Amplifying HEOR Studies Using Real-World Electronic Health Record Data

Since the adoption of electronic health records (EHRs), healthcare institutions have collected a massive amount of broad, real-world clinical data. This data has the potential to power retrospective HEOR studies of unprecedented scale and depth, but it can be challenging to harness.

This presentation will showcase how nference leverages language models and AI-assisted curation to de-identify and harmonize EHR data from leading health systems. nference's nSights platform makes the complete patient journey readily accessible for research, enabling breakthrough discoveries and collaborations as well as the creation of new venture companies that harness various data modalities.

nference will highlight specific HEOR studies that were successfully executed using the nSights platform. Examples will include the quantification of healthcare resource utilization (HCRU), determination of risk factors for high HCRU, and stratified outcomes analysis based on disease stage, each derived from broad EHR data (diagnosis, lab data, AI-analyzed pathology reports, and biopsy data).