Stanford Health Care recently launched a pilot of a new AI-powered automation designed to support quality teams in identifying patients whose clinical documentation may indicate a Surgical Site Infection, or SSI. The solution uses a large language model to review clinical notes and surface patients for retrospective review by quality specialists.

This pilot adds to our growing ChatEHR Automation suite and reflects SHC’s commitment to integrating AI into workflows that support high-quality, patient-centered care.

Supporting Timely, Efficient Infection Surveillance

The automation reviews documentation for patients who have undergone colorectal, or COLO, surgeries and generates a list of patients whose notes may meet SSI criteria. Importantly, the model does not make clinical judgments — it simply flags cases for further review, helping prioritize efforts and reduce the cognitive and administrative burden on reviewers.

The output of the system feeds directly into an existing retrospective report used by quality reviewers, streamlining their workflow and supporting timely, accurate reporting in alignment with national standards. The quality reviewers provide feedback on the patients flagged for monitoring and future improvement.