Introducing Layer Health
Today, we are thrilled to introduce Layer Health, a healthcare AI company spun out of MIT and backed by $4 million in funding from GV (Google Ventures), General Catalyst, and Inception Health. We are committed to solving the information problem in healthcare.
The information problem begins the moment a patient visits a health institution. Every interaction generates a trail of breadcrumbs, including clinical notes, lab results, and patient messages. When combined, these breadcrumbs tell a rich story of a patient’s health journey and clinical care. However, the most nuanced and valuable data is unstructured and hard to understand, often inaccessible without a team of nurses and data scientists. This issue consumes significant resources across the healthcare ecosystem and makes it difficult to scale new businesses and health innovations.
Layer Health’s first product, Distill, tackles the information problem by using AI to quickly perform any clinical, administrative, or research task that requires chart review from unstructured data. This includes registry submissions, quality measurement, curation of real-world evidence, clinical document improvement, and revenue cycle management. Distill integrates into existing products and workflows, ingesting clinical notes and analyzing them at scale. Behind the scenes, Layer Health’s machine learning (ML) algorithms leverage the power of large language models (LLMs) to deliver accurate results without the need for labeled data, reducing development time from months to as little as a day. Distill also learns and adapts from customer interactions, creating highly efficient customer-specific models fine-tuned for specific use cases.
Our Customers
A number of beta customers are using Distill, and we are excited to onboard more soon.
xCures, a health technology company with a rich repository of clinical data, is using Distill to organize and structure health data for more precise cancer treatment recommendations and efficient clinical trial matching. Using our language models trained and validated on xCures' unique data, the company can more accurately extract intricate and nuanced details from patient medical records. Distill stands to significantly expedite xCures' and their customers’ workflows in curating real-world evidence, ultimately enhancing patient care.
The Froedtert & the Medical College of Wisconsin health network, is using Distill to support quality improvement efforts. Our AI platform will supercharge the organization’s nurse abstraction team during chart review, enabling them to quickly and effectively find and submit data to clinical registries. Clinical registries are essential for benchmarking across clinical specialties and identifying areas for improving the quality of care. We’re excited to work with them as they utilize our technology to help transform healthcare.
Our Commitment to AI Safety
Safely deploying AI in healthcare is of the utmost importance, going well beyond security and privacy. Our previous work has given us both deep expertise and humility. We believe that LLMs, although incredibly powerful, are not a panacea for healthcare and that AI will make mistakes. This is why we are building transparency into Distill from the start. For example, the platform enables customers to understand the evidence for every prediction, and we have a clinical team dedicated to validating our models across diverse datasets and clinical use cases. Additionally, our research team, at the forefront of advancing AI in healthcare, is also developing the methods necessary to ensure AI safety.
Our Team
Members of the Layer Health team have worked together for nearly a decade, earning recognition as leaders in AI and ML. With ties to MIT and Harvard, we have been at the forefront of using LLMs to solve the information problem for healthcare systems, providers, payers and researchers. The founding team includes:
David Sontag, CEO, a leader in AI for healthcare and MIT professor with over 100 published papers in AI and ML.
Divya Gopinath, an ML engineer and architect focused on trustworthy AI, previously a founding engineer at TruEra and a researcher at MIT.
Luke Murray, an expert in human-computer interaction who previously built MedKnowts while a researcher at MIT and Know Your Data at Google.
Monica Agrawal, a pioneer in large language models and recent PhD from MIT, who previously built some of the first ML models for Flatiron Health.
Steven Horng, an emergency physician and clinical informatician at Harvard Medical School with over 15 years of experience deploying ML in live clinical settings.
Want to learn more?
Reach out to us at hello@layerhealth.com or visit our website.
For job or hiring inquiries, please reach out to recruiting@layerhealth.com.
