What Is Misophonia, Exactly?

The word misophonia comes from the Greek for “hatred of sound.” But that description sells it short. It isn’t simply an aversion to noise. It’s a disorder in which specific sounds, usually soft, repetitive, and made by another person, set off an intense chain reaction in the body and mind. Researchers define it as a condition characterized by strong emotional, physiological, and behavioral responses to sounds that most people barely notice.

The most common triggers include chewing, swallowing, lip smacking, slurping, throat clearing, sniffling, and breathing. Tapping on a keyboard, pen clicking, and the crinkle of a wrapper can also set it off. Some people also react to visual cues, such as watching someone’s jaw move, even without sound.

Estimates of how many people have misophonia vary, but multiple studies suggest that between 5% and 20% of the population experience symptoms significant enough to interfere with daily life. It often starts in childhood or early adolescence, with an average onset around age 12 to 13. It can persist for decades if left unaddressed.

What’s Happening in the Brain?

For a long time, people assumed this was a personality quirk or a sign of anxiety. New brain imaging research tells a very different story.

A landmark study published in Human Brain Mapping in early 2026 examined brain scans from 939 adults and found a specific connectivity disruption unique to misophonia. The culprit is a brain region called the anterior insula, a hub of the brain’s salience network. This is the area that decides, in a fraction of a second, what information deserves your full attention. In people with misophonia, the connection between the auditory cortex and the anterior insula is wired differently. The brain flags trigger sounds as urgent threats before the person has any chance to think about it.

Critically, this pattern did not appear in people with anxiety, depression, or autism, even when researchers analyzed the same brain data. It appears to be a misophonia-specific neural signature. That distinction matters enormously for treatment.

Earlier fMRI research confirmed that when a person with misophonia hears a trigger sound, specific regions fire up fast: the right insula, the anterior cingulate cortex, and the superior temporal cortex. Heart rate increases. Skin conductance rises. The emotional response arrives before reasoning can step in. This is why telling someone with misophonia to “just ignore it” is about as useful as telling someone with a broken leg to walk it off.

Research presented at the 2025 Misophonia Collaborative Forum added another dimension. Brain regions that become overactive during trigger exposure respond similarly whether a person is actually hearing the sound, watching a silent video of it, or simply imagining it. This tells us that misophonia isn’t purely a hearing problem. It involves memory, expectation, and mental imagery, too.

More Than Irritation: The Emotional and Physical Toll

The emotional range that people with misophonia report goes well beyond irritation. Anger is the most common reaction, but disgust, anxiety, panic, and even shame are also common. Physically, people describe muscle tension, sweating, nausea, a tightened chest, and a racing heart. The urge to flee the situation can feel overwhelming.

The source of the sound matters significantly. Research published in 2025 confirmed that sounds made by other people, especially people the listener knows, produce far stronger reactions than the same sounds made by strangers or machines. This is why family dinners can become unbearable war zones, while the same sounds in a crowded restaurant cause far less distress. It’s personal in the most literal neurological sense.

Research also finds that trigger sounds interfere with a person’s ability to concentrate on what they’re doing. People with misophonia are measurably worse at staying on task when triggers are present. Over time, the condition leads many people to avoid shared meals, open offices, public spaces, and sometimes their own families. The social and professional consequences can be severe.

Who Gets Misophonia?

Misophonia shows up across populations, but some groups appear more vulnerable. A 2025 systematic review found that between 12.8% and 35.5% of autistic people experience it, with 79% of those individuals also having anxiety, OCD, or other sensory sensitivities. It also appears frequently alongside mood disorders and obsessive-compulsive disorder in the general population.

Misophonia is not currently listed as a stand-alone diagnosis in the DSM-5 or ICD-11. But the field is moving toward formal recognition. In 2022, a consensus definition was published for the first time. Since then, standardized assessment tools have been developed, prevalence studies have grown in size and rigor, and clinical trials have finally begun.

What Can Actually Help?

Good news arrived in 2026 in the form of the field’s first randomized controlled trials, meaning research with a real comparison group and rigorous standards. Two studies confirmed that specific forms of therapy produce meaningful reductions in misophonia symptoms.

Cognitive behavioral therapy, or CBT, remains the most studied approach. A 2025 review presented at the World Tinnitus Congress confirmed that CBT delivered by both psychologists and audiologists significantly reduces the impact of misophonia on quality of life. Online CBT programs also show positive results, though dropout rates are higher than with face-to-face treatment.

Acceptance and commitment therapy, or ACT, also showed strong results in 2026 trials. ACT doesn’t try to eliminate the emotional response. Instead, it teaches people to tolerate distress without letting it control their behavior. For misophonia, this can mean staying at the dinner table, completing a workday in a shared office, or staying present in a relationship that might otherwise be derailed by triggers.

On the technology front, researchers at Duke University’s Center for Misophonia and Emotion Regulation are collaborating with a team at the University of Washington to develop a sound-suppression platform that uses headphones and an app. The goal is to allow a person to select which specific sounds they want filtered out while still hearing everything else.

Perhaps the most exciting frontier is neurostimulation. Clinical trials are underway to test whether transcranial magnetic stimulation, which uses magnetic pulses directed at specific brain regions, can calm the misfiring salience network at its source. If successful, this approach would be the first to directly target the underlying brain mechanism rather than managing its downstream effects.

You’re Not Alone, and You’re Not Broken

If any of this sounds familiar, the most important thing to know is that misophonia is real, it’s measurable, and it isn’t a personal failure. It also isn’t a life sentence. With the right support, people can and do find ways to manage their reactions, protect their relationships, and reclaim spaces that triggers have stolen from them.

Science has only recently begun to take misophonia seriously. The brain imaging findings of 2026 that show a disorder-specific neural signature are exactly the kind of evidence that turns skeptics into allies and moves conditions from the margins of medicine to its center. That shift is happening now.

Talk to a psychologist or psychiatrist who is familiar with sensory processing disorders. Be honest about what triggers you, how strongly, and how much it costs you in daily life. You deserve a professional who takes this seriously, because the science finally does.

The post That Sound Is Making Me Snap appeared first on Medika Life.

Its leaders were visible throughout the convention, participating in discussions on biopharma innovation, precision medicine, artificial intelligence, investment, genomics and policy. Partnership announcements emerged throughout the week. Delegations moved between panel discussions and private meetings with investors, entrepreneurs, researchers, and industry leaders. The message was clear. Abu Dhabi is making a strategic effort to become a notable player in biotechnology and the life sciences.

The timing is not accidental.

Around the world, governments increasingly view biotechnology as a strategic investment industry. Scientific innovation drives economic growth. Advanced therapeutics create new manufacturing opportunities. Genomics and precision medicine are reshaping approaches to disease prevention and treatment. Nations that attract talent, investment and scientific expertise position themselves at the forefront of one of the century’s most consequential industries and life-sustaining movements.

Abu Dhabi is determined to be among those countries.

Building the Innovation Foundation

Substantial investments and partnerships in infrastructure, research, education and health innovation support the Emirate’s ambitions.

In April 2025, Abu Dhabi launched the Health, Endurance, Longevity and Medicine (HELM) Cluster, an initiative designed to establish a globally competitive ecosystem spanning biotechnology, pharmaceuticals, medical technology, genomics, digital health, artificial intelligence and advanced manufacturing.

Officials project the initiative will contribute AED 94 billion to the economy by 2045, attract more than AED 42 billion in investment and create approximately 30,000 jobs.

Those figures reflect more than economic development goals. They signal a growing recognition that biotechnology is a cornerstone of future growth and global competitiveness.

The Emirate is not starting from scratch. Organizations including M42, PureHealth, Mubadala Bio, Khalifa University, and the Mohamed bin Zayed University of Artificial Intelligence have established a foundation that combines research, clinical capabilities, advanced analytics and investment resources. The UAE Genome Program has surpassed 900,000 sequenced genomes, making it one of the world’s largest population genomics initiatives and providing a valuable resource for scientific research and precision medicine.

These investments provide the ingredients necessary to compete. Partnerships provide the opportunity to accelerate progress.

Building Through Collaboration

Biotechnology has long been a collaborative enterprise among private equity and entrepreneurs, academic medicine and corporations, and, now, nations working side by side with other countries’ governments.

Scientific discovery depends on the exchange of knowledge among researchers, entrepreneurs, clinicians, manufacturers, regulators, and investors. No single country possesses every advantage. Successful ecosystems learn how to connect their strengths with those of others.

Abu Dhabi’s recent actions suggest its leaders understand this reality more than anyone else. They act on it.

During the BIO International Convention, the Department of Health – Abu Dhabi announced a strategic collaboration with Sanofi focused on vaccine development and life sciences innovation. The previous year at BIO in Boston, the Department established a partnership with Boehringer Ingelheim that expanded access to the company’s OpnME research platform, creating new opportunities for translational research and scientific discovery.

An additional 2025 agreement was signed with Abbott, focused on pharmaceutical innovation, manufacturing capabilities, and emerging technologies.

These were not isolated announcements. They represented a broader effort to connect Abu Dhabi with leaders across the global life sciences community. That strategy continued at BIO 2026.

On June 23, 2026, the Department of Health – Abu Dhabi announced a strategic partnership with Biocom California, one of the world’s largest life sciences associations representing more than 1,800 biotechnology, pharmaceutical, and medical technology organizations. The agreement creates a formal gateway between the California innovation ecosystem and Abu Dhabi’s growing life sciences sector, strengthening opportunities for collaboration among researchers, entrepreneurs, investors, and innovators across both markets.

The significance of the announcement extends beyond California. It reflects Abu Dhabi’s effort to connect itself to some of the world’s most influential innovation networks and to participate in the exchange of scientific knowledge, talent, and investment that increasingly defines biotechnology leadership.

Moving Up the Biotech Value Chain

If the Biocom agreement demonstrated Abu Dhabi’s commitment to global collaboration, a second announcement made the Emirate’s ambitions even clearer.

On June 24, 2026, the Department of Health – Abu Dhabi, M42, and Mammoth Biosciences announced a partnership to advance gene-editing therapies, clinical research, and advanced therapy manufacturing in Abu Dhabi. The agreement seeks to leverage insights generated through the Emirati Genome Program while supporting the development of next-generation treatments for inherited diseases.

This announcement stands out because it moves beyond ecosystem building and into the development of future therapies.

Mammoth Biosciences was co-founded by Nobel Prize-winning scientist Jennifer Doudna, whose pioneering work helped bring CRISPR gene-editing technology into medicine. Under the agreement, Mammoth will contribute its proprietary gene-editing platform, M42 will provide genomics, health, and clinical research infrastructure, and the Department of Health will support the regulatory and research environment needed to advance development.

The collaboration includes plans to introduce Mammoth’s lead clinical candidate, MB-111, into Abu Dhabi’s research ecosystem, support advanced gene-editing clinical trials, establish advanced therapy manufacturing capabilities, and develop local expertise through workforce training programs.

Perhaps most significantly, the partnership highlights how Abu Dhabi is leveraging its genomics investments. The Emirati Genome Program has created one of the world’s most comprehensive population genomics initiatives. The Mammoth agreement represents an effort to translate those insights into therapies targeting inherited diseases.

As H.E. Dr. Noura Al Ghaithi noted when announcing the partnership, Abu Dhabi is focused on translating genomic insights into therapies to address some of the most complex inherited diseases affecting populations in the region and worldwide.

Leadership Matters

The prominence of Abu Dhabi at BIO 2026 reflects sustained engagement from senior leaders. H.E. Dr. Noura Khamis Al Ghaithi, Undersecretary of the Department of Health – Abu Dhabi, and Dr. Mohamed Al Ameri, Division Director of Genome and Biobank at DoH, are among the officials representing the Emirate’s vision throughout the convention.

Their participation reflects a broader commitment. During Abu Dhabi’s 2025 strategic mission to the United States, approximately 40 representatives from 12 organizations attended more than 20 strategic meetings, conducted 16 institutional visits, participated in 9 BIO-related panels, and established 7 new partnerships and agreements.

Such activity underscores an important point. Building a biotechnology ecosystem requires more than investment capital. It requires leadership, patience, and a willingness to build relationships across borders and disciplines.

A Must-Watch Innovation Hub

For decades, discussions about biotechnology leadership have focused on a familiar collection of cities and regions. Boston, San Diego, Basel, London, and Singapore earned their positions through scientific excellence, entrepreneurial activity, and investment.

Abu Dhabi is pursuing a different path.

The Emirate is leveraging capital, scientific infrastructure, genomics, artificial intelligence, policy support, and international partnerships to establish a presence in the global biotechnology landscape. Its strategy recognizes that modern biotechnology advances through collaboration and that scientific leadership increasingly depends upon connecting talent, expertise, and resources across borders.

BIO 2026 demonstrated that Abu Dhabi is no longer simply expressing an ambition to participate in the life sciences sector. Through initiatives such as the HELM Cluster, partnerships with organizations including Sanofi, Boehringer Ingelheim, Abbott, Biocom California, and Mammoth Biosciences, and investments spanning genomics, research, and advanced therapies, the Emirate is laying the foundations for a biotechnology ecosystem with global aspirations.

Whether Abu Dhabi joins the ranks of the world’s leading life sciences hubs remains to be seen – but it should be watched closely. What is increasingly difficult to overlook is the depth of its commitment and the speed with which effort is being translated into action.

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A growing body of research is connecting GLP-1 receptor agonists, the class of drugs behind brand names like Ozempic, Wegovy, Mounjaro, and Zepbound, to a serious eye condition that can cause permanent vision loss. The condition has a long medical name: non-arteritic anterior ischemic optic neuropathy, or NAION. Eye specialists sometimes describe it as a stroke of the optic nerve. And once the damage is done, there is currently no treatment that can undo it.

This article is not written to frighten you or push you off your medication without talking to your physician. Millions of people are benefiting from these drugs every day. But the question health experts are now asking out loud is this: when a rare side effect starts appearing in large numbers of people, does it stay rare?

What Are GLP-1 Drugs and Why Are So Many People Taking Them?

GLP-1 stands for glucagon-like peptide-1. These drugs mimic a hormone your gut naturally releases after eating. They slow digestion, reduce hunger, and help control blood sugar. Originally developed for type 2 diabetes, they became household names when studies showed they could also produce significant weight loss.

The popularity of these drugs has been extraordinary. Roughly 15 million people in the United States are currently taking GLP-1 medications, and that number keeps climbing. Many of these users do not have diabetes at all. They are taking the drug specifically to lose weight, often without a full picture of what the long-term risks might look like.

The Eye Condition No One Was Expecting

NAION occurs when blood flow to the front portion of the optic nerve is cut off or severely reduced. The optic nerve is the cable that carries visual signals from your eye to your brain. When that nerve loses its blood supply, even briefly, it can suffer damage that leads to permanent partial or total vision loss in that eye. Health authorities, including the World Health Organization, confirm that this vision loss is usually permanent.

The condition is not brand new. It was already known to affect adults over 50, people with high blood pressure, and people with diabetes. What caught researchers off guard was a cluster of cases appearing in people who had recently started taking semaglutide-based medications.

The alarm was first raised in 2024, when physicians at Massachusetts Eye and Ear, a Harvard Medical School-affiliated hospital, published findings in the journal JAMA Ophthalmology. Their retrospective study of more than 16,000 neuro-ophthalmic patients found that people with type 2 diabetes or obesity who were taking semaglutide had a significantly higher rate of NAION compared to those taking other medications. Among diabetes patients in the study, semaglutide users showed a hazard ratio of 4.28, meaning the risk of developing NAION was more than four times higher than in comparable patients on other glucose-lowering drugs.

A separate Danish and Norwegian study that same year, drawing on data from more than 424,000 patients with type 2 diabetes, found that once-weekly semaglutide use more than doubled the five-year risk of NAION compared to patients taking other diabetes medications.

A Small Percentage Times Millions of People

Here is where the math matters. NAION is classified as “very rare,” meaning it may affect up to 1 in 10,000 people. The European Medicines Agency, which regulates drugs across 27 countries, formally added this classification in June 2025, recommending that product information for Ozempic, Wegovy, and Rybelsus be updated to include NAION as a side effect. The World Health Organization issued its own safety alert shortly after.

But consider what “very rare” actually means when tens of millions of people are taking a drug. If even 1 in 10,000 semaglutide users develops NAION, and 15 million Americans are using GLP-1 medications, that translates to roughly 1,500 potential cases in the United States alone. And that figure is based on the most conservative estimate.

The American Optometric Association’s clinical guidance report put it bluntly: “There is a low risk of serious ocular side effects. But a low risk of a big number is a big risk.”

The University at Buffalo researchers who published a related case series in JAMA Ophthalmology noted something else that raised eyebrows. NAION almost always strikes one eye at a time. But some patients on GLP-1 drugs were presenting with the condition in both eyes simultaneously, which is considered atypical and potentially more alarming.

The Research Is Still Sorting Itself Out

To be fair, the picture is not entirely clear-cut. A large February 2025 retrospective study that pooled data from 37 million diabetes patients across 14 international databases found that semaglutide users showed about 14 to 15 NAION cases per 100,000 patients annually, and when compared to other GLP-1 drugs, the risk was not significantly different. This suggests the vision risk may apply to the entire class of GLP-1 medications, not just semaglutide specifically.

A separate large cohort study published in JAMA Network Open, covering 185,000 individuals on GLP-1 drugs, found a slightly higher risk of developing diabetic retinopathy, but a similar rate of NAION compared to those on other treatments. And two studies presented at the American Academy of Ophthalmology’s 2025 annual meeting offered conflicting signals: one tied GLP-1 drugs to increased NAION risk and diabetic retinopathy risk, while another suggested the drugs might actually protect against dry age-related macular degeneration.

Scientists are careful to note that none of the current evidence proves that GLP-1 drugs cause NAION. What exists is a statistically significant association that has now been observed across multiple studies, multiple countries, and multiple drug databases. That is enough to prompt regulatory bodies to act and researchers to dig deeper.

Who May Be at Highest Risk?

Physicians are paying special attention to patients who already have underlying vascular risk factors. High blood pressure, high cholesterol, diabetes, a history of cardiovascular disease, and a structural eye condition called a small optic disc are all considered risk factors for NAION independent of GLP-1 use. When these pre-existing vulnerabilities are combined with a medication that may affect blood flow to the optic nerve, the potential for harm may be higher.

The symptoms to watch for are specific and sudden: vision loss in one eye that seems to come on without warning, often noticed upon waking. There may be a dark or blurry area in part of your field of vision, or a sense that something has been “wiped away” in one corner of sight. There is typically no pain, which is part of why people sometimes wait before seeking care. Any of these symptoms should be treated as a medical emergency.

Where Things Stand Right Now

As of June 2026, the European Medicines Agency has updated its labeling requirements for semaglutide to include NAION. The World Health Organization has issued a formal safety alert. And a multidistrict litigation involving GLP-1 vision loss lawsuits was consolidated in the Eastern District of Pennsylvania in December 2025. Legal analysts report that over 1,800 lawsuits had been filed by mid-2025, with more expected as scientific review continues.

The U.S. Food and Drug Administration has not yet added a NAION warning to American drug labels for semaglutide. Novo Nordisk, which manufactures Ozempic and Wegovy, has not yet updated its U.S. prescribing information to reflect the risk. Public health advocates and some legal experts have called for a black box warning, the FDA’s highest-level alert.

The American Academy of Ophthalmology and the North American Neuro-Ophthalmology Society have both weighed in, stating that they do not recommend that all semaglutide users stop their medication immediately if they develop NAION, since the benefits of the drug may still outweigh individual risks, depending on the patient’s overall health. But both organizations agree that sudden vision changes of any kind require immediate medical evaluation.

What This Means for You

If you are currently taking a GLP-1 medication for weight loss or diabetes management, here are the most important things to keep in mind.

1. Do not stop your medication without talking to your physician. For many people, the health benefits of these drugs are substantial, and an abrupt stop can create its own risks.
2. Tell your physician if you have existing eye problems, high blood pressure, or a history of cardiovascular disease. These factors may influence how closely you should be monitored.
3. Take sudden vision changes seriously. If you wake up one morning and something looks wrong with one eye, that is not something to wait out. Call your physician or go to an emergency room. Time may matter.
4. Ask questions. Ask your physician whether NAION has been discussed in your care plan. Ask whether your specific risk factors warrant more frequent eye exams. You have the right to that conversation

The Larger Question

GLP-1 medications have been genuinely life-changing for many people. They have helped reduce the burden of obesity, lower cardiovascular risk, and control blood sugar in ways that were difficult to achieve before. None of that is in dispute.

But when a drug reaches the scale of tens of millions of users, even rare side effects become a public health question. A risk that affects fewer than 1 in 10,000 people in a clinical trial still produces thousands of real individuals with real and permanent vision loss when multiplied across the population taking these drugs. Those individuals deserve answers, updated labels, and the chance to make informed decisions before the lights go out.

Research is ongoing. Regulatory conversations are happening. In the meantime, staying informed, staying in communication with your physician, and taking any sudden change in vision seriously are the most important steps you can take.

The post The Weight-Loss Drug Nobody Warned You About: When the Scale Goes Down, and Your Sight Goes With It appeared first on Medika Life.

Bringing Conversational AI to Scientific Research

The announcement arrives as artificial intelligence continues to reshape nearly every corner of the health sector. Much of the attention has focused on applications designed for consumers seeking information or clinicians seeking support in managing increasingly complex workloads. Briya is directing its attention to a different priority audience: medical researchers in academia, hospitals and life science companies.

The decision reflects a recognition that scientific inquiry often remains constrained by barriers that have little to do with science itself. Researchers routinely navigate fragmented data sources, technical requirements, analytical platforms and resource limitations before they can begin testing a hypothesis or exploring an observation.

The Next Step in Briya’s Evolution

Briya is executing on the established understanding that artificial intelligence can help reduce those barriers.

Researchers using AIRE will be able to explore public health information, including data from the U.S. Centers for Disease Control and Prevention, through a browser-based conversational interface. Rather than writing code, users can ask questions in natural language, refine their inquiry through dialogue and review the analytical pathway used to produce results.

“The last few years proved that AI can generate answers,” Briya co-founder and CEO David Lazerson told Medika Life. “The next challenge is making AI capable of generating trustworthy science. That requires a fundamental shift from general-purpose AI systems to research environments built around transparency, epidemiological methodology and scientific accountability.”

The announcement represents the latest step in Briya’s evolution. Founded in 2020 by Lazerson and Chief Technology Officer Guy Tish, the company initially centered efforts on helping organizations connect fragmented health data while maintaining privacy protections, governance requirements and institutional control over sensitive information.

Medical records rarely exist in a single location. Information is often distributed across electronic medical records, laboratory systems, imaging platforms, physician notes and institutional databases. Briya developed a federated approach that allows information to remain within source organizations while supporting approved research across participating data environments.

AIRE expands that mission from data access to data exploration.

The platform is designed to support cohort construction, endpoint validation, treatment pathway analysis, chart review and the exploration of structured and unstructured clinical information. Researchers interact with the platform through conversation rather than code, allowing them to start with a scientific question rather than a technical workflow.

The strategy mirrors an approach that has proven successful in other areas of artificial intelligence. Consumer platforms such as ChatGPT and Perplexity accelerated adoption by allowing users to experience the value of AI before deciding whether additional capabilities justified a subscription.

Reducing the Distance Between Questions and Answers

Briya is applying a similar philosophy to research. Many health technology companies continue to pursue adoption through enterprise purchasing processes, institutional pilots and lengthy implementation cycles. The Briya approach places the researcher at the center of the experience and allows investigators to determine the platform’s value through direct, frequent use.

The company believes that approach may be particularly meaningful for researchers working outside large academic medical centers and major pharmaceutical companies. Those institutions often have access to dedicated data science teams and sophisticated analytical resources. Smaller universities, physician-scientists, public health investigators and community-based researchers may not.

The absence of resources does not diminish the importance of the questions they seek to answer. In fact, as many attending HLTH EU head from Amsterdam to BIO International in San Diego, many of the biggest life-changing advances start in smaller research settings.

Giving Researchers a Seat at the Table

A physician observing an unusual treatment response, a public health researcher investigating a local health pattern, or an early-career investigator evaluating a new hypothesis all face the same challenge: transforming observation into evidence. That process frequently requires technical expertise and infrastructure that are not universally available.

Reducing those barriers could expand participation in research and potentially broaden the range of questions being explored. Accessibility alone, however, is not enough.

Scientific inquiry requires transparency, reproducibility and methodological rigor. Researchers must understand how conclusions are reached, what assumptions influence an analysis and where potential bias may exist.

A Move from Observation to Evidence

Recognizing those requirements, Briya recently appointed internationally recognized epidemiologist Professor Jonathan Samet, MD, MS, as Chief Epidemiologist. Dr. Samet is Professor of Epidemiology and Occupational and Environmental Health, and the former Dean of the Colorado School of Public Health.

“Scientific rigor and accountability cannot be layered onto AI after the fact,” Dr. Samet told Medika Life. “If these technologies are going to play a meaningful role in healthcare research, transparency, reproducibility and epidemiological methodology must be built directly into the system itself.”

Samet added that researchers need to understand more than an AI-generated conclusion: “Researchers need to understand not only what an AI system concludes, but how it reached those conclusions and what risks may exist along the way.”

His appointment reflects a broader challenge facing artificial intelligence in research environments. While generative AI systems can produce clear and persuasive responses, researchers and institutions must be able to evaluate the methods, assumptions and analytical pathways behind those outputs.

Trust, governance and cybersecurity have become as important as speed and convenience. Health information remains among the most sensitive categories of personal data. Institutions considering AI-enabled research environments must evaluate privacy protections, security controls and governance requirements alongside scientific capabilities.

Briya says its architecture is designed to allow data to remain within source organizations while supporting anonymization, compliance controls and auditable pathways for approved analysis.

Briya’s decision to open access to AIRE arrives at a time when researchers are under increasing pressure to produce meaningful scientific output while navigating growing volumes of health information. The platform’s no-cost entry point reflects a broader shift occurring across technology, where organizations increasingly recognize that adoption begins with customer experience. By allowing researchers to engage directly with data through a conversational interface, Briya is reducing barriers that have traditionally separated scientific questions from scientific exploration and adoption.

The announcement broadens the conversation surrounding artificial intelligence in health. Much of the industry’s attention has focused on consumer and clinical applications. Briya is directing attention to another critical constituency whose work influences every future therapy, diagnostic and public health intervention.

As HLTH Europe begins, the company is making the case that empowering researchers may represent one of the most consequential applications of artificial intelligence in health. If successful, the approach could help accelerate discovery, expand participation in research and provide investigators with a direct path from observation to evidence to implementation.

The post At HLTH Europe, Briya Opens No-Cost Access to AI-Powered Research appeared first on Medika Life.

The Centers for Medicare & Medicaid Services released the rules on June 1. That deadline was set last year in the GOP tax-and-spending law known as the One Big Beautiful Bill Act, which established a work requirement for certain people enrolled in Medicaid, the state-federal health insurance program for people with low incomes or disabilities.

Medicaid agencies are scrambling to rework IT systems and make sure they have staff to effectively enforce the rules, while also keeping enrollees from losing coverage for administrative reasons, such as difficulty navigating state eligibility portals.

The newly announced regulations offer a clearer picture of what roughly 18.5 million Medicaid enrollees will have to do to prove they qualify for benefits.

Jim Torres, who helps people enroll in health coverage at the Samuel U. Rodgers Health Center in Kansas City, Missouri, said a “very small percentage” of his clients have heard of the changes coming to Medicaid.

“These folks have very busy lives. They’re doing the best they can to get by,” he said. “It’s just not a top-of-mind thing for most of them.”

Health policy researchers and consumer advocates said enrollees should keep a few things in mind as the Jan. 1, 2027, rollout approaches in most states.

1. The work rules won’t apply to everyone.

The new rules will apply to people covered through what’s known as Medicaid expansion. Since 2014, more than 40 states and the District of Columbia have decided to allow more people into their Medicaid programs, generally low-income adults without dependents. Georgia and Wisconsin offer coverage to some people in this group, so they’ll be subject to the rules.

Children and pregnant people, as well as individuals with disabilities who receive Social Security payments — all groups that already qualify for Medicaid — won’t be subject to the rules. Nor will people determined to be “medically frail,” or too sick to work.

People subject to the work rules are “crowding out” people in the Medicaid program who are “truly in need,” CMS Administrator Mehmet Oz claimed during a June 1 press call. “Work requirements are going to turn this around, we hope.”

The rules are set to take effect in most places in January. Nebraska started enforcing them in May. Montana plans to start in July but won’t kick people off until October. Arkansas will do a “soft” launch in July — it will start enforcing the rules but with no penalties until next year.

2. States will take your word that you’re too sick to work. For now.

Federal officials have stressed that states should make the process of reporting hours and requesting exemptions as simple as possible for Medicaid enrollees by creating automated systems and using existing data sources, such as unemployment and education records.

If states cannot determine you’re performing 80 hours of qualifying activities a month using those data sources, you may be allowed to “self-attest” to that in 2027, health policy researchers said.

People will also be allowed to “self-attest” that they are too sick to work in 2027, and do so one time in 2028. Then states will start asking for proof, if they can’t find it through available data.

But after the initial rollout, the burden of proof is likely to still fall on many enrollees, said researchers and consumer advocates.

People may need to dig up pay stubs, medical records, and doctors’ notes and submit them for state review, said Morgan Henderson, who has studied Medicaid work programs in Georgia and Arkansas at The Hilltop Institute, a research center at the University of Maryland-Baltimore County.

“The higher this manual reporting burden, the less people are going to do it,” he said. “That means that we’re going to see coverage drop-offs.”

3. The rules are tougher than expected for people too sick to work.

One of CMS’ primary goals has been to “protect vulnerable populations” through “strong exemptions to make sure people who can’t reasonably be expected to work are not subject to the requirements,” Dan Brillman, a deputy administrator at the agency, said during the June 1 press call.

Consumer and patient advocates, however, said the final rules’ exemptions are more restrictive than expected. Enrollees will eventually have to provide documentation, such as a statement from a medical professional, to prove that a health condition keeps them from working. And each individual state will have to determine the severity of beneficiaries’ medical conditions.

“Someone could be medically frail in Nebraska but not medically frail in Delaware,” said Carolyn Sheridan, associate director of state policy for the National Organization for Rare Disorders, which lobbies for patients with rare diseases. She said her group had hoped the rules would offer a standardized definition of who counted as medically frail and not leave the decision up to states.

Trump administration officials have publicly crusaded against fraud in government health programs, such as Medicaid, and states could face financial penalties for incorrectly granting people exemptions from the work rules, said Jennifer Tolbert, who researches Medicaid at KFF, a health information nonprofit that includes KFF Health News.

“States may be more cautious,” she said. “That will likely lead to people losing coverage who may still be eligible.”

4. Only certain qualifying activities count.

Enrollees can satisfy the rules by working 80 hours a month. They can also be enrolled in college courses, volunteer through a community organization, or do “in-kind” work that doesn’t result in pay.

The rules set out, in detail, how many academic credit hours translate to 80 hours a month — students need to be enrolled in six credit hours per semester to meet the “half-time” requirement. An unpaid internship can count toward the 80 hours.

People can also prove they’re volunteering with “a document from a community service organization.”

Consumer advocates say it might be hard for people to obtain proof they’re performing these kinds of informal activities. But supporters of the rules say volunteerism can already be tracked.

“If you run into trouble with the law and the judge says, ‘Hey, you need some volunteering and community service to serve your time,’ there are already ways that we verify that,” said Niklas Kleinworth, who works on state health policy for the conservative Paragon Institute.

5. You have time to prepare.

Make sure your state Medicaid agency has your current mailing address and keep your eye on your mailbox, said researchers and consumer advocates. State Medicaid agencies must inform you in two ways if you’ll be subject to the rules — by either regular mail or email, and by one other form of communication, such as a text or phone call or by posting a notice online.

“The important stuff comes by mail,” Henderson said.

And check in with your state Medicaid agency, said researchers and advocates. Some states, including Arkansas, California, and Wisconsin, have already posted information about the work rules on their websites. If you can’t find what you’re looking for there, visit or call a local office. A caseworker should be able to tell you whether you’ll be subject to the rules.

“Get ahead of this,” said Joan Alker, who is executive director of the Georgetown University Center for Children and Families and studies Medicaid. “So that you don’t end up going to the pharmacy one day and they say, ‘Oh, you’re not insured anymore’ when you’re trying to get your prescriptions refilled.”

KFF Health News correspondent Samantha Liss and senior correspondent Rachana Pradhan contributed to this report.

The post Final Rules for Medicaid Work Requirements Are Out. Here’s What You Need To Know appeared first on Medika Life.

Health care is not short on ideas. It is not short on innovation, intelligence, technology or ambition. What it risks losing is focus on why those ideas matter and who they are meant to serve.

That concern shaped a fireside conversation with Tom Lawry at the Digital Health & AI Innovation (DHAI) Summit. Tom and I came to the stage from parallel and connected bodies of work. He is a best-selling author, the author of Health Care Nation: The Future Is Calling and It’s Better Than You Think, Hacking Healthcare and his classic, AI in Health: A Leader’s Guide to Winning in the New Age of Intelligent Health Systems.

Tom is one of the most respected voices on artificial intelligence and health information. My own book, Healing the Sick Care System: Why People Matter, another bestseller, looks at health care through the lives of patients, families and clinicians navigating a system that can be brilliant in moments and bewildering in motion.

Two Books, One Shared Concern

Our books were already in conversation before we arrived at the Summit. Tom wrote the Foreword to Healing the Sick Care System: Why People Matter, and I wrote the back-of-book review for Health Care Nation. We had each recognized the connection between the two. Readers, however, often encounter books separately. One may see Tom’s as a system-level call to redesign health care and mine as a people-first call to restore humanity to care. On stage, with an audience ready for the discussion, the relationship became easier to feel and internalize.

One book shares why the system must change. The other asks who that change must serve. Together, they return health innovation to the question that should guide every decision: whose life is made better because we invent?

That question was at the heart of Amir Lahav’s DHAI Summit. Curated by Amir Lahav, PhD, the Summit brings together people across artificial intelligence, digital health, health systems, research, investment and care delivery, from industry leaders such as Amazon Web Services, represented at the Summit by its Global Chief Medical Officer, Rowland Illing, MD, to trade groups like the Decentralized Trials Research Alliance championed by Craig Lipset, co-chair, and companies such as MassiveBio, an AI-powered platform to match patients to 19,000+ oncology and hematology trials worldwide, represented by Arturo Loaiza Bonilla, MD MSEd, are harnessing information to advance science and save lives.

Lahav’s achievement is far more than assembling experts. He has created a setting where different parts of the health community can bench-test their thinking against one another.  The action on the mainstage spills over to the hallways and receptions. That matters because AI in health cannot mature within a single discipline. Data scientists need clinicians. Clinicians need workflow support. Innovators need patient insight. Investors need to understand adoption. Health system leaders need to know when technology solves a problem and when it adds another layer of friction to an already complex ecosystem.

The audience brought energy to the room because the topic was more than technical. People wanted to talk about what AI makes possible. They wanted to talk about what health care cannot afford to forget. Health has become transactional. The operational aspects of care carry more friction than compassion. Patients are asked to coordinate their care across portals, referrals, insurance prior authorizations, clinical handoffs, and delayed communication. Clinicians are asked to heal while absorbing new layers of documentation, digital alerts, measurement and workflow pressure.

That is the context in which AI enters health care.

AI is curated knowledge and amplified pattern recognition. It can search for information no person could hold alone. It can surface signals, compare data, support decisions and make complexity more manageable. Used well, it can help clinicians, researchers, health systems and patients see what might otherwise remain hidden.

Harvey Castro, MD, MBA, a physician futurist and AI health-care innovator, understood that connection. He shared that he was heading to Portugal to speak on AI and health care, and that he would be reading Healing the Sick Care System during this flight. His encouragement reflected what made the Summit meaningful. The conversation was not ending with our fireside chat. It was traveling with people who are carrying the future of health AI into new rooms, new audiences and new decisions.

Insight, however, is different from wisdom. A pattern is different from a person. A recommendation is different from a relationship. AI can help reveal possibilities. People must decide how those possibilities impact the realities of illness, fear, family, access, culture and care.

That is where Tom’s work and mine meet. Health Care Nation asks why a country with extraordinary science, clinical talent, and technology continues to struggle with fragmentation, costs, incentives, and uneven access. Tom challenges the habit of waiting for someone else to fix what is broken. Policymakers, executives, payers, providers, employers, innovators and citizens all shape health care through choices, incentives, habits and expectations.

Healing the Sick Care System: Why People Matter starts from the same concern through the experience of a person seeking and delivering care. It asks what happens when a system with remarkable capabilities becomes so difficult to navigate that professional burnout leads to abdication, shifting more of the confusion, delay and uncertainty onto the very people seeking care. It looks at what care feels like when people seek treatment yet still feel lost, when they meet skilled professionals yet leave without understanding the next step, and when they are surrounded by technology yet feel lost and alone.

When Innovation Forgets the Person

Health care does not lack brilliance. It has extraordinary science, dedicated professionals, ambitious innovators and vast resources. Yet brilliance loses force and investment loses meaning when the system becomes more focused on transactions than on the people seeking care.

Soner Haci, CEO of PONS, captured that spirit after the session, writing that the story Tom and I shared was exactly why PONS was founded. His response mattered because it connected the fireside conversation to entrepreneurial purpose. Strong health companies often begin with the recognition that a problem people have learned to work around should no longer continue.

That is also why Lahav’s careful curation mattered. The Summit gave innovators a place to discuss more than what can be built. It invited people to consider whether what is being built is useful, human and ready for the realities of care. In health, possibility is never enough. The measure is whether the possibility improves the experience of the person seeking care and the person trying to provide it.

Tom is especially conscious of how many health professionals experience new technology. AI may be introduced as an aid, yet it can feel like another responsibility added to an already strained workflow. When a tool requires more clicks, more documentation, more review or more mental switching, it becomes one more demand on the people it was meant to support.

That concern should be central to the AI conversation. Implementation matters as much as innovation. AI earns trust when it reduces burden, fits the rhythm of care and gives clinicians back time for judgment, conversation and healing. A tool that adds work, noise or uncertainty to care has missed the purpose of health innovation.

Leanne West, innovation catalyst, patient advocate, connector, Chief Engineer of Pediatric Technology at Georgia Tech, and President of the International Children’s Advisory Network, reflected on LinkedIn that the fireside discussion was “speaking my language.” She highlighted a line from Healing the Sick Care System, that doctors should be people first and doctors second. Her reaction captured why the discussion resonated. The audience heard an AI conversation that kept returning to people.

That return to people is not sentimental. It is central to the challenge. People navigating illness often understand system failure with painful precision. They know where the instructions were confusing, where the portal failed, where follow-up disappeared, where a handoff became a gap and where no one seemed accountable for the whole experience.

Communication belongs in the same conversation. In health care, silence changes the experience. Confusing instructions, disconnected portals, delayed follow-up, fragmented records and unanswered questions become part of how people remember care. AI and digital health can help by making communication more useful, timely, and understandable. The goal is better understanding, not more automated volume.

Prevention also belongs in the same conversation. Health Care Nation argues that the health of people and the nation are inseparable. A country cannot continue spending enormous resources on illness while underinvesting in what helps people stay well. Healing the Sick Care System reaches that point through the patient’s experience. People should be seen, supported and guided before their physical and mental health reaches the snapping point.

This is the power of DHAI. Amir Lahav created a space where AI was discussed in the context of health’s larger obligation. Lahav even hosted a panel on pediatrics, where adults and children as young as six sat together on the mainstage, offering counsel. The conversation was not limited to algorithms, platforms or market opportunity. It asked whether innovation can reduce friction, protect health professionals, support patients, strengthen communication and make care more human.

Those are the questions that move AI from novelty to value. Can it help identify risk earlier? Can it make information easier to understand? Can it reduce administrative burden? Can it help match people to appropriate care? Can it support better conversations? Can it give clinicians back time to listen, think and guide? Can it help people feel less alone, less confused and more supported?

Together, Tom’s book and mine point toward priorities that health leaders should keep close: build around people, invest in prevention, reduce friction, protect clinicians, align incentives, listen to patients, measure outcomes and use technology wisely.

AI as Insight, Not Replacement

AI will not repair a fragmented system on its own. If incentives remain misaligned, AI may optimize the wrong outcomes. If patients remain peripheral, AI may scale impersonal care. If communication remains broken, AI may create more messages without creating more meaning. If trust is treated as an assumption, people will resist new tools for understandable reasons. This is why people absolutely matter.

The future worth building is hopeful. AI can help us see patterns earlier, connect knowledge faster and support better decisions. It can help researchers, clinicians and health systems work with greater insight. It can help people move through care with less confusion and more support. Its value grows when insight is joined with human judgment.

That was the heart of our fireside conversation, and that was why the audience response was powerful. We are not lacking ideas. We are not lacking innovation. We risk allowing health care to become ever more transactional at the very moment when technology should help us make it more connected, understandable and humane.

In Health Care Nation, Tom Lawry reminds us that we must stop waiting for someone else to fix the system. Healing the Sick Care System reminds us that every improvement must be judged by the lives of the people seeking care and the people providing it. These are companion calls to action.

“The future is calling,” as Tom writes. It may indeed be better than we think. It will become better when insight is joined with empathy, when innovation is guided by purpose and when the people touched by health-care systems shape what comes next.

AI can help us see more. People must decide what to do with what they see.

The next chapter belongs to us.

The post Health AI Faces a Human Test 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.

The post Colorado Charts Its Own Course on Vaccines Amid Federal Pullback appeared first on Medika Life.

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

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

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

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

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

Patients Remember the Journey, Not the Molecule

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

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

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

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

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

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

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

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

Health Technology Cannot Create More Work

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

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

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

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

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

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

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

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

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

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

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

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

Communication remains inseparable from the care experience.

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

The Companies That Win Will Simplify Complexity

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

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

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

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

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

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

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

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

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

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

Those experiences ultimately shape reputations.

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

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

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

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

Executive Summary

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

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

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

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

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

I: A Conversation That Sparked a Question

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

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

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

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

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

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

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

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

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

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

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

III: A Wicked Problem and a Strategic Dilemma

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

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

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

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

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

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

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

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

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

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

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

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

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

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

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

V: Three Conditions Hostile to Learning

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

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

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

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

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

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

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

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

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

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

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

Payer Interference: External Rules Overriding Internal Mission

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

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

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

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

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

Physician Burnout: The Human Output of a Broken System

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

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

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

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

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

VI: Thinking Design, Not Framework Prescription

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

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

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

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

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

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

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

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

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

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

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

The four DSRP patterns work as follows.

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

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

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

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

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

VII: Building the Ecosystem

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

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

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

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

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

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

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

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

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

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

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

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

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

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

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

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

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

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

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

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

IX: Strategic Implications — The Cost of Not Learning

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

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

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

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

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

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

Citations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

That distinction matters more than many realize.

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

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

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

That is where DHAI distinguishes itself.

The Power of Curated Expertise

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why Human Connection Still Matters in the AI Era

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

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

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

Those are conversations that require candor and mutual learning.

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

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

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

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