How Healthcare and Life Sciences AI Decisions Can Hold Up

An Architectural Framework for Drug Safety, Population Health and Better Outcomes Across Federal and State Programs

Federal healthcare programs are deploying AI for drug safety surveillance, population health, better health outcomes and quality measurement, fraud detection and clinical trial review.

In this webinar, Progress Federal Solutions will present an architectural framework that makes AI decisions accurate, consistent and defensible across a wide range of federal healthcare and life sciences work. The session demonstrates how querying across both structured and unstructured data in the same request, enriching data semantically, governing decision logic and tracing provenance over time work together as a single integrated foundation rather than as separate point solutions.

Four domains of worked examples:

• Drug safety signal detection draws on FAERS, Sentinel, published literature, and FDA review history in a single query
• Population health cohort identification integrates clinical, social services, environmental, and eligibility data across fragmented source systems
• Better health outcomes and quality measurement demonstrate how the same architecture supports accurate, consistent measure calculation and risk stratification across federal and state health programs
• Healthcare fraud detection applies semantic alignment and temporal pattern recognition to identify anomalies that cross provider, payer, and program boundaries

The session closes with practical guidance on where to start and how the Progress Data Platform provides this foundation across federal civilian healthcare and life sciences programs.