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

The post Garbage In, Garbage Out: The Organizational Crisis Beneath Healthcare’s AI Gold Rush appeared first on Medika Life.

That is what makes the Digital Health & AI Innovation Summit (DHAI), taking place June 8-9 in Boston, distinctive within an increasingly competitive field of AI and innovation conferences. The Summit certainly offers a high-caliber program and noted speakers. However, its real value proposition beyond the agenda lies in the conversations and takeaways.

The carefully curated forum, organized by Amir Lahav, PhD, and World BI, is intentionally designed for a smaller community of roughly 500 attendees and more than 150 speakers and innovators. The result is that the connections become as valuable as the presentations.

That distinction matters more than many realize.

Artificial intelligence and digital health are moving at extraordinary speed. Health systems, pharmaceutical companies, regulators, investors and technology innovators are all trying to answer the same questions: How do we apply innovation responsibly while improving outcomes for patients and clinicians? How do we integrate AI into the R&D process? How can we leverage information technologies to accelerate the recruitment of the right people for clinical trials? The challenge is no longer simply technological capability. The challenge is implementation, governance and integration into the realities of care delivery.

Those questions are difficult to answer from the back row of a ballroom.

They are more likely to be explored over coffee between sessions, during a shared meal, or in quieter moments when people can challenge assumptions, exchange experiences and discuss what is actually working in health systems, research environments, and patient care settings.

That is where DHAI distinguishes itself.

The Power of Curated Expertise

What gives a conference enduring value is not only the quality of its speakers, but whether those speakers remain accessible enough to challenge assumptions, answer difficult questions and engage in unscripted dialogue. That is increasingly uncommon in modern health conferences, where influence often feels managed from a distance.

At DHAI, the proximity to the experience of 150 presenters is intentional.

The next era of health won’t be built in silos and it certainly won’t be forged by focusing on the hype. It requires leaders willing to share their failures alongside their successes, and their fears alongside their visions,” shares Amir Lahav, PhD, curator and DHAI organizer. “The DHAI Summit provides an exclusive, trusted space for these unfiltered conversations that rarely happen on public stages. This is an exclusive invitation to join the health AI  pioneers who are moving the needle and step into the room where the real trajectory of medicine is being shaped,” he adds.

For attendees seeking to understand how artificial intelligence is moving from experimentation to clinical reality, few conversations may prove more valuable than those surrounding the work of Dr. Dennis Wall at Stanford University. His groundbreaking efforts to apply AI to accelerate diagnostics, particularly in neurological and developmental conditions, reflect the growing intersection of machine learning and patient-centered medicine. In most settings, hearing someone like Wall speak might last 20 minutes. Here, the opportunity to continue the discussion between sessions may be equally important as the presentation itself.

The same can be said for leaders shaping the future of pharmaceutical innovation through AI. Thomas Fuchs, Chief AI Officer at Eli Lilly and Company, operates at the center of one of the most significant transformations underway in life sciences. His work integrating AI, pathology and drug discovery reflects how computational science is redefining therapeutic development. With pharmaceutical companies investing billions into AI-enabled research ecosystems, the ability to exchange perspectives directly with someone navigating those realities daily carries extraordinary value.

Precision medicine also takes on a more practical dimension through leaders such as John Axerio-Cilies, Chief Data and Technology Officer at Tempus AI. Tempus has become emblematic of how data science, oncology and artificial intelligence are beginning to reshape personalized medicine and diagnostics. Yet the real insight often comes not from keynote slides but from candid reflections on implementation challenges, physician adoption, workflow integration, and trust in AI-driven systems.

What also distinguishes the program is its recognition that health innovation no longer lives within traditional boundaries. Biology, computational science, organizational leadership and entrepreneurship are rapidly converging, creating entirely new expectations for how innovation enters the health ecosystem.

That reality becomes especially clear when considering trusted voices such as Tom Lawry, author of Hacking Healthcare and one of the most respected global advisors on AI strategy in health. For years, Lawry has argued that artificial intelligence alone cannot transform the delivery of care. Institutions themselves must evolve alongside technology. Leadership structures, workflow, culture and decision-making all become part of the innovation equation. His perspective reinforces an increasingly important truth: AI implementation is not fundamentally a technology challenge. It is a human challenge.

That same intersection between innovation and execution is reflected in the participation of Sally Ann Frank, Global Lead for Health & Life Sciences at Microsoft for Startups. Her work focuses on helping emerging companies move beyond promising ideas toward scalable and commercially viable solutions. Through strategy development, technical enablement and go-to-market support, she works directly with startups navigating the increasingly complex realities of AI, digital health and life sciences innovation. At a time when thousands of companies are entering the AI marketplace, Frank brings an unusually practical understanding of what separates experimentation from sustainable impact across the global health ecosystem.

The scientific and technical dimensions of the Summit are equally compelling. Arturo Loaiza-Bonilla, MD, MSEd, Co-Founder and Chief Medical AI Officer of Massive Bio, Network Chief of Hematology and Oncology at St. Luke’s University Health Network, whom I met recently during HITLAB Health Innovation Week in New York, champions an important evolution in medicine, where clinical leadership, oncology, data science and AI innovation are interconnected. His work sits at the intersection of precision medicine, clinical trials and responsible AI application, demonstrating how technology can expand access and support informed care decisions while keeping physicians and patients at the center of the experience.

The program also grounds innovation in the realities of patient care and health system operations. Through her leadership at NYC Health + Hospitals, Dr. Komal Bajaj has focused extensively on quality, equity and implementation within one of the nation’s largest public health systems. Her perspective introduces an important layer of realism into discussions that can sometimes become overly theoretical. AI may promise efficiency, but health systems must still ensure that innovation improves care delivery rather than complicates it.

That balance between aspiration and practicality is also reflected in leaders such as Lauren Li of Novartis, whose work in AI and innovation strategy demonstrates how global life sciences companies are integrating AI responsibly across research, development, and commercialization. The questions facing companies like Novartis are no longer whether AI will shape health innovation, but how to apply it responsibly while preserving scientific rigor and public trust.

Equally important to the DHAI agenda is the presence of Jeremy Walsh, Chief AI Officer at the Food and Drug Administration. At a moment when AI is moving rapidly into research, clinical decision support, diagnostics and operational health systems, regulatory leadership must provide oversight. FDA voice addresses a growing concern that innovation and governance cannot operate on separate tracks. The future of AI in health will depend not only on technological capability, but on transparency, accountability and safety. His perspective brings a policy and regulatory dimension to a conversation too often dominated by technology.

Taken together, these leaders represent more than expertise. They reflect the convergence of medicine, data science, biotechnology, health systems, patient engagement and policy. The global health ecosystem is entering a period in which barriers between disciplines are dissolving. Clinicians must understand data science. Technologists must better appreciate patient experience and the realities of workflow. Pharmaceutical leaders must think beyond molecules toward digital ecosystems and longitudinal patient engagement.

Why Human Connection Still Matters in the AI Era

That convergence changes the value of gatherings like this one. Large conferences often showcase these worlds side by side. Smaller curated forums create the possibility for those worlds to interact.

That dynamic is particularly important in digital health, where enthusiasm can sometimes outpace evidence. AI is neither a miracle nor a menace. It is a tool shaped by human intention, data quality and leadership. The most important conversations in AI and health today are not only about capability. They are about judgment.

How do we reduce physician burnout without depersonalizing medicine? How do we use predictive analytics responsibly? How do we ensure that innovation improves access rather than deepens disparities? How do we maintain trust while integrating increasingly autonomous technologies into patient care?

Those are conversations that require candor and mutual learning.

As someone attending and stepping to the stage during DHAI, I believe that may ultimately become its greatest differentiator. In health, relationships still matter. Communication still matters. Shared perspective still matters. Technology may accelerate insight, but human interaction remains essential to wisdom.

Health innovation does not advance through presentations alone. It advances through collaboration, challenge and conversation. Those exchanges between sessions often become the catalyst for strategies and unexpected ideas that continue long after this event comes to a close.

In a global health environment often defined by complexity, there is growing value in spaces where innovation feels ambitious and human. The DHAI appears designed to deliver that ROI.

The post The Value of Health AI Conferences Is No Longer the Stage. It’s the Hallway Conversation appeared first on Medika Life.

An operational model developed in Algeria may show a way that countries can make childhood vaccines more effective, more acceptable and more economical. It is a model that may provide a framework for middle-income countries across the globe, including many across the rest of Africa.

Six-in-one (or hexavalent) vaccines are cutting the number of clinic visits needed to prevent multiple life‑threatening infections and easing pressure on already stretched health systems. Growing economic evidence from Algeria and several Latin American countries suggests that while these vaccines may cost more upfront, the investment may be largely or entirely recovered through fewer appointments, streamlined logistics, and a reduction in cases of vaccine-preventable diseases and potential adverse events from vaccination. Yet the children who could benefit most – those living in low‑ and middle‑income countries are still the least likely to receive them, widening an avoidable gap between what modern vaccines can do and the protection children actually receive.

Most hexavalent vaccines save money in another way: they reduce the number of adverse events – side effects – that require treatment in a hospital or clinic. Acellular hexavalent vaccines include a type of protection against pertussis, or whooping cough, which is the gold standard for immunization in higher-income countries but has not yet been widely adopted beyond them.

With more than a decade of historical data supporting safety and efficacy, these acellular pertussis vaccines have a notable track record of improving vaccination coverage rates (VCR) and parents’ willingness to have their children protected, as they cause fewer painful adverse events [1].

Acellular pertussis (aP) vaccines are formulated using isolated antigens, which are purified and detoxified, thereby removing most of the components of the bacterium that cause undesirable reactions [2].

Most low- to middle-income countries still use whole-cell pertussis vaccines, which include a suspension of the entire inactivated Bordetella pertussis organism – some 3,000 antigens. Although the inclusion of far more antigens can result in a marginally higher immune response, the complexity of the vaccine leads to varying amounts of reaction-causing components between batches of vaccine and varying levels of protection [2].

The combination of more adverse events and variable efficacy means that developing countries bear a disproportionate share of the burden incurred through side effects. The side effects in children lead to an increased reluctance among parents to agree to future vaccines for their children and higher costs for the healthcare system. These problems often arise in healthcare systems that are inadequately equipped to deal with them.

Expert opinion from Algeria indicates that acellular hexavalent vaccination has improved vaccination coverage levels and simplified the vaccination schedule by reducing the number of appointments. It reduces the required number of immunization visits from ten to six. This eases pressure on overstretched health services, simplifies logistics and cold-chain management, and reduces indirect societal costs, including the time parents spend away from work.

Algeria is the third WHO African region country to adopt the acellular hexavalent vaccine into its national immunization schedule. Economic data from those countries and several in Latin America demonstrate that a rollout of the vaccine across African countries is not only possible but also economically advantageous [3,4,5,6].

Algeria’s vaccination metrics: an operational model for success

Vaccines have transformed child health in Algeria, as they have across the world. Since the initial introduction of vaccination in Algeria, followed by sustained efforts to expand the vaccination schedule, infant mortality rates have dropped dramatically from 163 per 1,000 live births in 1966 to 20 per 1,000 in 2023, a reduction of around 87% [7].

The percentage of children protected in Algeria has exceeded the targets set by the World Health Organization (WHO) for decades, with diphtheria, tetanus toxoid and pertussis (DTP) coverage consistently above 90% [8]. As in many countries, the COVID-19 pandemic disrupted healthcare systems, leading to a decline in vaccination rates, with DTP-3 coverage, a key measure of childhood vaccination, reduced to 77% in 2022 [9]. This situation was quickly improved, with coverage increasing to 92% by 2024 [9].

In 2022, three cases of polio caused by circulating vaccine-derived poliovirus type 2 were reported [10]. Rarely, the circulating vaccine-derived virus causes polio, highlighting the necessity of timely vaccination with IPV, with which these vaccine-derived cases do not occur [11].

The shift to hexavalent vaccination

Algeria’s shift from its former schedule to hexavalent vaccination was not a straightforward process. Initially, the five-in-one (or pentavalent) vaccine was replaced by a combination of the tetravalent vaccine (DTP-Hib) and the monovalent Hepatitis B vaccine (HBV), administered across 10 separate healthcare visits, necessitating additional appointments [12].

The change in the vaccination schedule resulted in delays in dose administration and a decrease in vaccination coverage. This issue was resolved with the introduction of the new schedule, which integrated an acellular hexavalent vaccine in 2023, reducing the number of required healthcare visits to six [13].

While polio vaccination was present in the previous schedule (with one IPV dose at 3 months and 3 OPV doses at 2, 4, and 12 months), inclusion as part of a hexavalent vaccine simplified the schedule (giving three doses of IPV at 2, 4, and 12 months associated with three OPV doses), helping to maintain the global strategy for polio eradication. The WHO recommends that all countries using OPV adopt a vaccination schedule with at least two doses of inactivated vaccine, which gives individual protection without the risk of vaccine-related polio [14].

The financial metrics of the switch

A recent whitepaper conducted a pharmacoeconomic analysis of the budgetary impact of transitioning from a whole-cell tetravalent vaccination schedule to an acellular hexavalent schedule. While the switch was associated with an increase in annual program expenditure of approximately 26 million Euros (around a 58% rise in upfront costs), this was substantially offset by nearly 19 million Euros in annual savings generated through the management of adverse events, improved logistics and transportation, and increased parental productivity [13]. Overall, roughly 73% of the upfront cost was offset by these savings.

Algeria is the latest in a series of examples where this is the case. The nominal, upfront cost of acellular hexavalent vaccines is typically higher than that of whole-cell vaccines; this has, in many cases, deterred countries from adopting them. However, there are many benefits at both the economic and systemic levels that recoup much of the costs of acellular hexavalent vaccines. In many instances, these costs are hidden and not factored into initial value calculations.

Similar experiences have been seen in other countries. In Argentina, Peru, and South Africa, the switch to hexavalent vaccines led to higher initial costs, but these were substantially offset by savings from fewer adverse events, lower programmatic expenses, and improved logistics. For example, in data from Argentina, roughly 90% of the initial investment into acellular hexavalent vaccines was recovered through fewer adverse event-associated costs and lower programmatic costs [15]. Peru reported a reduction in logistical costs by nearly 60%, with roughly 44% of the initial increase in costs recovered [16]. South Africa achieved overall savings of about 10 USD per child [3].

These calculations overlook benefits that are more difficult to quantify. For example, what costs are generated because of vaccines missed and infections caused by increased vaccine hesitancy on the part of parents. Across these settings, the higher upfront investment in hexavalent vaccines has proven to be economically viable, with much of the cost recouped through broader system efficiencies.

Programmatic benefits of hexavalent vaccination

Hexavalent vaccination offers the potential for simpler systems and higher levels of acceptance among patients. For the child, integrating six antigens into a single injection drastically reduces the number of needle sticks, alleviating injection-related anxiety and the prevalence of local reactions. This increases parental acceptance and helps to improve vaccination coverage.

Parents are relieved of the burden of coordinating multiple medical appointments, covering travel costs, and dealing with lost workdays. By reducing parental anxiety and the strain of repeated visits, combined vaccines help mitigate vaccine hesitancy within communities. This has been demonstrated in multiple studies across Africa, with investigations in Gambia and South Africa documenting concerns among parents about a child receiving more than two injections in a single visit [17,18]. Limiting the number of healthcare visits is also a crucial factor in increasing vaccine coverage in areas with limited healthcare infrastructure, such as those in rural southern Algeria.

For healthcare professionals, particularly in resource-limited settings such as rural areas in Africa, the adoption of combined vaccines helps to ease the administrative burden of multiple appointments. These formulations optimize consultation efficiency by drastically reducing the required administration time and simplifying inventory management [19].

The use of ready-to-use liquid vaccines, such as the hexavalents, has been shown to simplify and enhance the safety of the vaccination procedure when compared to vaccines that come as a powder that has to be reconstituted [20]. The preference for this approach among frontline workers is overwhelming: one study indicated that 97.6% of healthcare providers favored these liquid, combined formulations in their daily work [21]. Evidence supports this preference, demonstrating that the switch led to a dramatic reduction in administration errors (from 42.8% to 4%) and needlestick injuries (from 42.3% to 9.5%), while also yielding an average time savings of 1.1 minutes per dose [22].

The case for Hexavalent vaccination across Africa

Across the WHO Africa region, VCR has improved significantly over the last few decades; however, unfortunately, this improvement has stalled. The level of coverage for the third dose of DTP-containing vaccines, a standard benchmark for immunization system performance, has sat in the mid-70s for over a decade, with a current coverage of 76% [23].

This stagnation of the VCR is reflective of the ongoing issue of inequality. The gap in vaccine access runs not only between Africa and higher‑income regions, but also within the continent itself, where some countries consistently outperform others. Diseases, however, do not recognize borders; any outbreak that affects one country is likely to increase the risk to surrounding countries. Air travel enables a disease case to be spread to virtually any country in the world within just two days [24].

The COVID-19 pandemic was a clear example of the rapid spread in today’s world. Within a period of weeks, the virus spread from its origin in China to the entire globe, despite public health measures and lockdowns. With this in mind, any country that is falling behind on vaccination coverage becomes a weak link in a global chain where diseases can flourish and form reservoirs of cases that can allow diseases such as polio to spread unchecked.

Bringing vaccine equity to lower- and middle-income countries is therefore vital to addressing global health concerns. Hexavalent vaccination has demonstrated its ability to increase vaccine coverage in these countries. Among the WHO Africa region, Mauritius, which adopted hexavalent vaccination in 2017 [4] currently stands notably above the average for the region, with 96% coverage for the first dose of inactivated polio vaccine, and 93% for the benchmark based on DTP-containing vaccines [25].

Vaccine coverage translates into increased prosperity. Vaccination cannot be viewed as an inconvenient expense but as an investment. The WHO estimates that for every dollar spent, vaccination can yield a return on investment of around 54 USD – provided, of course, that the vaccines actually find their way into the arms of children [26].

As the Algerian case study demonstrates, higher upfront costs for acellular hexavalent      vaccines are often paid back by improvements in logistical efficiency, reduced healthcare burdens, and improved parental compliance. By bridging the gap between high- and low-income immunization standards through this investment, the life-saving benefits of gold standard vaccinations can become more than a privilege of geography, but a universal foundation for human health.

[This consensus paper is based on the findings of a white paper discussing the findings of a group of vaccination experts focusing on paediatric immunisation, supported by Sanofi. Intended for professional use.]

Reference list

1. Boisnard, F., Manson, C., Serradell, L., & Macina, D. (2023). DTaP-IPV-HB-Hib vaccine (Hexaxim): an update 10 years after first licensure. Expert Review of Vaccines, 22(1), 1196–1213. https://doi.org/10.1080/14760584.2023.2280236
2. World Health Organization (2017) The immunological basis for immunization series: module 4: pertussis, update 2017. Available at: https://www.who.int/publications/i/item/the-immunological-basis-for-immunization-series-module-4-pertussis-update-2017
3. Batson A, Glassman A, Federgruen A, et al. The world needs to prepare now to prevent polio resurgence post eradication. BMJ Global Health. 2022;7(12):e011485. doi: https://doi.org/10.1136/bmjgh-2022-011485
4. ReliefWeb. Hexavalent vaccine: less injections and more protection babies. Available at: https://reliefweb.int/report/mauritius/hexavalent-vaccine-less-injections-and-more-protection-babies
5. Olivera, I., Grau, C., Dibarboure, H. et al. Valuing the cost of improving Chilean primary vaccination: a cost minimization analysis of a hexavalent vaccine. BMC Health Serv Res 20, 295 (2020). https://doi.org/10.1186/s12913-020-05115-7
6. Romero M, Góngora D, Caicedo M. Cost-Minimization and Budget Impact Analysis of a Hexavalent Vaccine (Hexaxim®) in the Colombian Expanded Program on Immunization

Value in Health Regional Issues, 2021; 26, 150-159

7. World Bank Data. Available at: https://data.worldbank.org/indicator/SP.DYN.IMRT.IN?locations=DZ
8. World Health Organization. Immunization data: African region. Available at: https://immunizationdata.who.int/dashboard/regions/african-region/DZA
9. World Health Organization.  DTP vaccination coverage. Available at: https://immunizationdata.who.int/global/wiise-detail-page/diphtheria-tetanus-toxoid-and-pertussis-(dtp)-vaccination-coverage?CODE=DZA&ANTIGEN=DTPCV3&YEAR=
10. GPEI – Algeria. Available at https://www.archive.polioeradication.org/where-we-work/algeria/
11. Global Polio Eradication Initiative. GPEI-OPV. polio global eradication initiative . Published 2016. Available at: https://polioeradication.org/polio-today/polio-prevention/the-vaccines/opv/           
12. Practical Implementation Guide for the 2016 National Immunization Schedule in Algeria. Available at: https://cnpm.org.dz/wp-content/uploads/2024/01/Guide_Pratique_de_Mise_en_Oeuvre_du_Nouveau_Calendrier_Natio-1.pdf
13. Laichour A, Kihel M, Aissaoui A, Olivera G. Pharmacoeconomic evaluation of national immunization program realisation in Algeria: cost-minimization analysis of switch from DTwP-Hib + HBV + IPV to an acellular hexavalent (DTaP-HBV-Hib-IPV) vaccine. Poster presented at: ISPOR Europe 2023; November 2023; Copenhagen, Denmark. Value in Health. 2023;26(Suppl 2):S2-EE134.
14. WHO Polio Position Paper 2022. Available at: https://www.who.int/publications/i/item/WHO-WER9725-277-300  
15. Olivera, I., Pérez, C.G., Lazarov, L. et al. Cost minimization analysis of a hexavalent vaccine in Argentina. BMC Health Serv Res 23, 1067 (2023). https://doi.org/10.1186/s12913-023-10038-0
16. Seinfeld J, Rosales ML, Sobrevilla A, López Yescas JG. Economic assessment of incorporating the hexavalent vaccine as part of the National Immunization Program of Peru. BMC Health Serv Res. 2022 May 16;22(1):651. doi: 10.1186/s12913-022-08006-1. PMID: 35570278; PMCID: PMC9109284.
17. Idoko OT, Hampton LM, Mboizi RB, et al. Acceptance of multiple injectable vaccines in a single immunization visit in The Gambia pre and post introduction of inactivated polio vaccine. Vaccine. 2016;34(41):5034-5039. doi: https://doi.org/10.1016/j.vaccine.2016.07.021
18. Hanani Tabana, Dudley L, Knight S, et al. The acceptability of three vaccine injections given to infants during a single clinic visit in South Africa. BMC Public Health. 2016;16(1). doi: https://doi.org/10.1186/s12889-016-3324-2
19. Pelissier JM, Coplan PM, Jackson LA, May JE. The effect of additional shots on the vaccine administration process: results of a time-motion study in 2 settings. Am J Manag Care. 2000 Sep;6(9):1038-44.
20. Al-Bashir L, Ismail A, Aljunid SM. Parents‘ and healthcare professionals’ perception toward the introduction of a new fully liquid hexavalent vaccine in the Malaysian national immunization program: a cross-sectional study instrument development and its application. Front Immunol. 2023;14:1052450.
21. De Coster I, Fournie X, Faure C, Ziani E, Nicolas L, Soubeyrand B, Van Damme P. Assessment of preparation time with fully-liquid versus non-fully liquid paediatric hexavalent vaccines. A time and motion study. Vaccine. 2015;33(32):3976–82.
22. Esteve IC, Fernández PF, Palacios SL, Rodrı́guez MJ, Vino HP, Ortega BR, Nieto Nevot ML, Manch´on GD, L´opez-Belmonte J-L. Health care professionals’ preference for a fully liquid, ready-to-use hexavalent vaccine in Spain. Prev Med Rep. 2021;22:101376.
23. World Health Organization. Immunization data: African region. Available at: https://immunizationdata.who.int/dashboard/regions/african-region
24. Findlater A, Bogoch II. Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel. Trends Parasitol. 2018 Sep;34(9):772-783. doi: 10.1016/j.pt.2018.07.004. Epub 2018 Jul 23. PMID: 30049602; PMCID: PMC7106444.
25. World Health Organization. Immunization data: Mauritius. Available at: https://immunizationdata.who.int/dashboard/regions/african-region/MUS
26. World Health Organization (2025) Fully funded Gavi, the Vaccine Alliance, is a lifeline for child survival, says WHO. Available at: https://www.who.int/news/item/28-03-2025-fully-funded-gavi–the-vaccine-alliance–is-a-lifeline-for-child-survival–says-who

The post An Expert Perspective from Algeria on Hexavalent Vaccine Adoption appeared first on Medika Life.

The Strait also ships vaccines.

Save the Children has a consignment of urgently needed medicines stuck at a supplier’s warehouse in India. The road route is closed due to conflict. The usual fallback — air freight — has just doubled in price due to jet fuel prices. The charity’s chief executive, Janti Soeripto, put the situation to NPR earlier this month: “The transport for the drugs is more expensive than the drugs themselves.” That sentence is the story this piece is about. Not the Strait, not the oil, not even the war. The slow, awkward arithmetic by which a maritime closure thousands of miles away ends up determining whether a child in Kandahar gets a vial of antibiotics.

What the Strait actually carries

Commercial activity through Hormuz remains around 90 per cent below pre-war levels, according to analysis from the Council on Foreign Relations. Pre-conflict, around 3,000 vessels transited the strait each month; the latest House of Commons Library figures put current traffic at roughly five per cent of that. The strait is partially open, partially closed, and oscillating depending on the state of the Lebanon ceasefire and which side has most recently accused the other of violating it.

The pharmaceutical reading of those numbers takes a different shape. The Gulf Cooperation Council region serves as a transit hub linking Africa, Asia, Europe, India and the United States, and its pharmaceutical industry, valued at $23.7 billion, relies on imports through Gulf airspace and the strait for around 80 per cent of its product. Most of what matters most moves by air, not by container ship. Wouter Dewulf, professor at the University of Antwerp and a specialist in pharmaceutical logistics, told Al Jazeera last week that 35 per cent of pharmaceuticals move by air, and around 90 per cent of life-saving pharmaceuticals and vaccines do. He estimates that 22 per cent of global air cargo flows are exposed to disruptions in the Middle East.

Why a closed sea lane raises the cost of a mosquito net

The mechanism is rarely intuitive. India, which produces almost half of US generic prescriptions, depends on the strait for around 40 per cent of its crude oil imports — and that crude is the upstream feedstock for the petrochemicals used in active pharmaceutical ingredient manufacturing. With oil trading above $100 a barrel, the cost of producing the ingredient rises before a single tablet has been pressed. Indian air cargo rates have climbed 200 to 350 per cent on some routes, according to industry analysis, and war-risk insurance premiums for vessels transiting Hormuz have, by some measures, surged more than 1,000 per cent since late February.

The exposure is not abstract. The US Pharmacopeia has flagged that 48 per cent of US amoxicillin oral suspension is produced in Jordan, alongside a quarter of doxycycline hyclate capsules — common antibiotics, sourced from inside the conflict’s regional footprint.

It travels further than that. Jean Kaseya, director-general of Africa CDC, told reporters earlier this month that fuel shortages are pushing up the cost of producing mosquito nets, which are made from polyester, which is made from petrochemicals, which depend on a sea lane currently being charged at over a million dollars a transit when it is open at all. Malaria control is now, by an unobvious chain of reasoning, also a Hormuz story.

And the medicines that can’t wait

Of all the downstream consequences, the cold chain is the most exposed. Vaccines, insulin, biologics, and cancer therapies must be maintained within a narrow temperature range, typically between 2 and 8 degrees Celsius. Most of those products move by air, not sea, and most of the world’s high-volume air corridors run through Gulf hubs that have been variously closed, struck or rerouted around. Prashant Yadav, senior fellow for global health at the Council on Foreign Relations and one of the leading specialists in the field, has pointed to the timing problem with characteristic clarity: cargo carriers need roughly a week and a half to recover for every week of suspended shipments.

The arithmetic compounds.

It is partly a structural constraint. Yadav has also noted that European airlines and the two African carriers that have stepped in are unlikely to add new cargo capacity, as the disruption might continue for a few more months. Capacity is not bought overnight, and the current ceiling is, more or less, the medium-term one.

The countries most exposed are those already short of a buffer. The European Union has a stockpiling mechanism. The UK has flagged the risk of medicine shortages within weeks, but holds some reserve. The United States ordered a six-month stockpile of essential medicines last year. Sub-Saharan Africa, by contrast, imports around 70 per cent of its pharmaceuticals and runs far closer to the wire — arriving at this moment as aid budgets across major European donors are repackaged as investment rather than grants. Routine immunisation in much of the region relies on Gavi-procured stock that travels through the same air corridors, and the cold chain in those settings was already fragile before any of this began. How long current buffers hold is a function of variables nobody is in a position to forecast confidently. Bob Kitchen, vice-president of emergencies and humanitarian action at the International Rescue Committee, who is based in Nairobi, told NPR that he had not seen a comparable convergence in his career — pandemic, Ukraine and the current crisis included. A UN-managed depot in East Africa is currently holding stocks bound for Sudan, Ethiopia and other acute crises that cannot be released.

Save the Children’s drugs are still in India. As of late April, the strait remains effectively closed despite a conditional ceasefire, with Iran and the United States locked in a dual blockade as Pakistan-mediated talks continue. France and the UK have signalled that they will lead an international defensive mission once a sustainable ceasefire holds. None of that gets a vial to Kandahar this week.

What is the longer-term lesson? Supply chain analysts have been writing it for years, and now have a vivid case in front of them. A global medicine system optimised for cost works only as long as nothing goes wrong in three or four key chokepoints. Hormuz is one. The Suez and the Bab al-Mandeb are others. The Panama Canal is a fourth. The system functions until it doesn’t, and the people who feel the failure first are rarely the people the system was designed for.

Soeripto’s sentence is worth reading again. The transport for the drugs is more expensive than the drugs themselves. It is not, on its face, a sentence about war or oil or even shipping. It is a sentence about who, in a system held together by chokepoints, ultimately pays the bill. The strait will reopen. The arithmetic — and the question of who absorbs it — will not.

The post The Strait That Ships the World’s Vaccines appeared first on Medika Life.

From court decisions, recent analysis challenging vaccine skepticism polling results, and congressional testimony, the past month reflects a vindication of the value of immunization, scientific advancement and plain old common sense over ideology-based medical beliefs untethered from clinical evidence.

It would be nice to think that cooler heads have prevailed and we are seeing a return to science-backed decisions guiding American vaccine policy. But the reality is that a resurgent defense of immunization practices may be driven by two bigger forces: measles and midterms.

As I wrote five years ago, disease can be a powerful change agent. Thanks to the ultra-high effectiveness of the measles vaccine to prevent measles outbreaks, an entire generation of kids, parents and healthcare providers had never seen the disease or knew what kind of devastation it could bring. With measles out of sight and out of mind, it was easy for a small band of vocal critics to cast doubt on the value of the measles vaccine. Instead of building on a culture of collective action against disease, we allowed that small band of critics to grow into a chorus of public health freeloaders.

Measles had other ideas. With our weakening herd immunity – a result of declining vaccination rates – it didn’t take much for the virus to quickly reintroduce itself. Since the start of the year, there have been more than 1,700 cases of infection across 19 outbreaks throughout the country. We haven’t seen case numbers this high in 35 years. And if the deaths of American children from measles aren’t tragic enough, we are now on the verge of losing our status of officially eliminating measles. As a preeminent leader in immunology science, it is a startling embarrassment for the U.S. to accept this public health defeat.

Americans of all political stripes now seem to be paying attention. Following a decade of significant decline in vaccination, particularly among Republicans, there now is a push to back away from hardline anti-vaccine rhetoric ahead of the midterm elections. It’s easy to see why: at a time when measles outbreaks are a highly visible example of failed policy by the incumbent ruling party, politicians are not willing to risk being associated with practices that are out of step with the direction in which most U.S. voters want to go.

It’s telling that 66% of MAGA voters support vaccination as a requirement for kids to attend school. The measles outbreak has done a lot to educate people on the value of vaccines, which may be one reason why last week’s congressional testimony by RFK Jr., in which he was forced to admit that the measles vaccine is both safe and effective, and the timely appointment of Dr. Erica Schwartz, a physician and vaccine supporter, to lead the CDC may reflect the political liability posed by alternative vaccine doctrine in the months leading up to the midterm elections.

With growing distrust in federal vaccine messaging, there is a vacuum of credible sources for Americans to turn to for vaccine guidance. That void may actually be an opportunity in disguise for vaccine communicators.

Health care providers, including pharmacists, are still the most trusted source for reliable vaccine information: 4 out of 5 Americans look to these professionals to provide the right mix of personalized, empathetic communication with credible safety and efficacy information. Their stories can carry the power of connection, compassion and candor that we need right now.

Vaccination may seem like a black and white decision for a lot of us, but health care providers know that many parents need help navigating the gray areas. How we tell those stories – and who tells them – is essential to strengthening the national conversation around immunization.

Working more closely together, vaccine makers, innovators in antibody science, medical institutions and non-profit advocacy groups can create more compelling, unified communications that reach people when they are closest to making immunization decisions. This can be done by leveraging the voices of medical professionals to convey the emotional value of protecting our children against preventable disease instead of defaulting to statistics-heavy, complex messaging; pulling those stories through in coordinated media and policymaker engagement; and linking back to credible research sources that feature more prominently in online searches.

If the current measles epidemic in the U.S. is a crisis of our own making, it’s our responsibility to leverage the harsh health and economic lessons from this experience. We must act, not for the political convenience of the midterm elections, but to create better, more durable immunization policies and communications that again can unite Americans against our common disease enemies.

The post Of Measles and Midterms appeared first on Medika Life.

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.

Perhaps we should not have been surprised. He was always on the go, ate healthy meals, never smoked, and enjoyed being with friends.

He always seemed calm and collected, so on his 101st birthday, I asked how he had managed his stresses — his ship, the Canberra, was torpedoed during WWII with the loss of many of his buddies, and he was nearly killed. He had lost his daughter to cancer when she was forty, and his wife to cancer when she was 67. Yes, he had grieved greatly, but somehow, he was resilient and came back strong each time. He had been very sad but lived through his grief and always appeared unperturbed. “Well,” he said, “I guess I just let stress roll off my back.”

Organs decline about 1% per year.

One of the most important things that happens with aging, sort of like a car, “old parts wear out.” Most organ functions decline by about one percent per year. Of course, there is great variation from person to person, year to year, organ to organ, but 1% is a pretty good average to consider. We usually think of a person being in their prime through at least age 60. In many ways, that is correct, but you may be surprised to learn that the 1% decline starts in early adulthood and continues throughout life, speeding up as we age. Fortunately, most of our organs have significant redundancy so that we can tolerate these declines without any appreciable impact. But eventually, the decline may get to the point where we have a functional impairment that can be serious or at least impair our day-to-day activities.

I recently turned 84. I don’t feel “old,” but I do know I can’t do everything I used to do, or at least not as quickly. My hearing is less; my vision is reduced. My muscle mass and strength are definitely much less than they were in the recent past. My balance is OK, but not as good as it used to be, and so on. It has been a set of changes that came slowly at first but are now progressing faster. I always enjoyed splitting wood for the fireplace. I kept a woodshed filled with wood split and logs drying for a year before splitting. As time went on, I realized that I couldn’t keep at it for as long before wanting to take a break. As one friend in his early 80s told me, “I was fine until about age 78, and then it seemed that the aging process was suddenly there and moving fast.” Those declines, developing “under the radar” for decades, had accelerated and become overt.

The 1% Per Year Decline, Author’s image

Hearing decline begins at about age 25 but is not noticed until much later. Many of you will need reading glasses by age 40, even though you have had excellent vision for years; cataracts may occur later. Balance starts its inevitable decline early, although it, too, will not be noticed until much later. Meanwhile, internal organs, including the heart, lungs, and kidneys, are slowly declining, and so too is brain function, especially cognition.

This normal aging process of old parts wearing out is universal and is progressive, but you can slow it and sometimes reverse it, at least partially. Let’s use bone mineral density (BMD) as an example. BMD is easily measured to demonstrate the sturdiness of our bones — how strong they are. We start life with cartilage rather than bones. As we grow from toddlers to children to teenagers, calcium and other minerals, along with a protein-collagen matrix, are laid down in our bones, and they become increasingly strong, reaching a peak around the age of twenty. Once that age and that peak are reached, it can’t go up any further — that’s it. Then there is a plateau, and at about age thirty to forty it starts to decline at a rate of about 1% per year. At age twenty, men’s bone mineral density is, on average, higher than women’s.

Nevertheless, for women as for men, the decline is about one percent per year. Menopause changes this; the rate of loss increases to perhaps three percent per year for a few years and then returns to the one percent average decline until reaching osteopenia and then osteoporosis. There are three important points to consider. If you live long enough, your bone mineral density will decline to a level where, if you fall, a bone fracture becomes more likely. Since women start at a lower level and because they have this increased loss of BMD during menopause, they’ll reach that fracture threshold in life earlier than men. Since women tend to live longer than men, in total, more women than men will have a fracture at some point in their lives. We might just say this is one of the risks of living longer.

But why will you fall? Because your balance mechanism is likewise declining, and your muscle mass and strength are not as capable of “catching” your fall. The three combine together in a very negative manner!

Muscle mass and strength decline in a similar fashion, resulting in what doctors call sarcopenia. Most people lose perhaps 30% of their muscle mass between ages 50 and 70, and the loss continues at an even faster rate thereafter. Older individuals who exercise find it takes more effort to maintain their muscle mass and strength, but regular exercise and good nutrition have a significant beneficial impact and slow the process considerably.

Cognitive function is another example; your brain loses some of its abilities as you age. Cognitive abilities and brain volume do not decline in lockstep but do have a clear relationship. A fascinating study published in the journal Nature in April 2022, pulled together 123,984 MRI scans taken at over 100 institutions from 101,457 individuals ranging from 115 days post-conception to 100 years of age — from fetuses to centenarians. At the age of three years, the brain had reached 80% of its maximum size. The gray matter, which consists of the actual brain cells, reaches its maximum by about age 6, whereas the white matter, the inner connections between brain cells, does not reach its peak until the late 20s. The decline in brain volume thereafter is slow but accelerates after about age 50. These changes can be seen in the figure, which shows the growth trajectories of gray and white matter. The charts show volume (in mm3) across age, beginning before birth and ending at about 100 years.

If you are over 65, you have probably noted that you can’t memorize as well, recall names as quickly, etc. When I was in medical school, memorization was relatively easy; not so today! This is normal. This loss of cognitive function over time should not be confused with the disease Alzheimer’s. Nearly everyone who lives long enough will suffer from some cognitive decline, but only some will develop Alzheimer’s. As with BMD, you reach your peak cognitive function around age twenty; it plateaus for about 10 years, then starts that slow decline. Given the great redundancy in your brain, it is not noticeable for some time. Eventually, you reach a functional threshold where your cognitive function begins to impair your ability. This becomes more apparent when an older person is engaged in highly technical activities, very fast-paced activities, or stressful situations (emotional, physical, or health-related). Those cognitive challenges are less apparent in highly familiar situations.

This 1% annual loss is normal. No, it is not an exact number, nor is it the same for every person or every organ, nor is it exactly 1% in the same person at all times. But 1% is a good proxy for what is happening throughout your body throughout adulthood and into your elder years.

Slowing the aging losses

Here are some suggestions to slow that steady decline in functions. If you start at age twenty with very strong bones (i.e., a high BMD), then you have a longer way to go down before reaching that level of potential bone fracture from a fall. Perhaps too late for you if you are older, but encourage your children and grandchildren to eat a good diet, play/exercise daily, preferably outdoors, manage their stress, and get a good night’s sleep. And for their brains, do just the same (quality food, plenty of exercise, sound sleep, and managed stress) as for their other organs. Then, continually challenge their brains with new learning. Those with more years of schooling will start out with greater reserves, so the 1% decline will take much longer to cause difficulties. Muscle mass and strength are similar. Encourage them to build it up now as a teen. No need to be a muscled bodybuilder, but regular exercise and a good diet will mean more strength at the start of that long decline.

Most people seem to accept that, with age, comes a decline in function. “That is just what happens when we get older.” They may not like it, but they do not realize that they can substantially modify the downhill course. You can slow this continuing loss of body functions, including cognitive decline, and you can start at any age. It is never too late. It is not hard to do, and it does not cost money, but it does take time and persistence. It is all about how you move, what you eat, how you manage chronic stress, get adequate sleep, avoid tobacco, not too much alcohol, plus challenge your brain and keep socially engaged.

Stephen C Schimpff, MD, MACP, is a quasi-retired internist, professor of medicine, former CEO of the University of Maryland Medical Center, and author of Longevity Decoded — The 7 Keys to Healthy Aging, and is co-author with Dr. Harry Oken of BOOM — Boost Our Own Metabolism

The post Normal Aging — A Steady Decline in Organ Size and Functions appeared first on Medika Life.

Suicide remains one of the leading causes of death in the United States, with young people particularly affected. These are not abstract figures. Each life lost represents a story interrupted, a family altered, and a community left to navigate grief and unanswered questions. Public health requires that we confront this reality not only with data, but with a commitment to building systems that respond to human experience in real time.

From Fragmentation to Continuity Across the Lifespan

For many years, suicide prevention in the United States has been shaped by dedicated organizations working across research, advocacy, education, and crisis response. The American Foundation for Suicide Prevention has played a central role in advancing scientific understanding, funding critical research, and advocating for national policy changes that recognize suicide as a preventable public health issue. Its work has helped elevate awareness, influence legislation, and bring suicide prevention into mainstream health conversations.

The Jed Foundation has taken a complementary path, focusing on upstream prevention by strengthening emotional health among adolescents and young adults. Through partnerships with high schools, colleges, and universities, JED has worked to embed mental health support within the environments where young people live and learn. Its programs have helped institutions move beyond reactive approaches toward more proactive models that build resilience, identify risk earlier, and foster a sense of belonging.

Each organization has demonstrated meaningful impact over time. Each has contributed to saving lives and shaping how mental health is understood. Their efforts, however, have largely operated within distinct domains. One has advanced national research and advocacy. The other has transformed youth and campus mental health systems. Both have addressed critical points along the continuum of care, yet the broader system has remained fragmented.

The decision to merge as equals reflects a recognition that suicide prevention cannot be addressed in silos. Public health challenges of this magnitude require continuity across the lifespan. Early emotional support, community-based intervention, crisis response, and long-term recovery must function as part of an integrated system rather than a series of disconnected efforts.

Connection, Not Scale Alone, Defines Public Health Impact

Public health is often described through infrastructure and policy. Those elements are essential, yet they are insufficient on their own. Public health is ultimately about connection. It connects evidence to action, systems to individuals, and care to lived experience.

Suicide prevention sits at the intersection of these connections. Risk is influenced by social conditions, access to care, stigma, and the environments in which people interact. Protective factors such as trusted relationships, purpose, and community support can alter outcomes when they are present and accessible. The challenge has not been a lack of understanding. The challenge has been delivering that understanding in ways that are coordinated, equitable, and sustained.

A unified organization has the potential to bridge long-standing gaps. It can align research with real-world application, ensuring that scientific insights inform programs that reach people earlier. It can connect youth-focused interventions with broader public awareness efforts, creating continuity rather than gaps as individuals move through different life stages. It can also strengthen advocacy by bringing together complementary perspectives into a more cohesive national voice.

Scale introduces both opportunity and responsibility. A larger organization can mobilize resources, influence policy, and expand reach. Public trust, however, is built in local and personal interactions. The effectiveness of this merger will depend on its ability to maintain proximity to individuals and communities while expanding its national impact. Size alone does not create connection. Intentional design does.

The combined organization is expected to operate with substantial resources, which creates an opportunity to accelerate progress. Resources must translate into accessible programs, stronger partnerships with schools and health systems, and tools that enable families, educators, and clinicians to act with confidence. Public health systems succeed when they reduce friction for those seeking help and make support visible before a crisis emerges.

This moment also offers a broader lesson for the health sector. Fragmentation is not unique to suicide prevention. Across chronic disease, health equity, and digital health, organizations often operate with shared purpose but limited alignment. The willingness of these two organizations to merge reflects an understanding that structural change may be necessary to achieve meaningful outcomes.

The integration process will require thoughtful leadership and a clear sense of purpose. Combining cultures, programs, and strategies requires discipline and humility. Success will not be measured by organizational scale or visibility. It will be measured by whether fewer individuals reach a point of crisis without support and whether more people experience a system that feels connected, responsive, and human.

Suicide is often described as preventable, which places responsibility on the systems designed to address it. Prevention requires more than awareness. It requires intentional coordination, early recognition, and sustained engagement across the continuum of care.

This merger does not resolve the complexity of suicide prevention. No single organization can. It does represent a meaningful step toward greater alignment in how society responds to one of its most pressing public health challenges. Connection is not an abstract ideal in public health. It is the foundation upon which progress depends.

For more information about both organizations, visit these organizations’ websites at afsp.org and jedfoundation.org. 

The post Suicide Prevention Is a Public Health Imperative, Not a Patchwork Effort appeared first on Medika Life.

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

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

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

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

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

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

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

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

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

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

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

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

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

The post How Every Doctor Can Improve The Healthcare System Immediately appeared first on Medika Life.