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

Executive Summary

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

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

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

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

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

I: A Conversation That Sparked a Question

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

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

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

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

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

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

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

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

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

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

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

III: A Wicked Problem and a Strategic Dilemma

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

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

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

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

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

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

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

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

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

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

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

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

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

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

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

V: Three Conditions Hostile to Learning

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

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

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

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

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

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

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

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

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

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

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

Payer Interference: External Rules Overriding Internal Mission

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

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

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

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

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

Physician Burnout: The Human Output of a Broken System

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

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

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

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

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

VI: Thinking Design, Not Framework Prescription

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

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

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

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

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

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

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

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

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

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

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

The four DSRP patterns work as follows.

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

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

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

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

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

VII: Building the Ecosystem

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

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

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

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

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

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

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

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

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

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

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

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

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

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

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

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

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

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

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

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

IX: Strategic Implications — The Cost of Not Learning

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

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

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

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

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

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

Citations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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According to a Commonwealth Fund report, the US ranks last among high-income countries in healthcare access, efficiency, and equity. Our health system has vast potential and can achieve Moonshot-like outcomes, but it still struggles to address people’s day-to-day needs.

The world’s largest health ecosystem comprises five fundamental stakeholders — patients, payers, product innovators, policymakers, and providers. And while they should work in harmony, they remain primarily disconnected, operating in silos with financial performance as the measure of success. With the fear of economic failure nipping its leaders’ heels, the system prioritizes fiscal responsibility over patients’ healthy longevity. It leads to a “sick-are” over healthcare strategic mindset.

A System Disconnected

While medicine is a “team-support,” the health industry positions its sectors like boxers — each pitted against the other. The concept of a fragmented health system is not new. Nearly 20 years ago, Dr. Alain C. Enthoven, a renowned health economist, argued that the US health sector suffered from a fundamental lack of coordination and misaligned incentives, leading to inefficiencies that primarily burden patients.

In her book Fragmented, Ilana Yurkiewicz, MD, illustrates the real-world consequences of this disconnect. Patients often find themselves bouncing between specialists dedicated to one piece of their patient’s anatomy, grappling with conflicting medical advice, and navigating a bureaucratic maze that often results in disparate, delayed, or denied care.

Economic pressures fueling consolidations and layoffs have led major players to go slow on much-needed ambitious health information initiatives. The much-heralded digital transformation, which promises AI-driven operational efficiency, seamless data exchange, and improved patient outcomes, remains unrealized.

Meanwhile, patients are finding themselves trapped in a complex, hard-to-navigate medical maze where medical records are often inaccessible, treatment plans are inconsistent, payer decisions feel opaque, and pricing remains elusive. The effect is profoundly dehumanizing.

The Patient is Out of the Picture

Financial pressures outweigh the fundamental goal of healing, and the loss of patient focus and a significant decline in empathy remain the system’s most glaring obstacles. This absurd reality is captured in a satirical moment from Yes Minister, where a lawmaker appointed by the UK Prime Minister visits an award-winning hospital — shockingly operating without patients. While comedic, the scene reflects an all-too-real aspect of modern health systems. Administrative structures, insurance approvals, and reimbursement models dictate operations, often sidelining the very individuals who seek care and healing.

As economic priorities continue to reshape industry, hospitals that once promised solutions struggle to sustain themselves, leaving patients increasingly marginalized. While improving patient outcomes should be the central focus of government policymakers, they too often become secondary considerations.

As the US Congress considers budget decisions, cutting Medicaid state subsidies does not mean economically struggling patients will be denied urgent care. Instead, when they seek treatment in the ER, hospitals will absorb the cost. This shifts the financial burden, further straining healthcare systems already operating on razor-thin margins.

Doctor Burnout: Primary Symptom of a Broken System

Physicians enter medicine purpose-centered with a desire to heal, yet the system relentlessly applies administrative and financial pressures that shift their attention elsewhere. Many doctors experience burnout, driven by excessive paperwork, prior authorization hurdles, and unrealistic patient quotas. According to a Mayo Clinic Proceedings study, nearly 63% of physicians report signs of burnout, including emotional exhaustion and depersonalization.

Designed to streamline care, electronic medical records (EMRs) are still a significant source of frustration. Physicians spend hours dealing with complex interfaces rather than engaging with patients. The demand to see more patients in less time — dictated by billing and reimbursement structures — further erodes the doctor-patient relationship, leading to dissatisfaction on both sides.

A 2023 survey by the Primary Care Collaborative and the Larry A. Green Center found that 62% of primary care physicians report not having enough time to adequately meet patients’ needs, which impacts treatment and preventive care discussions.

This dissatisfaction manifests as a breakdown in communication. While effective dialogue between providers and patients across specialties and within the broader health ecosystem is essential for treatment and prevention, patients and physicians struggle to find time to connect.

Burnout is not simply a professional hurdle; it’s a crisis that directly affects patient outcomes. Overworked, exhausted doctors are likelier to make errors, experience lower job satisfaction, and even leave the profession altogether. If the system fails to address professional despair and its causes, the shortage of health professionals will only worsen, limiting access and compromising care.

The Business of Health Over the Health of People

While business considerations shouldn’t take precedence over people, the financial structures underpinning the health sector drive decision-making that deemphasizes people’s health. The silos within the health ecosystem exacerbate this issue, preventing a shared understanding of economic and operational realities, limiting coordinated action and shifting the focus away from the patient.

Insurers prioritize cost control without recognizing the pressures this places on providers. Pharmaceutical companies innovate without fully considering how reimbursement models shape prescribing behaviors. Policymakers create regulations with a limited understanding of their clinical innovation impact. This lack of cross-sector awareness perpetuates fragmentation and inefficiency, and patients ultimately pay the price.

Digital health companies, once hailed as the future of care efficiency and effectiveness, are struggling with financial sustainability, forcing many to pivot or shut down entirely. Biopharmaceutical companies triage drug development pipelines, looking for future medicines with a high probability of regulatory success rather than encouraging research that may fail but illuminate a pathway to even more significant discoveries. Again, it is the patient who pays the price.

The Cost of Failing to Invest in Care and Caring

There is enough blame to go around, and we must stop looking for a convenient villain. If we attack one piece of the ecosystem rather than look at the totality of our problem, we will fail to make meaningful changes.

We can’t throw our health system out and start from scratch; we must find a way to work within and through it effectively. The primary lever that will drive reform of the health system and improve its functioning is bringing everything back into empathetic focus, with the patient at the center of care. Whether we are payers, product innovators, policymakers or providers, we must ask if our actions are built on a foundation of empathy — from heartfelt “care.” First and foremost, we must cooperate in the service of the patient.

The health sector is an industry, but it must be more than that. At its core, it should recognize that we are always people — only sometimes patients. Yet, financial pressures have overtaken its fundamental purpose: healing. This system-wide disconnect erodes trust, drives up costs, and leaves too many without the care they need. Until the key players in the health ecosystem realign around a unified, patient-centered mission, fragmentation will persist, outcomes will fall short, and lives will remain at risk. The stakes are too high to accept anything less.

Unless we build on a foundation based on “how would I feel” empathy, our system will not be built to last. It will be structured to fail.

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Health system CFOs across the country face a unique quandary. Operating margins remain low, staffing shortages restrict services, industry-wide buying power is waning, organizational credit ratings are at risk, and inflation rapidly affects reimbursement and patient affordability. From the CFO’s point of view, it is prudent to look toward other industries and adopt a holistic approach to managing their organizations’ financial health.

The following seven habits can be employed by health system CFOs leading through turbulent economic times:

Embrace Adaptation in the Post-Pandemic Era

The pandemic disrupted traditional operations, requiring a departure from established norms. In this post-pandemic era, CFOs must acknowledge that returning to the pre-pandemic status quo is not feasible. Rising labor costs and supply chain challenges in the face of capped payor contracts demand fresh strategies. Relying solely on traditional approaches and partners in this new environment will not work. To remain effective, CFOs and their operational leaders must lead their organizations in adapting to the new post-pandemic reality.

Repurpose Savings for Clinical Impact

Non-clinical departments often struggle to inspire clinicians, but CFOs can bridge this gap by strategically connecting clinical goals with repurposed savings from overhead departments. Healthcare leaders should embrace innovative cost-saving approaches, such as considering non-clinical spending as a strategic category of costs to negotiate. CFOs can funnel those savings back into the clinical environment, easing pressure on physicians and nurses and enhancing patient care. An annual habit of exploring non-clinical spending to fund clinical initiatives can pay off for both resources and morale.

Shift from Volume to Efficiency

The age-old adage “volume is everything” in healthcare may not go away entirely, but it is evolving. CFOs increasingly steer their organizations toward cost-efficient practices rather than merely focusing on volume growth. Shifting performance measures from volume to efficiency is worthwhile, even if it is a departure from traditional thinking. This habit is needed as the industry acknowledges the importance of cost-effective care delivery—and the value within risk-based payment programs.

Execute Beyond Technology

Technological solutions typically rely on business cases and aim to provide essential tools for modern operations. However, this technology assessment must go beyond adoption and implementation. The true value lies in the execution of the solution and the insights gained from its use and performance. Sound systems require good data to meet their potential. As an example, a national home goods retailer conducted an RFP and needs assessment for its 1,200-truck delivery fleet. The organization derived information to extend the contract with a current vendor. The negotiation resulted in over $1.8M in savings through invoice credits, cash retention bonuses, and contracted cost savings. Most notably, the negotiation never disrupted distribution. The habit of “focus and finish”—connecting technology with non-tech performance measures—ensures that insights are effectively translated into actionable strategies. The discipline to perform a rigorous “look back” analysis on major technology investments is essential to achieving the promised ROI.

Embrace Data and Strategies Beyond Your Four Walls

There is a world of insights and opportunities beyond the healthcare industry. Although healthcare is unique compared to other industries, there are insights and lessons to be learned from looking at how business is conducted outside of healthcare. For example, wouldn’t a hospital want to know what the local university pays for groundskeeping in the same city? What about bank processing and other financial fees? In one case, the CFO of a big-box retailer looked beyond the industry for insights and saved over $10M by uncovering overcharged credit card processing fees that had slipped past their internal process for more than four years. With this information, the CFO and team negotiated a refund of the fees and all credit card agreements.

Knowledge sharing across industries can be valuable for all organizations. The habit of expanding a network of partners and collaborating with businesses outside the industry can provide fresh perspectives and solutions, which ultimately benefit the health system’s financial performance.

Question the Status Quo

CFOs should embrace the habit of challenging industry norms and targets. It’s a requirement to push change management boundaries in pursuit of effective solutions. One industry norm worth challenging is the singular reliance on group purchasing organizations (GPOs) to aid in spend analysis and negotiations. With GPOs addressing only 25% of non-clinical spending, there is an excellent opportunity to address cost savings in these categories. Unlike direct clinical spending, health systems compete with all industries to secure optimal non-clinical product and service contracts. Leading companies outside healthcare often devote more time and resources to secure better contracts. Continually setting the bar higher—to renegotiate contracts, adopt cutting-edge technology, or pursue innovative partnerships—can drive tangible results and transform the financial trajectory of health systems, no longer leaving money on the table.

Manage Non-Clinical Costs Proactively

Non-clinical cost allocation, akin to investments in a personal portfolio, requires constant monitoring and adjustment. CFOs should adopt a proactive habit of managing non-clinical costs that consume approximately 20% of revenue. By acknowledging the dynamic nature of healthcare finances, CFOs position their organizations for financial success. Also, the CFO can no longer assume that existing structures and partners are sufficient to control these non-clinical costs. Research shows that 75% of non-clinical spending falls outside GPO and procurement contracts. With today’s financial challenges, this leaves a gap in controls that must be addressed. The habit of managing non-clinical costs proactively pays off.

Proactive management of v costs has been proven to result in favorable contracts and significant savings in both annual direct costs and signing bonuses. This outcome was demonstrated by a discount retailer that conducted an RFP and needs assessment with their 10-year incumbent Warranty provider. The proactive assessment resulted in a more favorable contract with higher vendor engagement and training support from a new vendor. Beyond the service value, the resulting savings were $4M in annual direct costs and a $4M signing bonus in the contract’s first year, with the new vendor covering all transition costs.

Today, the role of a health system CFO extends beyond traditional financial management. CFO leadership requires a blend of strategic vision, adaptability, and innovative thinking about benchmarking and managing change. CFOs who embrace these seven habits can navigate the healthcare industry’s challenges in a post-pandemic economic climate, foster financial resilience, and ultimately contribute to enhanced patient care, organizational success, and highly competitive service delivery.

******

Special thanks to co-author Mark Van Sumeren, who has more than 40 years of experience in healthcare strategy, operations, and supply chain management. As the General Manager of the healthcare practice at LogicSource, he spearheads cost-savings initiatives within nonclinical healthcare supply chains. Previously Mark held senior executive positions at Owens & Minor, Ernst & Young, and Detroit Medical Center, collaborating with major integrated delivery networks and academic medical centers to enhance operational efficiency and supply chain practices. 

The post Seven Habits of a Highly Effective Health-System CFO appeared first on Medika Life.

Media colleague Eric Wicklund, associate content manager and senior editor for Innovation, Technology, and Pharma for HealthLeaders, has penned a ViVE preview piece with a power grabber: “CAN VIVE 2024 SOLVE HEALTHCARE’S BIGGEST DILEMMAS?” That headline question isn’t rhetorical and doesn’t require an answer.  Resolution is not the responsibility of the ViVE co-organizers CHIME Health and HLTH.  ViVE puts the collaborative opportunity for health ecosystem players to face the challenges and opportunities in motion.

At the heart of these dilemmas is how payers, patients, product innovators, policymakers, and providers source and maximize health information.  No doubt everyone is jumping on the AI, ChatGPT, and GenAI bandwagon, excited that technology will solve the problems of system-wide fragmentation.  But like any tech shift or bolus of information, it all comes down to what innovation theorist John Nosta calls the “Introduction of the Cognitive Age.”

While some express trepidation that this technological revolution is the dawn of all fears that smart machines will replace health professionals, Nosta suggests that the “Cognitive Age leverages AI as a partner, augmenting and enhancing people’s ability to access and act with heightened intellectual and even technical (i.e., physical ability).

Once again, ViVE has teamed up with Galen Growth to provide insights on digital health in the US health systems. Galen Growth connects the importance of partnership and recent trends influencing not only the US health system – but also the close reader how health information and innovative technologies will transform the delivery, quality, and cost of patient care – including the influence on clinical evidence, decentralized clinical trials, and connective and home care.

Access the Galen Growth 2024 Digital Health Report here: LINK

ViVE is the space where all these conversations – the good, bad, and ugly – of the (predominately US) health ecosystem can be explored, and ideas can be identified and pursued.  VIVE partners with Galen Growth – the global digital health research and analytics specialist. It uses its proprietary database HealthTech AlphaTM to track more than 680 million data points, documenting 14,200 digital health ventures worldwide to produce its “Digital Health Innovation | 2024 Report.”

“In an era of rapid technological advancement, the 6,129 hospitals within 407 Health Systems1 across the United States are at the forefront of transforming healthcare delivery for diverse communities nationwide, notes Julien de Salaberry, a co-founder and CEO of Galen Growth. As the Digital Health landscape evolves, Health Systems play a pivotal role in bridging gaps, enhancing patient outcomes, and addressing persistent pain points.”

But the most significant challenges to accessing the potential of these new digital health technologies are not in ideas themselves – rather the innovators’ understanding of how the US health system works (or is dysfunctional); too often, the founders of these companies are unaware of how to engage leading hospital systems – find champions, integrate within the system, and address cost benefits and ongoing support issues.  Failure to navigate the complexity of countless decision-makers too often leads to ventures running out of capital and vanishing.  The Galen Growth Digital Health Innovation Report documents the roadway to potential success.

CTOs and CIOs within significant health systems are not immune to unprecedented challenges facing hospital economics and the cost of technology. Digital health leaders also recognize that they will hit a brick wall without clearly expressing ROI.  Health systems require measurable outcomes and evidence-based results from digital health partners, and to succeed mutually, they are creating incubators, venture groups and partnership models.

Health systems – hospitals, long-term care communities, and home care providers – will make giant leaps very shortly, drawing on information and technological changes.  The Galen Growth Report issued at ViVE on Monday, February 25^th, is the analysis and roadmap to understand better the economic priorities and investments driving these changes.

What is evident from the Galen Growth Digital Health Innovation Report is that while the total dollars of investment in digital health saw a steep decline from the exuberant investment during the height of the COVID pandemic, the category has gained greater importance among health systems seeking to make significant advances in how patient-care, work-flow processes, preventive care and reimbursement analyses play out.

AI, ChatGPT and GenAI investment is not aspirational – the focus is mainly practical and operational.  Those accessing the full report during ViVE will immediately gain valuable information to guide internal planning. Medika Life will update this article tomorrow with a link to the full report.

Will ViVE solve – as Eric Wicklund teases readers – the health system’s “biggest dilemmas?” Is that even it’s mission?  Likely not. But the people who attend ViVE will be looking for insights and information that can guide solutions. The Galen Growth “Digital Health Innovation | 2024 Report” will be one of the big takeaways that will spark conversation, ideas, and hopefully, as Eric reflects, some concrete solutions.

The post Galen Growth + ViVE 2024 Champion Digital Health Innovation as a Salve for an Ailing US Health Ecosystem appeared first on Medika Life.

While San Francisco certainly has a historical impact on global health innovation, the Boston health ecosystem is fertile ground to be the nation’s power hub for innovation, research, and economic growth. Boston has emerged as a key player – perhaps the place – in the life science sector with its world-class universities, renowned hospitals, and investment community. Innovation accelerates when talent, infrastructure, and finance converge on a geographic area. 

Boston’s concentration of top-tier academic institutions, including Harvard University, MIT, and Tufts Medical School, attract brilliant minds worldwide, leading to groundbreaking discoveries and pioneering research in biotechnology, devices, genomics, and pharmaceuticals. These innovations applied to patient care have improved health outcomes and fostered collaborations between academia and industry, driving innovation and the development of life-saving therapies.

Employment Opportunities Abound in Boston

The Massachusetts Biotechnology Education Foundation’s “2023 Massachusetts Life Sciences Employment Outlook” reports that the life sciences sector in Massachusetts is home to 132,000 jobs. This study predicts another 42,000 new jobs will come online in 10 years.

The presence of world-class hospitals and community safety nets like Boston Medical Center, Massachusetts General Hospital and Brigham and Women’s Hospital offer scaled and start-up biotech, contract research organizations, digital health, medical device and pharmaceutical companies a base to listen, learn and apply. These institutions attract top medical talent, providing a foundation for companies to tap into great minds as advisors on the practical issues facing innovators – how to navigate the fragmented US health ecosystem – even how to recognize revenue.

Not at BIO, but at another nearby life science digital health meeting. Barry Finette, MD, Ph.D., founder of ThinkMD, a physician, who has worked around the world in emergency medicine, is among those who have transitioned from full-time medicine to the innovation sector reflects why change is so urgent:

“The US has the most sophisticated high-quality healthcare in the world if you can afford it and have access to it. For too many Americans, neither is the case. The US healthcare system is in crisis, even though we spend more on healthcare per person per year (>$11K) than any other country in the world, and yet, the US is ranked in the 20-30s in the world for most key health indices and have a life expectancy that has been decreasing.”

Beyond BIO, Digital Health Leaders Meet at the Digital Healthcare Innovation Summit to Explore Needed Changes

The Digital Healthcare Innovation Summit 2023 (#DHIS2023 and #DHISEast) meeting is a hidden gem.  Though the meeting rooms are packed, this annual gathering held in Boston still needs to be discovered.  Its attendees and speakers represent the health ecosystem of decision-makers who must learn more about each other’s business agenda – how their models work and thrive economically.

The unscripted panel sessions almost guarantee that the mainstage conversationalists will “tell it like it is.” The speakers are drivers of change and include Sree Chaguturu, MD, chief medical officer of CVS; Christopher Brooker, partner, Frist Cressey Ventures; Magann Vaughn Watters, vice president, New Ventures & Strategic Alliances, LabCorp; Tarun Mehra, vice president, Healthcare Strategy, M&A and Partnerships, Microsoft.  The impressive list of speakers and attendees reinforces the value of this gathering – more so – it underscores the power of Boston as a microcosm of the national health system and its possibilities.

[Photo: Moderator Nancy Brown, General Partner, Oak HC/FT explores with panelists Neel Shah, MD, CMO, Maven Clinic; Lori Evans Bernstein, Co-Founder & CO, Caraway; Angela Glyder, VP, Clinical Operations, Lucina Analytics, and Sukanya L. Sonderland, senior vice president Blue Cross Blue Shield of Massachusetts, on “Women, Families, and Health – Understanding the Market, Its Impact and How You Measure Success”]

The Summit jumped into pressing access to-care challenges – sometimes conflicts – facing the US health system. CVS’s Chaguturu outlined the retail pharmacy giant’s strategy around the acquisition of Oak Street Health and the evolution of its Minute Clinic model. CVS has also invested in Maven Health to improve greater access to women’s health.

Maven Women’s Health founder and CEO Kate Ryder smartly outlined how this unicorn enterprise has succeeded by focusing on its customers’ – women’s health – needs rather than enticing tech platforms.  Maven has built one of the more comprehensive platforms providing support across fertility, pregnancy, adoption, parenting, and pediatrics. The company received the Digital Healthcare Innovator – East Coast Award.

Boston – A Model for Harmonious Health Innovation and Navigating the Health Ecosystem

“Culture crushes change,” reflects health innovation theorist John Nosta, president of Nosta Labs.  Too many significant changes that might help heal the disjointed health ecosystem fail – not because the invention will not improve people’s care – it doesn’t mesh with the system’s economic sensibilities.  The greater Boston region may be part of the treatment for failure.

Listening to the speakers at #DHIS2023, it’s noticed that many of the leaders on the stage and conference room are not engineers; they are rooted within the medical system. Some are trained health professionals and others work within the health system – they are part of a closed-loop community and seek collaboration. 

Instead of bemoaning the interference of AI and ChatGPT, they imagine how these technologies can integrate successfully into their workflow. Instead of looking for ways to reject ideas that will change the care process, they are rolling up their sleeves to figure out how to untangle the links in the supply chain of patient care.

If BIO is the place to explore business development and partnerships, DHIS is the forum for experts in the trenches of digital and health tech to transparently examine how all this work comes together as the system seeks to change.

We’ll be back in Boston later this month to explore how these leaders will change the future of health innovation development through diversity in clinical trials.  Stay tuned.

The post Life Science Dominates Boston in June as BIO (and DHIS) Take Over Bean Town appeared first on Medika Life.

To suggest patients can influence the machine that drives modern healthcare is naïve and indicative of a flawed understanding of the realities of the industry. It is nothing more than pandering to the cash cows of the industry – you and I.

It is not merely the patients who are powerless cogs in the machinations of healthcare. Their care providers suffer a similar fate. Caregivers are not responsible for the systematic erosion of patient centered medicine, they too are victims. Focusing on relationships between provider and patient is therefore a wholly fruitless endeavor.

Systemic remedies are required

As an analogy, consider if you will, the provider and the patient as cellmates in a high security prison constructed and manned by pharma, insurers and their intermediaries. No amount of debate and planning between the two cellmates is going to result in their release or an improvement in their living conditions. Their only hope lies in reprieve and relying on the humanity of their jailers. If it is lacking, the pair are doomed.

Call me cynical (guilty as charged) but I cannot envision a world in which healthcare companies suddenly decide to eschew profitability for the benefit of their customers. If anything, with every passing decade, rampant capitalistic profiteering will likely worsen the lot of patients and providers, as more intermediaries emerge in the ever growing chain of healthcare and market share declines with negative population growth.

In short, our current health system globally, warped over decades from a humanity based, service industry into the profit based system of exploitation we now deal with, is as good as it gets for you and I. It cannot and will not change.

Appeasing the masses

No one likes to feel they are trapped in a system over which they have little or no control. As the healthcare system evolves and refines itself, incremental changes will center care on establishing dependency (treatment, not cure) and further isolating the farmers (providers) and their flocks (you and I) into managed farming units focused on maximizing revenues.

If you’ve seen intensive chicken farming, you get the idea.

All the signs are there and have been for the last two decades, but like all unpleasant realities, we simply turn a blind eye, as we understand on an instinctive level that we are powerless to affect change. And yet, we continue to posture, we continue to whistle into the wind. All to no effect.

While it is true that patient advocacy groups do occasionally secure small victories, these victories inevitably come at a price, usually exacted on services or pricing in a fashion so subtle that the further erosion of control goes unnoticed.

Patient activism is even encouraged by the industry to pacify the masses. The industry will go as far as sponsoring and participating in many of the patient advocacy groups that proliferate the healthcare market. Appearances matter, results however, cost extra and never at the expense of the bottom line or without a quid pro quo.

Escaping Alcatraz

So what choice then, for patient and provider, caught up in the gears of a machine intent on using them for the pursuit of profits? It is too late to walk any of this back and it cannot be undone. We lack the social backbone to address it and those who can address it lack any form of incentive that supersedes profit.

How do we return healthcare to its former heady days of doctors serving their patients, of ethical medicine, of deep bonds of trust and respect between both patient and provider. How do we ensure that future generations can benefit from technology and medical advances to improve their health, rather than being cycled into a chain of dependency, for those fortunate enough to afford anything approximating to care.

If we cannot change the existing system, the unpleasant alternative is to part ways with it. To strike out into the medical wilderness, forgoing healthcare insurers, pharma and the wretched leeches that inhabit the spaces between. To separate, permanently, the ties that bind both patient and provider to flawed systems that perpetuate abhorent levels of care.

I firmly believe this to be our only option if we are to ensure fair and free access to healing for future generations.

How this future would look and what it would encompass for doctors and patients remains to be seen, but the break needs to be made, before the gates shut for good on the flock.

The post Patient Engagement is a Pointless Pursuit appeared first on Medika Life.

The digital health sector has grown significantly during the past decade, primarily mobilized to address unmet health-system and care needs. From $2 billion invested in 2011, that figure morphed 28-fold to $56 billion in 2021. In the shadow of the COVID pandemic, unprecedented investment poured into digital health during this two-year window, accelerating the adoption of AI, remote patient monitoring, telehealth, digital therapeutics, wearables, VR and more.

DOWNLOAD THE FULL REPORT HERE

Galen Growth and FINN Partners have collaborated – for a second time – to offer the entrepreneurial invention and investment industry a wealth of data that can guide financing life-sustain – perhaps saving – enterprises.  With this detailed report, data is shared not as “one-off” ideas but as puzzle pieces assembled to provide actional insight into a dynamic and evolving digital innovation sector that offers the transformative power to find effective detours around health ecosystem fragmentation.

The Damocles Sword of Access and Outcomes

The two companies avoided hype and biases to create this analysis enabling investors or potential business partners to weigh subjective decisions alongside objective facts in recognizing that the fragmented health ecosystem centers around preserving the system itself are vital to helping product innovators and patients navigate the healthcare labyrinth. That economic return on investment is weighted along with patient experience and outcomes.  This is the Damocles sword that inventors and investors must always consider – reimbursement that ensures access to care and meaningful and measurable outcomes.

While digital health venture capital fell short of the exorbitant funding of 2021, it closed ahead of 2020 levels. A closer look at the year-on-year development of funding value by funding stages shows that funding in 2022 nearly maintained the level of 2021 for the Early Stage and Series A funding rounds. However, it lagged behind the 2021 values across late-stage enterprise funding.

Investors learned and began to put their due diligence into action – targeting their energies toward specific geographies, sectors and needs (e.g., clinical trials).  They looked for therapeutic categories receptive to digital health innovation.  Within the disease-specific investments, oncology saw the highest participation of investors (9%), followed by mental health (7%) and women’s health (7%).  C-Suite leaders and their funders will do well to study the report data and to determine next steps.

Just Facts – 250+ Million Data Points Analyzed

While other studies highlight “media grabbers” and numbers disconnected from insight into where the money is going — from the investment stage to disease states— The Global State of Digital Health connected more than 250 million critical analytic dots. This report will guide C-Suite leaders, business development and therapeutic area heads, private equity funds, and financial analysts to make savvy decisions that drive return on investment—whether the goals be financial or patient care.

After an extraordinary two-year pandemic funding spree, there are apparent shifts in how investors place their economic bets. In this year of geopolitical turmoil and uncertain financial performance, digital health remains vital to the future of the health economy and in addressing public health press points. However, the development stages in which funds are directed, therapeutic categories selected, provider systems that demand transformation, and the regions where innovators establish headquarters are now investment factors.

Bottomline, funding levels exceed the pre-pandemic values. Digital health ventures are busy – submitting more than double the number of regulatory filings in 2021 than before the COVID era. There is a dramatic increase in partnering activity over 2021. If dollars and Euros are the only indicators of energy and commitment, it is easy to see why hand-wringing news stories prevail. Look at all the metrics that determine a vibrant sector.

In combining investment, regulatory filings for new product introductions, and partnerships, it is apparent that 2022 has been a formative period. Investors and innovators are putting in place the processes and systems – the people and science – that drive adoption and market traction. Investors are no longer looking at the sector as monolithic.

Putting the Right People and Processes on the Innovation Bus

If 2021 was the year of exuberance, 2022 was, as Jim Collins, author of the classic business bestseller “Good to Great,” dedicated to putting the right people – leaders and digital health sectors – on the bus. For example, medical diagnostics ventures had an exceptional year by representing 12 percent of regulatory filings of Digital Health ventures across the globe, taking the top spot for ventures with significant Clinical Evidence (Evidence Signal >40), and capturing the highest investment volume share (17%), with the total cluster funding value falling short of 2021 by 16 percent.

As the report suggests, 2022 is a turning point for digital health – a shift from Digital Health 1.0 to the 2.0 era, with Digital Health evolving into a more mature ecosystem. The recent data reaffirm that perspective and a fast-paced realignment occurring within the sector.

Looking at the past year, it is evident that investor mindsets matured in 2022, and lessons learned were applied. Obstacles – from regulatory hurdles to payer pushback – made incubators, accelerators and private equity groups much more selective in their investment and partnership decisions. They looked beyond “invention” to innovation – where the market seeks to embrace a new product and rally to Foreword its access within the health ecosystem.

The 2022 “Global State of Digital Health Report” does far more than examine monies invested in the broad digital health category — it offers a guide for decision-makers on how multiple data points translate into market shifts and how to pinpoint trends that provide predictive insight and best practices in investment strategies, across regions, Digital Health categories, therapeutic areas and technologies.

Decisions will be made based on this collaborative effort. This report offers a new perspective – the data this report provides – finding the balance between hype and hope to offer balanced optimism is a guide.

The post Digital Health Innovation Continues to Press Forward at Warp Speed appeared first on Medika Life.

In my earlier posts in this series, I have written primarily from the perspective of what primary care physicians can do to improve their patients’ health and reduce total costs of care. Concurrently, they can reclaim their right to practice in a non-frustrating environment with a limited number of daily patient visits. Torinus approaches improving health care from the perspective of a business leader faced with rising health care costs. Here I will quote and paraphrase from Torinus’ book and, since I basically agree with his recommendations, will amplify with some of my own thoughts.

He argues that company CEOs must make health care a strategic priority since it is one of the top three costs for any company. In addition, healthcare costs can make the company noncompetitive if not managed aggressively. However, strategic priority to him also means it is essential for the company to attend proactively to the health and wellness of its employees, not just be the provider of an insurance plan.

CEOs need to consider the long term for their companies and employees. The company and the employee together spend about $16,000 per year for a family for insurance as of his 2014 book publication, obviously much greater than that today! Using his $16,000 estimate, an employee who works for a company for 25–40 years represents an insurance expenditure over a lifetime career that could be as much as $400,000 to $640,000 in 2014 dollars. This drives home the point that it only makes sense to have a long-term view of employee health, beginning with an aggressive approach to maintain wellness, actively reduce risk factors and manage disease as it occurs.

Some repetition here is worthwhile. Don’t just focus on treating disease but prevent disease and maintain wellness. Of course, that will cost some money now, but the end result will be a substantial decrease in total costs over the years. And, don’t just cover disease care but arrange for each employee and family member to have outstanding primary care to avoid unnecessary excessive specialty care.

He observes that the current health care system focuses on specialty care, whereas it needs to focus on the care recipient with high-quality primary care — the patient/consumer/employee. But to be effective, the patient/consumer/employee needs to be engaged. Instead, the current healthcare system disengages the patient — it removes responsibility because the patient is not the doctor’s customer.

In his company, expenses were rising to double digits by 2003, but with their new plan in place, it dropped to 2% or less per year.

Torinus’ “prescription” for all companies (and what his company initiated beginning in 2004) follows:

First, every company, including small companies, should self-insure with an added stop-loss catastrophic policy.

Second, employees should be offered only a consumer-directed healthcare policy (CDHP), in essence, a moderately high deductible plan (often about $2500) with either an associated health savings account (HSA) or a health-related account (HRA.) The company should prefund the account with an amount (often about $1500 or more) that the individual can use for any health care needs with the assumption that since it is now the individual’s money, he or she will spend it more wisely — employee/patient engagement.

Third, the company should insist that each provider have price transparency. Since that’s often difficult to obtain, Serigraph uses various companies like Alithias Inc to provide that for them so that they can compare one provider to another. For example, they determine the price of the all-inclusive (gastroenterologist, anesthesiologist, and facility fee) along with quality data of colonoscopies at the nearest five centers and then rank them. The employee or family member who needs the colonoscopy is told that, for example, the company sees it as appropriate preventive care and so will cover the cost, in this case up to $1,500. [His book appeared before the ACA became law, so the insurance component would cover colonoscopy now, but the principle is still valid.] This is an amount that will pay for, say, four of the five local centers, but if he or she selects a provider that charges more, they are on the hook for the remainder.

Fourth, if the company is large enough, it should provide an on-site primary care clinic at no cost to the individual. For example, at Serigraph, the clinic includes a concierge-type physician (meaning that the physician is salaried, has a low number of patients under care, and gives ample time and energy to each employee/family member patient, consistent with some of my previous posts) plus a nurse practitioner, a health coach, a dietician, and a chiropractor. On the other hand, if the company is too small to justify a full-fledged clinic, then the company can pay the retainer for a nearby direct primary care/membership/concierge physician who works with others, such as a health coach.

Fifth, the clinic, with particular attention by the health coach, gives all employees a health risk assessment annually and then works one-on-one with each employee (and family member) at no cost to maintain wellness and health, including the use of behavioral change programs around diet, nutrition, exercise, stress management, and smoking cessation.

Sixth, the clinic staff gives very intense management of chronic diseases and coordination of specialist visits when needed. (See my earlier article on care coordination by the PCP)

Seventh, Serigraph uses what Torinus calls Centers of Value for procedures beyond those that the primary care physician does. These are doctors/institutions that have outstanding quality records yet a competitive price for, say, a knee replacement. Serigraph gives their employees $2,000 toward the deductible or covers the deductible for the surgery when they use these Centers of Value.

Seventh, his company gives (and he recommends others do likewise) generic drugs for free, and all of the above prevention and wellness programs are supplied free of charge. Finally, the company makes free counseling available for developing advanced directives, and if an individual requires end-of-life care, hospice is available free of charge.

I notice that his company spends considerably on extensive/comprehensive primary care, including wellness maintenance, proactive prevention, behavioral health, and chronic care management. In return, the company is rewarded in return with lower total costs and healthier workers.

Given that healthcare has become a company strategic priority, then it needs to be managed, and that requires data. Hence, he urges all companies to develop health-related management dashboards, including both a financial dashboard (how much is the company spending) and a health dashboard (how many individuals in the company have uncontrolled blood pressure, uncontrolled asthma, uncontrolled cholesterol, have not had appropriate mammography or colonoscopy, etc. — all information collected from the clinic in an unidentified manner to protect individual privacy).

These approaches are based on fundamental principles, including individual responsibility; marketplace discipline — installing consumerism, steering business to the best quality and price (“do good work and you get our business”); proactive care — maintaining employees’ health and wellness and give extensive care to those with chronic illnesses; and sound management — putting those who pay, i.e., the employer and the employee, in charge.

Torinus suggests that there are multiple rewards for following this basic approach (I added number 2 since he implied but did not write it.)

1) The reward for business is a healthier workforce and more affordable healthcare expenditures.

2) The reward for individuals is more health and wellness, less illness, and fewer dollars spent.

3) The reward for high-value providers is more business.

4) The reward for entrepreneurs comes if they innovate with better care provided at a lower cost

5) There could be a reward for taxpayers — if governments (federal, state, and local) were to utilize these approaches.

Sound advice? I certainly think so.

The post A Business Approach To Reducing Healthcare Costs appeared first on Medika Life.

My friend Susan in the first article of this series was a good example. Presenting to her PCP with a somewhat unusual symptom, she was sent from specialist to specialist without ever learning what was causing her symptom, much less resolve it. It was a true waist of time, money and her emotions when the answer was there if only a doctor spent some time to listen to her.

To further exacerbate the problem, the doctor and patient no longer have a “contract;”. The patient and doctor are bystanders to the decision-makers. Frustration by doctors and patients is high, and physician burnout has become rampant.

 Add to this is a significant change in the common serious diseases – complex, chronic illnesses, mostly preventable, for which American medical care has not established suitable methods of prevention or adequate methods of care. In addition, what should be the role of the primary care physician has been compromised by the insurance industry (both commercial and government-sponsored) that puts the incentives in the wrong places. The result is a sicker population, episodic care, and expenses that are far greater than necessary.

Our current delivery system was designed early in the past century with the expectation that the patient would pay the doctor a reasonable fee for the effort, skill, and time involved.

Insurance developed during the past 70 years initially to pay for unexpected, highly expensive care, such as surgery or hospitalization. But over time, insurance transitioned into what is essentially prepaid medical care and along the way eliminated the financial “contract” between you and your primary care physician (PCP or Nurse Practitioner.) The contract today for both you  and the doctor is with the insurer The patient and doctor are bystanders to the decision-makers. Frustration by doctors and patients is high, and physician burnout has become rampant.

Worse yet, insurance pays primary care providers a pittance, driving them to “make it up in volume” by seeing too many patients per day, often 24 or more. Of course, this means short visits, perhaps three per hour, which translates into about 10-12 minutes of actual face time with you.

The delivery system was developed to deal with acute medical problems, where it is reasonably effective. For example, consider the pneumonia that a single internist can treat with antibiotics, an appendicitis that can be cured by the surgeon, or the fractured arm that the orthopedist can cast. But our medical care system works poorly for most chronic medical illnesses and costs far too much. Chronic illnesses include diseases like diabetes with complications, cancer, heart failure, chronic lung and kidney disease, and Alzheimer’s.

These chronic illnesses are increasing in frequency at a rapid rate and consume the bulk of health care expenditures. They are largely (although not entirely) preventable.

A century ago, the most common causes of adult death were infections – pneumonia, typhoid, and tuberculosis. Today these are uncommon and treatable. Now the most frequent causes of death are chronic illnesses – heart, cancer, and stroke, with Alzheimer’s and diabetes just behind. Other than some cancer, most others are not curable.

Most chronic diseases are related to lifestyles and are preventable. A myriad of social, environmental, financial, and personal reasons has led to non-nutritious diets, lack of exercise, chronic stress, inadequate sleep, smoking, and excess alcohol. Obesity is now a true epidemic, with one-third of Americans overweight and one-third obese. The combined result is high blood pressure, high cholesterol, and elevated blood glucose, which, combined with the long-term effects of the above behaviors, leads to diabetes, heart disease, stroke, chronic lung problems, kidney disease, and cancer.

No one pays for prevention, for maintaining health and wellness. Insurance is for disease care. Government does little (except with tobacco) to assist. As a result, as a country, we do not attend to actual healthcare and maintaining wellness, which in turn means greater pressure on the medical care delivery system. We don’t have a health care system, it is a medical care system that focuses on disease, its diagnosis, and treatment. Wellness and prevention are largely ignored. That is unfortunate because most of today’s chronic diseases could be prevented. Attention to prevention is the logical method to maintain and improve health and is much less expensive than treating a disease once it occurs.

When any of these chronic diseases develop, except for some cancers, it usually persists for life. These are complex diseases to manage and are often very expensive to treat – an expense that continues for the rest of the person’s life. Preventing them is equally complex but a lot less expensive.

Although not adequately appreciated, primary care physicians can handle most of today’s chronic illness care. They have the knowledge, experience, and skill level to do so. But this does not happen with short visits. All too frequently, the patient is referred to one or multiple  specialists when the PCP could have dealt with the problem had they had enough time. That extra time would not have cost much, but the referral, of course, means an increase in the costs of care, often substantial.

Some patients with chronic illnesses will need a team of caregivers, but the various specialists and the PCP are not a true team working in a unified manner. For example, consider a patient with lung cancer who may need a surgeon, radiation oncologist, medical oncologist, pulmonologist, pain specialist, palliative care team, nurse practitioner, and many others. Primary care physicians generally do not have the time needed to coordinate the care by the specialists. This is very unfortunate because coordination is absolutely essential to ensure good quality at a reasonable cost. You might think that one of the specialists might take on that role but that rarely occurs. More often the patient starts with a surgeon who refers him on to a radiation oncologist who then refers to a medical oncologist who then may or may not call-in others as needed. 

Any team needs a quarterback, and in general, that person is or should be the primary care physician. The PCP needs to be the orchestrator of the various specialists when needed in these complex patients. This need for a team and a team quarterback for the patient with a chronic illness is much different than the needs of the patient with an acute disease in which one physician can usually suffice. A team quarterback dramatically reduces the total costs of care if only because it means continuity and organization of care, keeping the patient’s welfare upper most in mind.

This shift to a population that has an increasing frequency of chronic illnesses mandates a shift in how medical care is delivered. Unfortunately, our delivery system has not kept up with the need. This is no way to run a railroad.

Join me with the following articles as I address more of the Whys and Hows and What to Do.

The post Complex, Chronic Diseases Are Rampant Today appeared first on Medika Life.

Not too long ago, our family joined the rare disease community. For years, our child exhibited a multitude of disconnected symptoms that all seemed to have different explanations, if they had explanations at all. The growing list included dizziness, rapid heart rate, stomach aches, rib pain, joint pain, unexpected dislocations, migraines and others.

Spider-Web-like Health Ecosystem

The symptoms accumulated, along with a cascade of specialist appointments, diagnostic tests and treatments. So did the hours of speaking – and negotiating – with our health insurance carrier. Along the way, we encountered all sorts of personalities in our spider-web-like health ecosystem, most compassionate and wanting to help our child, others bewildered or frustrated, and still others deaf to our worries and requests. The boldest and best among the health professionals we worked with were those willing to confess their uncertainty. In medicine, the response “I don’t know” is now an act of courage.

Fifty years ago, when modern medicine came of age, there were far fewer treatments for common, non-communicable diseases such as high blood pressure, cardiovascular diseases or mental illness. While medicine has become more advanced and specialized, it has also become increasingly fragmented. The important family physician – who should have sufficient time and compensation to coordinate care – is often out-of-the-loop as patients rush to a myriad of medical specialists – each hyper-focused on their piece of the biological puzzle.

Fee-for-service care disadvantages primary care medicine and a patient’s coordinated care.  This financial model may work for simple – in and out – cases, but when it comes to chronic illnesses, it does not. Add to that the complexity of electronic medical record systems with limited interoperability. Now, specialists face “telephone-game” obstacles to accessing colleagues’ clinical notes and diagnostic data for a shared patient.

Now, shift from common non-communicable diseases to needle-in-the-haystack conditions.  It’s baffling for patients, parents and providers.  Over time, doctors may even become frustrated with these patients. People with multiple, disparate symptoms with no “one pill to heal them all” are sometimes labeled problem patients owned by no one. In these circumstances, young female patients often experience gender bias and provider judgments that the puzzling symptoms must be “in their heads.”

Communication is Part of the Care

When you hear hoofbeats behind you, think horses, not zebras. As 14^th-Century theologian and philosopher William of Occam would suggest, the more common explanation is the correct diagnosis. But medical students – and the doctors they become – need to be careful not to develop a foolish consistency.  This is not the Middle Ages of Medicine.  It is the 21^st Century of miracle medicine where patients must have a voice.

While it is easier to treat confusing and contradictory symptoms than to ask why a patient is experiencing them, savvy doctors rely more on their patients’ collaboration to help them solve medical mysteries. Information, communication, and advocacy can build a bridge linking doctors and patients.

Traumatized by the Medical System

But, most often in healthcare, the gatekeepers – providers and payers – feel they know best. This overconfidence can quickly unravel in the face of the challenges of rare whack-a-mole diseases. Physicians trying to help feel helpless and grasp at straws when their initial diagnoses and treatments do not solve their patient’s problems. Over time, many patients are traumatized by a medical system that seeks to help, but is seen as fallible.  As different solutions are sought, the system pays more and more for that same patient over time. The patient’s underlying illness remains unaddressed. No one wins.

This two-decade journey to arrive at my child’s diagnosis (Ehlers Danlos Syndrome) has taught me several lessons.

• TEAM:  Any successes result from the passion of individuals —parents, physicians, payers or policymakers — who are determined to work together to find solutions and willing to listen.

• TECH:  Good health information begins to force our fragmented health system to converge around the patient. When data is accessible, artificial intelligence finds needle-in-a-haystack solutions, uniting the myriad of like cases so that health professionals can learn, engage, and arrive at answers sooner.  Physicians who also engage patients promptly through the EHR system are more than answering the questions of anxious patients; they demonstrate partnership in the care.

• TRUST: I have seen how critical advocacy is. As a health communicator, I have been fortunate to serve on the boards of organizations such as the American Academy of Pain Medicine Foundation, American Heart Association, Leukemia & Lymphoma Society, The Marfan Foundation and Let’s Win for Pancreatic Cancer, which unite healers and patients, and participate in the Centers for Medicare and Medicaid Part D Working Group: all great forums for accurate information that improve public health through informed decisions.

These lessons all point to passionate, informed collaboration as the key to restoring clarity and sanity to the fragmented health system and driving quality care for patients, whether they have common ailments or rare conditions. I integrate these lessons into my work to benefit clients and, most importantly, change the lives of the patients they serve.

Our family’s lives were changed by one cardiologist who dared to utter a simple sentence: “You need a team.” Her idea to put one together should not have been so revelatory, nor so atypical, but it was. She understood collaboration fundamentally and saw patients and their caregivers as her partners in healing.

Collaboration – Overused Word; Underused Strategy

For those whose work touches patient care and public health, I encourage you to collaborate, talk, and merge your experiences with others. Remember, this work directly touches people’s lives. This work makes a difference. Give thought to how you can bond with others to change the course of care. Collaboration is an often-overused word but is too often underplayed as a behavior.

I call on my readers and colleagues to look to the innovators of ideas, products and relationships and recognize that through collaboration with each other, we have an opportunity to recast the health ecosystem. Recognize that our work is a life-saving effort about being part of people’s transformational moments. And be courageous enough to know when you don’t know.  From there, we can begin the journey toward healing, together.

The post Looking for Zebras: Medical Mysteries and Transformational Patient Moments appeared first on Medika Life.