Building trustworthy AI starts with better clinical data

Summary

Health systems are investing heavily in AI, but many are building on fragmented clinical data. Clinical decisions, population health reporting, quality metrics, and AI outputs are only as reliable as the data behind them.

IMO Health analysis shows that nearly one in five problem list entries may be outdated, duplicative, or clinically irrelevant, quietly introducing risk across the data ecosystem that these initiatives depend on.

This session will examine how poor clinical data quality creates downstream consequences across care delivery, reporting, and AI performance. We’ll also explore how leading organizations are improving data integrity at scale to reduce manual effort, strengthen reporting accuracy, and create more reliable, AI ready clinical data.

Objectives:

Examine how poor clinical data quality impacts clinical decision making, quality reporting, population health initiatives, and AI performance.
Identify common sources of inaccurate, duplicative, or outdated problem list data and the downstream risks they create across the healthcare data ecosystem.
Get practical insights on how to reduce manual data cleansing efforts, strengthen reporting accuracy, and strategies for maintaining reliable AI-ready clinical data.