The healthcare AI revolution is generating headlines daily. AI systems that detect cancer earlier than radiologists. Language models that summarize patient histories in seconds. Algorithms that predict adverse events before they happen.

The promise is extraordinary. The reality is messier.

Because every healthcare AI company faces the same fundamental problem: getting access to the data that makes AI useful.

The Data Gap Nobody Talks About

Talk to any healthcare AI founder and they'll tell you about their models, their accuracy metrics, their clinical validation studies. Ask them about data access and the tone changes.

The truth is that most healthcare AI operates on surprisingly limited data:

Claims data tells you what was billed, not what actually happened clinically. It's useful for population health and risk stratification, but it lacks the clinical nuance needed for personalized recommendations.

Wearable data captures vital signs and activity patterns; valuable, but disconnected from clinical context. Your Apple Watch doesn't know your medication history or lab results.

Research datasets enable model development but don't reflect real-world deployment. Training on curated data, then deploying against messy clinical reality, creates performance gaps.

The data AI systems actually need - comprehensive, real-time clinical records - remains locked inside EHR systems.

Why EHR Data Is So Hard to Get

Healthcare data access has historically required one of three approaches, each with significant limitations:

Provider partnerships give you access to specific health system data, but require lengthy negotiations, IRB approvals, and data use agreements. You're limited to that system's patient population and subject to their governance.

Research collaborations provide curated datasets for model development but create distance from clinical deployment. Models trained on research data must be validated again in production environments.

Synthetic data enables certain types of model development without privacy concerns, but synthetic records can't replace real clinical data for applications that need to understand individual patients.

What's been missing is a scalable way to access patient data across multiple health systems, authorized by patients themselves, in real-time.

The Regulatory Environment Just Changed

The HTI-5 proposed rule released in December 2025 represents a significant shift in how regulators view AI and health data.

For the first time, federal rule-making explicitly addresses autonomous AI accessing patient health information. The proposed rule updates definitions to "allow autonomous AI to retrieve and share health data" and articulates a goal of advancing "AI-enabled interoperability solutions."

This isn't a coincidence. Regulators see AI as healthcare's future. They're actively creating pathways for AI systems to access the data they need, through patient authorization.

The patient-directed data access framework established by the 21st Century Cures Act was designed for consumer health apps. But the same infrastructure enables AI applications. If a patient authorizes your AI system to access their records, providers must make that data available through standardized APIs.

Patient-Directed Access: The AI Data Layer

Here's what patient-directed data access enables for AI healthcare companies:

Comprehensive Clinical Context

Instead of operating on claims data or wearable signals, your AI has access to the patient's full clinical picture: diagnoses, medications, lab results, allergies, immunizations, care team notes. The same data their doctor sees.

Real-Time Information

Patient-directed APIs connect to live EHR systems. Your AI operates on current data, not snapshots from six months ago. Medication changes are reflected immediately. New lab results are available as soon as they're filed.

Cross-System Aggregation

Patients receive care across multiple health systems. Patient-directed access lets you aggregate records from wherever the patient has been treated, creating a longitudinal view that no single provider has.

Patient Trust

When patients explicitly authorize data sharing, the privacy and consent dynamics shift. Your AI isn't accessing data through a side door; the patient chose to share it. That consent relationship creates trust that enables deeper engagement.

The Infrastructure Problem

Understanding the opportunity is easy. Executing is harder.

Implementing patient-directed data access requires integrating with EHR systems that each have their own APIs, authentication flows, and data formats. The FHIR standard helps, but doesn't eliminate implementation complexity.

For AI companies, this creates a build-vs-buy decision: invest engineering resources in healthcare data infrastructure, or partner with companies that have already built it.

We've spent over two years building integrations across Epic, Cerner, athena, eClinicalWorks, NextGen, Flatiron, and Modernizing Medicine. Our API provides access to patient-authorized clinical data in a normalized format so AI companies can focus on models and algorithms rather than data plumbing.

The AI Healthcare Stack

The healthcare AI companies that will succeed aren't just those with the best models. They're the ones that solve the full stack: data access, clinical integration, regulatory compliance, and user experience.

Data access is foundational. Without comprehensive clinical data, even sophisticated AI systems operate with one hand tied behind their back. With it, they can deliver on the promise that's generating all those headlines.

The regulatory tailwinds are here. The patient-authorization framework exists. The infrastructure is available.

The data problem has a solution. The question is whether you'll access it.