Layer Health Secures $21M to Scale AI Chart Review: What It Means for Clinical Data Integrity and Hospital Operations

Layer Health, a healthcare AI company founded by former MIT, Harvard, Microsoft, and Google technologists, has raised $21 million in Series A funding to expand its chart abstraction and clinical inference platform. The round was led by Define Ventures, with participation from Flare Capital, GV (formerly Google Ventures), and MultiCare Capital Partners—underscoring the platform’s relevance across health systems, digital health, and life sciences.

At the heart of Layer Health’s offering is an AI engine that combines large language models (LLMs) with longitudinal patient data to analyze both structured and unstructured clinical information. The company claims its platform can achieve clinician-level accuracy in automating chart review—a labor-intensive process that costs health systems millions each year and delays everything from reimbursement to quality reporting.

The Chart Review Bottleneck

Manual chart review remains one of the most costly and error-prone processes in healthcare operations. Nurses, medical assistants, and coding specialists spend thousands of hours each year reviewing provider notes, lab results, and historical encounters. According to a 2022 Health Affairs study, more than 40% of U.S. health systems report delays in quality reporting or clinical research due to limited abstraction capacity.

Layer Health’s LLM-based platform automates the extraction of clinically relevant information with traceable accuracy. Every AI-generated conclusion is accompanied by references to the original source text—an essential feature for adoption in regulated environments like hospitals and CROs.

Early Results: Efficiency and Accuracy

The company reports strong early outcomes. Froedtert & the Medical College of Wisconsin reduced abstraction time for clinical registries by more than 65% using Layer Health’s platform, freeing up staff to focus on patient-facing roles. In collaboration with a leading cancer research institute, the platform processed real-world data abstraction for dozens of patients in hours—a process that traditionally took more than a year (Layer Health Press Release, 2025).

The technology is already used for a range of use cases, including:

• Automating clinical registry submissions and quality measure reporting
• Streamlining clinical research data abstraction for real-world evidence studies
• Enhancing CDI and coding accuracy for revenue cycle optimization
• Providing synthesized clinical histories to support physician decision-making

AI That Reasons Like a Clinician?

Unlike traditional NLP tools that rely on fixed rules or taxonomies, Layer Health’s engine is built to infer clinical meaning in context—synthesizing across different parts of the chart and adapting to nuanced scenarios. CEO David Sontag, an MIT professor and healthcare AI researcher, describes the platform as capable of “reasoning like a clinician.” The company’s focus on explainability and auditability is designed to build trust among both clinicians and compliance teams.

According to Sontag, the endgame is simple: “reduce administrative burden, improve care quality, and unlock revenue opportunities.” The company’s tech-forward approach has already attracted partnerships with health systems, life sciences companies, and data infrastructure platforms.

Implications for Health Systems and Tech Vendors

Hospitals and health systems navigating staffing shortages and revenue pressures are increasingly turning to automation for core operations like CDI, quality reporting, and coding. Layer Health’s approach—tying AI abstraction directly to clinical and financial workflows—puts it in direct competition with more established players like 3M M*Modal, Health Language (Wolters Kluwer), and Nuance (Microsoft).

The funding also signals increasing VC appetite for applied AI in healthcare—especially tools that drive measurable ROI in documentation, reimbursement, and compliance-heavy processes. As chart review automation evolves from point solution to platform capability, enterprise IT teams will need to ensure seamless integration with EHR systems, clinical data warehouses, and research platforms.

AI-First Abstraction May Soon Be the Norm

Layer Health’s $21 million raise is not just a vote of confidence in AI—it’s a recognition that the manual chart review model is unsustainable. As abstraction moves from clipboard to algorithm, the winners in this space will be those who can deliver accuracy, traceability, and workflow alignment at scale. The next few years will likely determine whether AI-driven abstraction becomes a core layer of enterprise health IT—or another hyped experiment that failed to meet the demands of clinical operations.