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.

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

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

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

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

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

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

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

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

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

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

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

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Early in the book, Nosta writes, “The solved can never touch the whole.”  That line lingers. It captures the essence of his argument. AI can solve, generate, synthesize, and accelerate. Yet something about the human experience of thinking, the struggle, the friction, the meaning-making, exists beyond resolution.

This tension defines the book. It is not anti-technology. Nosta is deeply engaged with AI and candid about its value. He describes large language models as tools that “move faster and connect more disparate concepts than our minds could ever manage on their own.”  He is equally clear that this capability introduces a subtle risk. We may begin to outsource not just tasks, but thought itself.

That distinction matters more than many may be willing to admit.

From Tools to Thought

One of the most compelling contributions of The Borrowed Mind is its framing of AI not as the next step in computing, but as a turning point in cognition. Nosta traces a clear arc. Gutenberg unlocked words. Google unlocked facts. AI, he argues, is unlocking thought. 

That progression is elegant, yet also unsettling. Words and facts could be externalized without fundamentally altering the structure of human reasoning. Thought is different. It is intimate. It is identity. It is how we become.

Nosta reminds us that thinking once required effort, a type of natural friction that created sparks of innovation. “The distance between question and answer created space for our discernment.”  Within that space, judgment formed, curiosity deepened, and understanding took root.

AI compresses that distance. It removes friction. It delivers coherence with remarkable speed.  One of the book’s most important insights emerges here. Coherence is not the same as understanding.

Nosta introduces the concept of “anti-intelligence,” describing it as “fluency without understanding. Coherence without experience.”  AI does not think. It mirrors the structure of thinking. It produces language that resembles reasoning without sharing its origin.

In health, where evidence, interpretation, and judgment must coexist, this distinction is not academic. It is operational. It shapes how clinicians trust tools, how leaders deploy them, and how patients ultimately experience care.

The Seduction of the Socratic Mirror

One of the most original sections of the book is Nosta’s description of the “Socratic Mirror.” He draws a parallel between classical dialogue and modern AI interaction. Socrates asked questions to surface the truth. AI, in a different way, reflects our thinking back to us, reframed, extended and sometimes clarified.

Nosta writes that the model “…does not tell me what to think but creates the conditions under which my own thinking could deepen.” This is where the book moves beyond critique and into possibility.

Used well, AI becomes a cognitive partner. It expands perspective, accelerates exploration, and invites iteration. In clinical research, patient engagement, and system design, this capacity holds enormous promise.

Nosta does not romanticize the relationship. He recognizes its asymmetry. The model has no interior life. It does not ponder. It does not carry consequence. It does not bear responsibility. That responsibility remains human.

Rethinking the Fear of Displacement

A persistent anxiety runs beneath every conversation about AI. Many fear it will become a job slayer, a force that displaces rather than elevates human contribution. That concern is understandable, yet not new.

Every meaningful advance in technology has reshaped how people work. The wheel did not eliminate labor. It redefined movement. The stethoscope did not replace physicians. It extended their ability to listen and interpret. The tollbooth transponder did not end transportation roles. It changed the flow and focus of human involvement. Each innovation shifted roles, demanded new skills, and expanded what people could do.  AI belongs in that lineage.

What distinguishes this moment is not the elimination of work, but the redistribution of cognitive effort. The real risk is not that machines will think for us, but that people may become less inclined to think for themselves. Nosta’s warning is subtle yet profound. Surrendering curiosity, judgment, and reflection to systems that generate answers with ease risks dulling the very faculties that define human intelligence.

This is why The Borrowed Mind is such an important read at this moment. It does not dismiss concerns around job displacement. It reframes it. The central challenge is not protecting roles as they exist today, but strengthening the uniquely human capacities no system can replicate. Creativity, discernment, ethical reasoning, and the ability to navigate ambiguity are not diminished by AI. They become more essential.

The book offers reassurance without complacency. The future of work will favor those who sharpen their thinking, engage deeply with ideas, and remain active participants in their own intellectual development. The machine is not the adversary. Neglecting the development of one’s own mind is a danger.

Composite Intelligence and the Limits of the Machine

Nosta introduces “composite intelligence” to describe the interaction between human and machine cognition. Composite does not mean blended into sameness. It means distinct contributions working in concert. The model brings speed and breadth. The human brings depth.

This triad becomes one of the most useful frameworks in this book. AI excels in velocity and scale. Depth, the slow transformation of understanding, remains human. As Nosta writes, “Models do not ponder.” 

In health, this distinction is profound. Data can inform. Algorithms can suggest. The act of deciding, especially in moments of uncertainty, requires something more. It requires what Nosta elevates as the defining human contribution. Virtue.

Drawing on Aristotle’s concept of practical wisdom, Nosta reminds us that judgment is forged through experience, consequence, and accountability. A model can generate options. It cannot live with outcomes.

This is where the book resonates most deeply for those working in health. Intelligence is becoming abundant. Discernment is becoming scarce and, therefore, more valuable.

The Risk of the Borrowed Mind

The book’s title is not metaphorical. It is a warning. Nosta argues that as engagement with AI deepens, internal dialogue begins to change. The model becomes a cognitive tuning fork, subtly shaping how questions are framed, how ideas are explored, and how answers are anticipated. This dynamic is not inherently negative. It can elevate thinking, accelerate learning, and make complex domains more accessible. Dependency remains the concern.

Reliance on generated thought risks weakening the muscle of original thinking. Access can be mistaken for understanding. Individuals may become, in Nosta’s words, “cognitive clones.” 

This concern is particularly relevant in health ecosystems already strained by time, complexity, and administrative burden. The temptation to offload cognitive work will be strong. The discipline to remain intellectually engaged will be essential.

A Book About AI That Is Not About AI

What makes The Borrowed Mind stand apart is that it is not ultimately about technology. It is about humanity. Nosta writes, “This book is not really about technology. It is about you.”  That idea anchors this work.

Readers are challenged to consider what it means to remain “the authors of our own minds.”  Not passive recipients of generated insight, but active participants in meaning-making.

This question sits at the center of the health ecosystem’s future. As AI becomes embedded in clinical workflows, research, and patient engagement, the issue is not whether it will improve efficiency. It will.

The deeper question is whether it will deepen humanity or dilute it. Will it create space for clinicians to think more deeply, connect more meaningfully, and act more wisely? Or will it create a system that values speed over reflection, output over understanding, and coherence over truth?

Nosta offers no simple answers. He offers a framework for asking better questions.

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Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

The post AI Will Not Fix Health Care – Leadership Might appeared first on Medika Life.

History, however, offers a useful caution. Breakthrough technologies in medicine rarely achieve their full potential simply because they exist. Their real impact depends on whether the institutions responsible for health-care delivery know how to adopt them wisely, integrate them responsibly and align them with their mission to improve patient health.

Artificial intelligence now stands at that same threshold. The industry has moved beyond fascination with what algorithms can do and entered a more demanding phase: determining how these tools should be evaluated, governed, and integrated into the environments where care is delivered. At the same time, some health professionals are turning to AI – not to augment their knowledge – but assuming the information is patient-care ready.

Across the health ecosystem, leaders are discovering that the most important questions about artificial intelligence are not technological. They are organizational, ethical and operational. Which AI systems genuinely improve clinical decision-making? Which tools strengthen the efficiency of hospitals and health systems? Which innovations introduce complexity without delivering measurable benefit?

Answering those questions requires a perspective that bridges policy leadership, real-world care delivery, and the scientific foundations of biomedical informatics. That convergence of experience sits at the center of a “Views From the Top” mainstage discussion at the HIMSS Global Health Conference & Exhibition, where some 35,000 leaders whose work spans the global health ecosystem will examine how organizations can recognize the true value proposition of artificial intelligence applications before embedding them into health-care systems.

The perspectives shaping this discussion reflect three essential dimensions of responsible artificial intelligence in health: governance frameworks that guide innovation, operational insights from large-scale health care delivery, and scientific rigor grounded in biomedical informatics. Together, these vantage points illuminate the path from technological promise to practical value.

Governing Innovation in a Rapidly Changing Health Ecosystem

Digital transformation in health rarely succeeds simply because technology exists. It succeeds when organizations develop leadership frameworks capable of evaluating innovation, managing risk and aligning new tools with patient-centered goals.

Few leaders have observed the evolution of digital health across as many national systems and institutional environments as Hal Wolf, president and chief executive officer of HIMSS, Ran Balicer, MD, PhD, chief innovation officer of Clalit Health Services and Isaac Kohane, MD, PhD, chair of biomedical informatics at Harvard Medical School. The three will step onto the mainstage at HIMSS to share their “View from the Top” in a session titled: “Recognizing the ‘Value Proposition’ Criteria While Selecting AI Applications.”

Through his work with global government health ministries, hospital networks, and technology innovators worldwide, Wolf has consistently emphasized that technological progress must be anchored in governance and trust.

“Digital health transformation is not about technology alone. It is about leadership, governance, and the trust that allows innovation to improve care,” Wolf has said in discussions about global digital health transformation.

Artificial intelligence intensifies this leadership challenge because its influence extends far beyond traditional clinical tools. AI systems increasingly operate across multiple layers of healthcare delivery. Some applications assist clinicians by analyzing medical data or suggesting treatment options. Others function within hospitals’ and health systems’ operational infrastructure, helping manage patient flow, prioritize diagnostic reviews, and allocate scarce resources.

These operational algorithms rarely capture headlines; however,  they shape the environment in which health care is delivered. Decisions about which cases are reviewed first, how clinicians allocate their attention, and how health systems manage capacity can profoundly influence patient outcomes.

For leaders responsible for health systems, artificial intelligence cannot be treated as simply another technological upgrade. It must be evaluated through governance structures capable of understanding how algorithms function, what assumptions shape their recommendations, and how their use aligns with institutional priorities.

Without that oversight, innovation risks amplifying complexity rather than improving care. Instead of informing, it can spread misinformation.

Aligning Artificial Intelligence With the Values of Medicine

Governance provides the policy foundation for responsible adoption of artificial intelligence, but real-world implementation reveals a second challenge: ensuring that AI systems operate effectively within healthcare delivery itself.

Large population health systems increasingly use advanced analytics to anticipate risk, manage chronic disease, and allocate clinical resources across diverse communities. Within these environments, artificial intelligence is no longer a theoretical innovation. It is already influencing how health organizations prioritize patients, coordinate care and deploy limited resources.

That operational perspective is central to Ran Balicer, MD, PhD, of Clalit Health Services, one of the world’s most advanced data-driven health systems. The Clalit integrated infrastructure connects hospitals, clinics, and community health programs through longitudinal datasets that support predictive analytics at the national scale.

Experience within such systems reinforces an important insight: artificial intelligence models do not function independently of human judgment. They reflect priorities embedded in their design and the assumptions guiding their deployment.

“Algorithms are opinions embedded in code,” Balicer has observed in discussions about the role of artificial intelligence in population health.

In practice, this means that AI systems interpret clinical data through frameworks shaped by human choices. The way a model defines risk, prioritizes cases, or recommends interventions reflects decisions about what matters most within a healthcare environment.

Those decisions carry ethical implications. When artificial intelligence helps determine which patients receive immediate attention or which cases are escalated for further review, transparency about how algorithms function becomes essential to maintaining trust among clinicians and patients alike. The scientific frontier of health-care AI reinforces that concern.

Isaac Kohane, MD, PhD, who has also served as a co-author of the Institute of Medicine Report on Precision Medicine, which has served as the template for national efforts, has spent decades exploring how machine learning can advance medicine while preserving the judgment that defines clinical practice. His research emphasizes that artificial intelligence in healthcare must align with the ethical traditions and professional responsibilities of medicine.

“AI systems in medicine must ultimately reflect the values of the profession they serve,” Kohane has written in discussions about AI alignment in biomedical informatics.

This perspective highlights a crucial distinction between technological capability and clinical responsibility. Many AI models entering healthcare environments were originally designed for broader computational tasks rather than the nuanced realities of patient care. Medicine operates within a landscape shaped by uncertainty, empathy, and accountability, and technologies introduced into that environment must reflect those values.

Ensuring that artificial intelligence aligns with the principles guiding health-care delivery, therefore, represents one of the most important scientific and ethical challenges facing the future of health.

The Discipline Required to Make Innovation Matter

The health sector has experienced waves of technological enthusiasm before. Electronic health records promised seamless information exchange, but then introduced administrative burdens on health professionals when implemented without thoughtful workflow design. Data analytics promised unprecedented insight, but sometimes led to fragmentation when systems failed to communicate across institutions.

Artificial intelligence now stands at a similar moment in the evolution of health technology.

Its capabilities in supporting decision-making flow are extraordinary, yet realizing them will require disciplined leadership to evaluate, integrate and govern AI tools within health-care delivery systems. Health leaders must learn to ask deeper questions before embracing the next algorithmic breakthrough. What problem does this system truly solve? How does it strengthen clinical practice? What assumptions guide its recommendations? How does its use advance the mission of improving patient health?

These questions move the conversation beyond technological novelty toward operational practicality. It’s among the many reasons these three global leaders step to the HIMSS stage together.

Artificial intelligence will undoubtedly reshape the health ecosystem in the years ahead. Its long-term impact, however, will not be determined solely by the sophistication of algorithms or the speed of technological progress. Along with how to leverage AI, ChatGPT and LLMs, users require heightened cognitive awareness.

It will be determined by whether the health community develops the discipline and ability required to translate innovation into systems that strengthen care, support clinicians and improve the health of the populations they serve.

The real story of artificial intelligence in health is no longer about what machines can do. It is about how wisely the health sector chooses to use them.

The post From AI Excitement to Execution: Why Health Leaders Must Now Master the “How” appeared first on Medika Life.

Primary care continuity allows physicians to develop a longitudinal awareness that no episodic encounter or health tech tool can replicate. Over time, physicians learn what is normal for each patient and what represents meaningful clinical change. Subtle physiological shifts, early symptoms or emerging risk factors appear not as isolated data points from a blood exam, but as part of a social narrative unfolding across time. Early recognition allows earlier intervention, often before disease takes its profound toll.

Clinical evidence confirms the protective effect of continuity. It’s not a matter of opinion. A systematic review published in BMJ Open found that patients with sustained continuity of care had significantly lower mortality than those with fragmented care. Continuity did not just improve satisfaction; it altered survival. The physician who knows the patient can detect disease earlier and guide care more effectively.

Listening allows physicians to detect patterns that laboratory values alone cannot explain. Patients share information differently when they believe that their physician understands them and remembers their history. This sustained awareness allows physicians to identify emerging illnesses without relying solely on reactive diagnostics. Continuity transforms listening into clinical intelligence and a deeper care partnership.

In Healing the Sick Care System: Why People Matter, which has become a surprise Amazon bestseller, one insight repeatedly emerges: patients do not seek care only for treatment; they seek reassurance that someone who knows them is guiding their journey. Physicians who listen across time accumulate knowledge that cannot be captured in a chart alone. That memory allows earlier recognition, more accurate interpretation, and wiser intervention. Healing begins in that continuity of understanding.

Transactional Care Solves Symptoms but Sacrifices Understanding

Health has, for some time, been undergoing a structural shift toward transactional encounters. Walk-in clinics, urgent care centers, and virtual platforms provide speed and accessibility that patients value. These models address immediate symptoms efficiently and fill important gaps in care delivery. Accessibility has improved, yet continuity has weakened.

Transactional medicine treats episodes rather than trajectories. Each encounter begins without the benefit of longitudinal understanding. Clinical decisions are made with time-stamp specific knowledge of how symptoms emerged or how physiology has changed over time. Care becomes reactive rather than interpretive.

Research demonstrates the consequences of this fragmentation. Studies published in the Annals of Family Medicine show that sustained primary care continuity reduces hospitalizations and lowers healthcare expenditures. Early recognition prevents complications that require more invasive, costly interventions. Fragmentation delays recognition and increases clinical risk.

In fact, physicians in the vanguard of building relationships encourage their patients to ask questions.  In their co-authored book Let Patients Help! A “Patient Engagement” handbook – how doctors, nurses, patients and caregivers can partner for better care by “e-Patient Dave” deBronkart with Daniel Z. Sands, MD, MPH, the founder of the Society for Participatory Medicine, offer 10 suggestions that clinicians say to encourage patient engagement.

This shift also alters how patients engage with care. Connections that develop over time can be lost quickly when continuity disappears. Patients become consumers navigating isolated services rather than partners guided across time. The clinical relationship weakens, and with it the interpretive depth that makes prevention possible.

Health systems globally recognize the value of continuity. The Organization for Economic Co-operation and Development (OECD), a Paris-based international organization that promotes policies to improve economic and social well-being globally, reports that hospital admissions for chronic diseases, often preventable through effective primary care, account for a substantial share of healthcare utilization. Systems that preserve physician-led primary care continuity achieve better outcomes and greater efficiency. Relationship stabilizes care.

Innovation Requires Connection to Fulfill Its Potential

This shift toward transactional care carries life-threatening implications that extend far beyond the patient experience. It also directly affects whether health innovation fulfills its promise or becomes a compensatory tool addressing fragmentation. Innovation depends on context to generate meaningful insight. Context emerges through continuity. That context can devalue life-saving innovations.

Artificial intelligence, predictive analytics, and remote monitoring technologies are designed to detect patterns across time. These tools require longitudinal clinical awareness to distinguish meaningful change from statistical variation. Physicians who know their patients can interpret innovation correctly and act earlier. Innovation becomes transformative when anchored in relationship.

Fragmented care weakens this interpretive capacity. Data collected across disconnected encounters lacks coherence. Predictive tools lose precision when longitudinal context is absent. Innovation becomes reactive, identifying disease after symptoms emerge rather than predicting disease before it develops.

Technology achieves its highest value when it extends the physician’s ability to listen and observe. Remote monitoring allows earlier recognition of physiological change. Predictive analytics strengthens preventive intervention. Innovation amplifies continuity when guided by sustained physician leadership.

Team-based primary care models reflect this principle. Nurse practitioners and physician assistants expand access while physician leadership preserves interpretive continuity. Research published in Medical Care Research and Review confirms that coordinated team-based care maintains strong clinical outcomes. Physician oversight ensures that innovation remains integrated within longitudinal care. It also improves health professional job satisfaction and reduces burn-out.

Innovation cannot replace the relationship at the center of medicine. Algorithms detect patterns but do not understand meaning, and they do not strengthen physician/patient ties. Devices collect data, but do not know the patient behind the data. Physicians translate information into guidance by integrating technology with human understanding.

The future of health innovation depends on preserving continuity between patient and physician. Technology deployed within sustained relationships strengthens prevention and improves outcomes. Technology deployed within fragmented systems often compensates for structural weakness rather than transforming care. Continuity determines whether innovation fulfills its promise.

Health systems now face a defining moment. Transactional care offers speed and convenience. Relational care offers understanding and prevention. Innovation will achieve its full potential only when it strengthens the continuity that allows physicians to listen, learn, and guide patients across time.

Healing begins with being heard. Health technology succeeds when it helps physicians listen more deeply and act more wisely in the service of the people who entrust them with their care.

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These innovations promise earlier diagnosis, greater precision and improved efficiency by augmenting the knowledge and insight that health professionals develop through years of care. Yet beneath this progress lies a more difficult question. Will we use technology to strengthen the physician–patient relationship, or allow it to redefine the nature of care?

As written in Healing the Sick Care System: Why People Matter, “…the system is not broken because it lacks innovation, talent, or investment, but because it has lost sight of the people it exists to serve.” Technology is not the epicenter of care. It is meant to support communication, deepen relationships, and strengthen the human bond at the center of medicine.

Yet as artificial intelligence becomes embedded in diagnostics, decision support, documentation, reimbursement and care navigation, extraordinary clinical potential is accompanied by a growing tension.

Two Encounters, One Technology

For instance, in a primary care practice, a physician begins a routine visit with a patient in their mid-50s who has diabetes and hypertension. An ambient AI system seamlessly documents conversations, captures symptoms, updates medications, and generates a clinical note. The physician no longer turns toward a screen. Connection with the patient is essential. The patient speaks openly about fatigue, stress, and concern about long-term health.

Midway through the visit, the electronic record surfaces an AI-generated prompt suggesting an adjustment in therapy based on predictive risk modeling. The physician pauses, not to mindlessly follow the algorithm, but to ask additional questions about daily routine, financial constraints, and willingness to adopt lifestyle changes. Technology informs conversation. It does not replace it.

When the visit ends, documentation is complete, the treatment decision is shared, and the patient leaves with confidence, clarity and a sense of partnership in care. The physician directs the encounter. Technology supports judgment and understanding. The visit feels thoughtful, personal and grounded in relationship.

Now imagine the same technology in a different environment. The documentation remains seamless. The prompts still appear. The system functions efficiently. But here, the pace is set as much by operational demand as by clinical judgement. The schedule tightens. The visit is short. The physician moves quickly from one room to the next, guided less by the patient’s story and more by the system’s tempo. The encounter becomes transactional and compressed. Technology has not changed. What has changed is who is directing the care.

This is the quiet divide now shaping modern medicine. One path preserves physician-directed care, where technology supports human understanding. The other reflects system-directed transaction, where efficiency begins to overshadow the relationship. The difference lies not in the tool but in the priorities that shape its use.

This question of direction is not theoretical. It reflects a deeper shift in how technology may shape human judgment itself. Innovation theorist John Nosta, whose work has long been rooted in the health sector and now spans a broader landscape, cautions in his Psychology Today column: “Artificial intelligence is far from neutral, and we need to be careful by calling it simply a tool. By simulating understanding, it may reshape what humans expect from thinking itself. Over time, it can erode the habits required for discernment. And this danger is cumulative. It doesn’t announce itself as failure. It arrives as convenience.” Nosta is also the author of the upcoming book: The Borrowed Mind—Reclaiming Human Thought in the Age of AI.

When Technology Reflects the System Around It

Technology itself is not the challenge. When developed in partnership with physicians, nurses, and other health professionals, it can be transformative. Many of the most effective innovations emerge when developers observe the realities of care and design tools that strengthen human interaction rather than disrupt it.

John Whyte, MD, MPH, CEO of the American Medical Association, has emphasized that artificial intelligence must support physicians and care teams, not replace clinical judgment, and that technology should strengthen, not weaken, the physician–patient relationship.

A clear example of this tension is emerging in the context of prior authorization. Health professionals and administrative staff often spend more than a dozen hours each week navigating authorization requirements, time taken directly from patient care. New AI-enabled platforms, such as Optum’s Digital Authorization Complete powered by Humata Health, are designed to remove that burden by embedding real-time automation into clinical workflows and reducing manual steps. These innovations restore something invaluable: time.

Now, the deeper question is not technological but human. When time is returned to the system, how will it be allocated to the health professional? Will it allow clinicians to deepen their understanding of patient needs and strengthen their connection? Or will it simply enable the system to see more patients during their shift? The technology is neutral. Its meaning is shaped by people’s intent.

Health care operates within systems shaped by financial and operational pressures. In a transactionally driven environment, even well-intentioned technology can be redirected toward productivity rather than connection. A tool designed to restore time can become a mechanism to increase throughput. A system intended to support thoughtful care can accelerate volume in a fee-for-service environment. Technology inevitably reflects the values and objectives of the system in which it is deployed. It is not the technology that directs decisions and action; it’s the leadership.

The scale of investment underscores the stakes. The global AI in health market, estimated at roughly $36–39 billion in 2025, is projected to grow substantially in the coming decade. Investment shapes priorities. Priorities shape design. Design shapes experience. And experience shapes trust.

Emerging guidance aligned with the American Medical Association emphasizes that artificial intelligence must remain under meaningful clinical oversight. Technology must support physicians and care teams, not replace judgment or responsibility. Governance, transparency, and continuous evaluation are essential to ensure that technology strengthens patient safety, clinical reasoning, and trust.

This perspective aligns with participatory medicine. Dr. Danny Sands of the Society for Participatory Medicine has described health care not as a service transaction, but as a collaboration between patient and clinician. In that view, technology should support relationship-centered care, not redirect medicine toward system-driven throughput.

The Direction of Care

Health systems face real pressures: workforce shortages, clinician burnout, chronic disease, and financial strain. These realities demand smarter and more scalable solutions. Artificial intelligence offers meaningful progress. It can detect disease earlier, reduce administrative burden, and support more informed decisions. But efficiency is not healing.

Healing occurs when patients feel understood, supported, and guided by clinicians who have the time and space to listen and respond with care. When technology restores time and that time deepens connection, it fulfills its promise. When reclaimed time becomes additional volume, something essential is diminished.

Artificial intelligence will continue to shape medicine. The deeper question is not whether technology will advance, but who will decide how it is used and for what purpose.

If guided primarily by efficiency, care risks becoming faster but less human. If guided by partnership with physicians and patients, it can restore time to listen, space to understand, and the ability to decide together. Technology is not the healer. People are.

When guided by clarity of purpose, with the patient at the center of effort, and grounded in physician-guided judgment, technology becomes what it was always meant to be: a force that strengthens knowledge, deepens understanding, and restores the bond between physician and patient. Systems matter. They enable scale, coordination, and progress. Yet their purpose is fulfilled only when they serve people. Health care is at its best when human connection and well-designed systems work together in the service of healing.

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Lydia Romero strained to hear her husband’s feeble voice through the phone.

A week earlier, immigration agents had grabbed Julio César Peña from his front yard in Glendale, California. Now, he was in a hospital after suffering a ministroke. He was shackled to the bed by his hand and foot, he told Romero, and agents were in the room, listening to the call. He was scared he would die and wanted his wife there.

“What hospital are you at?” Romero asked.

“I can’t tell you,” he replied.

Viridiana Chabolla, Peña’s attorney, couldn’t get an answer to that question, either. Peña’s deportation officer and the medical contractor at the Adelanto ICE Processing Center refused to tell her. Exasperated, she tried calling a nearby hospital, Providence St. Mary Medical Center.

“They said even if they had a person in ICE custody under their care, they wouldn’t be able to confirm whether he’s there or not, that only ICE can give me the information,” Chabolla said. The hospital confirmed this policy to KFF Health News.

Family members and attorneys for patients hospitalized after being detained by federal immigration officials said they are facing extreme difficulty trying to locate patients, get information about their well-being, and provide them emotional and legal support. They say many hospitals refuse to provide information or allow contact with these patients. Instead, hospitals allow immigration officers to call the shots on how much — if any — contact is allowed, which can deprive patients of their constitutional right to seek legal advice and leave them vulnerable to abuse, attorneys said.

Hospitals say they are trying to protect the safety and privacy of patients, staff, and law enforcement officials, even while hospital employees in Los Angeles, Minneapolis, and Portland, Ore., cities where Immigration and Customs Enforcement has conducted immigration raids, say it’s made their jobs difficult. Hospitals have used what are sometimes called blackout procedures, which can include registering a patient under a pseudonym, removing their name from the hospital directory, or prohibiting staff from even confirming that a patient is in the hospital.

“We’ve heard incidences of this blackout process being used at multiple hospitals across the state, and it’s very concerning,” said Shiu-Ming Cheer, the deputy director of immigrant and racial justice at the California Immigrant Policy Center, an advocacy group.

Some Democratic-led states, including California, Colorado, and Maryland, have enacted legislation that seeks to protect patients from immigration enforcement in hospitals. However, those policies do not address protections for people already in ICE custody.

More Detainees Hospitalized

Peña is among more than 350,000 people arrested by federal immigration authorities since President Donald Trump returned to the White House. As arrests and detentions have climbed, so too have reports of people taken to hospitals by immigration agents because of illness or injury — due to preexisting conditions or problems stemming from their arrest or detention.

ICE has faced criticism for using aggressive and deadly tactics, as well as for reports of mistreatment and inadequate medical care at its facilities. Sen. Adam Schiff (D-Calif.) told reporters at a Jan. 20 news conference outside a detention center he visited in California City that he spoke to a diabetic woman held there who had not received treatment in two months.

While there are no publicly available statistics on the number of people sick or injured in ICE detention, the agency’s news releases point to 32 people who died in immigration custody in 2025. Six more have died this year.

The Department of Homeland Security, which oversees ICE, did not respond to a request for information about its policies or Peña’s case.

According to ICE’s guidelines, people in custody should be given access to a telephone, visits from family and friends, and private consultation with legal counsel. The agency can make administrative decisions, including about visitation, when a patient is in the hospital, but should defer to hospital policies on contacting next of kin when a patient is seriously ill, the guidelines state.

Asked in detail about hospital practices related to patients in immigration custody and whether there are best practices that hospitals should follow, Ben Teicher, a spokesperson for the American Hospital Association, declined to comment.

David Simon, a spokesperson for the California Hospital Association, said that “there are times when hospitals will — at the request of law enforcement — maintain confidentiality of patients’ names and other identifying characteristics.”

Although policies vary, members of the public can typically call a hospital and ask for a patient by name to find out whether they’re there, and often be transferred to the patient’s room, said William Weber, an emergency physician in Minneapolis and medical director for the Medical Justice Alliance, which advocates for the medical needs of people in law enforcement custody. Family members and others authorized by the patient can visit. And medical staff routinely call relatives to let them know a loved one is in the hospital, or to ask for information that could help with their care.

But when a patient is in law enforcement custody, hospitals frequently agree to restrict this kind of information sharing and access, Weber said. The rationale is that these measures prevent unauthorized outsiders from threatening the patient or law enforcement personnel, given that hospitals lack the security infrastructure of a prison or detention center. High-profile patients such as celebrities sometimes also request this type of protection.

Several attorneys and health care providers questioned the need for such restrictions. Immigration detention is civil, not criminal, detention. The Trump administration says it’s focused on arresting and deporting criminals, yet most of those arrested have no criminal conviction, according to data compiled by the Transactional Records Access Clearinghouse and several news outlets.

Taken Outside His Home

According to Peña’s wife, Romero, he has no criminal record. Peña came to the United States from Mexico in sixth grade and has an adult son in the U.S. military. The 43-year-old has terminal kidney disease and survived a heart attack in November. He has trouble walking and is partially blind, his wife said. He was detained Dec. 8 while resting outside after coming home from dialysis treatment.

Initially, Romero was able to find her husband through the ICE Online Detainee Locator System. She visited him at a temporary holding facility in downtown Los Angeles, bringing him his medicines and a sweater. She then saw he’d been moved to the Adelanto detention center. But the locator did not show where he was after he was hospitalized.

When she and other relatives drove to the detention facility to find him, they were turned away, she said. Romero received occasional calls from her husband in the hospital but said they were less than 10 minutes long and took place under ICE surveillance. She wanted to know where he was so she could be at the hospital to hold his hand, make sure he was well cared for, and encourage him to stay strong, she said.

Shackling him and preventing him from seeing his family was unfair and unnecessary, she said.

“He’s weak,” Romero said. “It’s not like he’s going to run away.”

ICE guidelines say contact and visits from family and friends should be allowed “within security and operational constraints.” Detainees have a constitutional right to speak confidentially with an attorney. Weber said immigration authorities should tell attorneys where their clients are and allow them to talk in person or use an unmonitored phone line.

Hospitals, though, fall into a gray area on enforcing these rights, since they are primarily focused on treating medical needs, Weber said. Still, he added, hospitals should ensure their policies align with the law.

Family Denied Access

Numerous immigration attorneys have spent weeks trying to locate clients detained by ICE, with their efforts sometimes thwarted by hospitals.

Nicolas Thompson-Lleras, a Los Angeles attorney who counsels immigrants facing deportation, said two of his clients were registered under aliases at different hospitals in Los Angeles County last year. Initially, the hospitals denied the clients were there and refused to let Thompson-Lleras meet with them, he said. Family members were also denied access, he said.

One of his clients was Bayron Rovidio Marin, a car wash worker injured during a raid in August. Immigration agents surveilled him for over a month at Harbor-UCLA Medical Center, a county-run facility, without charging him.

In November, the Los Angeles County Board of Supervisors voted to curb the use of blackout policies for patients under civil immigration custody at county-run hospitals. In a statement, Arun Patel, the chief patient safety and clinical risk management officer for the Los Angeles County Department of Health Services, said the policies are designed to reduce safety risks for patients, doctors, nurses, and custody officers.

“In some situations, there may be concerns about threats to the patient, attempts to interfere with medical care, unauthorized visitors, or the introduction of contraband,” Patel said. “Our goal is not to restrict care but to allow care to happen safely and without disruption.”

Leaving Patients Vulnerable

Thompson-Lleras said he’s concerned that hospitals are cooperating with federal immigration authorities at the expense of patients and their families and leaving patients vulnerable to abuse.

“It allows people to be treated suboptimally,” Thompson-Lleras said. “It allows people to be treated on abbreviated timelines, without supervision, without family intervention or advocacy. These people are alone, disoriented, being interrogated, at least in Bayron’s case, under pain and influence of medication.”

Such incidents are alarming to hospital workers. In Los Angeles, two health care professionals who asked not to be identified by KFF Health News, out of concern for their livelihoods, said that ICE and hospital administrators, at public and private hospitals, frequently block staff from contacting family members for people in custody, even to find out about their health conditions or what medications they’re on. That violates medical ethics, they said.

Blackout procedures are another concern.

“They help facilitate, whether intentionally or not, the disappearance of patients,” said one worker, a physician for the county’s Department of Health Services and part of a coalition of concerned health workers from across the region.

At Legacy Emanuel Medical Center in Portland, nurses publicly expressed outrage over what they saw as hospital cooperation with ICE and the flouting of patient rights. Legacy Health has sent a cease and desist letter to the nurses’ union, accusing it of making “false or misleading statements.”

“I was really disgusted,” said Blaire Glennon, a nurse who quit her job at the hospital in December. She said numerous patients were brought to the hospital by ICE with serious injuries they sustained while being detained. “I felt like Legacy was doing massive human rights violations.”

Handcuffed While Unconscious

Two days before Christmas, Chabolla, Peña’s attorney, received a call from ICE with the answer she and Romero had been waiting for. Peña was at Victor Valley Global Medical Center, about 10 miles from Adelanto, and about to be released.

Excited, Romero and her family made the two-hour-plus drive from Glendale to the hospital to take him home.

When they got there, they found Peña intubated and unconscious, his arm and leg still handcuffed to the hospital bed. He’d had a severe seizure on Dec. 20, but no one had told his family or legal team, his attorney said.

Tim Lineberger, a spokesperson for Victor Valley Global Medical Center’s parent company, KPC Health, said he could not comment on specific patient cases, because of privacy protections. He said the hospital’s policies on patient information disclosure comply with state and federal law.

Peña was finally cleared to go home on Jan. 5. No court date has been set, and his family is filing a petition to adjust his legal status based on his son’s military service. For now, he still faces deportation proceedings.

The post ‘I Can’t Tell You’: Attorneys, Relatives Struggle To Find Hospitalized ICE Detainees appeared first on Medika Life.

Systematically speaking, the key to identifying challenges lies in examining fundamental issues, not just their consequences. For example, if we want to fix the flaws in the learning process, we must first redefine the roots of deep learning and its underlying mechanics. We may even need to redefine them repeatedly to understand how to solve the problems arising from mind-based technologies.

Let me explain what I mean through one of the most debated topics of our time: the mental laziness caused by the way AI is rewriting our brain’s habits. To understand this, we need to look at the dynamics of deep learning in the brain. By grasping this process through interdisciplinary research, we might find ways to make AI learning feel more like natural deep learning.

The goal isn’t just to know the biochemistry of cells. Before looking at what happens inside an organism, we should ask:

Why do we usually prefer learning through AI over the effortful, traditional human way?

You might say the answer is obvious: because learning with technology is effortless and fast.

As a learning specialist, I’d like to answer this from a theoretical perspective.

 First, we must accept a reality: Human deep learning is naturally a challenging process. It is fundamentally different from the vast amounts of data we consume today through formal or informal education assisted by LLMs.

The Logic of Immediate Reward: From Skinner to the Present

There is strong research showing that learners prefer a small, immediate reward over a larger, delayed one. This was first highlighted by B.F. Skinner (1953), the pioneer of operant conditioning. (I’ve previously written about how this connects to AI. )

Later, others expanded on this effortless reward preference. In short, according to the behavioral economics of Skinner’s theory, humans look for shortcuts. 

AI is currently the ultimate shortcut, giving the best answer in seconds without any real struggle. From this view, it’s not just about the mind; it’s about behavioral economics.

A behavior that leads to a quick reward will always be repeated.

Richard Herrnstein (1961), a student of Skinner’s, developed a mathematical formula called the Matching Law. He showed that organisms don’t just look at one reward; they choose between options. If given two choices, a living being will put its energy into the one that pays off faster and more directly. 

In behavioral economics, this phenomenon is known as temporal discounting (Ainslie, 1975). The value of a reward drops the longer you have to wait for it. Simply put, the reward loses its shine in the organism’s mind because it requires patience.

We observe this phenomenon every day with AI users, particularly those utilizing ChatGPT. Students, for instance, might feel that spending hours writing a thesis is stupid or inefficient when they can get an answer in a split second. They don’t just feel productive; they feel smart for bypassing the effort. 

Even if you tell them that the struggle is what actually builds their brain, they often won’t listen. They choose the immediate payout over the long-term value. 

Evolutionary psychology explains this too: an immediate reward is guaranteed, while a future one is uncertain. Since we are wired for survival, we grab what’s available now.

Brain Biochemistry and the Deep Learning Process

When we learn something deeply, three key things happen at a neurological level:

1. Exposure to New Information: The nervous system makes its first contact with data for which it has no existing pattern.

2. Cognitive Load: This is that stuck feeling when a mental process is harder than expected. It’s the effort the brain needs to process unfamiliar data (Sweller, 1988). This friction is essential.

3. Processing and Protein Synthesis: If the information is processed correctly, chemical signals trigger the creation of proteins that physically change the brain’s structure to store that knowledge (Kandel, 2001).

This is why sleep is so vital. Most of this protein synthesis happens while we rest. 

One of the most beautiful parts of learning is when we stop thinking about a problem, but our brain keeps working on it. 

Through the Default Mode Network or DMN (Raichle, 2015), the brain makes random, creative connections. This is where true creativity is born.

Toward Friction-Based AI

If deep learning is the result of protein synthesis triggered by challenge, then the paradox of modern AI is clear: By removing the friction, technology is removing the learning. 

We are facing a biological crisis where human brains, instead of producing genius and problem-solving skills, are becoming mere terminals for receiving quick hits of dopamine.

My proposal is simple: How can we turn AI from a passive answer-giver into a Cognitive Challenging Provocateur? 

We need to design models that don’t bypass cognitive load but manage it in a personalized way. 

I call this Friction-based AI; a model where algorithms are programmed not for the shortest path, but for the most effective learning path. This is an open invitation to researchers, neuroscientists, and AI architects to collaborate on this new paradigm. My ideas are ready to be turned into actionable proposals.

As a final note, I believe the way we interact with AI is a skill in itself. Even if everyone has the same tools, the results aren’t equal. Efficiency depends on the how. 

I am currently developing a startup idea to address these exact challenges in EdTech.It’s EdTechxDr. Atefeh F.

References

• Ainslie, G. (1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin.

• Herrnstein, R. J. (1961). Relative prevalence of response in relation to the relative frequency of reinforcement. Journal of the Experimental Analysis of Behavior.

• Kandel, E. R. (2001). The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses. Science.

• Raichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience.

• Skinner, B. F. (1953). Science and Human Behavior. Simon and Schuster.

• Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science.

The post Why Biological Learning Demands the Friction We Seek to Delete? appeared first on Medika Life.

It was the fall of 2022 when large language models and Generative AI burst out of research labs and onto Main Street. Since then, every day seems to bring another AI breakthrough that challenges how work gets done.

In my role advising organizations on AI strategy and deployments, I see a consistent pattern among healthcare leaders: excitement about what AI could unlock, paired with exhaustion from the volume of noise, pressure, and competing claims.

Welcome to 2026.

As predictions flood inboxes and social feeds, focused on what AI might do next, I want to ground the conversation in something more useful. Rather than forecasting outcomes, let’s focus on three forces already at work—forces that will determine whether AI delivers real value in healthcare or quietly stalls.

Will 2026 be a year of boom, bust, or backlash?

The honest answer is yes.

Boom: Early Wins—and an AI Arms Race

Let’s start with what’s working.

Healthcare is seeing real, if narrow, gains from AI:

• Ambient documentation reduces administrative burden
• Imaging and pathology tools iare mproving speed and consistency
• Operational and revenue cycle applications driving incremental efficiency

These are not moonshots. They are targeted solutions addressing specific pain points. And they matter.

At the same time, healthcare is now firmly in an AI arms race.

Every EHR vendor, medical device company, life sciences firm, and digital health startup is racing to declare itself “AI-native.” Roadmaps are packed with copilots, assistants, agents, and automation claims. No vendor wants to be perceived as falling behind.

That pressure is accelerating innovation—but it’s also compressing timelines, encouraging over-promising, and pushing organizations to adopt faster than they can realistically absorb.

Boom energy is real.

But it is also uneven and fragile.

Prediction: Within two years, most AI used in provider organizations will arrive embedded inside core systems and devices already in use. Intelligence will not be something teams “add on”; it will be something they inherit.

Recommendation: Understand where AI is already embedded across your vendor ecosystem and what’s coming next. Engage early through advisory councils or pilots. Engage and prepare clinicians before introducing these capabilities into workflows. AI should never arrive as a surprise.

Bust: When Pilots Multiply, but Value Doesn’t

Generative AI has dominated innovation agendas, yet only a fraction of pilots ever reach sustained production. A survey cited by MIT reports that roughly 95% of business AI pilots fail to generate measurable returns.

This is not evidence that AI lacks value.

It is evident that many organizations lack discipline.

High failure rates are normal in early markets. Technology matures. Tools improve. But value only materializes when leaders focus on fundamentals: design, data readiness, workflow integration, and ownership.

Most AI initiatives fail not because the technology doesn’t work, but because success is never clearly defined. Projects are launched out of curiosity, vendor pressure, or fear of being left behind. Clinical impact, operational accountability, and economic value are clarified too late—if at all.

Equally damaging is the underestimation of the human systems AI enters. Healthcare work is relational, regulated, and trust-dependent. When AI is introduced without redesigning workflows, preparing staff, or clarifying responsibility, it creates friction—not relief. Adoption then stalls quietly.

Prediction: In 2026, organizations will run fewer AI pilots—but with much higher expectations. Boards and executives will require clearer evidence of clinical, workforce, or financial value before approving new initiatives.

Recommendation: Move from “fail fast” to “fail before you scale.” Define success upfront, assign ownership early, and redesign workflows in tandem with technology. AI initiatives without a credible path to value should be halted immediately.

Backlash: Fear, Workforce Anxiety, and the Trust Gap

The most underestimated force shaping AI’s trajectory in 2026 is neither technical nor financial.

It’s human.

History offers context. When automobiles first appeared, they were seen as dangerous and socially disruptive. Red Flag laws required people to walk ahead of vehicles waving flags and capped speeds at just a few miles per hour. These laws weren’t about innovation—they were about fear, control, and adjustment.

Healthcare AI is entering a similar phase.

Workforce research shows healthcare workers are among the most cautious about AI adoption, citing concerns about trust, transparency, and job impact. This caution is not irrational. Healthcare has a long history of technology being imposed rather than co-designed.

As a result, scrutiny is increasing—particularly from labor organizations and state legislators. Recent bills, including those limiting AI’s role in clinical decision-making and licensed practice, reflect not anti-innovation sentiment, but unresolved trust and knowledge gaps.

Innovation does not scale without trust.

In 2026, AI scrutiny will intensify, especially with labor organizations and at the state legislative level.

As I write this, the Chair of the New York State Senate Committee on Internet and Technology just introduced a bill (S7263) to “protect patients and front-line care workers from the adverse effects of AI tools in risky or untested settings.”  The bill prohibits chatbots from performing the duties of licensed nurses and puts strong guardrails around the use of AI in healthcare settings.”

I often write about the need for a balanced approach to defining both the “gas and guardrails” that guide AI’s use in health and medicine. Incentives and safeguards are equally important.

Prediction: Expect increased legislative activity and labor engagement around AI in healthcare throughout 2026. Such actions should not be dismissed simply as anti-innovation. They reflect something deeper: a trust and knowledge gap that needs to be closed.

Recommendation: Create durable AI value by investing in workforce and consumer education. Clinicians need clarity—not just on how AI works, but on how it supports professional judgment rather than replaces it.

From Awe to Analytical

The year ahead will test the resolve of leadership. Transformation in healthcare is rarely linear—and never clean.

Vendors will continue to showcase breakthroughs. The hype will continue. But 2026 is not the year for cheerleading.

It is the year for realism.

The most effective leaders are moving from awe to analysis—recognizing that AI value does not come from the technology itself, but from the opportunity it creates to rethink how work gets done.

In that sense, AI value is—and always will be—a uniquely human process.

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