Matching Patients with Clinical Guidelines

Healthcare systems, payers, and medical societies invest massive effort to maintain evidence-based clinical guidelines for a variety of conditions. However, when patients are in the hospital, often clinicians just don’t have the time to research or read these guidelines, leading to major gaps in how consistently they are applied. Recent advances in Medical AI can shortcut this problem by automatically reading the full history of a given patient, finding the most recent and relevant guideline for their clinical history, and presenting it in context.

This session will walk through a solution architecture for an end-to-end solution that does this, using a state-of-the-art healthcare-specific LLM, that can be deployed locally within an organization’s security perimeter to ensure privacy, compliance, and the ability to read organization-specific guideline documents. We’ll also show how to handle formatting of clinical guideline documents that are challenging to general-purpose LLMs like flowcharts, decision trees, and visual decision tables.