Despite this extraordinary pace of innovation, something fundamental remains broken. Patients still struggle to navigate care. Physicians continue to wrestle with fragmented systems, administrative overload and technologies that often add work rather than reduce it. Health innovators repeatedly introduce sophisticated tools into environments overwhelmed by operational complexity, lack of governance, cybersecurity concerns, workflow disruption and communication gaps.

The issue is no longer whether innovation benefits care. The issue is friction.

Consumers compare health care experiences to every interaction in daily life. They compare health care to Apple, where design simplifies complexity, to Amazon, where communication is continuous and immediate, and to banking and travel platforms providing real-time updates and seamless transactions. Some may even compare it to Domino’s Pizza, which promises delivery within 15 minutes or the pie is free. Expectations surrounding responsiveness and convenience have fundamentally changed.

Then they enter health care environments where forms are repeated, portals fail to communicate, prior authorizations delay treatment and updates disappear into silence. Patients are left to navigate disconnected systems during moments of vulnerability. The expectation gap between consumer and health care experiences continues to widen and increasingly shapes reputation.

In Healing the Sick Care System: Why People Matter, the observation is made that “Health care isn’t failing because we lack innovation. It’s failing because the system around that innovation has calcified.” The statement remains painfully real because innovation alone does not create confidence. Experience does.

Patients Remember the Journey, Not the Molecule

The patient and physician experience is shaped less by what a product promises and more by what happens after that promise enters real life. A medicine may be clinically meaningful, yet the experience surrounding it can still become exhausting if coverage is difficult to secure, prior authorization is confounding, specialty pharmacy coordination is slow, follow-up instructions are unclear or support programs require patients to become navigators of their own care.

In those moments, people are not judging science on its own merits. They are judging the total experience of trying to make that medicine or care available and understandable.

Physicians face their own administrative version of friction. A therapy may be medically appropriate, yet before treatment can begin, office staff must determine coverage, complete documentation, respond to payer step-through requirements, manage rejection appeals and explain delays that were never created in the exam room. Every additional administrative step consumes time, stretches staff and places additional strain on the physician-patient relationship. Even non-medical formulary changes can force physicians to restart conversations, explain unexpected medication switches and reestablish patient confidence in treatment decisions already made.

Patients remember counting the hours as they waited for answers. Physicians remember losing uncompensated time navigating systems and approvals. Nurses remember caring for patients through computer screens while typing notes into laptops on rolling carts in crowded hallways. Office managers remember the relentless cycle of paperwork, rejected claims, disconnected portals and endless callbacks trying to move care forward.

The therapy may eventually do its job, yet the pathway becomes inseparable from the memory associated with the brand, the company and the broader health care system. Every new process, technology and treatment promises improvement. For patients and health professionals, however, if the path to care feels uphill, the friction surrounding the experience can overshadow the value of the benefit.

For many patients, repeated uncertainty, delays and administrative obstacles contribute to a form of medical PTSD, where anxiety surrounding the system becomes inseparable from the treatment experience. For health professionals, the constant burden of navigating fragmented systems, managing approvals and compensating for communication gaps has become a leading contributor to burnout.

Friction is rarely remembered as an operational issue inside organizations. Patients and physicians experience it personally. This is why communication must be elevated operationally within health care. Communication is not marketing layered onto innovation after development is complete.

Health care organizations often think they are going through the process of delivering a product, therapy or platform. Patients and physicians experience something more personal: time invested in every interaction surrounding the innovation is time lost forever.

Health Technology Cannot Create More Work

The same reality applies to health technology startups and digital health innovators. Technological advancement alone does not guarantee adoption within health care environments already burdened by operational complexity and workforce fatigue.

Health care organizations do not merely evaluate whether technology works. They evaluate whether it integrates with existing workflows, whether cybersecurity standards are state-of-the-art, whether onboarding is manageable, whether interoperability gaps create additional burdens, and whether the institution can trust the accuracy of data.

Every additional step is a friction point, while every unresolved operational issue becomes part of the patient and physician experience surrounding the journey.

A sophisticated AI platform that requires clinicians to validate outputs continuously adds cognitive burden. A monitoring platform generating clinically important alerts contributes to fatigue. A system that requires extensive retraining or manual workarounds may succeed in demonstration but stumble in real-world conditions.

Innovation may arrive elegantly designed; however, it enters health care environments already strained by workflow complexity, disconnected systems, cybersecurity demands and administrative fatigue. The operational realities surrounding implementation often become as important as the innovation itself.

That reality does not diminish the importance of continuous invention. It reinforces the importance of implementation, communication and operational design within real-world clinical environments.

This shift is increasingly visible across the global health innovation marketplace itself. At HLTH Europe 2026, conversations are moving well beyond excitement surrounding artificial intelligence, digital therapeutics and next-generation platforms. The agenda sessions focus on interoperability, workflow integration, governance, patient engagement and operational implementation. Conference themes repeatedly emphasize connected systems, coordinated experiences and technologies that reduce fragmentation rather than add to a growing list of patches.

One of the more revealing themes from HLTH Europe focuses directly on interoperability and the longstanding frustration surrounding disconnected systems. The conference site notes that clinicians continue spending enormous energy managing platforms that fail to communicate effectively with one another. At the same time, artificial intelligence is increasingly viewed not as a replacement for care, but as a bridge helping systems “finally speak the same language.”

Another major focus involves provider realities. HLTH Europe speakers highlight workforce fatigue, cyber risks, operational strain and workflow challenges facing clinicians and health systems across Europe and beyond. These agenda themes reinforce a growing recognition throughout the industry that innovation cannot succeed if it increases the burden for the people expected to use it every day.

Health professionals increasingly describe a workplace dominated by more screens, more alerts, more documentation and less time with patients. Technology interrupting workflow rather than integrating into it creates resistance, regardless of how advanced the platform may appear. The hidden work behind implementation often becomes the defining experience for the people expected to use the system every day.

Cybersecurity provides another important example. Health professionals and patients may never fully understand the technical architecture protecting health information, yet they absolutely understand the emotional consequence of uncertainty surrounding data privacy, reliability and trust. Confidence in health technology is not built solely through functionality. It is reinforced through consistency, service, transparency and confidence that information is accurate, protected and responsibly governed.

Communication plays an equally important role here. If clinicians are left uncertain about updates, system changes or data governance responsibilities, confidence weakens. If patients do not understand how information is protected, trust erodes, regardless of how advanced the technology.

Communication remains inseparable from the care experience.

The organizations most likely to lead the future of health care will not distinguish themselves solely through technological achievement. They will reduce friction around the user interface, workflows and data accuracy.

The Companies That Win Will Simplify Complexity

This reality explains why access organizations such as Hims & Hers Health and Cost Plus Drugs deserve careful study from across the health care sector, regardless of whether industry leaders agree with every aspect of their business models. These organizations are built around reducing friction in how people access and experience care.

Their importance extends beyond convenience or pricing. These companies recognize that many traditional health institutions have underestimated: people increasingly expect health care experiences to reduce anxiety, simplify decision-making and provide continuity throughout the care journey.  They are “Amazon-like,” offering a “Buy It Now” simple click medical oversight option.

The rise of concierge medicine, direct-to-consumer health platforms and walk-in clinics with reduced wait times reflects a broader market signal the health sector cannot ignore. Patients are increasingly gravitating toward experiences where communication is clearer and access is more immediate.

For those able to afford concierge care, the attraction often extends beyond physician access itself. Patients value responsiveness, shorter wait times, easier scheduling, follow-up communication and the sense that someone is helping coordinate their journey through the system. Walk-in clinics and urgent care centers appeal for similar reasons. People are searching for environments where care is readily accessible, understandable and administratively manageable. The downside of loss of care continuity is offset by immediacy, which is what the consumer values most.

This migration reflects frustration with friction embedded throughout the trending health care experience. Long hold times, delayed callbacks, countless portals, disconnected records, repeated paperwork on clipboards and uncertainty surrounding next steps all shape how people perceive quality of care.

Communication once again sits at the center of the experience. Patients rarely separate operational snafus from expert care. They experience the entire journey as one connected reality – positive or negative.

The lesson is not that health care should behave exactly like retail commerce. Medicine carries ethical, scientific and regulatory responsibilities far beyond consumer transactions. Nevertheless, the operational expectations consumers now bring into the setting have changed.

People increasingly expect health care to be as responsive as the communication they experience elsewhere in life. Is that expectation reasonable?

The pharmaceutical industry, payers, providers, and health technology innovators must recognize that they no longer own just the patents on therapies, platforms or services. They also own the surrounding user experience.

Patients experience health as a continuous journey, not a “build your own adventure” exercise in navigating fragmented systems. Most people enter the system anxious and seeking reassurance from their health professionals. A delayed approval, clinically sterile information delivered through a diagnostic portal or a physician struggling to navigate complexity alongside them deepens that burden. These experiences shape how health care is remembered more powerfully than advertising campaigns or corporate positioning statements.

Those experiences ultimately shape reputations.

The future winners in health care will not simply develop innovative products. They will reduce friction around the human experience surrounding those products. They will recognize that communication, workflow design and responsiveness are not secondary considerations attached to innovation. They are part of the experience.

Patients and physicians rarely remember the elegance of molecular or system architecture behind a therapy or platform. They remember whether the experience made care delivery easier and more humane during moments that mattered.

The post Health Innovation Has a Friction Problem appeared first on Medika Life.

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

Executive Summary

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

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

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

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

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

I: A Conversation That Sparked a Question

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

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

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

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

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

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

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

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

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

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

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

III: A Wicked Problem and a Strategic Dilemma

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

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

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

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

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

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

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

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

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

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

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

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

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

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

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

V: Three Conditions Hostile to Learning

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

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

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

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

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

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

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

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

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

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

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

Payer Interference: External Rules Overriding Internal Mission

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

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

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

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

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

Physician Burnout: The Human Output of a Broken System

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

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

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

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

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

VI: Thinking Design, Not Framework Prescription

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

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

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

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

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

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

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

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

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

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

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

The four DSRP patterns work as follows.

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

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

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

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

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

VII: Building the Ecosystem

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

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

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

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

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

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

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

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

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

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

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

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

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

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

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

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

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

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

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

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

IX: Strategic Implications — The Cost of Not Learning

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

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

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

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

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

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

Citations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A Conversation Without Borders

Among the diverse voices at the Forum were three standout attendees—one from Ghana, another from Brazil, and still another from India—all deeply committed to advancing scientific discovery and digital transformation in health, all sitting at one table coincidentally. Their presence reinforced the idea that innovation emerges not from a single system or nation but from a mosaic of lived experiences, cultural insight, and shared human purpose.

At a time when geopolitical divisions grow and xenophobic rhetoric clouds practical need, this convening of minds from across continents stands as a counterpoint: progress in medicine and public health demands openness, not isolation.

Today, two out of five HIMSS members live outside the United States, representing the tremendous growth in its international reach.

Global Minds and Shared Missions

Consider the stories behind some of the most transformative scientific breakthroughs. Dr. Tu Youyou, who drew upon traditional Chinese medicine to isolate artemisinin, reshaped malaria treatment and saved millions. Tu received the 2011 Lasker Award in clinical medicine and the 2015 Nobel Prize in Physiology or Medicine jointly with William C. Campbell and Satoshi Ōmura for her work.

Dr. Salvador Moncada, born in Honduras and later based in the UK, changed the future of cardiovascular medicine through his work on nitric oxide. And Dr. Pardis Sabeti, born in Iran and raised in the United States, played a critical role in genomic tracking during the West African Ebola outbreak. These are not anomalies—they are the natural result of cross-border learning and purpose-driven science. In recognition of his tapping into the power of collaboration to accelerate biomedical discoveries, Dr. Salvador was nominated by the President of Honduras to serve as the country’s first Ambassador to China. 

Such examples underscore a larger point: global health challenges—from infectious disease to chronic illness—cannot be solved in silos. They require knowledge sharing, inclusive research, and the integration of clinical science, population health data, and epidemiological insights gathered across geographies. HIMSS is paving the way for people and countries to come together.

Today, health information flows freely across continents. Clinical trials are increasingly multinational. Genomic datasets used to train AI models include samples from diverse populations. Epidemiological patterns—from outbreaks to noncommunicable disease trends—are informed by data from regions that span income levels and infrastructure capacity. This global interconnectedness of knowledge is not only valuable—it is vital.

Health innovation now depends as much on access to ideas and information as on access to raw data or funding. Each individual—whether a clinician, data scientist, policymaker, patient or communicator—contributes to this ecosystem through their choices within their workplace, organization, advocacy group and community. These local actions ripple outward to impact global outcomes.

When people are empowered to think boldly and act collaboratively—regardless of where they are from—their influence transcends borders. This is especially true in a world where diseases migrate, health inequities persist, and environmental factors increasingly shape population health. No one country has a monopoly on the future of medicine, and no one person is immune to illness.

Science and Technology as a Bridge

Science is not merely technical; it is relational. It is built on trust, transparency, and the willingness to share. When data is exchanged openly—on disease trends, therapeutic outcomes, or environmental health risks—it becomes a force for public good. When it is withheld or politicized, it delays solutions and costs lives.

As HIMSS convened global thinkers in a borough symbolic of reinvention, the message was clear: advancing AI in health is not just about algorithms—it’s about equity, empathy, and inclusion. Those values begin not with policy mandates but with people. Beneath sessions on technology and policies, the conversation continually returned to the reality—it’s about people working collaboratively.

Every organization has the power to foster a culture where global voices are welcomed, collaboration is incentivized, and ideas are judged not by origin but by merit. The future of health will be shaped by how willing we are to embrace human potential, wherever it begins, and work with people who can help advance human health wherever they call home.

Brooklyn as a Setting and Symbol

Brooklyn is a fitting backdrop for these conversations. A city defined by generations of immigrants—scientists, healers and visionaries—stands as a beacon for what is possible when people are welcomed, not walled off. HIMSS is hosting its APAC meeting July 16-18 in Malaysia.

The HIMSS AI in Healthcare Forum brought together technologists, clinicians, ethicists and entrepreneurs. But more than that, it reminds participants of something timeless: when diverse minds come together, knowledge is not only shared—it is elevated. When human potential is honored without prejudice, the possibilities for better health are limitless.

The post Science Has No Borders – And Neither Should Human Potential appeared first on Medika Life.

Since its inception, Diligent Robotics has been revolutionizing healthcare automation with Moxi, the only humanoid robot actively operating in complex, real-world environments like hospitals. Alongside this incredible delivery milestone, Moxi has:
• Saved nurses and other clinical staff over 1.5 billion steps allowing them to remain in their units and closer to their patients
• Saved clinical staff over 575,000 hours, giving them back that time to focus on being at the bedside and being more creative in their work
• Surpassed 125,000 autonomous elevator rides, highlighting the complex nature of autonomous operations in real-world environments, especially in a
busy hospital.
• Currently partnered with 23 health systems, representing 31 hospital-level partnerships and growing nationwide. Customers range from multi-hospital
health systems to small community hospitals in rural areas.

“This milestone is a testament to Diligent Robotics’ leadership in healthcare automation and our commitment to delivering impactful solutions that support clinical teams,” said Dr. Andrea Thomaz, CEO and Co-Founder of Diligent Robotics. “While many companies talk about integrating robotics into healthcare, we’ve been successfully doing it since 2020. Moxi’s achievements showcase the transformative power of embodied AI in addressing real-world challenges. As we look to the future, we’re excited to push the boundaries of innovation, advancing robotic capabilities to create even greater efficiencies, support and milestones in healthcare and beyond.”

Pioneering Innovation in Healthcare Robotics

Unlike traditional automation systems, Moxi seamlessly integrates into hospital workflows, handling routine tasks such as delivering supplies, lab specimens and medications. With an average task time of 20-26 minutes, Moxi ensures consistent and reliable delivery while streamlining hospital operations.
“One of the things I noticed when shadowing nurses during their day-to-day work is how often they get pulled away from patient care to go and run tasks, to go and get things,” said Trish Fairbanks, Chief Nursing Officer, Endeavor Health. “This is a huge dissatisfier for nurses. They like to be with their patients and Moxi doing the running around for them is just super cool.”

Behind the scenes, Diligent Robotics leverages multiple terabytes of performance data collected weekly from its fleet to drive continuous innovation. This data is critical to refining Moxi’s capabilities and accelerating the development of new product iterations. By combining robust AI models with real-world feedback, Diligent Robotics maintains its edge as the true leader in healthcare AI.

Transforming the Future of Work

Moxi’s success is a clear indicator of the growing role of humanoid robots in healthcare. Robotics in healthcare, once considered an experimental innovation, has evolved into a transformational tool, delivering operational efficiencies and reshaping hospital workflows. Health systems increasingly rely on solutions like Moxi to streamline workflows, alleviate workforce shortages and combat clinician burnout. This shift highlights the growing recognition of robotics as an essential component in addressing the complex challenges of modern healthcare, transforming how care is delivered and managed. With its unmatched ability to navigate complex environments and learn from data, Moxi continues to redefine what is possible in hospital settings.

“It’s been incredible to see how perceptions of Moxi have evolved over time,” added Thomaz. “Initially, there were natural concerns about how this technology might replace jobs or operate efficiently in a busy hospital. Now, Moxi has become an indispensable part of the team, taking on routine tasks that allow staff to focus on patient care. Watching clinical teams interact with Moxi as if it’s a real member of the team—saying good morning, giving it high-fives, and even naming it ‘Employee of the Week’—has been one of the most rewarding human-robot interactions I’ve seen in my career. Seeing Moxi become such a valued part of healthcare teams reaffirms our mission to create technology that truly supports people in meaningful ways.”

As Diligent Robotics enters 2025, the company remains committed to pushing the boundaries of what embodied AI and humanoid robots can achieve. To learn more about Diligent Robotics and its groundbreaking work, visit www.diligentrobots.com.

# # #

About Diligent Robotics
Founded in 2017, Diligent Robotics is an Austin-based A.I. company that creates robot assistants that help people with their chores so they can focus on the work they care most about. Moxi is our hospital robot assistant that helps clinical staff with routine, non-patient-facing tasks so they have more time for patient care, and hospitals save money on staff burnout and turnover costs. Moxi has been successfully supporting several US health systems and focuses on tasks such as gathering supplies and delivering them to patient rooms, delivering samples to the lab and retrieving items from central supply to nursing units. As a company founded by social robotics experts, we’re proud to be at the forefront of creating robots that incorporate mobile manipulation, social intelligence and human-guided learning capabilities. We believe that if we can give people the resources that they need to do the work they care most about, we will transform the meaning of “work.” www.diligentrobots.com

The post Not a Pilot, Not a Prototype—Diligent Robotics Hits 1 Million Humanoid Deliveries Across Fleet at Healthcare Customers appeared first on Medika Life.

In the year and a half I worked in that department, I learned more about medicine, human rights, patients’ responses to loss, and the ambivalent relationships of medical personnel than throughout my entire medical work history and career as a nurse.

Although I later moved on to direct care positions in multiple departments (OB and surgery, Mother/Baby/PP, Med-Surg, Trauma), the emergency room experience was my formal introduction to many foundational aspects of the medical environment, especially regarding the unaddressed conflict in the relationships between the differing roles of providers in medicine.

The main concerning dynamic I observed was that nurses were generally dismissed and disregarded by many physicians, as both professionals and as necessary components in the practise of medicine.

In my experience as a professional, this aspect has still not changed over time and spans throughout all specialties in medicine.

When I became a nurse in the year 2000, I was no longer just the observer of adverse or lack of communication toward nurses from physicians or the frequent poor treatment of physicians toward nurses. I became the receiver of both.

Nursing Expertise Is Still Mostly Misunderstood

Many physicians do not see the nursing staff as an imperative extension of their own care and knowledge. Many are unaware of what most nurses do and how much they know. They do, in fact, just expect their orders to be carried out and quite often neglect to understand the gap that nurses must close from orders of care to implementation of care and then to continued follow-up of care. The latter two skills are what create and sustain patient health and wellness.

Nursing responsibilities, experience and skills remain a neglected and misunderstood facet of healthcare. Most lay people see nurses as the medical personnel carrying out their doctor’s orders, making the necessary calls to patients and hopefully, effectively understanding the medical reasoning and intricacies behind the care and information they are delivering.

However, true nursing goes beyond this.

Learning to regurgitate orders and instructions is not what gets a good nurse through school or what keeps his/her patients alive. Critical thinking, research, and observation while responding appropriately in and to emergent, acute, and chronic situations, listening when no one thinks we are listening, and knowing when the wrong medicine or treatment has been ordered or recommended are.

The doctor will not go to jail if the nurse gives an inaccurately ordered medication, resulting in an adverse event or fatality; it is the nurse.

We are, first and foremost, the buffer between a physician and his patient.

And both patients and physicians need this.

What Effective Nursing Offers To Physicians’ Care of Their Patients

Good nurses listen to their patients and have a knack, not just the training for, for excellent triage. Body language tells more than a patient’s report. Patients’ verbal reports must be delicately and discreetly screened for hidden information that is critical in many cases, to appropriate safe care and orders. Nurses hone in on things not said, or that are mis/underrepresented, which often results in a totally different approach to treatment than at first written.

Nurses’ bedside experience yields a wealth of information and patient history that frequently change the initially documented needs and treatment of the patient’s condition. The following are some common examples: (Note that global and national MyChart EMR records now give access to patient medical information and have greatly improved providers’ knowledge of documented patient information.)

1. A patient comes into the emergency room or the clinic reporting a “terrible headache” and is nauseated and dizzy but denies a history of hypertension. Vital signs reveal a dangerously high pressure, but the patient defines themselves as non-hypertensive because they are normally prescribed hypertensive medications, so they consider themselves “cured.” This is a much more common thought process than is understood, especially for elders.

Further nursing triage reveals that the patient is “between” PCPs (very often this is code for the patient’s dislike for their previous one and so they just quit going to visits) and the patient has been out of their medication for two months (due to an inability to cover changing Medicare/other insurance costs). This knowledge prevents the ordering of further hypertensive medications (for perceived acute/undiagnosed episodes) by the ER physician or urgent care clinic doctor which could cause a dangerous drug interaction and/or overdose because the patient is very likely to refill the original medication as well at a later date. This is another common problem among elderly patients, especially. Gaining a full picture of the patient’s circumstances in this situation will also predicate running lab tests which may have not been ordered otherwise or ordered differently. This would offer additional insight to the patient’s current cardiac and renal status/risk in association with current signs and symptoms.

Nursing also contacts the inhouse social worker to assist the patient in funding available to cover the cost of medications and to elicit a list of PCP’s in the immediate area that take patient’s insurance (this is providing SW is as thorough as expected.) Nursing also provides a follow up call a few days after the visit to ensure that patient has had their needs addressed.

2. Patient presents with guarded abdominal pain. Their eyes are dark, their pupils pinpoint, and they are jittery and talking fast. The nurse notices skin irritations and sores and a “slack jaw” appearance in the patient. Many physicians immediately write this patient off as an addict, document “drug-seeking behaviour” as cause for visit and stop there. This has been both my personal and professional experience. Given the patient’s appearance which concurs with heroin/meth addiction, this may be a correct standing diagnosis. However, there is always more to know and investigate. This patient is a human being in need of care and thorough assessment. The pain the patient complains they have often has another root source besides withdrawal. The nurse notices after the doctor leaves the exam room that the patient winces when standing and limps on the right side. An astute nurse will pull the physician back in and subsequent due diligence medically reveals appendicitis. A life is saved.

*A more frequent finding with patients in addiction is bodily injury due to violence perpetrated against them from the population they associate with. Since shame is a huge factor in this group, the patient will often not divulge a criminal act against them and associated injury is easily missed in assessment.

3. Patient complains of generalized dizziness and imbalance. She mentions that she notices one side of her body seems to be “lagging.’ The neurological “tug” test is performed along with the routine balance test. No present abnormalities are observed, yet the patient insists she is experiencing increasing episodes. Although labs are ordered to check for abnormalities in hydration, glucose, and possible tell-tale results of a recent stroke or myocardial infarction (cardiac enzymes and CRP), they come back normal. As the physician is writing discharge orders for PCP follow-up recommendations, the nurse checks in with the patient.

The patient is sitting with her head down. Her off-handed mumbled comment catches the nurse’s attention. “I feel like I’m literally living in darkness and am scared most of the time.” This comment strongly hints at mental health issues. Adverse mental health conditions that are left untreated will absolutely affect the body (altered stature, weight balance, gait, eye movement, posture, cognitive word halt/jumble.) Upon further assessment, the patient also reveals long-term anxiety-related insomnia, one hallmark (though not entirely definitive) of compromised mental health.

A discussion with the doctor now adds a psych evaluation, a mental health consult to her PCP follow up and community referrals. The patient’s time is not wasted reaching out to the medical community because a nurse made the decision to follow the cornerstone of his/her medical training to observe/listen to the patient. Nurses are taught to observe both the presence and absence of information and body language and many other factors. The picture presented when first meeting a patient is most often just the tip of the iceberg.

The Benefits Of Honoring and Respecting One Another as Providers

When physician and nursing roles support and complement each other’s expertise and knowledge, and each respects the other’s insight and practice, great results occur for patients:

• a much more in-depth picture of the patient’s overall physical and mental health is revealed.
• potential risks and needs that often go unidentified are exposed.
• the patient receives a much more comprehensive, relative treatment plan.
• patient trust in the medical community increases

Better patient health is achieved, and a much-needed deeper level of patient trust in their care team begins to be restored.

Unified medical forces create reliability and safety for all involved.

Patient compliance is directly related to patient trust for their provider.

When physicians respect the nurses they work with and understand that good nursing staff are an immeasurable source of support and diverse medical knowledge, the target of healthcare, patients, benefit the most.

They are why there are doctors and nurses in the first place.

The post How Physicians Benefit From The Experience and Knowledge of Nurses appeared first on Medika Life.

Like an endangered species, preventive medicine and chronic disease management—the US primary care system—face extinction. With nearly 30% of American adults lacking a source of care and 28 percent reporting delaying or not getting care due to cost, the consequences are far-reaching.  The focus on chronic disease prevention and addressing its root causes demands greater attention, as the health of the system—and the people it serves—depends on it. If we are frustrated about something, this is worth the outrage.

It has been almost impossible for elected officials, who too often look for singular villains, to grasp the extent of this system-wide dysfunction. This crisis extends beyond consumer comfort with technology or the cost of medicines. Primary care medicine—the basis for health delivery—is marginalized as an honored medical discipline. Somehow, we opt for a national health system prioritizing sick care over healthcare.

Primary care providers are grappling with burnout and inadequate compensation compared to their specialist counterparts, and the system often prioritizes paperwork over quality of care. Economics drives health delivery and access, and it’s simply not working to the advantage of consumers and primary care physicians.  

Finger-pointing and Senate HELP Committee photo ops cannot solve this nation’s care crisis. What’s needed is a fundamental shift in our approach to illness, prevention, and access—one that addresses the root causes of our failing primary care system and ensures that quality healthcare is accessible to all Americans, regardless of zip code or digital literacy. That will reduce our total health costs.

Obesity and Heart Disease: A Multigenerational Threat

America’s waistline is changing—we are adding notches to the nation’s belts. Obesity rates among younger Americans are climbing, creating an abundance of chronic diseases that once seemed confined to older generations. Alarmingly, heart disease, which had been in decline for decades, is creeping back up.

The invention of new weight-loss drugs like GLP-1 receptor agonists helps many struggling with chronic weight issues and mitigates some health risks. Yet, these drugs are not a complete answer to the challenge. They do not adequately address the underlying risks—heart disease, diabetes, and other chronic conditions—that require ongoing, consistent engagement with health professionals. Without this, even those who benefit from these medications – looking trim – may still end up battling old health challenges.

The persistent challenge of obesity across various age groups in the US, which hovers at +/- 40 percent, reinforces worrisome trends that impact people by age, race and region. A rate stable at 40 percent is not something to celebrate – it requires action. It’s a tipping point for illness.

Prediabetes: A Perfect Public Health Storm

Prediabetes is the nation’s silent epidemic. Close to 90 million adults—more than 1 in 3 Americans—have it, and 90% don’t know they do. Left unchecked, some 20 percent of these people “graduate” to Type 2 diabetes and other complications annually. The rise in obesity among younger populations only exacerbates this issue, setting the stage for an earlier onset of chronic diseases that worsen over time.

Prediabetes demands a dedicated behavior-focused treatment plan. Without significant lifestyle changes, individuals are on a fast track to diabetes and its life-altering complications. And yet, the primary care system—our first line of defense—is buckling under pressure, unable to provide the consistent support patients need. It’s not just the use of medications – it’s understanding that obesity is a multi-system condition and a unique disease that transcends more belt notches.

The Limitations of GLP-1 Drugs:

GLP-1 drugs do reduce weight and lower the risk of diabetes and heart disease. But they are not a substitute for comprehensive care. The underlying dangers—poor cardiovascular health, insulin resistance, and other metabolic issues—don’t disappear with weight loss alone. Without engagement with allied health professionals trained to address the complexities of obesity to monitor and address these risks, consumers will face new challenges despite these drugs’ initial success in losing pounds.

We live in what Katherine Saunders, MD, DABOM, a Weill Cornell Medicine’s Comprehensive Weight Control Center and co-founder of FlyteHealth, calls the “Obese-a-genetic” era.  Her efforts at FlyteHealth leverage the latest in science, technology, patient support, and a range of medications to individually tailor weight treatment based on a person’s unique biology alongside the complexity of obesity treatment:

“Overweight and obesity are misunderstood medical conditions that are more complex than calories in and calories out. The advice many patients receive—to eat less and exercise more—often fails to address the problem.”

Saunders and her colleagues are at the cutting edge of results-oriented care, but she is among the handful who have dedicated their careers to this pressing clinical discipline.

Walk-In Clinics are about Convenience

Convenience of care is essential to people’s well-being. Entrepreneurial internists have recognized this, creating “pop-up” vaccination and care centers to bring services closer to those in need and better work/life balance. But convenience alone isn’t enough. Urgent care clinics underscore one of the nation’s most pressing public health threats—the erosion of primary care—has reached a retail-like inflection point.

Walk-in clinics and telehealth check-ins are helpful but do not offer dedicated follow-up. They are geared to address the consumer’s immediate need and are not structured for the longitudinal engagement for the hard-to-tackle considerations that call for comprehensive support.

We are stuck between a system that focuses on its self-preservation and what is in our and national long-term interests – protecting our most important asset – our health.

The Rise of the Make American Health Again Movement

Primary care physicians, the cornerstone of preventive health, are becoming extinct as a medical profession species. The reasons are many: medical school debt driving doctors to higher-paying specialties, they are paid by the number of patients seen daily burnout, and the rise of retail clinics offering quick, transactional care.

While these clinics improve access, their focus is not on a long-term patient-physician relationship. This shift leaves a dangerous gap in the medical safety net, particularly for chronic conditions like obesity, prediabetes, and heart disease. Without a trusted health provider to guide them, patients are left to navigate their health journeys solo—often with devastating consequences.

Many are aghast at Robert F. Kennedy Jr.’s nomination to the Department of Health and Human Services as Secretary of the nation’s key organization setting national health policy. This justified anxiety centers on his stated positions on vaccines and his off-hand comments dismissing the importance of medicines in preventing more serious illnesses. However, his thoughts about America’s poor health report card grades deserve attention regardless of the outcome of the Senate confirmation hearings.

His Make America Healthy Again movement has an approach that deserves consideration: the need to tackle the chronic disease epidemic, which has become the leading cause of death in the US and, later, drives massive costs in hospitalization.

“There are some things that RFK Jr. gets right,” says Resolve to Save Lives CEO Dr. Tom Frieden, who was appointed Director of the Centers for Disease Control and Prevention during the Obama Administration. “We do have a chronic disease crisis in this country, but we need to avoid simplistic solutions and stick with the science.” Frieden made his comments in an NPR interview on the RFK Jr. nomination.

We need (much) more than medications and pop-up clinics to address America’s growing health crises. The health ecosystem must be reimagined to center around people’s health outcomes – not a one-size-fits-all approach to keeping them well. We must foster long-term patient-provider relationships, ensure easy access to understandable health data, emphasize nutrition and physical education in schools, and make care accessible to people across racial and generational lines.

As the ticking time bombs of obesity, prediabetes, and heart disease continue to warn, the urgency for change cannot be overstated. The frustration over the current complexity of access underscores what happens when we prioritize the system over prevention. Access to care isn’t just a convenience—it’s a matter of survival. To prevent the collapse of this fragile house of cards, we must act decisively and collaboratively to build a health system that sustains us all.

The post Conceptually, the “Make America Healthy Again Movement” Needs a Nod appeared first on Medika Life.

It encouraged me to not only accept, but to normalize, and even embrace a cancer diagnosis. I counted how many times the word cancer was repeated as I waited.

Twenty. The words “your cancer” was repeated so many times, my head was swirling.

For cancer treatment to get an ok with the patient, it must first get the patient to be ok with the presence and word cancer; to even give it a home in the thoughts, body, and present awareness.

There is nothing about an adverse health condition that merits giving it permission to take up residence in your body. Disease comes and goes in our lives, but to remain healthy and to stay in perspective of the value and length of life we want, even with the worst physical diagnosis, never do we simply accept a diagnosis and NEVER do we call it our own.

Whether chronic or acute, sickness is not a natural state of the body and does not belong in it.

I have read many a breast cancer patient post on “supportive” websites vehemently owning a breast cancer diagnosis, at the behest and example of their medical providers.

Breast cancer patients are not taught by and large that breast cancer is an unwelcome enemy.

Rather, it is inundated into society for women to expect a diagnosis of breast cancer and then accept it and “fight” it. This is a propaganda technique designed to perpetuate an illness mindset.

Truthfully, there is only ONE fight to be had when it comes to illness, terminal or not and that is the fight to convince your mind that that disease process does not belong in you.

Disease is a separate, dangerous enemy from your body.

As a woman who is still feeling the effects of subsequent treatments and undergoing surgeries post treatment for breast cancer, I can say firsthand that I have lived this truth since diagnosis. I have refused to allow myself to call this attack on my life and purpose my own. It is not from God, so it is not mine.

Our healing is in the unseen, supernatural realm, not in the unseen physical, natural realm. Complete healing is brought into the natural realm through our realizing and believing in the Unseen. The Unseen truths are materialized in the Seen realm through our holding firm to that faith that we possess cancer-free, pain-free and complication-free bodies, spirits and souls (mind, thoughts, emotions, will and all things) in this realm where God our Father lives and has manifested this miraculous freedom from (all) earthly symptoms and diagnoses. Healing is every one of our’s destiny, through faith in His powerful healing desires for us.

No one was destined to be born to die from cancer, addiction, chronic illness, sudden tragedy or ANYTHING else. The Lord, our God, designed us to live long, beautiful lives.

His gentle, but firm command is for us to align ourselves, our thinking and beliefs, with HIS diagnosis of LIFE…not with the enemy’s of Death.

Let every ache and pain be crushed under your feet, under the weight of His promise of complete healing, but stand as a reminder that what we feel (fear, fright, apprehension, ruminations, death visions) is not what we are or where we are going.

Believe this moment that you will live. Looking over your shoulder only in expectation to see great Love running toward you, not to cringe with fear. See your long and healthy life in front of you and begin planning for it.

He has you. He has us.

Believe HIS prescription for health, not Oncology’s for death.❤️

The post Stop Owning a Cancer Diagnosis appeared first on Medika Life.

The migration of healthcare workers from low and middle-income countries (LMICs) to high-income countries (HICs) — the so-called “medical brain drain” — is not a new phenomenon. But its scale and impact have reached a point where we can no longer turn a blind eye.

Consider this: some HICs draw as much as one-fifth of their physician workforce from LMICs. This exodus is happening against a backdrop of a global shortage of 2.8 million physicians, with LMICs bearing the brunt of this deficit. It’s akin to siphoning water from a drought-stricken village to fill swimming pools in wealthy neighbourhoods.

The drivers of this migration are complex. Healthcare workers often cite poor working conditions, limited career advancement opportunities, and socioeconomic challenges in their home countries as push factors. As one study notes, “the top five reasons for respondents choosing to emigrate from their home country were: socioeconomic or political situations in their home countries; better education for children; concerns about where to raise children; quality of facilities and equipment; and opportunities for professional advancement.”.

But the consequences are far from complex — they are devastatingly clear. Beyond the immediate loss of skilled professionals, there are significant economic costs to LMICs. A study by Saluja et al. estimated that “LMICs lose nearly US$16 billion annually (95% CI $3.4 to $38.2) due to the cost of excess mortality that results from physician migration to HICs.” This figure represents the direct financial investment in training these professionals and the potential lives lost due to their absence.

The impact on healthcare systems in source countries is equally severe. As Misau et al. point out, “The health care system in the developing countries faces many problems, human resource being one of the majors. The system is structurally and systemically fragile and weak to provide effective service where it most needs. Brain drain appears to have complicated the situation and made matters worse.”

It’s easy to point fingers at HICs for “stealing” healthcare workers from LMICs. But this oversimplifies a complex issue. Many argue that individuals can seek better opportunities and living conditions for themselves and their families. One commentary notes, “when health-workers leave, they exercise their autonomy in pursuing their life plans; the freedom to leave one’s country and free choice of profession are codified as human rights in the UDHR.”.

So, what’s to be done? The World Health Organization has developed a global code of practice for the international recruitment of health personnel. But as Brugha and Crowe point out, “the code is ultimately voluntary. Recent research has suggested a lack of awareness of the code among relevant stakeholders and that the code has not affected policies, practices, or regulations in Canada or other developed countries.”

LMICs need to implement strategies to retain their healthcare workers. Kamarulzaman et al. suggest, “Investing in and strengthening domestic health care, providing career opportunities and attractive remuneration, and investment in research and development in a context of political stability are necessary to attract and retain health workers.”

But HICs cannot absolve themselves of responsibility. Ethical recruitment practices, support for health system strengthening in LMICs, and partnerships for medical education and training can help mitigate the negative impacts of healthcare worker migration.

The current situation is unsustainable and detrimental to global health equity. As Eaton et al. argue, addressing this issue requires “a comprehensive approach that considers the rights and aspirations of individual healthcare workers, the needs of source countries, and the ethical responsibilities of destination countries.”

The medical brain drain is not just a problem for LMICs — it’s a global health emergency. And like all emergencies, it demands immediate, concerted action. The health of millions depends on it.

The post Medical Brain Drain: A Global Health Emergency We Can No Longer Ignore appeared first on Medika Life.

Medika Life was given an opportunity to interview four HIMSS leaders – to hear more about plans for the Orlando global meeting and the priorities staff embrace year-round. It was clear that HIMSS staff see their roles as a professional responsibility and largely a public health mission.

Here are the #HIMSS24 Content Highlights:

• HIMSS Connect
• Keynotes
• Policy Issues
• Nursing and Informatics
• Start-Ups & Entrepreneurs

Gil Bashe, Editor-in-Chief, Medika Life: The health industry has many essential trade associations of different types that we know of.  PhRMA and BIO, and to some extent, HIMSS, could be seen that way, but it has grassroots membership. There are certainly very senior people in our industry who are HIMSS members. You could say HIMSS has grassroots and grasstops affiliations.

HIMSS – NOW 125,000 Strong

Christine Buck, Chief Marketing and Communications Officer, HIMSS:  Yes.  That’s right.  Let me offer context for Medika Life readers about HIMSS.  HIMSS is a 60-year-old organization and a global society that has evolved and changed.  It’s inspiring to see our community working to find solutions, thinking together as opposed to thinking in silos. And that’s what we need. We need to be the bridge. We need to be a transparent provider of democratized information.

Our membership has grown to more than 125,000 members. We landed on “Creating Tomorrow’s Health” because we are about the future, about bringing together individuals who care about generative AI and the patient. This is the difference between where we think about solutions not for the sake of an organization but the ultimate audience we all serve – patients. So that’s an exciting responsibility.

Bashe: I would very much appreciate knowing a bit about the fact that when we get together in two weeks in Orlando, some of the policy conversations will occur, from modernizing HIPAA to cyber security aspects. HIMSS is at the forefront of community building. It also tries to create a safe environment so that information can be used to improve the human condition. Could you share a little bit about that?

Tom Leary, SVP, Head of Government Relations, HIMSS:  Our policy initiatives are set by the board of directors and society members.  We are focusing on health equity. Everything needs to be pointed toward health equity so that we can leverage technology and data science to improve the human condition you’re talking about.

Specifically, concerning global conferences, we’re anticipating a lot of conversations in several key areas.

Artificial intelligence has just overtaken all the discussions around public policy. Several camps are starting to form. Some think AI is the panacea for the patient’s condition, provider burden, or any other categories we might want to discuss or that stakeholders might want to discuss.  The other camp is the fearful individuals- whether AI is taking their jobs or AI is making decisions where providers are not in the middle. It’s those kinds of conversations around artificial intelligence that we anticipate having.

Cyber security and data privacy.  The more that health care remains in the top five targeted sectors, we would fully anticipate a lot of conversation around how to maintain a high degree of vigilance and preparedness, and, quite frankly, both policymakers and our members want to talk about it, such as  “What do you know that I need to know so that I can better prepare my organization against a cyber-attack?”

Data modernization is a third key area for us that we anticipate a lot of dialogue on, particularly from the US perspective. What we saw from the global pandemic was a borderless global issue. Much investment in some areas, particularly in the clinical setting around technology advancements, resulted in excellent preparedness. However, we are still dealing with many paper-based approaches in the realm of public health and population health. So, how do you modernize the public health community?  We are facilitating ideas and conversations to address global public health priorities.

We’re very excited. A critical development in the last 48 hours is that the CDC Director, Dr. Mandy Cohen, will be the first CDC Director and, in close to 15 years, the only Director to address any HIMSS audience. More specifically, the top session that she’ll be sharing with the office of the National Coordinator is on the whole issue of data monetization. Those are three key areas that we’re looking at, particularly from a conference perspective. We can also get into some other year-round topics.

Challenges of Technology Linked to Patient Care

Bashe: I will want someone to address year-round topics because I see the annual meeting as the beginning or culmination of the year. I do have a question regarding innovation because we often talk about information. Still, I often find that many people from the digital health innovation sector attend HIMSS annually and at the national meeting.

Some of them come from chief technology offices or chief information offices or people involved in information services, or they’re people developing systems in terms of augmented intelligence or Chat GPT, the application of higher technologies to synthesize information.

Other people are looking at the integration of tools. Smart wearables, all these intelligent applications. I would very much appreciate your perspective as HIMSS leaders in talking a little bit about the role that HIMSS plays in supporting digital health innovation that collects and shares information.

Toni Laracuente, Senior Vice President & Global Head of Analytics, HIMSS:  My background in the clinical space is as a thoracic ICU nurse, which was my specialty for many years, and then I moved into quality improvements and Hospital Administration.

In the early days of electronic medical records, what you just described with nurses saying, “Oh, I don’t have time to look at that,” those were kind of the dark days of digital health, when systems were designed primarily as billing systems or departmental systems that were very focused on billing but had limited functionality and workflow process for the clinician, for the nurses, the doctors, physical therapists, and any other kind of ancillary professional service the patient may encounter.

Our practice within HIMSS analytics is genuinely focused on digital transformation. When I say digital transformation, I’m talking about the innovative, intentional, and clinically designed use of data and technology systems.

But people first, tech last. We look at people, processing data, and then technology because our approach is for digital transformation to be successful; it has to be person-centric and focused on the needs of the people who will be using and experiencing that technology.

It’s not a “one size fits all”.  The work we do with our digital maturity adoption models – you may have heard of the MRAM and ERAM adoption models. That’s the most well-known. But we have digital maturity models that span the entire care continuum. Suppose you consider any care delivery environment where a person may seek and access health care. In that case, our maturity models can assist and provide guidance and strategy for the digital transformation of those care delivery environments. The focus is on the person-centric or the patient-centric digital health ecosystem.

That is one of the presentations I’ll give at the global conference. It’s the use of digital maturity and digital transformation to build a person-centric digital health ecosystem. It needs to be a seamless process from the first encounter to the end of the encounter, from Pre-Natal right through to the End of Life.

Most importantly, it has to work seamlessly for everybody coming into contact with the system. So, that means using innovative technology. But the technology needs to work in the background. Then, the people using that technology can focus on their jobs. And we don’t have those situations where the nurses say, “Well, I don’t have time to read a digital health record.”

The response should be: “That is a tool that enables me to do my job so much better because I have access to the information that I need when and where I need it.” And the same is true for anybody, whether that’s the patient, the patient’s family, the physician, or any other healthcare provider.

Our maturity models are the focus of our work. All our maturity models have eight stages, and in the very beginning, at that stage of 0, 1, 2, which is pretty much where 95% of US and global healthcare systems sit in that early stage of digital maturity.

We often encounter process automation in some ways, but not digital transformation. Our tools and methodologies are designed to guide organizations from that piecemeal, fragmented use of technology and healthcare delivery to digital transformation, where you start at Pre-Natal and go through End of Life.

Leary: Regarding innovation, we’re very excited to have the ARAPH, the new Advanced Research Program Agency for Health (ARPA-H), which is a bipartisan development. They have about $2.5 billion that they want to spend on innovation, and they have a mandate to get the word out. Their deputy director is coming to the global conference to educate the healthcare community, from startups to large organizations, and to work with them.

Innovation can be stretched; what’s the next version of innovation in healthcare? They must find those innovative thoughts and voices and adequately fund them in a tight budgetary environment. Here in the US, they have $2.5 billion. And it’s a bipartisan belief that we need to continue to invest in that kind of approach.

It’s very similar to what happened with the 21st Century Cures Act. ARPA-H is new, and we’re very excited to have their senior officials there with us in the meeting with the startups and large organizations.

Global Meeting Hosting Government Leaders

Buck: I want to add that we have the South Korean Minister of Health is expected to attend, and representatives from Samsung Medical Center, which you’re going to be hearing a lot more about in terms of the innovation that they’re driving as a Stage 7 hospital system, but the message and the outcomes that they are going is a great touchpoint for all organizations around the world.

Bashe: To your point about the South Korean Minister of Health coming. It’s truly a global meeting, although it’s hosted in the United States.  

Hong Kong spends about 6 to 7% of its GDP on Health. They live about 15 years longer than we do in the United States. One of the things I find hopeful is that when I’m visiting and speaking to colleagues in other nations, most health information is digitized, consumers have their health records, and they are moveable.

Christine, to your point: when you and the senior staff are dealing with members of other governments worldwide, I imagine you’re serving an unofficial diplomatic role for the US health system with other people interested in sharing best practices. Could you give a glimmer about your global insights of not just dealing with the membership of HIMSS? I think you said there are 125,000 members now.

Buck: Toni, when you were speaking, the individuals in HIMSS come from a technology background; I have a FinTech background, and Toni is an actual patient care advocate. Everyone is invested in some way.  Hal Wolf, our CEO and President, is a person who works toward democratizing information between countries, dignitaries, and organizations, and that, to me, is a profound shift where we’re not keeping the information in for our benefit or someone else.

Our senior team comprises practitioners and developers of these new ideas, drawing ideas from the entire HIMSS staff and community. They’re leading panels like Toni or developing products.

Leary: The excellent collaboration between our organization and your experience has been so helpful to my growth—the beauty of what’s happening at global conferences. Perhaps we’ll get as high as 80 countries this year. They’re all searching for that digital health transformation approach, and sharing what they’ve learned and lessons they could learn from others is the key to the conference conversation and throughout the year.

The world looks at the 10-year investment that the United States made in digital health transformation, from 2010 through 2021, in the Medicaid/Medicare providers and all the providers that updated their systems. When the pandemic hit the United States, for all its politics around vaccination and immunization, aside from that, the United States was technologically ready to layer on telehealth services and various capabilities such as data and analytics because they’ve invested.

What we’re seeing in different parts of the world is curiosity about how the US did it, what kind of investment is needed, and what you would avoid if you were to do it again.

The Germans, for example, last year held a very impactful conversation with several members of the US Government on “If you had to do meaningful use over again, what would you do differently?” and struggling with the issue of not only provider burden but providers and patients “opt-in opt-out” of a program, whether it’s somewhere in Europe or Asia, or Central and South America.

They all want to get to that transformation—part of this conversation we’ll be having at our second Ministerial summit. Several years ago, we experienced the Minister of Tajikistan in one room, and the Columbian Minister of Health was in the next room, and they didn’t interact.

What we designed last year and again this year is a Ministerial Summit to talk about health equity, technology, and some of the capabilities we can all embrace as a community.

We’re expecting senior leaders from between 15 and 20 countries to sit around a table for two hours on Wednesday afternoon and continue the conversation into the evening at the international reception. They have those leaders together, talking about the vision for the future.

Bashe: The NGO element of HIMSS comes across. It’s a membership-based NGO. You are trying to advance collaboration and standards around how information can improve people’s lives and be somewhat universal. A nation’s borders do not trap information, and information shared can accelerate our understanding of how to deal with everything, from social determinants of health to the cost of health to the efficient use of health personnel, all that is driven by information. HIMSS is a depository of how processes impact performance in terms of health information. Would that be accurate?

Preparing for the Next Pandemic

Leary: That’s absolutely part of what we’ve seen over the last couple of years, which is that information sharing helps to advance not only individual countries but regional and global initiatives. The conversation that we had around the European health data spaces they developed for the EU was a big piece of legislation. There was concern that individual countries would be able to lock down their data.

If that is the case, what happened with the pandemic? What was the response to the pandemic, where data on COVID-19 from the early days of Asia would not have made their way to Europe, the United States, the Americas, and Africa? Researchers were able to work together because the data flowed from place to place, turning it into actionable information and vaccination and policies.

Suppose we don’t have those global dialogues around the power of the data and turning it into actionable information. In that case, you don’t have that kind of rapid response in a global pandemic.

Bashe:  As the HIMSS C-suite team, you’re part of a bigger puzzle. Your pieces have to align together, and I appreciate that. But regarding your mandate responsibility to the HIMSS community, could you share an expectation or hope you’d like to see come out of the upcoming meeting?

Buck:  My expectation and hope are that we create the energy for people to not just convene at one moment in time for a few days, but that it carries on 365 days a year and that we also create a stronger connection to the value that HIMSS bring to members, to organizational affiliates, to providers and patients. My goal is to get the human message out there and have all the passion and hard work come out from the team you see here and everyone who attends and creates that community.

Jim Burnett, Vice President, Engagement Strategies, HIMSS: HIMSS is an entire constellation involving research, analytics, government relations, professional development, and our chapter community. What we have is this community, which offers a wealth of engagement opportunities. In going through the study of all our different member communities, what I’m finding most clear is we need to draw people into that community from this important event. They need to come into the government relations group from the government; they need to go into the analytic tool sets to roadmap their digital transformation.

At the event, I hope our broader community is getting a lot more exposure and that we are helping people understand that it’s not this point in time. It’s not this individual session that you’re sitting in. It starts there and then crawls through that spider web through all these other supportive products and services that come out with the association.

Bashe: I notice that HIMSS members are deeply committed to the organization. It’s not like I’m sending in my annual check; they feel that by being a member of HIMSS, they are part of that conversation, part of the process of the evolution of the system, of how the information will be used.

Jim Burnett, how much of your role is spent in terms of looking at member retention and member acquisition, but also making sure that people understand the culture, the inherent values of HIMSS, and the responsibility that HIMSS feels in terms of how the information will be used to improve the health system?

Burnett: It’s about the overall value driven by the community and the collaboration. Most hospital systems aren’t in competition with one another. It’s the rising tide lifting all boats. To ensure that all these voices are heard, that their case studies are involved in our research, that their voices are indeed heard in the community, and that they can share their best practices.

That is the actual value of that community piece. It’s not necessarily the most prominent voices. You need to be able to pull from the corners of the room, draw people out, and get their perspectives on the conversation. I think that’s a critical point.

Professional Development to Initiatives on the Hill

Bashe: It’s not about digitally emailing your membership that their dues are up. Tom, how big is your policy team?

Leary: Our policy team is seven people. We deputize everyone on this screen, the rest of the organization, and the membership, so our policy team is 125,000 people. We’re seven staff. It’s a good thing.

Bashe: Is there a piece of legislation or conversation you and the team are hyper-focused on right now? What do you feel concerns the American health system here in the US?

Leary: We’re very focused on proper Office of National Coordinator funding.  They haven’t had a reasonable budget since their first year. They’re the little agency that can. 

There are two other pieces of legislation that we’re pounding on for this year. One is the telehealth provisions from the pandemic, which were extended through December of this year. And we’re working across the coalition to make that policy permanent. We’ve seen the benefit of telehealth. We’ve all benefited from it. The greater access and better healthcare outcomes. Those were set to go back to March 12, 2020, in terms of policy structure if we don’t make that policy permanent.

Laracuente: From an analytics product perspective, we are launching our newest digital maturity model, the infrastructure adoption model, and that launch is happening at our booth at 4 pm on Tuesday.

We’ve invested heavily in completely rewriting and modernizing this model. Throughout this session, we discussed using artificial intelligence, cyber security, and technology infrastructure. Overarching the in-frame model enables an organization to manage the risk of having technology. Financial risk, data, security, and privacy risk. And the risk that comes with user adoption and getting the greatest return on investment.

We’re launching that. But from an all-encompassing digital health ecosystem view. I love that you started this conversation by discussing the connections between patients with health problems. Jim mentioned that hospitals work together to try to lift each other in healthcare. When we look at health outcomes, all of the work we do in digital transformation is focused on improving the health of populations everywhere.

Part of what I’m touching on in my presentation on Tuesday is – are you familiar with the quintuple-

aim of health from public health improvement? Everything we do is focused on improving population health outcomes. Still, worldwide health care is focused on that overarching premise of the quintuple aim – improving access to care, improving patient experience, workforce experience, health, equity, reducing costs, and enhancing value for money.

We’re positioning our work in digital transformation and the maturity models in alignment with that. We’re very focused on how the digital security models deliver health outcomes and how those outcomes contribute to our provider organizations achieving success across those five domains of the quintuple.

You mentioned innovation earlier, and one thing that I wanted to touch on is when it comes to artificial intelligence, there’s a lot of discussion and hype around it, i.e., AI will take our jobs in healthcare. That couldn’t be further from the truth. AI will help us do our jobs much better, the jobs we’re all here to do, and why we get out of bed every day.

AI Will Unleash Waves of Practical Applications that Improve Patient Care

Bashe: There was a recent trending piece in Medika Life about pathologists and AI that reduces their attention when they feel they have more specificity and improve accuracy.

Laracuente: AI is perfectly positioned to do that when we look at the HIMSS mission around equity and health outcomes. I always start any conversation about AI and the workforce with the premise that AI will not replace healthcare providers or physicians. However, the physicians who use AI to do their jobs better and more efficiently will replace those who don’t.

Bashe:  The technology of the horseless carriage replaced blacksmiths through time. Technology has replaced professionals. I know that that’s very true. Humanity wins, hands down. It is unleashing as Innovation Theorist John Nosta proclaims, “the cognitive age.” Open-minded people who can hone their curiosity and ask the right questions will be valuable. Those who can’t put two words together will be in trouble.

Buck: That’s what I love about HIMSS – we’re substantive. We’re the real deal. We’re not fly by night. I love that about this whole conversation, but the entire organization and our community worldwide are the substantive providers of the suitable types of information people seek.

Bashe:  Everything is rooted in information. How we respect information, access it, and democratize it is essential. HIMSS is in incredible hands thanks to your collaborative leadership.  Thank you for sharing these thoughts and essential priorities for improved healthcare access and delivery to Medika Life readers.  I’m looking forward to continuing this conversation in Orlando.

Here is a special preview of the “Health Unabashed” interview with HIMSS CEO and President Hal Wolf on Healthcare NOW Radio from Monday, March 11th to Sunday evening, March 24th.

***

Special thanks to Albe Zakes, HIMSS Director, Corporate Communications for facilitating this conversation with HIMSS leadership.

The post Exclusive Medika Conversation with HIMSS Top Leadership – Inside Scoop on Key 2024 Priorities appeared first on Medika Life.

I primarily worked with Medicaid and Medicare patients because they are the forgotten and most destitute of patients. There were many reasons for the depth of hopelessness and despair evident in the faces and home environment of my patients.

Lack of effectual and compassionate care in the home health situation remains one reason.

Many factors made this job a difficult one, but the patient was the one who suffered the most when home health did not perform as they should.

Problems With the Home Health Situation

The task of assessing all previous diagnoses, intervening to find newer ones, and then beginning the process of teaching those diagnoses is a necessary component of the proper care of each patient.

Discovering that these crucial initial steps were usually not taken by either the admitting nurse or the previously assigned home health nurse of one of my patients was daunting and frustrating.

The heartbreak and difficulty in this nursing position was watching the majority of these patients (whose primary morbid diagnosis was longstanding, at least 10 years) neglect to follow through on their care plan and instructions, second to their loss of faith in their healthcare providers.

Unfortunately, one of the biggest barriers I experienced as the patient’s advocate (the number one job of a nurse) was disassembling the negative and discriminatory attitudes and perceptions that the patient’s medical providers had toward the patient.

Too many physicians and colleagues would give me reports of “combative and noncompliant” statuses for these patients, citing that they “just don’t take their medicine” or “they won’t keep their doctor’s appointments.”

Each report made me think differently of the messenger as my experience in many medical environments taught me early on that no one wants to be or stay sick. I understood that there is always an excess of conflicts and obstacles to the patient obtaining the necessities required to follow instructions or make it to their appointments.

Prescribers and providers often never address, investigate, or consider these important factors.

There are still only a handful of reputable providers who recognize this reality and attempt to bring some resolution to it by rightfully requiring their nursing staff to assist the patient in acquiring necessities, such as medical transportation covered by their insurance, or assistance in finding prescription programs to cover medicine copays.

Meeting just those needs goes a huge distance in increasing compliance of care instructions and appointment keeping. I have worked for just a few home health companies whose own creed to its patients was to liaise for them in this manner and those were the most fulfilling positions I had.

Miscommunication or lack of communication regarding the patient’s need for proper resources would result in the patient’s distrust of the home health company and the medical providers involved in meeting their needs. This perpetuated most “noncompliance.”

The home health nurse new to a patient should consider that a patient who has not had any previous home health or any positive previous home health experience may be slower to respond to recommendations and compliance-based instructions (medication regimen as an example.)

Purpose of Home Health Is To Improve and Stabilize A Patient’s Health, Not To Line Pockets.

The home health field originated to end repetitive emergency room visits and “unnecessary” multiple office visits. It soon became a tool for the medical community to monitor better higher-risk patients’ daily compliance and decisions in diet, medication management, new onset of disease and progression, or reversal of co-morbidities (secondary diagnoses that complicate the primary diagnosis) among other issues.

It is a fantastic idea if implemented with honest intentions, intentions void of the notion to use this vulnerable population to glean as much revenue as possible from Medicaid and Medicare without providing the excellent care documented as performed.

In other words, be very aware of the pervasive cheats and frauds when searching for a home health company or in assessing the one a patient may already have.

They are everywhere and many times, hard to discern.

Extravagant agency offices seem to elicit false confidence in a “successful” company and smooth-talking Case Managers who promise the world usually deliver absolutely nothing.

If something feels wrong upon meeting with staff from a new company, or promises are not delivered, or visits are infrequent, and the patient is not getting better or fully supported, you need a new home healthcare provider.

Most of the home situations I walked into included the patients’ lamenting how a prior nurse had visited only once that month, yet they were supposed to have been seen once or twice a week. When investigated, it was documented (and meticulously, I might add) that there had been several visits that month that had already been billed to the insurance.

This dishonesty and unethical behavior left the patient without their medication refills, without the necessary medical oversight for their conditions and without advocacy between them and their providers, which, incidentally placed each one on the brink of an ER visit-the very situation that home health is there to prevent.

Much of the time I sent patients to the ER for very high blood pressure, infected wounds that had not been treated or dressed, or for the latent symptoms from a heart attack suffered at home. They had not gone to the ER and could not get a hold of their home health nurse to report the incidents.

These are real and frequent occurrences.

Professionally, I experienced the disregard for patient safety that some medical providers showed at times for their patients due to their refusal to provide proper basic care. These kind of providers often had signed contracts with local hospitals to divert patient emergencies away from the emergency room for “cost-effective” measures for those hospitals, in lieu of that physician taking steps to treat an emergency themselves. The intent on paper was to divert non-emergency situations from the ER, not true emergencies: however, this is not the situation I experienced in home health with some providers.

Heart attacks, strokes, extreme hypertension, and other serious conditions cannot be treated outside of the emergency room effectively. Patients lose their lives due to corporate contracts to “divert ER visits.”

Medicaid and Medicare patients were the only patients I knew of who were coaxed into signing these contracts with their physicians, as the providers had to have a patient’s signature to ensure their compliance in such situations.

Many patients relayed to me that they were informed they would have to find another doctor if they refused to sign these “call me first, do not go to the ER” contracts.

This flagrantly discriminatory tactic was and is incomprehensible and is not discussed openly with most nurses. My superiors had heard about it but did not have the details of the nuances of the contracts.

One physician, whom I called to report my patient having had two strokes between doctor’s visits, told me to send this patient (who was currently having stroke signs and symptoms) to his office, not the ER, because “My patients don’t go to the ER. They go through me, first.”

To clarify, this is medical negligence.

This particular patient had sat in the doctor’s office twice in the previous months while suffering active strokes and was now blind due to those incidents. Even after this physician saw the patient on those days, he declined for her to go to the ER for care and sent her home only with a higher dose of medication for her blood pressure.

This patient was not too interested in taking her medication as she didn’t trust her provider. After reporting the incidents and the situation to my supervisor, I assisted her in finding another medical provider. And, yes, I sent her to the ER.

Empower Yourself: Taking A Stand To Demand The Care You Deserve.

To determine if the home care services you are currently receiving are deserving of your insurance dollars and time, ask these 7 questions:

1. From the time your MD ordered home health, how long did it take to receive a call from a home health company? (Ideally, the patient would want to investigate local home health agencies in the area and choose one for the MD to fax the order to, but not all patients have this capability or assistance to do so.)

Two weeks is a normal timeframe. Past that, call your MD and ask which company was faxed the order, call that company, or have the office nurse do this, and inquire about any holdups. Sometimes, insurance is the holdup. Other times, the chosen home health agency did not receive the order, lost the order, or was so disorganized that they themselves don’t know why they hadn’t called you.

2. When you do receive the initial call from a Case Manager to schedule your admission to home health, are they respectful of your time and schedule? If they communicate to you that they “only have this date and time available” to interview for admission (not all patients ordered home health will be admitted to any home health agency,) be wary. As nurses, we are being invited into your home to offer our services to you. You are not at our mercy. Your time and schedule should be considered for each agency appointment to visit you.

3. When a nurse comes to your home, do they respect your home environment? We need to know if you have animals or annoying/intrusive family members that would complicate a visit. Still, we certainly do not pass judgement regarding those or any other issues/situations in the home, barring criminal acts including drug use or abuse of the patient, which would have to be reported and reconciled through proper law and state enforcement.

4. Was your initial admission assessment thorough? This can last up to 2 hours. Use this guideline:

a) Full medical, social, and environmental (complete list of diagnoses and medical issues, family history and current status and issues, risks to your safety in the home) assessment taken?

b) Full assessment /inquiry as to any needs you may have?

c) Complete physical assessment including all body systems check, vital signs, blood sugar checked, equipment needs and checks, and oxygen saturation levels for oxygen-dependent patients. Needed equipment should be ordered immediately and delivered within a week at most.

d) Have all your questions answered to your satisfaction?

e) Explanation of the process of home health visits, documenting in the home (tablet or laptop usually) and how and when to reach either your nurse or the agency or the ER for health issues or questions you may have.

You should be provided with a working number answered by a nurse or knowledgeable agency staff at all times, day or night.

5. Have all of your medications been documented and all have recent refills and correct counts per the nurse? This is imperative to effectively managing your health and improving its status. This must be addressed and handled at EVERY home health visit.

6. After discussing your care plan (which includes the frequency of your visits, the diagnoses nursing will address and individual interventions, teaching and nursing tasks to be provided) do you understand it and agree to it?

7. Has an Emergency Plan been created and discussed with you? This includes exact instructions for the patient and family for handling weather emergencies, local, national disasters, and personal health crises.

In addition to the above, as a patient or family member of the home health patient, make sure that changes in health or problems with any delivery of care (such as not receiving ordered medical equipment) are reported to the home health nurse preferably at the time of occurrence but certainly at the very next visit.

Additional Tips To Maximizing Your Home Health Visits: Be Active In the Improved Health Outcome You Seek

As a combination strategy, medication refills are to be called in per the nurse and the patient/family member. However, ensure that refills are in the home before a home visit so that the nurse can properly dispense medication in a medbox if ordered.

When home visits are scheduled the patient must be educated on the fact that their nurse should arrive within an hour before or after the scheduled time due to several factors: length of time of the nurse’s previous home visit, possible emergencies or other patient needs, and the time to travel to the next patient.

Reporting the names of new doctors and any upcoming scheduled provider visit is imperative at each home health visit as there is usually a need during that home visit to include calls to the PCP or specialist.

In revisiting the original reason for home health, remember that the patient is to be properly and successfully cared for and should expect an outcome of improved health and stability, which helps reduce excessive in-clinic appointments, emergency room visits, and hospitalizations.

If these necessary benefits are absent from your home health care, determine your part in that, if any, and take action to change either your assigned nurse or the healthcare company immediately.

Yes, your life does depend on it.

References:

Expected standards of nursing care in the home:https://bmcnurs.biomedcentral.com/articles/10.1186/s12912-017-0264-9

This article shows how contracts should be handled in relation to reducing ER visits and NON-emergent health issues. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4038086/

The requirements in this article are to be expected of nurses, as well. https://www.homecaremag.com/february-2019/chart-homecare-career

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