Health IT Perspectives

A Google Health executive walked into a room full of health IT founders and said something that stopped everyone cold. AI developed on licensed data has no future. Primary data ownership is the only defensible moat in health IT. I brought that statement directly to Dr. Yin Ho — and her response shaped everything about how Symetrique thinks about data.

The Statement That Changed My Thinking

Most health IT AI companies today are building on licensed data. They access datasets from EHR vendors, clearinghouses, payer networks, and data aggregators — paying for the right to run their models on someone else's asset. The AI is theirs. The data is not.

This is a fragile position. The moment a data supplier changes terms, raises prices, decides to compete directly, or simply loses market position — the company built on top of their data is exposed. There is no moat. There is only a licensing agreement.

The Google Health executive's statement was not just a competitive strategy observation. It was a warning about the fundamental architecture of health IT AI — and which companies will still be standing in ten years.

Dr. Ho's Response — Validated and Extended

When I brought the Google Health executive's statement to Dr. Yin Ho, her response was immediate and direct.

Unfortunately that is true. The problem is you cannot develop an AI model on data that is not accurate, not quality level, or not representative.

She validated the thesis completely. But she did not stop there. She extended it in a direction I had not fully considered — and in doing so, she identified the most important white space in health IT data strategy today.

Large companies have volume. The EHR vendors, the clearinghouses, the payer networks — they have accumulated data at a scale that is difficult for entrepreneurs to match. And many of these organizations are reluctant to share. As Dr. Ho put it, entrepreneurs are almost forced to partner with organizations that may not want to share their data. That is the current reality of the marketplace.

But here is where the conversation took a turn.

Ownership of data still belongs to the patient. How can patients pull their data together and then provide permission to use their data for other purposes?

The Twelve Step Problem — Why Patients Cannot Access Their Own Records

This is the moment in the interview that stopped me completely. Dr. Ho did not just identify the opportunity. She mapped the obstacle with a specificity I had never heard articulated so clearly.

Putting together a complete patient record today requires navigating a twelve-step gauntlet that most patients simply cannot complete. Dr. Ho walked through it step by step:

First — knowing everywhere you have been seen over your entire medical history. Second — knowing which EHR system each of those physicians uses. Third — understanding how your data is stored in each system and how accessible it is. Fourth — navigating different permission and consent forms for each system. Fifth — understanding the statute of limitations each provider imposes — some will only release 90 days of records, others 120 days. Sixth — recognizing that even inside a patient portal, you will not get all of your data. Seventh — figuring out how to string all of these fragmented records together into a coherent format. Eighth — understanding what format each system exports data in. Ninth — determining whether the compiled record is actually complete. Tenth — navigating what happens when you have been seen across state lines. Eleventh — dealing with the different state laws governing data access in each jurisdiction. Twelfth — reconciling records from multiple different kinds of health systems that may not interoperate at all.

The challenge for a patient to put together their record is already an example of how hard it is for anyone to put it together in such a way that it is a large enough volume to run an AI model on.

The Opportunity Hiding Inside the Problem

Dr. Ho's twelve-step map of the patient record challenge is not just a description of the problem. It is a blueprint for the solution. Every step in that gauntlet is an opportunity to build something — a tool, a service, a platform — that makes the next step easier.

The founder who builds the infrastructure that helps patients compile their most complete longitudinal record — navigating the permissions, the formats, the state laws, the portal limitations — that founder builds the most defensible primary data asset in health IT. Not by acquiring data from institutions. By empowering patients to exercise rights they already legally have.

As Dr. Ho framed it: this might be a moment in time to think about not only empowering patients to access their data, but how to start putting together better, more complete records on behalf of the patient — so that you have the most complete set of records that exists. That is the opportunity. That is the moat.

What This Means for the AI Wrapper Problem

The Google Health executive's warning about AI on licensed data is a warning about commoditization. As foundation models improve and become accessible to everyone, the AI layer itself becomes less differentiated. The question is not whether your model is better. The question is whether your data is irreplaceable.

Licensed data is replaceable — by definition. Your competitor can license the same dataset. Primary patient data built on consent, trust, and genuine value exchange is not replaceable. It cannot be scraped, synthesized, or licensed away. It compounds over time as more patients join, as more data flows in, and as the platform proves its value to the people at the center of it.

What This Means for Founders

If you are building an AI product in health IT, ask yourself one question: if your primary data supplier changed terms tomorrow, would your product still exist? If the answer is no — you are building on licensed data and you do not have a moat. You have a dependency.

The path to primary data ownership in healthcare runs through patients. It requires building genuine trust, delivering genuine value, and creating a consent architecture that patients understand and choose. That is harder than licensing a dataset. It is also the only way to build something that lasts.

About Symetrique

Symetrique is a healthcare analytics and intelligence company serving pharma and biotech, payers, health systems, and medical device companies. We combine healthcare analytics, commercial analytics, market research, and real world evidence into an integrated service offering — giving our clients the complete intelligence picture they need to make faster, more confident decisions.

Our approach is built on a simple belief: data without context is noise. Symetrique brings together the structured and unstructured data layers of healthcare — claims, clinical records, physician observations, and patient journeys — and applies AI to turn them into actionable intelligence across the full commercial and clinical lifecycle.

Whether you are a pharma team navigating a competitive landscape, a payer making formulary decisions, a health system optimizing care pathways, or a medical device company building your market strategy — Symetrique delivers the evidence and analytics that move you from questions to answers.

This blog is part of our ongoing series — Conversations at the Edge of Health IT — featuring insights from leading voices in healthcare, technology, and life sciences. Subscribe at www.thesymetrique.com to receive each new episode directly.

www.thesymetrique.com | Madhav Kathikar, Founder & CEO | 224-566-9880

About Rushing Headlong

Rushing Headlong: Health IT's Legacy and the Road to Responsible AI is written by Dr. S. Yin Ho, MD, MBA. It has a foreword by J.D. Kleinke and an endorsement from Stéphane Bancel, CEO of Moderna. Available on Amazon.

Flatiron Health built a $1.9 billion company by going impossibly deep in one disease area. But most founders get the Flatiron story wrong. It did not succeed because oncology was special. It succeeded because oncology was the only disease area where the economics could justify the cost of what it actually took to build the platform.

Why Flatiron Only Did Oncology

When I asked Dr. Yin Ho which disease area is most ready for a founder to replicate the Flatiron model today, she reframed the question entirely — starting with a piece of history most people miss.

When Flatiron launched, AI was not ready. Natural language processing was in its infancy. Building a disease-specific clinical intelligence platform required something expensive and slow — highly trained clinical abstractors reading through thousands of physician notes by hand, turning unstructured data into research-grade datasets. That manual labor was extraordinary in both cost and complexity.

The only disease condition that could justify the high cost of the manual labor was oncology. No other disease condition paid enough from a therapeutic perspective to justify what it took to pull out that data.

The Flatiron model was not a strategic choice born from a love of oncology. It was an economic necessity. The therapeutic dollars available in oncology — from pharma companies developing cancer drugs — were the only ones large enough to fund the manual abstraction infrastructure Flatiron needed to build. Every other disease area was simply not profitable enough to justify the cost.

What Generative AI Changes About the Economics

The cost of abstraction has decreased. If you have an AI-enabled abstraction process, you can do something very similar to what Flatiron did with fewer resources. Suddenly the economic equation starts to move in favor of many other disease conditions.

AI-enabled clinical abstraction dramatically reduces the number of trained abstractors required. The same work that previously required a team of clinical professionals reading notes manually can now be augmented — and in many cases largely automated — by generative AI that reads unstructured physician notes, extracts structured clinical data, and builds longitudinal patient records at scale.

When the cost of abstraction falls, the economic equation that previously confined the Flatiron model to oncology opens up. Suddenly it does not have to be the highest-priced therapeutic. It can be a therapeutic that treats a larger volume of patients — or one that addresses a bigger unmet need for society.

The Two-Path Framework — The Insight Every Founder Needs

But Dr. Ho did not stop at identifying which disease areas are ready. She drew a distinction that fundamentally separates two different types of data entrepreneurs — and which path you are on determines your entire strategy.

Path 1 is about abstracting meaning from data that already exists but has never been properly used for research. The data is there — in physician notes, in pathology reports, in clinical documentation — unstructured, disorganized, and untouched by research methods. For this path, AI-enabled abstraction is your core capability. And the best disease areas to target are the ones where there is plenty of existing data and active pharma investment — immunology, cardiovascular, metabolic disease, and other high-volume conditions.

If your goal is to abstract meaning from data that already exists but that you have not been using to support research — AI-enabled abstraction methods are basically where you think when there is plenty of data but it is still unstructured and has not been organized to make research easy.

Path 2 is fundamentally different. It is about generating new observed data in areas where not enough exists in the first place. This is not an abstraction problem — it is a collection problem. And the disease area Dr. Ho points to most clearly for this path is women's health.

Women's health becomes more interesting because there is a dearth of data there. There is just not enough observed data from women's health. So that would be a very different aspect of the data entrepreneur journey.

Which Path Is Right for You?

The answer depends entirely on what you are trying to build and who you are building it for.

If you are building for pharma clients who need RWE to support drug development, HTA submissions, or commercial intelligence — Path 1 is your direction. The data exists. The buyers are funded. The AI capability is ready. Pick a high-volume disease area with active pharma investment and go impossibly deep.

If you are building for a population that has been systematically excluded from clinical research — and you believe the long-term opportunity lies in generating new observed data rather than mining existing records — Path 2 is your direction. Women's health, rare disease, and other data-poor areas offer the chance to build a genuinely differentiated primary dataset that no amount of abstraction can replicate.

Both paths are real. Both have paying customers. But they require fundamentally different capabilities, different go-to-market strategies, and different timelines to revenue. The founders who fail are the ones who try to pursue both paths simultaneously before proving either one.

What This Means for Founders

The Flatiron blueprint is now available to every founder with access to generative AI and a clear disease area thesis. The economic barrier that confined the model to oncology has fallen. The question is no longer whether you can afford to build it. The question is which path you are on — and whether you are honest with yourself about which one you are actually equipped to execute.

About Symetrique

Symetrique is a healthcare analytics and intelligence company serving pharma and biotech, payers, health systems, and medical device companies. We combine healthcare analytics, commercial analytics, market research, and real world evidence into an integrated service offering — giving our clients the complete intelligence picture they need to make faster, more confident decisions.

Our approach is built on a simple belief: data without context is noise. Symetrique brings together the structured and unstructured data layers of healthcare — claims, clinical records, physician observations, and patient journeys — and applies AI to turn them into actionable intelligence across the full commercial and clinical lifecycle.

Whether you are a pharma team navigating a competitive landscape, a payer making formulary decisions, a health system optimizing care pathways, or a medical device company building your market strategy — Symetrique delivers the evidence and analytics that move you from questions to answers.

This blog is part of our ongoing series — Conversations at the Edge of Health IT — featuring insights from leading voices in healthcare, technology, and life sciences. Subscribe at www.thesymetrique.com to receive each new episode directly.

www.thesymetrique.com | Madhav Kathikar, Founder & CEO | 224-566-9880

About Rushing Headlong

Rushing Headlong: Health IT's Legacy and the Road to Responsible AI is written by Dr. S. Yin Ho, MD, MBA. It has a foreword by J.D. Kleinke and an endorsement from Stéphane Bancel, CEO of Moderna. Available on Amazon.

Google Health failed. Microsoft HealthVault failed. Amazon Haven failed. Not because of bad technology. They had the best technology in the world. They failed because they treated healthcare like every other vertical — and healthcare is not like every other vertical.

The Question That Gives Every Health IT Founder Anxiety

I brought this question to Dr. Yin Ho with complete honesty — it is the question that gives me the most anxiety as a founder building in this space. What specifically did the big tech companies miss? And what must AI-native founders do differently to avoid the same fate?

Part of the reason why large technology companies did not do so well in healthcare despite multiple attempts and despite good intentions has a lot to do with the fact that they treated healthcare like every other vertical.

The Structural Reality That Makes Healthcare Different

In almost every other industry the person who uses the product is the person who pays for it. Consumer preferences drive product design. The person receiving value and the person paying for it are aligned.

Healthcare is the exception. The patient receives the care. A third party — the insurer, the employer, the government — pays for it. And the operating systems of healthcare — the EHRs, the billing systems, the clinical workflows — are designed to serve whoever pays the bills. Not whoever receives the care.

This is one of the few industries in which the user of healthcare is not the direct payer. The end customer tends to be who pays the bills, not the person receiving the care.

Why Big Tech Could Not Gain Scale

Big tech entered healthcare and tried to solve a structural incentive problem with better technology. Cleaner interfaces, faster data pipelines, smarter algorithms — none of it moved adoption. Making a transaction smoother does not resolve a structural incentive misalignment. Making information more available does not change the fact that the people who hold that information are economically motivated to keep it.

You would not see the adoption or utilization in a way that was meaningful. If you are a technology company with multiple verticals, you are going to rationalize — I am not going to put more money into something that is so complicated that I cannot even gain scale with it.

Dr. Ho's Direct Advice to AI-Native Founders

Before you build anything — map the incentives of every player touching the problem you are trying to solve.

Try and understand where all the different players are in that space. What is it for the patient? What is it for the physician? What is it for the payer? Draw the lines to understand where the incentives are and where the alignment is.

The incentive map tells you if a problem is worth solving, whether it is too broad for an early-stage company to tackle, and where a narrower entry point exists — one where at least one well-resourced stakeholder is sufficiently motivated to pay you to solve it.

The Incentive Map as a Founder's First Tool

Draw your problem space. Put every stakeholder on the map — patient, physician, payer, pharma, researcher, hospital, lab, regulator. For each one, ask: what do they gain from the current system, and what would they gain from the solution you are building?

Where those two answers create a net positive — where your solution gives a stakeholder more than the status quo does — that is your point of entry. Where the answers conflict — where your solution requires a stakeholder to give up something they currently profit from — that is your resistance.

What This Means for AI-Native Founders

The arrival of generative AI has created a new generation of health IT founders who believe — understandably — that this time the technology is different enough to overcome the structural challenges that defeated big tech. Some of them are right. But the ones who are right will not succeed because their AI is better. They will succeed because they understood the incentive landscape before they built, found the place where their solution creates aligned value for multiple stakeholders, and built trust that technology alone can never manufacture.

Healthcare is solvable. But it is solved differently than every other industry. The map you need to draw first is not a product roadmap. It is an incentive map. Start there.

About Symetrique

Symetrique is a healthcare analytics and intelligence company serving pharma and biotech, payers, health systems, and medical device companies. We combine healthcare analytics, commercial analytics, market research, and real world evidence into an integrated service offering — giving our clients the complete intelligence picture they need to make faster, more confident decisions.

Our approach is built on a simple belief: data without context is noise. Symetrique brings together the structured and unstructured data layers of healthcare — claims, clinical records, physician observations, and patient journeys — and applies AI to turn them into actionable intelligence across the full commercial and clinical lifecycle.

This blog is part of our ongoing series — Conversations at the Edge of Health IT — featuring insights from leading voices in healthcare, technology, and life sciences. Subscribe at www.thesymetrique.com to receive each new episode directly.

www.thesymetrique.com | Madhav Kathikar, Founder & CEO | 224-566-9880

About Rushing Headlong

Rushing Headlong: Health IT's Legacy and the Road to Responsible AI is written by Dr. S. Yin Ho, MD, MBA. It has a foreword by J.D. Kleinke and an endorsement from Stéphane Bancel, CEO of Moderna. Available on Amazon.

After eight conversations covering the original failures of health IT, the AI opportunity, the Flatiron blueprint, the seventeen year gap, and what big tech got wrong — I saved the final question for something different. I asked Dr. Yin Ho the question she had never been asked. Her answer is the most important thing in this entire series.

The Final Question

Is there a question you wish someone would ask you — something that gets at the heart of what you most want health IT founders to understand — that nobody has asked yet?

The question I keep hoping someone will ask — but no one has asked yet — is: how can we help physicians gain back their agency inside a world of technology from which they have been separated for so long? And how can we help patients support a system that supports both them and their physicians?

Why This Is the Most Important Question in Health IT

After decades of health IT investment — EHRs, interoperability standards, AI platforms, and digital health tools — the most important question still has not been asked. Not what tools should we build or how do we scale faster, but how do we give physicians and patients back their agency?

The Shift Dr. Ho Is Asking For

How can we encourage health IT companies to start thinking about listening to their physicians and listening to their patients? I think that is the more interesting question.

This is not about better interfaces alone. It is about designing systems where the physician’s clinical judgment is supported rather than overridden by documentation requirements, and where the patient’s understanding of their own condition and preferences shapes their care rather than being recorded as a data point.

A Connection Worth Making

When Dr. Ho shared this answer, it reminded me of a conversation with Dr. John White, CEO of the American Medical Association — who is similarly focused on bringing AI into healthcare while keeping patients and physicians at the center. Two leaders, approaching the problem from different angles, arriving at the same north star: technology in healthcare must ultimately serve the physician‑patient relationship.

What This Means for Founders — The North Star of This Series

Build technology that listens. Build products that restore agency. Build companies that put the physician and patient back at the center — not as data sources, not as end users, not as transaction nodes in a billing system — but as the reason the entire system exists.

About This Series

This is the eighth and final episode of Conversations at the Edge of Health IT — a series of conversations with Dr. Yin Ho, author of Rushing Headlong: Health IT's Legacy and the Road to Responsible AI. All eight episodes are available at www.thesymetrique.com.

Thank you to Dr. Yin Ho for your time, your honesty, and your extraordinary book. The health IT founder community is better for this conversation.

About Symetrique

Symetrique is a healthcare analytics and intelligence company serving pharma and biotech, payers, health systems, and medical device companies. We combine healthcare analytics, commercial analytics, market research, and real world evidence into an integrated service offering — giving our clients the complete intelligence picture they need to make faster, more confident decisions.

Our approach is built on a simple belief: data without context is noise. Symetrique brings together the structured and unstructured data layers of healthcare — claims, clinical records, physician observations, and patient journeys — and applies AI to turn them into actionable intelligence across the full commercial and clinical lifecycle.

This blog is part of our ongoing series — Conversations at the Edge of Health IT — featuring insights from leading voices in healthcare, technology, and life sciences. Subscribe at www.thesymetrique.com to receive each new episode directly.

www.thesymetrique.com | Madhav Kathikar, Founder & CEO | 224-566-9880

About Rushing Headlong

Rushing Headlong: Health IT's Legacy and the Road to Responsible AI is written by Dr. S. Yin Ho, MD, MBA. It has a foreword by J.D. Kleinke and an endorsement from Stéphane Bancel, CEO of Moderna. Available on Amazon.

We built a $5 trillion healthcare system around billing codes — not patient outcomes. That is not an opinion. That is history. And understanding exactly how it happened is the foundation of every meaningful health IT product being built today.

The Interview

I recently sat down with Dr. Yin Ho — physician, health IT entrepreneur, and author of Rushing Headlong: Health IT's Legacy and the Road to Responsible AI — to ask the questions every health IT founder needs answered. This is the first in a series of eight conversations covering the original failures of health IT, the AI opportunity that may finally change everything, and the hard lessons that only come from building inside this world for decades.

In this first conversation, Dr. Ho traces the single most consequential decision that locked the entire US digital health infrastructure into a billing-first architecture. Her diagnosis is both historical and urgent — because you cannot fix a system you do not understand.

What the Industry Got Wrong

The story of health IT is not a technology story. It is an incentives story. The systems that were built — the EHRs, the clearinghouses, the clinical documentation tools — were designed primarily to process payments. Not to understand patients. Not to generate knowledge. Not to support clinical decisions. To process billing transactions.

This was not an accident. It was the rational outcome of a set of economic incentives that rewarded transaction processing and penalized everything else. The HITECH Act of 2009 accelerated EHR adoption — but it accelerated adoption of a billing-first architecture that was already broken. It digitized the wrong thing.

The Physician as the Missing Link

The insight from Dr. Ho that surprised me most in this conversation was not about technology. It was about people.

The physician is the most trusted voice in healthcare. Patients make decisions based on what their physician tells them. They enroll in trials their physician recommends. They trust the treatments their physician prescribes. And yet — the physician is simultaneously the most excluded actor in the systems built around them.

The systems were not designed to support clinical judgment. They were designed to capture billable activity. The physician became a data entry operator in a billing system — spending more time documenting transactions than caring for patients. This is not a technology failure. It is a design failure. And it has consequences that ripple through every corner of health IT.

What This Means for Founders

If you are building in health IT, understanding this history is not optional. The incumbent systems you are navigating — the EHRs, the payer networks, the claims infrastructure — were all designed around billing-first logic. The data they produce reflects that logic. The gaps in that data reflect that logic. The resistance you encounter when trying to access or use that data reflects that logic.

You are not fighting a technology problem. You are navigating an incentives problem that has been decades in the making. The founders who succeed in health IT are the ones who understand this distinction — and build accordingly.

About This Series

This is Episode 1 of 8 from my conversation with Dr. Yin Ho. Over the next four weeks I will be publishing one post every few days — each one a focused insight for founders, builders, and operators working in health IT, pharma, and digital health.

Rushing Headlong is available on Amazon. It has a foreword by J.D. Kleinke — one of the founders of Truven Health Analytics — and an endorsement from Stéphane Bancel, CEO of Moderna. It is one of the most honest books written about health IT in a generation.

Every year, healthcare generates billions of data points. Almost none of it is clinically useful. This is not a storage problem or a technology problem. It is a structural problem that generative AI may finally be capable of solving.

A Founder's Frustration

I have spent months trying to build reliable real world evidence using claims data. And I kept hitting the same wall. The data was there — billions of data points. But the context was missing. The patient journey was missing. What looked like a complete dataset was actually a skeleton — transactions without story, codes without meaning, records without the human being at the center of them.

I was not alone in this frustration. In my conversation with Dr. Yin Ho, she named this problem with a precision I had not heard before — and explained why generative AI may finally offer a path through it.

The Claims Data Problem

Claims data is the most abundant data generated by the US electronic health system. It is also the most structurally limited for clinical purposes. Claims data was designed to process payments — to document what services were rendered, to whom, and at what cost. It was never designed to capture the clinical reality of what happened to a patient.

Turning claims data into clinically useful real world evidence has historically required something expensive, slow, and deeply human: trained clinical personnel doing what Dr. Ho calls detective work — reading through unstructured physician notes, piecing together a patient's longitudinal journey line by line, filling in the gaps that billing codes leave behind.

Turning clinical data into meaningful research datasets has been challenging and has required expensive, manual abstraction by clinically trained personnel.

Where the Intelligence Actually Lives

The richest clinical intelligence in the US healthcare system is not in the structured data fields of an EHR. It is in the unstructured narrative sections — the physician notes, the clinical observations, the treatment rationale, the patient reported symptoms that never make it into a billing code.

This is where the detective work happens. A physician documents not just what they did but why. They note the patient's hesitation about a treatment, the comorbidity that complicated the decision, the observation that did not fit the expected pattern. This information is irreplaceable. And it has been almost entirely inaccessible to researchers and analysts because no system could read it at scale. Until now.

What Generative AI Changes

Generative AI can be applied to augment and accelerate this abstraction process — reading unstructured clinical notes at scale, extracting the clinical context buried inside them, and building the accurate longitudinal datasets that real world evidence analysis demands.

This is not AI replacing clinical judgment. The human abstractor — the trained clinical professional doing the detective work — remains essential for quality validation and contextual interpretation. But AI can dramatically reduce the time, cost, and scale constraints that have made clinical abstraction prohibitively expensive for all but the largest pharma companies.

What This Means for Founders

If you are building in RWE, clinical intelligence, or health data — the abstraction problem is your opportunity. The companies that crack affordable, scalable, AI-assisted clinical abstraction will unlock a dataset that pharma, researchers, and payers have been trying to access for decades.

The technology is finally ready. The question is whether you can build the trust, the physician relationships, and the quality validation processes that make AI-abstracted clinical data credible enough to act on.

Seventeen years. That is how long it takes — on average — for a clinical discovery to reach a patient. A researcher identifies something that could save lives. And the average patient waits seventeen years to benefit from it. Understanding why this happens — and where it can be shortened — is the most important strategic question in health IT today.

Not One Wall — Many Small Ones

Most people assume the seventeen year gap is about clinical trials. The phases, the approvals, the regulatory pathway. But in my conversation with Dr. Yin Ho, she reframed it completely.

It is about a feedback loop. The ability for us to get information about what is happening is not very fast.

The seventeen year gap is not one bottleneck. It is dozens of small processes — each taking a few years — stacked on top of each other in a linear pathway. Discovery. Development. Phases I, II, III. Regulatory approval. Market launch. Post-market observation. Adoption. Every step moves slowly because the data feeding each step is slow, incomplete, or locked away where nobody can access it.

The Microprocess Opportunity

The path to closing the gap is not a single breakthrough. It is additive — shrinking multiple microprocesses simultaneously. Identifying patients faster. Documenting care more completely. Feeding information back about what is actually working in the real world — without waiting years to run another specific trial.

Dr. Ho frames it this way: whatever gets observed in clinical practice, how fast does that information come back to the researchers asking the question? And how well is it documented such that analysis becomes possible without designing a new study from scratch? Those two questions — speed of feedback and quality of documentation — are where the seventeen years hide.

The Data Layer Insight

But the insight from this conversation that stayed with me longest was not about feedback loops or microprocesses. It was about architecture.

For decades, clinical care and clinical research have been treated as two separate worlds. Different applications. Different workflows. Different teams. The EHR on one side. The clinical trial data capture system on the other. And we have been thinking about them at the application level — as if the interface is the problem.

There is a data layer that exists under both that actually could be used for more than one purpose.

The same data that documents a physician caring for a patient can fuel a clinical trial. The same patient journey that informs a treatment decision can generate real world evidence. The infrastructure already exists. We have simply been thinking at the wrong level — at the application level rather than the data level.

What This Means for Founders

If you are building at the intersection of clinical care and clinical research, the data layer is your strategic foundation. The founders who win in this space will not be the ones who build better EHR interfaces or better trial management tools. They will be the ones who build intelligence on top of the shared data layer that already exists — making it possible for the same data to serve multiple purposes simultaneously.

That is how you close the seventeen year gap. Not with a single breakthrough. With a fundamentally different way of thinking about data.

Less than 5% of eligible patients ever enroll in a clinical trial. Not because they are unwilling. Because they cannot find them. And the system was never designed to help them look.

The Question I Brought to Dr. Ho

I went into this conversation with a specific reframing I wanted Dr. Yin Ho to react to. Most companies in clinical trial recruitment solve this problem from the pharma side — finding patients for trials. What if the more powerful and more ethical solution is the reverse — finding trials for patients?

Her response was immediate and unequivocal.

I think you might end up with a better result if you shift the paradigm.

Why the Current Model Has Failed Patients

Patients have technically had access to lists of clinical trials for years. ClinicalTrials.gov has existed since 2000. And yet the enrollment rate has barely moved. Why?

Dr. Ho identifies three structural failures. First — patients often do not know trials exist at all. The information has not reached them. Second — they are almost entirely dependent on their physician to tell them, and most physicians do not have easy access to trial information either. Third — even when patients find a trial, understanding whether they qualify — the inclusion and exclusion criteria — is a complex, technical task that most patients cannot navigate alone.

The entire clinical trial information infrastructure was built in a B2B direction — to serve pharma, biotech, and CROs. The information flows from sponsors to sites to investigators. It almost never flows directly to patients or their community physicians. And so the patient — the most essential participant in the entire enterprise — has been an afterthought in a system designed around finding them rather than serving them.

The Community Physician — The Insight I Did Not See Coming

I expected Dr. Ho to validate the patient-first paradigm. What I did not expect was the insight she added unprompted — and it is the most important thing she said in this entire clip.

Most patients are not seen in academic centers. Most patients are seen in their community physicians around wherever they live. The information asymmetry has to be solved in two places — one for your patients and two for your physicians.

The information asymmetry in clinical trials is not just a patient problem. It is equally a community physician problem. The specialist at a major academic medical center knows about the trials happening in their institution. The primary care physician in a community practice — who sees the patient every six months and has the relationship that makes a trial recommendation trusted and actionable — has almost no access to that information.

We have been trying to solve the last mile of clinical trial recruitment by going directly to patients. But the more powerful last mile runs through the community physician who already has the patient's trust.

What This Means for Founders

If you are building in clinical trial recruitment or patient engagement, the paradigm shift Dr. Ho validates is your strategic direction. But the harder and more important insight is the community physician angle. The academic medical center market is crowded and well-served. The community physician market — where most patients actually receive care — is almost entirely untouched.

The founder who builds the intelligence layer that reaches community physicians — giving them real-time awareness of trials their patients may qualify for — will unlock an enrollment pipeline that the industry has never been able to access. That is the white space. That is the opportunity.