Oatmeal Health is an AI-driven cancer screening platform focused on lung cancer screening, partnering with Federally Qualified Health Centers (FQHCs), hospitals, and payers to close screening gaps in underserved communities.
Purpose The platform aims to address the problem of lung cancer screening failing due to fragmented systems: FQHCs have patient populations, hospitals have scanning equipment, and payers have incentive motivations. Oatmeal Health connects these three parties, with the goal of enabling eligible patients to be identified and guided to screening, thereby achieving earlier cancer detection.
Key Capabilities
- Patient identification: LungIQ can identify patients eligible for lung cancer screening, including mining buried smoking history from free-text notes, with a patient identification AUROC of 0.96 and 9 times faster than manual chart review.
- Multilingual guidance: Guides patients to lung screening in over 70 languages.
- AI second opinion: LungAI assigns a malignancy risk score from 0 to 100 to each nodule on every low-dose lung CT. The score appears in the viewer radiologists already use to support their interpretation, helping reduce missed cancers and unnecessary biopsies.
How to Use Eligible patients can be referred to partner imaging centers. The product can be integrated into existing systems: LungIQ is also used to identify eligible patients within the user's own system, and LungAI's scores are displayed directly in the viewer radiologists already use. The company states that no new coverage policies or billing models are needed.
Use Cases Suitable for FQHCs, health systems, and health plans. The company states that screening has been conducted in underserved FQHC communities in the United States, where screening gaps are the largest.
Limitations and Information Not Yet Provided Regarding specific pricing, costs, deployment steps, or integration details, the available information does not provide them. Some metrics mentioned by the company (such as the HEDIS lung screening measure expected to be adopted by NCQA, and outcome models built with actuaries) are planning or anticipated in nature, and specific implementation details are not yet provided.