At a time when Artificial Intelligence is rapidly weaving itself into the fabric of physical and mental healthcare, basic user-friendliness, processing speed, and market acceleration are no longer enough. To build digital solutions that actually work, we must grasp how humans learn, adapt, and transform their behaviors. This is exactly where the learning sciences become vital. Put simply, until we decode the mechanisms of ‘deep learning in humans’ through the lens of learning sciences, the concept of ‘Deep Learning’ in AI development will never reach its true potential.

Digital health, much like any other modern domain, is now permanently tied to technology. From education and corporate structures to parenting and economics, technology is built to streamline processes, widen access, and boost precision. At its core, technology was created to serve humanity across individual and social spheres, and digital health stands as one of the most critical testing grounds for this promise.

Yet, alongside this reality lies a much bigger issue—one that is gaining traction and deserves a rigorous, interdisciplinary look.

The question isn’t whether technology is inherently good or bad; it is that even the most advanced technology remains ineffective if it fails to align with human blueprints.

Today, more than ever, we need to look at AI and digital systems through a deeply human lens. This means moving away from treating an individual merely as a ‘user,’ a ‘data processor,’ or a passive ‘receiver,’ and instead recognizing them as a multi-dimensional, complex, living being.

In digital health, our core focus is the human being—the patient striving for recovery, the client seeking a precise diagnosis, the therapist requiring sharper diagnostic tools, or the physician leaning on technology to make high-stakes clinical decisions. The human is always the ultimate destination. If a digital tool is to succeed in this space, it must genuinely connect with real people, accounting for their cognitive, behavioral, biological, and experiential complexities.

Why Research in the Learning Sciences is Indispensable

In the digital health space, the real challenge is never just about getting someone to install an app or use a digital tool temporarily. The true measure of success is whether that tool can drive a real, lasting change in human behavior, attitude, and lifestyle. If a person engages with a platform for a brief period but experiences no sustainable shift in their health or daily habits, the technology has fundamentally missed its mark.

This is where the learning sciences help us elevate technology design far beyond surface-level mechanics and computational algorithms. When we understand how a person actually internalizes information, we can build better communication strategies, deliver more constructive feedback, apply the right behavioral reinforcements, and create environments that foster genuine trust, motivation, and user engagement.

Furthermore, this scientific backing allows us to grasp privacy and data security from the psychological standpoint of the user, since a patient’s willingness to trust a system is directly tied to how safe they feel sharing their data.

Two Foundational Pillars: Trust and Continuance Intention

To see how the learning sciences practically guide human behavior in the era of AI, we can look at two crucial dynamics in digital health:

1. The Mechanics of Trust
Trust is the ultimate currency in digital health, because users are asked to hand over highly sensitive personal, biological, and psychological data to an algorithm.

2. Continuance Intention and Habit Formation
Capturing a user’s attention at launch is relatively easy; keeping them engaged over time is where the tech industry routinely struggles.

Conclusion

The defining critique of modern AI is not its widespread adoption, but its lack of authentic human-centricity. Successful digitalization in healthcare cannot rely solely on technical scalability; it must place the complex human being squarely at the center of the design process.

Technology only gains meaning when it can understand human beings, build a relationship with them, earn their trust, and guide them toward lasting well-being.

Ultimately, the future of digital health will not be measured by raw processing power, but by the depth of the developer’s understanding of the human condition.

References

Sucala, M., Cole-Lewis, H., Arigo, D., Oser, M., Goldstein, S., Hekler, E. B., & Diefenbach, M. A. (2021). Behavior science in the evolving world of digital health: Considerations on anticipated opportunities and challenges. Translational Behavioral Medicine, 11(2), 495–503. https://doi.org/10.1093/tbm/ibaa034

Bai, B., & Guo, Z. (2022). Understanding users’ continuance usage behavior towards digital health information system driven by the digital revolution under COVID-19 context: An extended UTAUT model. Psychology Research and Behavior Management, 15, 2831–2842. https://doi.org/10.2147/PRBM.S364275

The post Human-Centered AI in Digital Health: Why Learning Sciences Matter appeared first on Medika Life.

A bill passed by the state legislature in March then signed into law by Democratic Gov. Jared Polis allows Colorado to further uncouple itself from federal guidance.

The law allows health officials to follow the recommendations of national medical groups when making decisions such as purchasing bulk vaccines for the Medicaid program.

“We are insulating our state from the dysfunction coming out of Washington,” said Democratic state Sen. Kyle Mullica, a co-sponsor of the bill and a registered nurse. “We’re going to rely on science.”

“From fighting during the pandemic for Coloradans to get vaccines as quickly as possible to combating the Trump Administration’s barriers to getting vaccinated, we have expanded access to vaccines for Coloradans who want them,” Polis said in a statement when he signed the law.

Colorado is one of at least 29 states that, along with Washington, D.C., have taken steps to bypass the new federal recommendations amid worries that the changes could chip away at public trust in vaccines and erode broad vaccine coverage.

Previously, Colorado, like most states, had followed federal guidance set by the Centers for Disease Control and Prevention. In January, CDC advisory panelists, selected by Health and Human Services Secretary Robert F. Kennedy Jr., removed six pediatric immunizations from the agency’s universal recommendation list.

Last year, doctors, scientists, local leaders, and other supporters came together to form an outreach and advocacy coalition called Colorado Chooses Vaccines.

The group aims to offer a clear, unified voice on the proven benefits of vaccines and reassure residents confused by the many federal changes.

Carol Boigon, a former Denver City Council member, joined the group because she wants more people to hear her own chilling story about vaccine-preventable illness.

“Every summer everybody got sick,” Boigon said, recounting her childhood in 1950s Detroit.

The illness was polio, a highly contagious viral disease that attacks the nervous system, sometimes causing partial or full paralysis.

During the summer of 1953, “the whole block was sick and some of us got crippled, and that was just the way it was,” she said.

New Group Steps Up

Boigon’s personal history will be part of the coalition’s work to educate new generations about the dangers of infectious diseases that were once common in the U.S. but are now relatively rare.

The group, which formed last September, will also compile vaccine information from medical groups and the state health department and advocate for policy proposals with the state government.

“It was in direct response to the federal threats,” said another coalition member, former state lawmaker Susan Lontine. She leads the nonprofit Immunize Colorado.

Another member, public relations specialist Elizabet Garcia, wants more outreach to Hispanics, whose vaccination rates lag behind other groups’.

“A lot of time it’s this fear that they’re going to have to pay out-of-pocket, that their insurance doesn’t cover it, that they might not even have insurance in general,” Garcia said.

Boigon was 5 when she got sick and was hospitalized for six weeks with a fever. The virus attacked her spine.

“None of my limbs worked immediately afterwards,” Boigon said.

Although she regained function in her other limbs, her right arm never fully recovered. She had to adapt, relearning everyday tasks such as reaching out to shake hands with people with her left hand.

In 1955, not long after she got sick, the new polio vaccine became more widely available to the public. As vaccinations took off, U.S. cases of polio, once one of the nation’s most feared diseases, dropped by an estimated 85%-90%.

Increasing Public Trust

State leaders have taken other steps to promote public health. After the Trump administration pulled the U.S. out of the World Health Organization, several states, including Colorado, decided to join the WHO’s Global Outbreak Alert and Response Network on their own.

Colorado also joined a multistate lawsuit challenging the Trump administration’s changes to the childhood vaccine schedule.

And the new state law has provisions besides allowing the state to diverge from federal recommendations. It codifies pharmacists’ ability to prescribe and give vaccines themselves. It also increases legal protections for healthcare workers who give vaccines.

“This law will provide more clarity to guide all Coloradans, including providers who administer vaccines,” Lontine said.

But the legislation has opponents who say it would interfere with parental choice and claim vaccines might be unsafe or ineffective.

“I just want to make sure we’re not just getting into a big political dispute between the federal recommendations — the CDC and so forth — and different political views in Colorado here,” said Republican state Sen. John Carson, who voted against the vaccine bill.

NPR contacted the U.S. Department of Health and Human Services about Colorado’s new law. Spokesperson Emily Hilliard answered in an email: “The updated CDC childhood schedule continues to protect children against serious diseases.”

Preventable Illnesses Surge

The flurry of statewide activity comes as Colorado and the nation have seen surges in illnesses such as flu and measles.

As of mid-May, Colorado had recorded 22 measles cases this year. In 2025, it registered 36 cases, according to the state health department, far surpassing totals from previous years.

Across Colorado, kindergarten vaccination rates for measles were 88% last school year — with only a few counties achieving rates of 95%, the level needed for herd immunity, according to data published by The Washington Post in December.

This has also been Colorado’s worst flu season in recent years.

Vaccination rates for both flu and covid-19 have dropped slightly in Colorado, according to the state health department.

Eight children in Colorado have died this season from flu; one from covid; and one from RSV, or respiratory syncytial virus. Vaccines for all three are available for children and recommended by the state’s health department.

Kennedy, a longtime anti-vaccine activist, has defended his decisions to overhaul the recommended schedule for childhood vaccinations.

In March, a federal judge put on hold many of the changes.

“We’re not taking vaccines away from anybody. If you want to get the vaccine, you could get it. It’s going to be fully covered by insurance just like it was before,” Kennedy told CBS News in January.

When a reporter suggested the new changes could result in fewer people getting a flu vaccine, Kennedy said: “Well, that may be, and maybe that’s a better thing.”

Boigon is sometimes incredulous at everything that has happened.

“It’s like we’re going backwards,” she said. “It’s like we have decided we don’t want a modern life; we want to be back in the 1950s, where children are sick and dying.”

This article is from a partnership with Colorado Public Radio and NPR.

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

Executive Summary

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

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

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

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

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

I: A Conversation That Sparked a Question

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

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

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

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

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

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

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

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

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

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

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

III: A Wicked Problem and a Strategic Dilemma

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

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

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

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

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

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

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

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

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

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

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

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

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

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

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

V: Three Conditions Hostile to Learning

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

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

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

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

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

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

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

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

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

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

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

Payer Interference: External Rules Overriding Internal Mission

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

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

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

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

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

Physician Burnout: The Human Output of a Broken System

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

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

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

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

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

VI: Thinking Design, Not Framework Prescription

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

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

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

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

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

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

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

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

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

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

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

The four DSRP patterns work as follows.

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

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

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

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

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

VII: Building the Ecosystem

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

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

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

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

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

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

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

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

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

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

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

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

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

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

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

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

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

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

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

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

IX: Strategic Implications — The Cost of Not Learning

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

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

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

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

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

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

Citations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Algeria’s vaccination metrics: an operational model for success

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

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

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

The shift to hexavalent vaccination

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

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

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

The financial metrics of the switch

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

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

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

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

Programmatic benefits of hexavalent vaccination

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

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

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

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

The case for Hexavalent vaccination across Africa

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

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

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

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

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

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

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

Reference list

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

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

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

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

The Day the Ordinary Disappeared

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

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

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

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

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

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

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

Where Care Is Action, Not Theory

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

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

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

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

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

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

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

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

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

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

The Human Thread Through Every Moment

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

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

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

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

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

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

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

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

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

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

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

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

That truth asks something of us.

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

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Healthcare is no exception, and in the wake of the murder of George Floyd and the national awakening to the scourge of systemic racism in our country, more and more conversations about racism in healthcare are being had, across our country, and in the medical literature. This is only a good thing, and I hope and pray they lead to rectification of the healthcare disparities faced by people of color in this country.

One such conversation was recorded in the excellent Critical Matters podcast, hosted by Dr. Sergio Zanotti — world-renown Critical Care Medicine specialist and Chief Medical Officer for Sound Critical Care, a national critical care medicine practice. He was speaking with Dr. Greg Johnson, Sound Physicians’ Chief Medical Officer for Hospital Medicine and thought leader within his field and a champion for diversity, inclusion, and belonging within medicine.

In this episode, Dr. Zanotti and Dr. Johnson have an honest conversation about healthcare, race, and racism in healthcare. It was eye-opening, and it is a must-listen for every healthcare professional in this country. You can listen to it here:

Dr. Johnson reiterated that many Black Americans must go to great lengths to let members of the healthcare team taking care of their families know the value of their loved one: what job they do; what they mean to their families; what they mean their communities.

First of all, I was shocked by this. And yet, when I thought back to my Black patients and their families, I realized that many of them did just that: go out of their way to let me know how important this patient in front of us was to them, their family, and their community.

Just the other day, I had a husband tell me, “This woman, she is the rock of our family.” Another family member of a patient with COVID-19 told me something similar. I never paid attention to this phenomenon, and I am so very grateful I heard this conversation to open my eyes to this reality.

This is wrong. This is horrific. This is terribly sad. No one should have to justify to me why their loved one is important, how prominent their loved one is in the community. It is absolutely horrible that Dr. Tyson’s had to show the healthcare professionals taking of his father that his father was a prominent lecturer in order to garner respect from them. How can this be?

Every patient we take care of has worth, has value. Black, Brown, Yellow, Red, or White, all our patients have worth, and the fact that Black Americans feel the need to describe how important their loved ones are to get respect is truly nauseating. And you know what they are saying to us? Black Lives Matter.

Painful though it may be, it is reality, and it is of absolute importance that we in healthcare are cognizant of this reality and work to rectify it. Indeed, I told that husband that his wife has worth, and I will do all that I can to care for her within her values and wishes.

Racism in healthcare is real; it is pervasive; it is indeed a pandemic. We need to acknowledge it and then work to overcome it. All of us in healthcare have a responsibility to do what we can to achieve this end. And for starters, all of us in healthcare should listen to this conversation about racism in healthcare to understand the problem and how to fix it.

The post Every Healthcare Professional Must Listen To This Conversation appeared first on Medika Life.

Inside the Westin St. Francis, a carefully curated (invite-only) group of life science companies presents to an audience that shapes markets, valuations and strategy for the year ahead. It is the most visible stage in health innovation and remains so for good reason.

However, innovation has never been confined to a single ballroom. It was once clustered along the corridors of New Jersey’s research parks, then radiated west to the Bay Area, before stretching across the Golden Triangle of London, Cambridge, and Oxford, finding parallel intensity in Paris, Rehovot and accelerating across Asia, from China to Singapore and beyond. Discovery has always followed talent, capital, and curiosity – not hotel addresses.

A Tale of Two Meetings in One City

Biotech Showcase reflects that reality. Each January, companies arrive in San Francisco carrying science born far beyond the Bay Area and find a setting designed to recognize promise wherever it originates. The meeting has become a convergence point for a distributed industry, where the next breakthrough is just as likely to come from Beijing or Boston as from Silicon Valley or Singapore, and where sound science and partnership, not proximity, determine what advances.

Parallel to that marquee gathering, another meeting has quietly and persistently grown into an indispensable part of the week’s architecture. Now in its 18^th year, the Biotech Showcase, co-produced by DEMYCOLTON and Informa, has become the gathering place where the broader biotech, pharma, and medical device ecosystems converge to drive the work that ultimately informs trade industry headlines. It is not an alternative to JPM; rather, it is the grassroots connective tissue that enables the rest of the week to function.

It’s Not the Place – It’s the Connections

“This meeting addresses a critical need for emerging biotech companies to be heard. We’re showcasing truly innovative companies, both as presenting companies and in our panel conversations. In fact, during one session, people walked in simply to hear what was new, the information they don’t hear elsewhere,” said  Sara Jane Demy, Founder & CEO of Demy-Colton, one of the gathering’s two convenors.  “We serve as the home for emerging and startup companies – seed-stage through Series A, B, and C, and small-cap. These are the companies developing the breakthrough therapies and technologies that will become the foundation of today’s science, leading to tomorrow’s therapies. We’re the innovation engine for the little guys,” she added.

When Biotech Showcase began almost two decades ago, its purpose was practical rather than aspirational. Many promising companies, mostly venture-backed, science-driven, and globally ambitious, were not on the JPM agenda. They still needed access to investors, strategic partners and business development leaders who were already flying into San Francisco for the “main show.” Biotech Showcase created a professional, disciplined, curated forum for those conversations to happen with intention rather than improvisation.

Dr. MaryAnne Rizk, head of Health AI, AWS, supports Ms. Demy’s words.  “The Biotech Showcase is where the future of medicine is previewed, bringing together an ecosystem of trusted innovators to share the next decade of healthcare.”

As JPM grew in scale and influence from the Hambrecht & Quist Healthcare Conference, it also became more constrained by space, protocol, and precedent. Presentation slots were scarce. Visibility became concentrated. Meanwhile, the number of companies advancing meaningful science expanded exponentially. New modalities emerged. Platform technologies matured. Innovation globalized. The industry needed a setting that could absorb this growth without diluting seriousness or credibility.

Today, the contrast between the two meetings is less about size than about function. JPM remains the industry’s loudest signal, a place where established players outline direction and investors listen for cues and dropped hints.

San Francisco during JPM Healthcare Week has become a global meeting ground for life-science leaders. Eden Ben, CEO of Amorphical, arrived at Biotech Showcase with a clear purpose: “I’m here during JPM Week and Biotech Showcase to present the Amorphical proprietary platform addressing metabolic bone and inflammatory diseases. We’re engaging investors around encouraging clinical results in osteoporosis, Crohn’s disease, and pancreatic cancer, with the goal of securing strategic investment to advance these programs into Phase 2b.”

Biotech Showcase Evolved into A Go-To Setting

Biotech Showcase, by comparison, is quieter but no less consequential. With more than 3,000 attendees, including more than 1,000 investors, and hundreds of presenting companies from around the world, it has become the driving force of the week. There are more than 350 company presentations.  Thousands of one-to-one meetings are scheduled in advance, not left to chance encounters in hotel lobbies. These are not symbolic conversations. They are the early architecture of partnerships, financing, and long-term collaborations.

What is striking is how intentionally this community has formed. A single profile does not define the companies that present at the Biotech Showcase. Some are early-stage and pre-clinical. Others are approaching pivotal trials or preparing for commercial transition. Many are international firms seeking a foothold in the U.S. market. Increasingly, they span disciplines that did not exist when the meeting began: AI-enabled discovery, data-driven trial design, diagnostics that blur the line between software and biology, and climate-adjacent technologies reshaping biomanufacturing and supply chains.

This community has lived through cycles together. It has seen exuberance give way to discipline, easy capital tighten into scrutiny, and promising science tested by unforgiving markets. During the pandemic, when in-person meetings were no longer possible, Biotech Showcase adapted, preserving its core function even as the format changed. When travel resumed after the COVID shutdown, there was a palpable recognition that these structured, face-to-face conversations were no longer a nice-to-have; they were essential.

Some gatherings that once anchored JPM week have shifted their center of gravity elsewhere, following the rise of new conference hubs and festival-style convenings. Biotech Showcase did not. It stayed rooted in San Francisco, aligned with the rhythm of JPM week, and doubled down on what it does best: creating order, access, and momentum for companies still earning their story.

In that sense, it has become the “other mega meeting” of the week, not because it competes for attention, but because it transmits the energy JPM generates and redistributes it across the ecosystem. Investors move fluidly between rooms. Business development leaders extend conversations that began elsewhere. Companies that may never stand at the Westin podium still find themselves in dialogue with mega partners who can change their trajectory.

What makes 2026 feel particularly distinct is the tone of the conversations unfolding inside Biotech Showcase. The industry is recalibrating. After years of volatility, there is a renewed emphasis on scientific rigor, capital efficiency, and partnerships built for durability rather than speed. Artificial intelligence is no longer presented as novelty; it is assumed, embedded, and evaluated for impact. Global health challenges, such as aging populations, the burden of chronic diseases, and climate-driven disruptions, are shaping what investors and innovators consider essential.

Less noise, and More Intent

In that environment, Biotech Showcase feels less like a workaround and more like an integral part of the infrastructure. It is where emerging companies can be evaluated on their merits rather than their market capitalization, allowing investors to see breadth without sacrificing depth. Where the future of health is assembled incrementally, through conversations that may never make headlines but ultimately shape outcomes.

JPM will always be the stage where the industry speaks to itself and the world. Biotech Showcase is where the industry listens, questions, and connects.

“We cover technology and therapeutics from A to Z, focusing on the earliest and most exciting stages of innovation, when the science is bold and the stakes are highest. This is also the most precarious phase, which is why visibility matters. Over the years, we’ve seen many of our companies graduate from this meeting to participate in the J.P. Morgan Healthcare Conference. When they reach that stage, they no longer need us, but there is always a new generation of innovators stepping in to take their place,” reflects Ms. Demy.

Eighteen years in, its importance is no longer anecdotal. It is measurable in attendance, in business development meetings scheduled, in companies that return year after year, either because something meaningful happened the last time they were here or they believe the quality of attendees warrants their attention. It is evident in the way the community shows up not to be seen, but to engage.

That is why Biotech Showcase flourishes, not as a counterpoint to JPM, but as its indispensable counterpart.

The post Biotech Showcase at 18: The Other Center of Gravity appeared first on Medika Life.

By Gaea Cabico, a freelance reporter in New York.

On the night Superstorm Sandy hit New York in October 2012, seawater from the Atlantic Ocean surged into the emergency room of what was then known as Coney Island Hospital. Staff scrambled to evacuate 28 patients to higher floors, wading through knee-deep water and holding flashlights to navigate dark hallways.

“The damage was extensive. The hospital was fully evacuated. We lost all power in the campus. It was multiple months before [the hospital] can be utilized again,” Svetlana Lipyanskaya, CEO of NYC Health + Hospitals/South Brooklyn Health, said in an interview. (Lipyanskaya, who was not at Coney Island Hospital in 2012, worked at Weill Cornell at the time.)

Today, the site, now called South Brooklyn Health, is fortified by a four-foot flood wall. A new hospital building, named after the late Supreme Court Justice Ruth Bader Ginsburg, features hurricane-resistant glass, and power and water systems designed to withstand future storms that could have once crippled the facility.

As climate change accelerates, South Brooklyn Health’s transformation reflects a growing recognition of the vulnerability of health care facilities to extreme weather events and the need for resilient infrastructure to maintain essential services. Michele Baker, policy coordinator of The Global Climate and Health Alliance, said many health facilities “were not built with these kinds of weather events in mind.”

“We learned what our vulnerabilities are,” Lipyanskaya said. “And so when we rebuilt, we rebuilt with that in mind.”

Crucial services moved to higher floors

In May 2023, the $923 million Ruth Bader Ginsburg Hospital opened, offering services to nearly 875,000 residents of South Brooklyn and nearby neighborhoods, many of whom are immigrants and low-income individuals. The Federal Emergency Management Agency funded the construction of the new hospital.

Related: Flooding is NYC’s most pressing public health threat, new city report says

One of the key features of the hospital is its emergency department located on the second floor to ensure continuity of care during floods.

“Our new building is built so that, post-event, we could be up and running very quickly because there’s literally nothing of any key importance on the first floor,” Lipyanskaya said. The hospital’s first floor houses just the lobby and a seven-foot bronze statue of Ginsburg.

Clinical services, along with power, heating, cooling, and water systems are located on higher floors. Aside from the flood wall surrounding the hospital campus, flood barriers are installed within the building. A wind-resistant envelope further protects the structure against severe storms. During Sandy, seawater from the Atlantic, just a mile away from the hospital, easily breached the facility, even over sandbags. Violent winds blew open the windows.

In addition to infrastructure upgrades, South Brooklyn Health has also trained staff to respond to disaster situations, and the hospital has implemented a robust supply chain system to ensure uninterrupted patient care during emergencies.

“We have backups of everything, and we’re able to replenish very quickly and move forward very quickly,” Lipyanskaya said, adding that the supply chain efficiencies were more a result of the Covid-19 pandemic than Sandy.

South Brooklyn Health is not alone in bolstering its defenses against climate impacts in the city. With a grant from FEMA, NYC Health + Hospitals Bellevue, Metropolitan and Coler, which were also devastated by Sandy, have installed flood walls to protect against storm surge, elevated electrical equipment and alarm systems, and put in place backup generators to ensure operations can continue for at least 72 hours during a power outage.

Atlanta hospital flooded during extreme cold

Many hospitals across the United States remain ill-equipped to withstand the increasing severity and frequency of climate-related disasters.

About 515 hospitals in North America could face high risk of a total or partial shutdown due to extreme weather events if global emissions continue to rise at a rapid pace, according to a December 2023 report by climate-risk data analysis company XDI. Large corporations, financial institutions, insurance companies, real estate developers, and governments rely on XDI’s data to assess the climate risks faced by their properties, infrastructure, and operations.

By 2100, Florida is expected to have the highest number of hospitals at high risk under a high emissions scenario, followed by Louisiana, California, Texas, and New York, according to the report.

Related: Climate change is delaying the arrival of spring in NYC parks, researchers find

Last year, Hurricanes Helene and Milton paralyzed health care facilities in Florida, Georgia, Tennessee, and other Southern states, and led to shortages of IV solutions.

In 2022, Grady Memorial Hospital, a public hospital that primarily serves low-income patients in Atlanta, was crippled after an HVAC system failure triggered by extreme cold led to widespread flooding. Dr. Jamaji Nwanaji-Enwerem, an emergency medicine physician, recalled how it damaged the burn unit and knocked out several CT scanners.

“These cascading failures revealed how aging infrastructure in safety-net hospitals, which already face financial and operational challenges, is especially vulnerable to climate-related events,” Nwanaji-Enwerem said.

Safety-net hospitals may lack funding for climate-proofing

While FEMA grants provide critical funding for disaster resilience, other programs often rely on tax-related incentives, such as credits, which require upfront capital investments, said Winslow Dresser, associate director of regional climate solutions at Health Care Without Harm, a nonprofit advocating for sustainable health care practices. Wealthier institutions with strong credit ratings and better access to capital are more likely to benefit from these programs, Dresser said.

“Many safety-net hospitals lack the financial flexibility to make these changes alone,” Nwanaji-Enwerem said. “Broader federal, regional, and state policies are needed to provide funding, technical support, and incentives for climate-resilient upgrades.”

Nwanaji-Enwerem added that strengthening partnerships with local governments and community organizations can also improve hospitals’ readiness and response.

For some hospitals, bolstering defenses against climate disasters can be especially challenging if they have not experienced a major storm or wildfire. In those cases, Dresser said, the hospital leader “does not have those kinds of risks top of mind.”

While South Brooklyn Health’s infrastructure upgrades were designed to ride out the challenges of a changing climate, its CEO stressed that resilience is about more than just disaster-proof buildings.

“We really need to focus on providing care to patients where they are and think much more broadly about what happens outside the walls of the hospitals,” Lipyanskaya said, adding the improvements have also allowed the hospital to expand its services to better support the surrounding community.

Authored by Gaea Cabico, a freelance reporter in New York, writing for HealthBeat.

The post Climate-proofing: How a Coney Island hospital rebuilt after Superstorm Sandy appeared first on Medika Life.

She found Forbes face down in the street in August 2023, two weeks before he had planned to move away from the empty storefronts, boarded-up houses, and poverty that make this one of the most troubled places in the nation.

Naketra Guy thought about how her son overcame losing his father at age 4 and was the glue of the family. She called him “humble” and “respectful,” a leader in the community and on the football field, where he shined.

Yet he could not outrun the grim statistics of his hometown. Bogalusa posts some of the worst health outcomes and poverty in Louisiana, a state that routinely ranks among the worst nationally in both. And Bogalusa has endured another indicator of poor public health: high levels of gun violence.

Since the beginning of the covid-19 pandemic, gun violence has shattered any sense of peace or progress here. Louisiana suffers the nation’s second-highest firearm death rate — and Bogalusa, a predominantly Black community with 10,000 residents, has seen dozens of shootings and a violent crime rate approaching twice the national average.

A nearby team refused to play football at Bogalusa High School in fall 2022, citing safety concerns.

Bogalusa’s mayor, Tyrin Truong, was elected in 2022 at age 23 on his promises to fix entrenched challenges: few youth programs and good jobs, and perpetual crime and blight.

“I ran for mayor because I got sick of seeing our city painted as mini-New Orleans,” he said, “due to the high levels of youth gun violence.”

In January, the Louisiana State Police arrested Truong, accusing him of soliciting a prostitute and participating in a drug trafficking ring that allegedly used illicit proceeds to buy firearms. He has said he is innocent. “I still haven’t been formally arraigned,” he told KFF Health News in late July, “and I haven’t been charged with anything.”

Every year tens of thousands of Americans — one every few minutes — are killed by gun violence on the scale of a public health epidemic.

Many thousands more are left to recover from severe injuries, crushing medical debt, and the mental health toll of losing loved ones.

Most headlines focus on America’s urban centers, but the numbers also reflect the growth of gun violence in places like Bogalusa, a pinprick of a town 75 miles north of New Orleans. In 2020, the gun violence death rate for rural communities was 40% higher than in large metropolitan areas, according to Johns Hopkins University.

Firearms are the No. 1 killer of children in the U.S., and no group suffers more than young Black people. More Black boys and men ages 15 to 24 in 2023 were killed in gun homicides than from the next 15 leading causes of deaths combined. Though overall U.S. homicides dropped sharply after the pandemic ended, adolescent gun deaths climbed even higher in the years after, according to research by Jonathan Jay, an associate professor in the School of Public Health at Boston University.

“It has all the markers of an epidemic. It is a major driver of death and disability,” Jay said. “Gun violence does not get the attention it deserves. It is underrecognized because it disproportionately impacts Black and brown people.”

Rather than bolstering efforts to save lives, federal, state, and local government officials have undermined them. KFF Health News undertook an examination of gun violence since the pandemic, a period when firearm death rates surged. Reporters reviewed government reports and academic research and interviewed dozens of health policy experts, activists, and victims or their relatives. They reviewed corporate earnings reports from gun manufacturers and data on the industry’s donations to politicians.

In polling published in 2023 by KFF, more than half of Americans said they or a family member had been impacted by gun violence such as by seeing a shooting or being threatened, injured, or killed with a gun.

American politicians and regulators have put in place laws and practices that have helped enrich firearm and ammunition manufacturers — which tout $91 billion in economic impact — even as gun violence has terrorized neighborhoods already damaged by white flight, systemic disinvestment, and other forms of racial discrimination.

President Donald Trump championed gun rights on the campaign trail and has received millions from the National Rifle Association, to whose members he promised, “No one will lay a finger on your firearms.” His administration has rolled back efforts under President Joe Biden to address the rise in gun violence.

Emboldened in his second term, Trump is pushing to allow more guns in schools, weaken federal oversight of the gun industry, override state and local gun laws, permit sales without background checks, and cut funding for violence intervention.

Trump ordered the attorney general to review all Biden administration actions that “purport to promote safety but may have impinged on the Second Amendment rights of law-abiding citizens.”

The Biden administration said “a historic spike in homicides” during the pandemic took its greatest toll on racially segregated and high-poverty neighborhoods.

Black youths in four major cities were 100 times as likely as white ones to experience a firearm assault, research showed. Gun suicides reached an all-time high, and for the first time the firearm suicide rate among older Black teens surpassed that of older white teens.

In Bogalusa, the pandemic gun violence spread fear. Among the victims killed were a 15-year-old attending a birthday party and a 24-year-old nationally known musician. Thirteen people were injured at a memorial for a man who himself had been shot. Residents said neighbors stopped sitting in their yards because of stray bullets.

Researchers say communities like Bogalusa endure a collective trauma that shatters their sense of safety. Two years after Forbes’ death, his mother says that when she leaves home her surviving children worry that she, too, might get shot.

Repercussions from the surge will last years, researchers said: Exposure to shootings increases risk for post-traumatic stress disorder, anxiety, suicide, depression, substance abuse, and poor school performance for survivors and those who live near them.

“We saw gun violence exposure go up for every group of children except white children, in the cities we studied,” Jay said. “Limits on government funding into gun violence research may stop us from ever knowing exactly why.”

Politics of Pain

The year before Forbes died in Bogalusa, Biden signed into law the Bipartisan Safer Communities Act, considered the most sweeping firearm legislation in decades.

In a matter of months, Trump has systematically dismantled key provisions.

Efforts to regulate guns have long proven ineffective against the power of political and business interests that fill the streets with weapons. In 2020, the number of guns manufactured annually in the U.S. hit 11.3 million, more than double a decade earlier, according to the federal government. In 2022, the United States had nearly 78,000 licensed gun dealers, more than its combined number of McDonald’s, Burger King, Wendy’s, and Subway locations, according to Everytown for Gun Safety, an advocacy group.

The Biden administration announced in 2021 it would attempt to reduce gun violence by adopting a “zero tolerance” policy toward firearm dealers who committed violations such as failing to run a required background check or selling to someone prohibited from buying a gun.

The federal Bureau of Alcohol, Tobacco, Firearms and Explosives, or ATF, which licenses gun dealers, has the authority to enforce laws meant to prevent illegal gun sales. In issuing an executive order, the Trump administration declared that, under Biden, the agency targeted “mom-and-pop shop small businesses who made innocent paperwork errors.”

From October 2010 to February 2022, the agency conducted more than 111,000 inspections, recommending revocation of a dealer’s license only 589 times, about 0.5% of cases, an inspector general’s report said. Even when it cited serious violations, the ATF rarely shut dealers down.

ATF leaders told the inspector general’s office that recommendations for license revocations increased after Biden’s zero-tolerance policy was implemented. In April, the Trump administration repealed it.

Surgeon General Vivek Murthy last year declared firearm violence a public health crisis. Within weeks of Trump’s inauguration, his administration removed the advisory. Of the 15 leading U.S. causes of death, firearm injuries received less research funding from the National Institutes of Health for each person who died than all but poisoning and falls, according to an analysis in 2024 by Brady, an anti-gun violence organization. Trump is trying to cut that funding, too.

Trump’s Department of Justice abruptly cut 373 grants in April for projects worth about $820 million, with a large share from gun violence intervention.

“We are going to lose a generation of community violence prevention folks,” said Volkan Topalli, a gun violence researcher at Georgia State University. “People are going to die, I’m sorry to say, but that is the bleak truth of this.”

Asked about its policies, the White House did not address questions about public health considerations around gun violence.

“Illegal violence of any sort is a crime issue, and President Trump has been clear since Day One that he is committed to Making America Safe Again by empowering law enforcement to uphold law and order,” White House spokesperson Kush Desai said.

Trump administration officials “want safer streets and less violence,” Topalli said. “They are hurting their cause.”

Garen Wintemute, an emergency medicine professor who directs the violence prevention program at the University of California-Davis, was among the first in the nation to consider guns and violence as a public health issue. He said race plays a significant role in perceptions about gun violence.

“People look at the demographic risk for firearm homicide and depending on the demographics of the people in the audience, I can see the transformation in their faces,” Wintemute said. “It’s like they’re saying, ‘Not my people, not my problem.’”

Eroding Gun Restrictions

Trump’s incursions against public health efforts to contain gun violence are backed by lobbying power.

Firearm industry advocacy groups made millions of dollars in political donations in recent years, mostly to conservative causes and Republican candidates. That includes $1.4 million to Trump, according to OpenSecrets, which tracks campaign finance data.

The assassination of civil rights icon the Rev. Martin Luther King Jr. helped lead to the passage of the federal Gun Control Act of 1968, which imposed stricter licensing rules and outlawed the sale of firearms and ammunition to felons.

While it remains the law of the land, over time, federal and state government actions have significantly weakened its protections.

Most states now allow people to carry concealed weapons without a permit or background check, even though research suggests the practice can increase the risk of firearm homicides.

In Louisiana, Democratic former Gov. John Bel Edwards, in office from 2016 to 2024, vetoed a bill that would have allowed people to carry concealed firearms without a permit.

Elected in 2023, Republican Gov. Jeff Landry signed a law to allow any person over age 18 to conceal-carry without a permit.

The Trump administration has created a task force to implement his executive order to end most gun regulations and which would allow more people with criminal convictions, including for domestic abuse, to own guns.

Figures vary, but some researchers estimate as many as 500 million guns circulate in the U.S. Sales reached record highs during the pandemic and publicly traded firearm and ammunition companies saw profits jump.

Donald Trump Jr. this summer joined the board of GrabAGun, an online gun retailer that went public in July under the stock ticker PEW. In a Securities and Exchange Commission filing, the company, which markets guns to people ages 18 to 44, cited “gun violence prevention and legislative advocacy organizations that oppose sales of firearms and ammunition” as threats to its sales growth.

Dave Workman, a gun rights advocate with the Second Amendment Foundation, said firearms are not to blame for the surge in pandemic shootings.

“Bad guys are going to do what bad guys are going to do regardless of the law,” Workman said. “Taking away gun rights is not going to reduce crime.”

David Yamane, a Wake Forest University sociology professor and national authority on guns, said the U.S. firearm debate is complex and the industry is often “painted with too broad a brush.”

Most guns will never be used to kill anyone, he said. Americans tend to buy more guns during times of unrest, Yamane added: “It’s part of the American tradition. Guns are seen as a legitimate tool for defending yourself.”

‘A Low Level of Hope’

Once called “the Magic City,” Bogalusa has become a grim symbol of deindustrialization.

Bogalusa emerged as Black people formed their own communities in the time of Jim Crow racial segregation at the turn of the 20th century.

Racism concentrated Black people in neighborhoods that became epicenters of poor health, reflected in high rates of cancer, asthma, chronic stress, preterm births, pregnancy-related complications — and, over recent decades, firearm violence.

Thousands flocked to Bogalusa after the Great Southern Lumber Company built one of the world’s biggest sawmills, establishing Bogalusa as a company town. Racial tensions soon followed.

Members of the local Deacons for Defense and Justice gained national attention in the 1960s for protecting civil rights organizers from the Ku Klux Klan, a hate group that burned houses and churches, terrorizing and killing Black people.

As the mill changed hands over the decades, Bogalusa’s fortunes slid. In the mid-20th century, the population surpassed 20,000, but it is now about half that.

International Paper, a Fortune 500 company based in Tennessee, runs the mill as a containerboard factory, employing about 650 people. In 2021, the state announced incentives for the company that included a $500,000 tax break, saying the move would help bring “prosperity.”

Businesses remain boarded up along the main drag. Houses still bear damage from Hurricane Katrina, and many streets are eerily quiet.

Nearly 1 in 3 people in Bogalusa live in poverty — 2½ times the national average.

Bogalusa’s violent gun crime rate reached 646.1 per 100,000 people in 2022, higher than Louisiana’s and 1.7 times the national one, according to the nonprofit Equal Justice USA, citing FBI Uniform Crime Reporting data.

In many rural towns across the South, “there is a level of desperation that is more apparent” than in other parts of the U.S., said Luke Shaefer, a University of Michigan professor of social justice and public policy.

“They don’t have the same infrastructure to have robust social services. People are like, ‘What are my life chances?’” Shaefer said. “People feel like there is nothing that can be done. There is a low level of hope.”

Missed Opportunities

Mayor Truong lamented the violence in Bogalusa after Forbes was killed, writing on Facebook, “When are we as a community going to come together and decide enough is enough?”

The federal government had offered one path forward.

The Biden administration provided billions of dollars to local governments through the American Rescue Plan Act during the pandemic. Biden urged them to deploy money to community violence intervention programs, shown to reduce homicides by as much as 60%.

A handful of cities seized the opportunity, but most did not. Bogalusa has received $4.25 million in ARPA funds since 2021. None appears to have gone toward violence prevention.

The Louisiana legislative auditor, Michael Waguespack, found that Bogalusa used nearly $500,000 for employee bonuses, which his report said may have violated state law. In some cases, the report says, payments were not tied to work performed.

Bogalusa officials did not respond to a public records request from KFF Health News seeking detailed information about its ARPA money.

Former Mayor Wendy O’Quin-Perrette, who served from 2015 through early 2023, told Waguespack in a June 2024 letter that the city used ARPA money to improve streets and pay the bonuses. “We would not have done it without being sure it was allowed,” she said.

O’Quin-Perrette did not respond to requests for comment.

In a 2023 letter to Waguespack, O’Quin-Perrette’s successor, Truong, wrote that Bogalusa officials didn’t know how the federal money was spent. When he took office, Truong alleged, officials discovered “tens of thousands of dollars of checks and cash” stashed “in various drawers and on desks” in city offices.

Truong defended his stewardship of ARPA funds, saying that about $1 million remained when he assumed office but that the money was needed for more urgent sewer infrastructure repairs. “I wish we could have invested more, invested any money in gun violence prevention efforts,” he said.

In an interview, Truong said the city has been “intentional” about bringing down gun violence, including through a summer jobs program. He pointed to statistics that show homicides decreased from nine in 2022 to two in 2024. “If you keep them busy, they won’t have time to do anything else,” he said.

Asked about his January arrest, Truong said he has political enemies.

“I’m the only Democrat in a very red part of the state, and, you know, I’ve made a lot of changes at City Hall, and that ticks people off,” Truong told KFF Health News. He said that he ended long-standing city contracts with local businesspeople. “When you’re shaking up power structures, you become a target.”

Josie Alexander, a Louisiana-based senior strategist for Equal Justice USA, said city officials missed an opportunity when they didn’t use ARPA funds for gun violence prevention. “The sad thing is people here can now see that money was coming in,” she said. “But it just wasn’t used the way it needed to be.”

‘Too Much Trouble Here’

Truong said the city is still reeling from the pandemic spike in violent crime. He said he was at Bogalusa High School’s homecoming football game in 2022 when one teen shot another. Shots rang out, Truong said, and he grabbed his 3-month-old son and “laid in the bleachers.”

“It’s not a foreign topic to hardly anybody in town, whether you’ve heard the gunshots in the distance, whether you have attended a funeral of somebody who passed due to gun violence,” he said. Many still grapple with trauma.

In December 2022, Khlilia Daniels said, she hosted a birthday party for her teenage niece, praying no one would bring a gun.

The hosts checked guests for weapons, she said.

Yet gunfire erupted, Daniels said. Three teens were shot, including 15-year-old Ronié Taylor, who died, according to police.

“When someone you know is killed, you never forget,” said Daniels, 32, who held Taylor until emergency responders arrived.

Tajdryn Forbes was planning his future when he was killed, likely because of a dispute that started on social media over lyrics in a rap song, Guy said.

In a Facebook post in January, Bogalusa police said they had arrested someone in connection with Forbes’ killing. Authorities had previously announced the arrest of a teen in connection with the homicide.

Forbes had been a high school football standout, like his late father, Charles Forbes Jr., who played semipro. When Forbes scored a touchdown, he would look to the sky to honor his dad.

The school praised Forbes for his senior baseball season in a social media post: “This young man makes a difference on our campus and on the field with his strong character.”

When hopes for a college football scholarship did not pan out, Forbes worked as a deckhand for a marine transportation company. He saved money, looking forward to moving to Slidell, a suburb of New Orleans.

“He would always say, ‘There’s too much trouble here’” in Bogalusa, Guy recalled.

The post Guns, Race, and Profit: The Pain of America’s Other Epidemic appeared first on Medika Life.

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

TL;DR Summary

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

Opportunities: Transforming Public Health with AI

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

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

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

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

Real-world Applications and Innovations

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

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

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

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

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

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

Practicalities and Implementation Challenges

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

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

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

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

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

Risks and Concerns of AI in Public Health

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

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

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

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

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

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

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

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

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

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

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

Ethical and Regulatory Frameworks

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

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

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

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

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

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

Future Directions and Recommendations

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

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

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

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