Layer Health, a health technology company focused on transforming chart review using artificial intelligence, has secured $21 million in a Series A funding round. The round was led by Define Ventures, with contributions from other notable investors.
Layer Health, founded by AI and clinical leaders from institutions such as MIT, Harvard, Microsoft, and Google, is tackling the challenge of extracting usable insights from fragmented medical records. The company’s AI platform uses large language models (LLMs) trained on comprehensive patient data. This system automates the review and interpretation of clinical data with clinician-level accuracy.
How Layer Health’s AI Works
Layer Health’s AI distinguishes itself from traditional software solutions by reasoning across a patient’s entire medical chart, much like a clinician would. This approach allows the AI to navigate complex scenarios, helping health systems reduce costs and improve patient care. It also opens up new revenue opportunities.
Use Cases and Value Proposition
Layer Health’s platform offers significant value across several chart review use cases, including:
- Quality Reporting & Clinical Registries: Automating data extraction for improved accuracy and efficiency in quality measurement programs and clinical registries.
- Clinical Research & Real-World Data Abstraction: Accelerating patient cohort identification for research studies and streamlining real-world evidence generation.
- Hospital Operations & Revenue Cycle Management: Enhancing clinical documentation integrity (CDI) and coding processes to optimize reimbursement, reduce denials, and improve financial performance.
- Clinical Decision-Making & Patient Care Optimization: Providing clinicians and care teams with real-time, AI-powered insights for personalized, evidence-based treatment decisions.
Addressing Chart Review Inefficiencies
The manual process of chart review is currently time-consuming and prone to human error. Healthcare professionals spend countless hours analyzing records, a costly inefficiency. It can strain health systems financially, hinder clinicians’ ability to practice efficiently, and potentially affect patient outcomes.
Early manual chart reviews can also lead to inaccuracies when reporting to clinical registries.
Early Success and Future Plans
Layer Health’s AI has already shown significant returns for its early ecosystem partners:
- Health systems: Froedtert & the Medical College of Wisconsin health network has streamlined quality data abstraction, reducing the time required by more than 65%.
- Life science and clinical research partners: Layer Health’s technology facilitates real-world data (RWD) abstraction to support clinical research. Layer Health completed RWD extraction for dozens of new patients in collaboration with a leading cancer organization in a few hours, a process that previously took over a year.
With this new funding, Layer Health plans to expand its offerings, enhance its AI models, and deepen partnerships with health systems and other stakeholders.
“Medical chart review has historically been a costly and time-consuming challenge for health systems, yet scaling it is key to decreasing much of the friction in healthcare. That’s why we’re committed to revolutionizing this process and to building technology that providers trust, empowering them to enhance care quality, drive financial growth and identify new revenue opportunities,” said David Sontag, Ph.D., CEO and Co-Founder of Layer Health and a MIT professor.
“We are thrilled to partner with these stellar investors who deeply understand healthcare, our long-term vision and our technology’s transformative power. By reducing administrative burdens and streamlining inefficiencies, we allow providers to focus on their ultimate priority – delivering exceptional patient care.”
