Essential insights for improving healthcare data quality in AI
Overcome adoption barriers with explainable AI
Discover how to bridge the trust gap by creating transparent models. We'll show you how to build AI that clinicians and risk leaders can confidently rely on for patient care.
Master semantic grounding and normalization
Learn the critical role of data quality, semantic grounding, normalization, and value sets. You'll understand how to standardize fragmented electronic medical record data so your models can process real-time insights without making dangerous assumptions.
Ensure safety and reliability at scale
Understand how to ensure your AI is reliable, safe, and understandable. By integrating governed clinical logic, you can protect sensitive patient data and maintain strict regulatory adherence.
Learn from real-world AI failures
Gain insights into real-world AI failures and how to avoid them. We examine case studies where poor data led to incorrect clinical recommendations, highlighting exactly how a strong data foundation prevents these costly mistakes.
The cost of ignoring AI in healthcare data quality
Many organizations underestimate the semantic readiness required to make AI work at scale. Recent industry studies and our own controlled experiments reveal the stark reality of unprepared data:
- Data fragmentation and poor quality remain the biggest barriers to AI success.
- Up to 95 percent of generative AI initiatives fail to reach sustained operational use.
- In a recent test identifying undiagnosed patients, an off-the-shelf AI model only identified four out of 13 charts correctly. When we provided the model with semantic grounding and clinical logic, it correctly identified 12 out of 13 charts.
- Nearly 42 percent of open-source value sets involve missing codes, which quietly breaks AI capabilities and basic reporting.
