Treating the Root Cause

We founded MTN to build the future’s clinical AI. We started this year convinced that no clinical AI will matter at scale until something deeper is fixed.

We started MTN with a thesis any clinician would recognize. The bedside is full of decisions that depend on patterns no single person can hold in working memory. Vitals, waveforms, labs in motion, and health records encode much of that pattern. Modern models can learn from them when the data is treated with care.

So we built clinical AI on top of that data. Real models, trained on real cohorts. We cleared the institutional review boards at two major health systems. Cleared their IT and security reviews after that. Each step took longer than the science had, and each step taught us more about the system. We were standing at the door, approvals in hand, ready to run.

We expected the constraint to be model quality. It was not.

The lesson came from the part of the job that did not look like medicine.

Most of the engineering effort behind any clinical AI deployment is not the model. It is the data underneath. Every site looks different. Every integration breaks again when an upstream system changes a field, a code set, or a vendor. EHR upgrades shift what a clinical observation looks like at the storage layer. New billing rules redefine how the same encounter is coded. Long before a model could run live, the integration questions had already decided whether it ever would. The schema work, the billing-code reconciliation, the reimbursement pathway, the data-access layer, all of it sat upstream of the deployment we never had to actually run to understand.

By the end of a typical month, we found ourselves spending far less time designing models than navigating billing codes and the surrounding apparatus of reimbursement and operations. The work that determined whether a deployment lived or died was rarely the model itself. It was the reconciliation of the data deciding whether the model’s recommendations could ever be paid for. The science was not the bottleneck. The data layer was, and the data layer decided what could be built and what could be paid for.

That experience repeated often enough to stop being an anecdote. It was the structural shape of the work.

If the data layer decides what can be deployed, then the question is what is wrong with the data layer. The answer is older than any of our models, and it is hiding in plain sight on every health system’s income statement.

Roughly a third of every healthcare dollar in the United States is spent on administration rather than care.[1] That is the binding constraint on American medicine. Even the best clinical AI lives downstream of a financial system that cannot afford to absorb new costs without offsetting them elsewhere. A model whose validation looks strong on retrospective data but adds friction to billing, prior authorization, or reconciliation will not survive the budget conversation that follows the approvals. It will be politely retired, framed as a learning, and quietly replaced by whatever protects the operating margin.

The problem is not primarily clinical. It is financial. And the financial problem is not primarily about insurance design or pricing or any of the other places the policy conversation usually settles. It is about the cost of running the system itself: the cost of making the data agree with itself across the dozens of platforms a single patient interaction touches.

The administrative tax is not a separate disease from the deployment problem. It is the same disease in a different costume.

Look at where the money actually goes. Denial rework. Reconciliation across payers and providers. Parallel platforms that grew through acquisition, and the perpetual schema mapping between them. Back-office teams whose job is to translate one system’s representation of an event into another system’s representation of the same event.

These are the same underlying condition: data that does not agree with itself, in volumes large enough that humans become the integration layer. Fragmented data is what makes administration expensive. Fragmented data is also what makes clinical AI hard to deploy. One disease, two symptoms.

Once we saw it that way, the next move was structural rather than strategic. The technology we had built to make clinical data usable was already pointed at the harder and larger version of the same problem. Per-source semantic annotation. Canonical concepts emerging across many annotated schemas. Integration or migration without requiring raw-data movement. We had been solving the financial problem all along, in the narrow shape of a clinical deployment.

So we took the engine and pointed it at the financial layer. We call it MTN Data Foundry.

• It makes fragmented data operable by adding a semantic description to each source.

• Each source is annotated at the schema level on its own terms, using evidence from the schema itself rather than a canonical mapping imposed from above.

• Shared semantic concepts emerge across many annotated schemas, either through cross-source alignment or by mapping to a target schema when one is specified.

• Integration and migration follow from those mappings without requiring a raw-data central store.

The clinical work taught us the shape of the problem. Data Foundry is the shape of the answer.

The same fragmented-data pattern shows up wherever companies grow by acquiring others, regardless of industry. The seams left behind by an acquisition rarely disappear; they get papered over by integration projects that ship just enough harmonization to close the books and then never quite finish. Years later, an operator inherits an environment where the same customer or the same transaction or the same asset is represented in three subtly incompatible ways, and the cost of disagreement compounds quietly until a migration, an audit, or a new system of record forces it into the open.

Working with adjacent verticals to healthcare is the fastest way to harden the product. Cycles are shorter outside healthcare. Migrations close in months instead of years. Procurement is faster. Regulatory exposure is narrower. Each engagement sharpens the semantic-annotation primitives and the canonical-concept extraction that the longer healthcare engagements will eventually depend on. The matured version comes back into the healthcare deployments with more reps behind it and fewer assumptions baked into it.

When administrative drag eases:

• The marginal dollar moves toward care.

• Clinicians spend their day with patients rather than with documentation.

• Plans approve in minutes rather than weeks. Audits become a reading exercise rather than a forensic one.

• New therapies reach the people who need them because the system around them can afford to deploy them.

• The deployments themselves stop being heroic projects that require a special team and start being routine ones that any operator can run.

None of that requires a new clinical breakthrough. It requires the system underneath the breakthroughs to compute. The clinical AI we set out to build still gets shipped. It gets shipped on top of a system that can pay for it, in a country whose health spending finally tracks its health outcomes.

This is bigger than one company. The operators, consultants, plans, platforms, and investors working on any part of this fabric are the ones who decide whether the next decade looks like the last. We followed our own work upstream until we found the prerequisite, and the prerequisite turned out to be more interesting than what we set out to build. If you are working on any part of this problem, in healthcare or in an industry that looks like it, we would like to be in the conversation with you.