Case Study: Optimizing EHR
Data Completeness with On-Premise AI (Aspirin Example)

This case study evaluates the impact of leveraging free-text data from electronic medical records (EMRs) using large language models (LLMs) to enhance the completeness and validity of medication-use information. Using aspirin intake during pregnancy as a case study, we demonstrate how excluding unstructured data sources leads to significant gaps in data completeness, underscoring the critical role of AI-powered data extraction in real-world evidence generation. This study was honored with the “Best Podium Research Presentation” award at ISPOR US 2024.

This case study provides insight into:

  • Gaps in EMR data completeness when unstructured sources are excluded
  • AI and NLP’s role in enhancing medication-use records
  • Aspirin intake patterns in pregnancy

 

Link to ISPOR Research abstract

Aspirin Case Study Mockup

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