Adoption Is the Product: Clinical AI, Scalable Connectedness, and the Next Era of Healthcare Value

by top100admin | Jun 5, 2026 | AIMEDENT Journal Vol 1:4

In clinical AI, progress is easy to signal and difficult to achieve. A model is validated. A pilot launches. A press release announces state-of-the-art performance. From the outside, it looks like momentum. Inside the hospital, the clinician works within the same constraints, the workflow carries the same load, and the patient experiences no difference. The model exists; adoption does not. That distance, the space between what an algorithm can do in validation and what it changes in care, is the central economic fact of healthcare AI, and almost no one is pricing it correctly.

The evidence describes a field investing in capability and waiting on impact. According to Rock Health, U.S. digital health funding reached $14.2 billion in 2025, up 35% over the prior year, with companies centering AI capturing 54% of it, up from 37% a year earlier. Yet a 2025 McKinsey survey found that only about 39% of organizations report any enterprise-level earnings impact from AI, and most of that impact is under 5%. Capital is flowing toward model capability while the return on it lags. The constraint is rarely model quality; less than 60% of the data healthcare organizations already generate is used in decision-making. The capacity to build models has outrun the capacity to absorb them, and almost no one is pricing that gap correctly.

Why AI Widens the Distance

AI does not close the gap between science and care by default; in important ways it widens it. Predictive models are extrapolation engines: they describe risk well and rarely change it, because a probability is not an intervention. Convergence AI, the merging of once-separate fields (biology, imaging, language, and clinical data) into systems that reason across all of them at once, compounds discovery faster than any prior wave of healthcare technology, and faster than delivery systems can absorb. Generative medicine, in which AI designs novel therapeutics and interventions on demand rather than merely predicting risk, is the sharpest current expression of that acceleration. Each advance enlarges the same distance: capability races ahead, and adoption waits.

The failure is specific, not vague. Models that perform in validation degrade in live clinical settings: distribution shift as local populations diverge from training data, workflow mismatch when an output arrives at the wrong moment in care, alert fatigue that turns signal into noise, and an integration burden that no benchmark measures. The pattern is documented. A widely deployed proprietary sepsis prediction model, live in hundreds of U.S. hospitals, was found on external validation to identify only about a third of sepsis cases while generating enough false alerts to fatigue the clinicians it was meant to help (Wong et al., JAMA Internal Medicine, 2021). The tool was not absurd; it simply did not survive contact with a real ward. Dentistry shows the same shape: AI that reliably flags caries or bone loss on a radiograph still changes nothing if it arrives outside the moment the clinician decides on treatment. A model can be accurate and still go unused, and an unused model changes nothing.

The contrast proves the point. A different sepsis system, deployed across five hospitals and monitoring more than 590,000 patients, was studied prospectively and reported in Nature Medicine in 2022. Among septic patients whose alert a clinician confirmed within three hours, in hospital mortality fell by roughly 18% (Henry et al., Nature Medicine, 2022). What separated this result from the failures was not a more brilliant algorithm; it was that the system earned sustained clinician use, near 89%, by fitting the way care already moved. The benefit tracked the adoption, not merely the prediction. Same disease, two models, opposite outcomes, and the difference lived in everything around the model rather than in the model itself.

This is why benchmark performance has become a poor predictor of clinical impact, and why the hard problem in healthcare AI has migrated from the model to everything around it.

The Thesis: Scalable Connectedness

This is the space I have spent my career studying, and it is the foundation of how I invest. I call it scalable connectedness. The premise is simple: science is accelerating faster than accessibility, and AI is the accelerant. The distance between what a model can do and what reaches the people who need it is widening, not closing. Most treat that distance as a gap to be lamented. I treat it as among the most underpriced spaces in healthcare AI, because it is where category-defining companies are built. Durable value does not come from a better model. It comes from connecting model capability to clinical adoption at scale.

That reframing changes what a strong AI company looks like and what a disciplined investor should underwrite. The defensible asset is not the algorithm; algorithms are increasingly abundant and quickly matched. The defensible asset is the ability to carry a model across the last and hardest distance, into a workflow, a reimbursement pathway, a clinician’s daily habit, a patient’s actual life. In clinical AI, the product is not the model. The product is the behavior change the model makes possible inside the system.

The Adoption Test

If adoption is the product, then adoption is where diligence should concentrate. The question that predicts outcomes is not “is the model accurate?” Most models are accurate in a demo. Three questions matter more:

1. What specific clinical behavior has to change for this model to be used?
2. Who owns that change, and do they have the authority and the incentive to see it through?
3. Once the change is made, is it durable, or does it reverse the moment attention moves elsewhere?

A team that can answer those three is solving the problem that actually separates the clinical-AI winners from the graveyard of accurate, well-funded models that no one adopted.

Three Audiences, One Direction

This thesis speaks differently to the three groups building healthcare AI, but it points them the same way. For those allocating capital, durable returns favor the work that moves a model into care (the integration, the workflow, the trust) over the one-time benchmark win that never travels. For founders, the most defensible position is the one closest to the patient: owning the distance between what a model can compute and what a clinician can act on. For physician innovators, the clinical vantage point is not a detour from the work. It is a design advantage, because knowing exactly where care breaks down is knowing exactly where a model can create value.

And it answers a question I am asked often: whether purpose and return are at odds. They are not. The companies that move AI from capability to bedside are, in the same motion, the ones that compound. Dignity delivered at scale and durable financial performance are not competing objectives in this field. They are one objective seen from two sides.

Conclusion

Healthcare’s next era will not be defined by who builds the most capable model. It will be defined by who connects model capability to the people it was meant to serve. That is an engineering problem, an investment problem, and, for those of us who came to this work from medicine, a moral one. The space between what AI can compute and what care can use is not empty. It is where the future of healthcare value will be built, by the founders and the funds willing to treat connectedness itself as the thing worth building.

About the author

Mohammed Quadri, MD, MBA, is a physician Enterpreneur, investor, and healthcare strategist focused on clinical AI, biotech, and life sciences from innovation through adoption. His work centers on scalable connectedness: the principle that durable value comes from connecting invention to adoption at scale, where purpose and returns advance together.

The Wound and the System: Why AI in Medicine Must Begin with Humanity

by top100admin | Jun 3, 2026 | AIMEDENT Journal Vol 1:4

As artificial intelligence reshapes clinical practice, one physician-executive argues that the real transformation is not technological — it is moral.

By Traci A. Kimball, MD, MBA

Physician Executive and Founder, The WISH Clinic® and Ekagra Health AI™

Every Patient Is Wounded Twice

Every patient who walks into my wound care clinic arrives with two injuries. The first is physical — a chronic wound that may have resisted treatment for months or years. The second is systemic: a deep, persistent loss of faith that the healthcare system is capable of healing them at all.

I became a wound care physician because I believed I could address both. Over decades of practice, I learned that the act of healing is never purely clinical. It is relational, spiritual even — a covenant between a suffering person and the professional who commits, without reservation, to walking with them until the wound closes or every ethical option has been exhausted. My mother called it simply: if you love your work, it is not work. I call it the laying on of hands.

Now, a new companion has joined us on that journey. Artificial intelligence is entering the exam room, the operating suite, and the billing department simultaneously. And the central question facing every physician, health system leader, and technology executive at this conference is not whether AI will transform medicine. It already is. The question is whether we will allow it to make medicine colder — or more human.

Three Eras, One Reckoning

To understand where we are going, we must reckon honestly with where we have been. Wound care — and medicine more broadly — has passed through three distinct eras in my lifetime.

Woundcare 1.0 was the humanist era. Bedside medicine. The physician as healer, diagnostician, and companion. Empathy was not a soft skill; it was the primary instrument of care. Bureaucracy was minimal. Trust was the currency of the clinical relationship.

Woundcare 2.0 arrived as the fee-for-service era consolidated its grip on American healthcare. Volume replaced value. Data replaced discernment. Physicians became documentation engines, their clinical time consumed by EHR fields that generated revenue but rarely improved outcomes. The wound care specialist who once spent forty-five minutes with a complex patient now had twelve. The system did not break physicians’ desire to heal. It simply starved it of oxygen.

Woundcare 3.0 is the era we are now entering — and it is, I believe, our last best opportunity to restore what was lost. Artificial intelligence, deployed with intention and governed by ethics, can return to the physician what the administrative burden stole: time, attention, and the cognitive space required for genuine clinical presence.

“AI must learn bedside manner before it learns billing codes.”

The Co-Pilot, Not the Replacement

At Ekagra Health, the AI platform I co-founded, we designed our system around a single governing principle: the physician is not a problem to be automated away. The physician is the point.

Our platform integrates EHR management, revenue cycle automation, real-time outcomes analytics, laboratory workflow, patient communications, and compliance infrastructure into a unified intelligence layer. But the architecture of that system reflects a deliberate moral choice. Every module was built to reduce the friction that pulls clinicians away from patients — not to replace the judgment that draws them toward them.

When AI absorbs the complexity of prior authorizations, denial management, and documentation, something remarkable happens: physicians remember why they went to medical school. They look up from their screens. They ask the question that the algorithm cannot yet ask, because it requires the full weight of human presence to ask it well: How are you, really?

This is not sentimentality. It is clinical strategy. Research consistently demonstrates that patient engagement, therapeutic alliance, and perceived empathy are among the strongest predictors of treatment adherence and wound healing outcomes. A physician restored to presence is a more effective physician. AI that serves that restoration is not merely efficient — it is therapeutic.

Leading Change, Not Just Adopting Technology

The implementation of AI in healthcare is, at its core, a leadership problem disguised as a technology problem. I have learned this the hard way.

For years, I believed that leading change meant having the answer first — arriving at the M&M conference with the diagnosis already formed, the solution already sketched. What my own leadership development revealed, uncomfortably, was that this instinct was not strength. It was a form of control that crowded out collaboration, silenced nurses who had seen what I had not seen, and made the team smaller than it needed to be.

The transformation I underwent — from the physician who needed to be the smartest person in the room to the leader who understood that the room itself is the intelligence — maps directly onto the challenge facing health systems today. Organizations that deploy AI as a top-down efficiency mandate will reap resistance, workarounds, and moral injury. Organizations that deploy it as an act of collaborative leadership — inviting clinicians into the design, celebrating early adopters, embedding new workflows slowly and with care — will reap culture change that lasts.

Kotter’s foundational insight remains true in the age of machine learning: change does not succeed because technology exists. It succeeds because people are ready. And people become ready when they are treated not as users to be onboarded, but as healers to be honored.

A New Oath for a New Era

At medical school graduation, we pledged to do no harm. The Hippocratic tradition asked us to hold the patient’s welfare above our own convenience, our own profit, our own pride. It was a moral framework sufficient for a world in which the physician was the most powerful actor in the clinical encounter.

That world no longer exists. Today, the algorithm is also present in the exam room. The insurance adjudication engine is present. The EHR vendor’s incentive structure is present. The venture capital thesis that underwrites the AI platform is present. Medicine has never been a more crowded moral space.

Which is why I would propose, for every technologist, executive, and clinical leader gathered at this conference, a supplementary oath: do no hubris.

Do not assume that because an algorithm performs better than a physician on a diagnostic benchmark, it is ready to be a physician. Do not assume that because a machine can predict wound deterioration with 94% sensitivity, it understands what it means to a 74-year-old woman to lose her ability to walk. Do not assume that efficiency and healing are the same thing, or that speed and care are synonymous.

The greatest wound in healthcare today is not biological. It is systemic. It is the accumulated damage of decades in which the infrastructure of medicine — its incentives, its technology, its administrative architecture — was designed to extract value rather than create it. Healing that wound requires the same qualities we bring to the bedside: honesty about what is broken, courage to change it, and the humility to know we will not do it alone.

The road ahead is long. But for the first time in my career, I believe the technology exists to walk it well. The only question is whether we have the wisdom to let it serve our humanity — rather than replace it.

About the Author

Traci A. Kimball, MD, MBA is a physician executive, wound care specialist, and the founder of The WISH Clinic® and Ekagra Health AI™. She will deliver the keynote address “From Wound Wizardry to Woundcare 3.0” at the Global Success Institute’s AI in Medicine Conference in June 2026. She writes and speaks on the intersection of clinical leadership, health technology, and the ethics of human-centered AI.