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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His fears did not dissipate after the birth of the baby. Instead, they worsened. One night when the baby began to cry, and his wife failed to get up immediately to soothe him, Jake yelled out, “Shut the F… up!” Horrified by his actions, his wife turned on him. “What is wrong with you?!” she asked.

Jake eventually discovered what was wrong, but not before his job and his marriage suffered the effects of his changed behavior. Jake had developed what one in ten new fathers (13 percent according to some estimates) suffer from. Until recently, it was believed that only women suffered from Postpartum Depression (PPD.) While there is still no diagnostic category for PPD in the DSM, it is subsumed under the general category of Major Depressive Disorder.

SYMPTOMS OF MALE POSTPARTUM DEPRESSION

Symptoms of male PPD share many similarities to those in women. A partial list, includes:

• Irritability, anger, or aggressive behavior.
• Easily stressed.
• Withdrawal from family and relationships.
• Poor concentration and difficulty focusing.
• Changes in appetite or sleep patterns (insomnia or oversleeping).
• Feeling overwhelmed, anxious, or hopeless.
• Suicidal thoughts.
• Risk-taking behaviors including substance and alcohol use.
• Physical symptoms including headaches and stomach aches.
• Indecisiveness.
• Restricted range of emotion

CAUSES OF MALE PPD

Several factors put men at risk for PPD, including sleep deprivation, a prior personal or family history of depression, or a feeling of being shut out from mother and child. Up to half of all men with a depressed partner also show signs of depression according to one study, but surprisingly, fathers also experience hormonal shifts that alter mood, especially a decline in testosterone.

A 2024 article on Understanding Paternal Postpartum Depression notes that while more women than men suffer PPD (one in 7 versus one in 10), men also experience hormonal changes.

Jonathan R. Scarff, in his article Innovations in Clinical Neuroscience; Postpartum Depression in Men, explains that low testosterone in general is linked to symptoms of depression in men, while low levels of estrogen, prolactin, vasopressin, and/or cortisol in new fathers negatively affect father-infant bonding/attachment.

PUSHED OUT BUT RESPONSIBLE

Jenna Berendzen, ARNP at UnityPoint Health, suffered severe postpartum depression and anxiety after the birth of her son. While Berendzen was admitted to the county perinatal psych unit, her husband was left to worry about her and to figure out how to single handedly care for their new baby. Two years later they discovered that he, too, had suffered PPD, yet he couldn’t say anything at the time because, in his mind, he hadn’t given birth, especially a C-section. Instead he felt pushed aside while trying to carry the load of the entire family,”

An Allied Health article notes, “Many dads want to be active participants in the care of their new baby, but sometimes end up feeling like they’re on the outside. As the bond between mother and child begins to strengthen, fathers may feel sidelined. “Many men have a breadwinner mentality that compels them to bottle up the pressure and downplay symptoms of PPD both as they are preparing for fatherhood and afterward.”

In an article I wrote for L.A. Baby when my own son was a baby, I noted how women tended to believe they knew what was best for their baby, and as a result, often pushed their husbands out of the nursery, which only added to the father’s feelings of ineptitude, rejection and even abandonment. The result was that dads didn’t really bond with their babies until the child was older.

BECOMING A FATHER

Despite the dearth of research on new fathers, some experts claim that the journey to fatherhood represents a unique and transformative time in a man’s life. According to one study, “A man does not become a father only at the moment when the child is born…it is a long-lasting dynamic process where the father’s identity is formed and sustained through various experiences.”

Once the baby is born, everything suddenly becomes real. Even the diminutive size and fragility of a newborn can feel overwhelming. Dads need support, reassurance and education about how to hold and care for an infant. This is where some men back off, preferring to abdicate the “tender tasks” to mom, rather than learn from her.

In my own book Midlife Parenting, A Guide to Having and Raising Kids in Your 30s, 40s and Beyond, I found that men who start parenting later in life are more mature and settled. However, many of them are also accustomed to more freedom and independence, which presents its own challenges.

CHANGING ROLES AND EXPECTATIONS FOR FATHERS

Researchers point out that the psychological process of becoming a father has changed in the last couple decades. As one study notes: “We can observe a shift in the perception of the father’s role in Western societies, and in younger generations there is a growing incidence of the so-called “new fatherhood” associated with emotional intimacy and availability of the father as well as increased involvement of the father in childcare and household care.” The authors point out that today men are not only welcomed, but are expected to attend parenting preparation courses and to be present during childbirth as well as postnatal care.

SO WHAT’S A DAD TO DO?

The good news is that there is treatment for male PPD. It begins the moment the couple learns they are having a baby. That’s when the conversations should start, says one researcher who offers the following advice:

• Invite Active Participation: Active participation in the pregnancy, supporting one’s partner, and learning what to expect during and after delivery will help fathers feel involved and prepared.
• Talk to a Financial Planner: Money is a top challenge. With proactive conversation and some professional guidance, new fathers will know what to expect and how to best navigate the expenses of having a child.
• Lean into Support: As the baby’s arrival date approaches, soon-to-be fathers should lean into their support system. Reinforcing relationships and being open to advice will help fight the fear of the unknown.
• Seek out Help: As men are preparing for fatherhood, it’s normal to occasionally feel overwhelmed. It’s important to seek out help sooner rather than later and work to solidify a healthy mindset before the baby arrives.

If symptoms do emerge, dads should seek out professional help. Jonathan Scarf suggests that in serious cases, psychotherapy, especially cognitive behavior therapy (CBT) and interpersonal therapy (IPT) have been shown to be effective, as well as daily morning light to correct circadian rhythms, which are related to PPD.

For some men, individual or couples therapy may be preferred over anti-depressants. Other times a combination may be most effective. Treatment can be short-term or long-term, based on whether there are deeper or more serious underlying issues, which are exacerbated by the birth of a child.

Other recommendations include meditation, yoga and other “mindfulness” approaches to stress. And finally, it would be helpful if employers or government programs supported paid paternity leave for men, recognizing the value of fathers in the earliest stages of parenthood.

Christi Taylor-Jones is a Licensed Marriage and Family Therapist, Jungian Analyst and writer. She is author of Midlife Parenting and Touched by Suicide. She is also a mother and soon-to-be grandmother.

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

The Day the Ordinary Disappeared

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

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

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

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

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

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

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

Where Care Is Action, Not Theory

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

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

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

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

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

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

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

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

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

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

The Human Thread Through Every Moment

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

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

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

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

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

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

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

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

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

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

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

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

That truth asks something of us.

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

The post The Moments That Shape Us: Why Life and People Matter Most appeared first on Medika Life.

One example would be pain. Research published in the Journal of Neuroscience found that when people didn’t know how much a painful heat stimulus would hurt — when they watched a group of others who disagreed wildly about it — they felt more pain than when the group agreed. The heat itself didn’t change. Only the uncertainty did. That single finding opens a door onto something much bigger: the way the brain interprets incoming signals doesn’t just affect physical pain. In fact, it shapes every experience, every emotion, and every belief we form about the world around us.

The Brain Is a Prediction Machine, Not a Camera

Your brain doesn’t work like a camera, passively recording what’s in front of it. It works more like a detective — making its best guess about what’s happening based on past experience, context, and whatever signals it can pick up in the moment. In fact, this is the way AI works the same way because it guesses what you intend when you are dictating to it. That’s based on what you have known to use before. It’s not original; it’s from something you’ve already said or thought.

Scientists call this predictive processing. Fancy words for something that’s simple. The brain is constantly generating a model of reality and checking it against what the senses report. Most of what you experience isn’t raw sensory data. It’s the brain’s best guess, already processed and interpreted before you’re even aware of it.

This has enormous consequences. Because your brain fills in gaps with guesses, your perception of any situation is shaped as much by what you expect as by what’s actually there. Research on how emotions are built in the brain confirms this same pattern. Feelings aren’t simple, automatic reactions that arise out of nowhere. They’re constructed — assembled by the brain from past learning, bodily signals, and whatever the surrounding context suggests is happening — all woven together into something that feels completely immediate and real. Fear, hope, dread, excitement — none of these are just responses to the world. They’re interpretations. And like all interpretations, they can be mistaken.

This might be unsettling to hear. But it’s also genuinely freeing, because it means your perception of reality isn’t fixed. It can be trained.

The Brain’s Thumb on the Scale

Here’s the catch. The brain doesn’t interpret experiences evenly. It has a strong, built-in bias toward the negative. This explains why negative information is so strongly entrenched in our minds. Negative information is stored more vividly in memory and carries more weight in the decisions we make than equivalent positive information does. This isn’t a character flaw. It’s an evolutionary feature.

Our ancestors survived by treating ambiguous situations as dangerous — if a rustle in the bushes might be a predator, it was safer to assume the worst and run. The cost of a false alarm was low; the cost of missing a real threat could be fatal.

In modern life, that same wiring creates serious problems. We’re exposed to more alarming information than any previous generation — not necessarily because the world is more dangerous, but because we carry a device in our pockets that streams us the worst of humanity around the clock. Research on how news consumption affects perception found that a steady diet of threatening content actively cultivates a distorted view of the world, pushing people to overestimate danger (The Scary World Syndrome) and feel a constant sense of impending doom that doesn’t match their actual circumstances.

In one study on risk perception during a health crisis, people overestimated their personal risk of dying from a disease by more than 270 times the actual probability. Their brains weren’t computing risk. They were amplifying fear.

Uncertainty makes all of this worse. Much worse. The same research that revealed how uncertainty increases physical pain also showed that not knowing what to expect activates a specific brain region — one that amplifies the intensity of an experience, for better or worse. And this effect isn’t limited to physical sensation.

Research on stress and health outcomes has found that the threat of losing a job can actually be more damaging to physical health than losing it outright, because the brain treats an uncertain threat as something to brace against continuously — a draining, exhausting posture that takes a real toll on the body over time. Sounds like burnout, doesn’t it? It isn’t just pain that uncertainty turns up. It’s almost everything the brain interprets as potentially threatening, which, given the negativity bias, covers a whole lot of ground.

What makes this particularly important in today’s world is that this feedback loop isn’t passive. The beliefs we form — shaped by perception, fear, and repeated exposure to alarming information — circle back and filter what we’re willing to notice next.

Research on how beliefs affect the brain’s processing of sensory information suggests that what we expect to see and feel actually controls what reaches our conscious awareness. Our beliefs aren’t just conclusions we reach. They become part of the filter that determines what evidence the brain even considers. This is like throwing the wheat away with the chaff.

What You Can Actually Do About It

Understanding how the brain constructs experience isn’t just interesting. It points directly to what we can do differently.

The first step is recognizing that your interpretation of a situation isn’t the same thing as the situation itself. When you feel dread about a conversation you haven’t had yet or are certain something’s going to go wrong, your brain is filling in a gap with a guess — shaped by past experience, current stress, and the negativity bias — not delivering a reliable preview of the future. That awareness alone, when you can genuinely hold onto it, changes your relationship with the feeling. You don’t have to argue with it or push it away. You just don’t have to treat it as truth.

The second step involves what you feed your brain. Because the brain builds its models of the world out of the patterns it encounters most often, the information environment you live in genuinely shapes how you perceive things — including things that have nothing directly to do with that environment. Heavy exposure to alarming content trains the brain to scan for threats even in neutral situations. Seeking out different perspectives, sitting with ambiguity instead of rushing to resolve it, and spending time in environments where uncertainty is met with curiosity rather than alarm — these gradually reshape the models your brain is running.

The third step is learning to treat uncertainty itself differently. That’s harder than it sounds, because not knowing really activates stress responses that narrow attention and make everything feel more urgent and more threatening. But evidence consistently shows that people who can stay open when they don’t know what’s coming — who can resist the pull toward premature conclusions — think more flexibly, solve problems more creatively, and make sounder decisions. The ability to hold more than one interpretation in mind at once isn’t a fixed personality trait. Like any other cognitive skill, it responds to practice.

None of this is an argument for forced optimism or pretending that hard things aren’t hard. Negative emotions carry real information and serve genuine purposes when they’re in proportion to what’s actually happening. The goal isn’t to replace one distortion with another. It’s important to notice when the brain’s interpretive machinery is running hot — turning not-knowing into catastrophe, amplifying uncertainty into doom — and to remember that what feels like reality is always, to some degree, something the brain has made.

The world you live in isn’t the world as it is. It’s the world your brain has built for you, piece by piece, out of everything it expects, fears, and has learned to look for. That’s not a reason for despair. Actually, it’s an invitation to get curious about the builder — and to ask whether the story it’s been telling you still has to be the only one.

The post Reality Isn’t What You Think: It’s How Your Brain Builds Everything appeared first on Medika Life.

This story also ran on NBC News. See below.

West Virginia’s Public Employees Insurance Agency enrolls nearly 215,000 people — state workers, as well as their spouses and dependents. The new law, which will take effect June 10, will allow plan members who have been approved for a course of treatment to pursue an alternative, medically appropriate treatment of equal or lesser value without the need for another approval from the state-based health plan.

“This legislation is rooted in a simple principle: if a treatment has already been approved, patients should be able to pursue a medically appropriate alternative without being forced to start the process over again — especially when it does not cost more,” Gov. Patrick Morrisey said in a statement after signing the bill into law on March 31.

“This is about common sense, compassion, and trusting patients and their doctors to make the best decisions for their care,” he said.

Delegate Laura Kimble, the Republican from Harrison County who introduced the legislation, told KFF Health News the measure offers “a rational solution” for patients facing “the most irrational and chaotic time of their lives.”

From Arizona to Rhode Island, at least half of all state legislatures have taken up bills this year related to prior authorization, a process that requires patients or their medical team to seek approval from an insurer before proceeding with care. These state efforts come as patients across the country await relief from prior authorization hurdles, as promised by dozens of major health insurers in a pledge announced by the Trump administration last year.

The West Virginia law was inspired by Eric Tennant, a coal-mining safety instructor from Bridgeport who died on Sept. 17 at age 58. In early 2025, the Public Employees Insurance Agency repeatedly denied him coverage of a $50,000 noninvasive cancer treatment, called histotripsy, that would have used ultrasound waves to target, and potentially shrink, the largest tumor in his liver. His family didn’t expect the procedure to eradicate the cancer, but they hoped it would buy him more time and improve his quality of life. The insurer said the procedure wasn’t medically necessary and that it was considered “experimental and investigational.”

Related coverage

After Series of Denials, His Insurer Approved Doctor-Recommended Cancer Care. It Was Too Late.

Eric Tennant’s doctors recommended histotripsy, which would target, and potentially destroy, a cancerous tumor in his liver. But by the time his insurer approved the treatment, Tennant was no longer considered a good candidate. He died in September. Read More

Becky Tennant, Eric’s widow, told members of a West Virginia House committee in late February that she submitted medical records, expert opinions, and data as part of several attempts to appeal the denial. She also reached out to “almost every one of our state representatives,” asking for help.

Nothing worked, she told lawmakers, until KFF Health News and NBC News got involved and posed questions to the Public Employees Insurance Agency about Eric’s case. Only then did the insurer reverse its decision and approve histotripsy, Tennant said.

“But by then, the delay had already done its damage,” she said.

Within one week of the reversal in late May, Eric Tennant was hospitalized. His health continued to decline, and by midsummer he was no longer considered a suitable candidate for the procedure. “The insurance company’s decision did not simply delay care. It closed doors,” his wife said.

Had the new law been in effect, Kimble said, Tennant could have undergone histotripsy without preapproval, because it was a less expensive alternative to chemotherapy, which his insurer had already authorized. The bill was passed unanimously by the state legislature in March.

U.S. health insurers argue that most prior authorization requests are quickly, if not instantly, approved. AHIP, the health insurance industry trade group, says prior authorization acts as an important guardrail in preventing potential harm to patients and reducing unnecessary health care costs. But denials and delays tend to affect patients who need expensive, time-sensitive care, studies have shown.

The practice has come under intense scrutiny in recent years, particularly after the fatal shooting of a health insurance executive in New York City in late 2024. Americans rank prior authorization as their biggest burden when it comes to getting health care, according to a poll published in February by KFF, a health information nonprofit that includes KFF Health News.

Samantha Knapp, a spokesperson for the West Virginia Department of Administration, would not answer questions about the law’s financial impact on the state. “We prefer to avoid any speculation at this time regarding potential impact or actions,” Knapp said.

In a fiscal note attached to the bill, Jason Haught, the Public Employees Insurance Agency’s chief financial officer, said the law would cost the agency an estimated $13 million annually and “cause member disruption.”

West Virginia isn’t an outlier in targeting prior authorization. By late 2025, 48 other states, in addition to the District of Columbia and Puerto Rico, already had some form of a prior authorization law — or laws — on the books, according to a report published in December by the National Association of Insurance Commissioners.

Many states have set up “gold carding” programs, which allow physicians with a track record of approvals to bypass prior authorization requirements. Some states establish a maximum number of days insurance companies are allowed to respond to requests, while others prohibit insurance companies from issuing retrospective denials after a service has already been preauthorized. There are also a crop of new state laws seeking to regulate the use of artificial intelligence in prior authorization decision-making.

Meanwhile, prior authorization bills introduced this year across the country, including in Kentucky, Missouri, and New Jersey, have been supported by politicians from both parties.

“Republicans in conservative states see health care as a vulnerability for the midterm elections, and so, unsurprisingly, you’ll see some action on this,” said Robert Hartwig, a clinical associate professor of risk management, insurance, and finance at the University of South Carolina. “They realize that they’re not really going to get much action at the federal level given the degree of gridlock we’ve already seen.”

Last summer, the Trump administration announced a pledge signed by dozens of health insurers vowing to reform prior authorization. The insurers promised to reduce the scope of claims that require preapproval, decrease wait times, and communicate with patients in clear language when denying a request.

Consumers, patient advocates, and medical providers have expressed skepticism that companies will follow through on their promises.

Becky Tennant is skeptical, too. That’s why she advocated for the West Virginia bill.

“Families should not have to beg, appeal, or go public just to access time-sensitive care,” she told lawmakers. Tennant, who sees the bill’s passage as bittersweet, said she thought her husband would have been proud.

During Eric’s final hospital stay, Tennant recalled, right before he was discharged to home hospice care, she asked him whether he wanted her to keep fighting to change the state agency’s prior authorization process.

“‘Well, you need to at least try to change it,’” she recalled her husband saying. “‘Because it’s not fair.’”

“I told him I would keep trying,” she said, “at least for a while. And so I am keeping that promise to him.”

—–

NBC News health and medical unit producer Jason Kane and correspondent Erin McLaughlin contributed to this report. KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF — the independent source for health policy research, polling, and journalism.

The post After Man’s Death Following Insurance Denials, West Virginia Tackles Prior Authorization appeared first on Medika Life.

Over 987 million people around the world now use AI chatbots regularly. Research shows that nearly half of Americans with ongoing mental health conditions have turned to a chatbot for emotional support in the past year alone. That’s a huge number of people relying on a technology that’s still very new in mental health care. So what does this mean?

Is it a big step forward in making help more accessible, or are we taking a risky chance? As with most things, the truth is somewhere in the middle. These tools offer real benefits, but they also come with real risks. It’s important to look at both sides honestly.

The Case for AI Chatbots in Mental Health

First, let’s look at why so many people are turning to these tools. There’s a mental health crisis, and not enough providers to help everyone who needs it. Long wait lists, high costs, and the ongoing stigma around seeking help all make it harder for people to get care. For someone who can’t afford therapy, can’t find an available provider, or feels too embarrassed to talk to someone in person, a chatbot that’s always available can feel like a lifeline.
Research supports this to some extent. Corporations are responding to this, and more TV ads are appearing that offer online therapy with or without chatbots.

A systematic review of 31 randomized controlled trials, which is considered the gold standard in research, found that AI chatbots helped reduce anxiety and depression symptoms in adolescents and young adults. Another meta-analysis of 14 strong trials found a clear positive effect on mental health outcomes, showing these tools are more than just placebos. For college students, who often face unique pressures and may avoid formal help, chatbots have shown promise in building coping skills and improving emotional well-being.

Anonymity is important, too. People are more likely to open up when they don’t feel judged. Studies show that users see the chatbot’s lack of social expectations as a big advantage. It’s easier to admit you’re struggling when you don’t have to worry about what someone else thinks. For people with anxiety, this low barrier could mean the difference between getting some support and getting none.

Mental health professionals have noticed these benefits, too. A 2025 study found that many clinicians see AI chatbots as a useful way to offer support between therapy sessions, provide education, and reach people who might not seek care otherwise. When the alternative is no help at all, the accessibility and scalability of chatbots are hard to ignore.

Where These Tools Can Cause Real Harm

This is where things get more difficult. The same qualities that make chatbots appealing, like being available, warm, and endlessly patient, can also make them risky for people in real psychological distress. We need to remember that chatbots are designed to keep users constantly engaged. It can be very hard to disconnect because the connection becomes so strong that it almost feels like leaving a friend.

Researchers have found something called a “compassion illusion” the strong feeling that an AI understands you, cares about you, and responds to your emotions in a meaningful way. An algorithm has no ability to “feel” or “care.” It feels real, but it isn’t. This gap between what people feel and what’s actually happening is where problems can start, especially for vulnerable people who may not realize they’re relying on something with no clinical judgment, no duty of care, and no way to notice if they’re getting worse.

A Stanford University study found that several popular therapy chatbots failed important therapeutic tests. They not only showed stigmatizing attitudes toward conditions like schizophrenia and alcohol dependence, but also gave dangerous responses in crisis situations. In one case, a chatbot responded to a subtle mention of suicidal thoughts by cheerfully naming tall bridges — something a good therapist would never do. Instances such as this have resulted in lawsuits related to suicides.

Another study tested ten AI chatbots using fictional teen mental health scenarios. Nearly a third of the time, the bots supported harmful ideas suggested by the fictional teens, such as dropping out of school or avoiding all human contact. None of the ten bots managed to challenge every dangerous suggestion. By any clinical standard, that’s a failing grade.

There’s also the problem of people relying too much on chatbots. Since these systems are always available and don’t make human mistakes, they can become someone’s main source of emotional support. Psychiatrists are now seeing cases of what’s called “AI psychosis” in patients, especially those with mental health vulnerabilities, who develop worse delusions or paranoia after spending a lot of time with chatbots. Because chatbots tend to agree and mirror rather than challenge distorted thinking, they can quietly make things worse over days or weeks.

This isn’t just a theoretical risk. It’s happening in clinical offices right now.

What We Still Don’t Know — and Why That Matters

The uncomfortable truth is that we don’t have enough research to know how often AI chatbots help, how often they cause harm, or who is most at risk. A review of 160 studies found that only 16 percent of the newer large language model-based chatbot studies had gone through clinical efficacy testing. Most are still in early testing stages. It’s like handing out a new drug before the clinical trials are finished.

Media coverage hasn’t made things clearer. Studies looking at news reports on AI chatbots and mental health found that journalism often focuses on the most severe, emotional outcomes, like suicides and hospitalizations, and presents them as clear cause-and-effect stories, even though the real evidence is much less certain. In most cases, there were already mental health conditions, substance use issues, or major life stressors involved. AI may have played a part, but it’s rarely the whole story.

Clinicians surveyed about AI chatbots have also raised concerns that aren’t getting enough attention. These include data privacy concerns, the risk that people will rely on chatbots instead of professional care, and the fact that these tools don’t know when to stop. They can’t pause a conversation, send someone to emergency services, or alert a family member. They can’t do the most important things when someone is truly in crisis.

The truth is that we’re still in the early days. Research is growing quickly — the number of studies on mental health chatbots quadrupled between 2020 and 2024. But strong, large-scale clinical evidence is still behind the technology. Millions of people are using these tools while science tries to keep up.

So what does this mean for you? An AI chatbot might really help you get through a tough night or teach you some coping skills. But it could also mislead you, support harmful thinking, or make you feel supported when you actually need a real person to help.

Use these tools carefully. If you’re dealing with serious depression, suicidal thoughts, trauma, or psychosis, they are not a substitute for professional care, no matter how warm or available they seem. If you’re using a chatbot for lighter support or just to sort out your thoughts, notice how you feel over time. Are you feeling more isolated or more dependent on it? That’s important to pay attention to.

This technology is here to stay. What we urgently need are clearer safety standards, better regulations, and more honest conversations about what these tools can and can’t do. Until then, a bit of healthy skepticism is helpful.

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Scientific Promise and Practical Benefits

A major strength of brain organoid research is its potential to improve our understanding of neurological and psychiatric conditions. Researchers can generate organoids from people with known genetic mutations to study how specific genes affect early brain development. This method has been used to study conditions like autism spectrum disorders, epilepsy, schizophrenia, and Alzheimer’s disease. It helps scientists find cell abnormalities that might not show up in animal studies.

Brain organoids are also useful for drug discovery and safety testing. Many treatments that work in animal models do not succeed in humans, especially for brain disorders. Organoids give scientists a human-based way to test how drugs affect neural cells. This can help spot toxic effects or benefits earlier, potentially lowering the risk of expensive late-stage failures and reducing unnecessary testing on people.

Limitations, Misconceptions, and Ethical Concerns

Even though brain organoids show promise, they have important limitations that are sometimes missed in public discussions. They lack blood vessels, immune cells, and sensory input, all of which are needed for normal brain function. Because they lack a vascular system, organoids obtain oxygen and nutrients only by diffusion, which limits how large and mature they can become. Most organoids end up looking like early fetal brain tissue, not fully developed brains. Does the appearance of something mean it will have the same abilities?

Variability is another challenge. Organoids grown in different laboratories — or even within the same lab — can vary in structure and cellular composition. This makes standardization difficult and complicates the interpretation of results. Additionally, reports of electrical activity within organoids have sometimes been mischaracterized as evidence of consciousness. Most neuroscientists agree that current organoids do not possess awareness, sensation, or thought, but the debate highlights broader uncertainties about how consciousness arises in biological systems.

As the science has advanced, ethical questions have also increased. There are concerns about informed consent when donor cells are used to make neural tissue, especially if donors did not know this could happen. Other worries come up when human organoids are put into animals, which raises questions about species boundaries and oversight. Although these experiments are closely regulated, many ethicists say clearer rules are needed as the technology develops.

Future Directions and Responsible Progress

Researchers are now trying to make brain organoids more realistic and useful. They are working on adding vascular-like systems, combining different organoid types to study how brain regions interact, and making results more consistent between labs. These improvements could help us better understand complex brain disorders and lead to more personalized treatments.

At the same time, ethical guidelines are changing to keep up with new scientific advances. Many experts say that as organoid research moves forward, it should be matched by openness, oversight from different fields, and regular public involvement. Brain organoids are not miracle cures or major threats; they are powerful but imperfect tools that can help neuroscience when used carefully. The future of this research will depend on both technical progress and a strong focus on ethics and public trust.

If all of this sounds like something from a Frankenstein movie, that would be one approach to take, but it isn’t realistic. We are only at the very beginning of understanding what the potential and the problems involved are for us. The research holds great promise, but it also requires informed restrictions that will not prevent advances.

The post Brain Organoids: Promise, Limits, and What Comes Next appeared first on Medika Life.

Primary care continuity allows physicians to develop a longitudinal awareness that no episodic encounter or health tech tool can replicate. Over time, physicians learn what is normal for each patient and what represents meaningful clinical change. Subtle physiological shifts, early symptoms or emerging risk factors appear not as isolated data points from a blood exam, but as part of a social narrative unfolding across time. Early recognition allows earlier intervention, often before disease takes its profound toll.

Clinical evidence confirms the protective effect of continuity. It’s not a matter of opinion. A systematic review published in BMJ Open found that patients with sustained continuity of care had significantly lower mortality than those with fragmented care. Continuity did not just improve satisfaction; it altered survival. The physician who knows the patient can detect disease earlier and guide care more effectively.

Listening allows physicians to detect patterns that laboratory values alone cannot explain. Patients share information differently when they believe that their physician understands them and remembers their history. This sustained awareness allows physicians to identify emerging illnesses without relying solely on reactive diagnostics. Continuity transforms listening into clinical intelligence and a deeper care partnership.

In Healing the Sick Care System: Why People Matter, which has become a surprise Amazon bestseller, one insight repeatedly emerges: patients do not seek care only for treatment; they seek reassurance that someone who knows them is guiding their journey. Physicians who listen across time accumulate knowledge that cannot be captured in a chart alone. That memory allows earlier recognition, more accurate interpretation, and wiser intervention. Healing begins in that continuity of understanding.

Transactional Care Solves Symptoms but Sacrifices Understanding

Health has, for some time, been undergoing a structural shift toward transactional encounters. Walk-in clinics, urgent care centers, and virtual platforms provide speed and accessibility that patients value. These models address immediate symptoms efficiently and fill important gaps in care delivery. Accessibility has improved, yet continuity has weakened.

Transactional medicine treats episodes rather than trajectories. Each encounter begins without the benefit of longitudinal understanding. Clinical decisions are made with time-stamp specific knowledge of how symptoms emerged or how physiology has changed over time. Care becomes reactive rather than interpretive.

Research demonstrates the consequences of this fragmentation. Studies published in the Annals of Family Medicine show that sustained primary care continuity reduces hospitalizations and lowers healthcare expenditures. Early recognition prevents complications that require more invasive, costly interventions. Fragmentation delays recognition and increases clinical risk.

In fact, physicians in the vanguard of building relationships encourage their patients to ask questions.  In their co-authored book Let Patients Help! A “Patient Engagement” handbook – how doctors, nurses, patients and caregivers can partner for better care by “e-Patient Dave” deBronkart with Daniel Z. Sands, MD, MPH, the founder of the Society for Participatory Medicine, offer 10 suggestions that clinicians say to encourage patient engagement.

This shift also alters how patients engage with care. Connections that develop over time can be lost quickly when continuity disappears. Patients become consumers navigating isolated services rather than partners guided across time. The clinical relationship weakens, and with it the interpretive depth that makes prevention possible.

Health systems globally recognize the value of continuity. The Organization for Economic Co-operation and Development (OECD), a Paris-based international organization that promotes policies to improve economic and social well-being globally, reports that hospital admissions for chronic diseases, often preventable through effective primary care, account for a substantial share of healthcare utilization. Systems that preserve physician-led primary care continuity achieve better outcomes and greater efficiency. Relationship stabilizes care.

Innovation Requires Connection to Fulfill Its Potential

This shift toward transactional care carries life-threatening implications that extend far beyond the patient experience. It also directly affects whether health innovation fulfills its promise or becomes a compensatory tool addressing fragmentation. Innovation depends on context to generate meaningful insight. Context emerges through continuity. That context can devalue life-saving innovations.

Artificial intelligence, predictive analytics, and remote monitoring technologies are designed to detect patterns across time. These tools require longitudinal clinical awareness to distinguish meaningful change from statistical variation. Physicians who know their patients can interpret innovation correctly and act earlier. Innovation becomes transformative when anchored in relationship.

Fragmented care weakens this interpretive capacity. Data collected across disconnected encounters lacks coherence. Predictive tools lose precision when longitudinal context is absent. Innovation becomes reactive, identifying disease after symptoms emerge rather than predicting disease before it develops.

Technology achieves its highest value when it extends the physician’s ability to listen and observe. Remote monitoring allows earlier recognition of physiological change. Predictive analytics strengthens preventive intervention. Innovation amplifies continuity when guided by sustained physician leadership.

Team-based primary care models reflect this principle. Nurse practitioners and physician assistants expand access while physician leadership preserves interpretive continuity. Research published in Medical Care Research and Review confirms that coordinated team-based care maintains strong clinical outcomes. Physician oversight ensures that innovation remains integrated within longitudinal care. It also improves health professional job satisfaction and reduces burn-out.

Innovation cannot replace the relationship at the center of medicine. Algorithms detect patterns but do not understand meaning, and they do not strengthen physician/patient ties. Devices collect data, but do not know the patient behind the data. Physicians translate information into guidance by integrating technology with human understanding.

The future of health innovation depends on preserving continuity between patient and physician. Technology deployed within sustained relationships strengthens prevention and improves outcomes. Technology deployed within fragmented systems often compensates for structural weakness rather than transforming care. Continuity determines whether innovation fulfills its promise.

Health systems now face a defining moment. Transactional care offers speed and convenience. Relational care offers understanding and prevention. Innovation will achieve its full potential only when it strengthens the continuity that allows physicians to listen, learn, and guide patients across time.

Healing begins with being heard. Health technology succeeds when it helps physicians listen more deeply and act more wisely in the service of the people who entrust them with their care.

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I have spent more than 30 years in healthcare with one consistent mission: helping people sustain and improve their lives. That mission has guided my work across large pharmaceutical companies, entrepreneurial startups, and academic institutions. It has shaped how I view innovation—not as a luxury, but as a necessity.

We often speak about healthcare innovation as if it exists in a vacuum. It does not. Innovation only matters if patients can access it, understand it, and afford it. Today’s system too often breaks that chain.

The U.S. healthcare system has evolved to prioritize risk management over prevention, short-term cost containment over long-term health, and utilization controls over patient outcomes. The consequences are real. Breakthrough therapies are delayed or denied. Preventive medicines are underused. Patients are left navigating complexity at the very moment they are most vulnerable.

However, innovation has repeatedly proven it can change the trajectory of disease—and lives—when it reaches patients.

Earlier in my career, I had the opportunity to help build Claritin into a household name. What made Claritin transformational was not just the molecule, but access. Non-sedating allergy relief allowed people to function—to work, learn, drive, and live daily life without compromise. We paired scientific innovation with brand-building, education, and emerging digital tools to enable patients to engage with their care in new ways. That experience taught me something enduring: innovation fails when it remains trapped behind complexity.

As digital channels emerged, I saw how virtual access could democratize care. Early online refill capabilities and digital front doors were not about marketing. They were about meeting patients where they were. Innovation is not only what happens in the lab; it is how solutions are delivered in the real world.

More recently, my work in cardiovascular and preventive medicine has reinforced this belief. Cardiovascular disease remains the leading cause of death globally, yet preventive innovation often struggles most to gain access. When therapies reduce future heart attacks, strokes, and hospitalizations—but do not show immediate cost offsets within narrow budget windows—they face resistance. This is actuarial logic colliding with human biology.

But prevention works. Inflammation matters. Long-term risk reduction matters. Helping people avoid catastrophic events enables them to remain productive, engaged, and present in their lives and with their families. The value of that outcome is difficult to capture on a quarterly balance sheet, but it is undeniable.

Innovation also matters because healthcare is not static. Populations are aging. Chronic disease is rising. Demand for care will only increase. Without continued pharmaceutical innovation—new mechanisms, better tolerability, improved adherence—we risk managing decline rather than enabling vitality.

Critics often frame innovation and affordability as opposing forces. They are not. The real tension lies between short-term system incentives and long-term societal benefit. When access to effective therapies is delayed or denied, costs do not disappear. They shift—reappearing as hospitalizations, disability, lost productivity, and diminished quality of life.

I have worked inside large organizations, small startups, and everything in between. I have seen how difficult it is to bring a medicine from concept to patient—and how fragile that final step of access can be. That is why innovation must be paired with thoughtful policy, modernized reimbursement, and a patient-centered view of value.

Healthcare should not be about simply surviving longer. It should be about living better for longer. Medical innovation, particularly in pharmaceuticals, plays a central role in making that possible. Even in a system burdened by complexity and constraints, innovation remains one of our strongest tools for advancing healthcare.

After three decades, my belief has not changed: when science, access, and mission align, lives improve. That is worth fighting to achieve.

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Since my field of study is educational psychology, and I am especially familiar with learning sciences, I write mostly about them. I believe combining research-based evidence is always more valuable and reliable than relying solely on personal ideas, even if they are logical.

In my writings and articles, I have repeatedly emphasized that sometimes we need to look back and integrate well-established scientific theories with modernity and artificial intelligence. I combine scientific evidence, including research articles and theoretical frameworks, with my own analyses, using them as a bridge to technology.

This approach and strategy prevent many potential risks. Instead of a preachy, rigid, or purely philosophical perspective, we adopt a systematic, scientific approach to derive practical solutions. One of the issues and concerns frequently discussed these days, which I have also mentioned in my recent articles, is the “consequences of excessive ease of performance through artificial intelligence.”In my latest article, I discussed the absence of “Fraction.”

In this article, I do not intend to discuss Fraction directly but rather focus on another challenge in the same area, which is not entirely unrelated to Fraction. This topic is the “level of anxiety and arousal resulting from facing performance.”

First, I will briefly explain this concept and then examine its connection to artificial intelligence systems.

Arousal Theory in Learning Psychology

One important theory in the neurophysiology of learning is Donald Hebb’s framework, which aligns with evolutionary approaches.

According to these perspectives, the human brain needs challenges to survive. The nervous system has evolved in challenging environments, and both anxiety and an optimal level of arousal have always been essential for survival. They increase alertness against potential risks and guide humans toward growth and the adaptation of necessary skills.

Donald Hebb, a neuroscientist, studied human learning, and one of his significant contributions was explaining the role of arousal in learning.

In Hebb’s framework, “arousal” is considered the fuel for the cerebral cortex to process information. Learning depends on neural plasticity, and this process occurs under an optimal level of arousal.

From this perspective, the brain is not simply trying to reduce tension but is seeking an optimal level of stimulation. If environmental stimuli are too low, the brain may create artificial stimuli or lose part of its natural efficiency.

As a result, neural firing and synaptic strengthening occur under the influence of arousal, and when arousal decreases significantly, the likelihood of forming or strengthening these connections decreases.

In addition to Hebb’s explanation, the classical “Yerkes-Dodson Law” also supports this necessity. According to this law, human performance improves with increasing physiological or mental arousal up to a certain point. When arousal is very low (a state toward which AI tools tend to push us), individuals experience reduced focus and cognitive motivation, and learning efficiency reaches its lowest point. In fact, a certain level of pressure or anxiety is not harmful; it is a prerequisite for achieving peak mental performance.

The “Arousal Gap” Challenge in Interaction with AI

As briefly explained in Hebb’s framework, the prerequisite for the neural interactions that lead to learning, perception, and cognitive actions is stimulation and arousal.

This moderate level of stimulation, which Hebb calls optimal arousal, is neither unpleasant nor at odds with the brain’s evolutionary nature in adaptation processes.

Now, imagine that a significant portion of our tasks is performed by an artificial partner and creates no direct cognitive responsibility for the individual. In such a scenario, what challenge will arise in human thinking?

These days, many articles and writings discuss the “excessive ease” challenge posed by AI tools. However, this article specifically focuses on reducing arousal levels and achieving optimal anxiety, according to Donald Hebb’s framework. Here, anxiety is considered one form of arousal, not equivalent to it entirely.

If most daily tasks are performed without prior stimulation or anxiety and without active cognitive engagement by AI, instead of the tools being under the consumer’s control, the consumer will be under their control.

From an evolutionary perspective, under such conditions, learning and cognitive adaptation processes will not align with the brain’s natural growth patterns, and the likelihood of effective knowledge adaptation will decrease.

The manifestations of this challenge will likely be observed in longitudinal studies as changes in the quality of cognitive performance and in neural circuit activity patterns.

References

Olson, M. H. & Hergenhahn, B. R. (2020). An Introduction to Theories of Learning (10th ed.). Routledge. 

Schachtman, T. R. & Reilly, S. (Eds.). (2011). Associative Learning and Conditioning Theory: Human and Non‑Human Applications. Oxford University Press.

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