non-functional design elements meant to provide a homey atmosphere but what homes also provide reinforced doors on nursing offices?

During times of ward stress, the staff would lock themselves in the offices and watch as the ward was disrupted in fury. Furniture, however, was from a specific company and very difficult to move because of its weight. But anything that wasn’t bolted down was fair game to be used as a weapon.

On one ward, infamous for the publicity it received when a state senator, using the information of a known criminal sex offender, became a staff member, had a special way of decorating their unit. The fake boughs of pine with decorative candy canes draped over the mantels did add an air of Christmas. The staff knew that those candy canes were too enticing to patients who had no access to candy, and they came up with a solution, insect spray.

A staff member carefully sprayed all the canes and all the decorations with the insect spray in the belief that knowing it was inedible, the patients wouldn’t touch it. Wrong.

These patients were seriously mentally ill, and a candy cane was too enticing. One or two of them grabbed a cane and began to eat it. They were, of course, sent to the local hospital since this hospital had neither qualified medical staff nor a place to treat them. Yes, some staff members even had board certification as pediatricians, although this was a hospital for adults.

Recently, there had been a young, psychotic man, believing that he could cure himself, who had eaten the pine needles of an on-grounds tree; the needles were deadly. Neither the nursing staff nor the physicians knew what to do and they tried an inappropriate medication meant for wounds and inflammation. He died.

I guess none of them had ever heard of Socrates or how he died, hemlock poisoning. Why were hemlock trees planted at a hospital for the seriously mentally ill in the first place?

But those ward decorations and the spraying of them with insecticide will always stand out in my mind. I’m sure the patients won’t remember because everything was kept from them. All they knew was that the decorations had been removed before Christmas and some patients were sent to area hospitals.

I can think of no place lonelier than a psychiatric hospital at Christmastime and it will always be that way. I hope things have changed around the country where state psychiatric hospitals are still functioning. I am, however, not sanguine that they have changed.

Dr. Farrell’s books can be found on Amazon: https://tinyurl.com/yckv2w6h and http://www.drfarrell.net

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