Developing an OMOP-Standardized Prostate Cancer Database and Improving Data Quality Using NLP and PSA-Based Algorithms

Objective: To develop and evaluate an Observational Medical Outcomes Partnership (OMOP) standardized prostate cancer database from the University of Texas Medical Branch (UTMB) Epic Electronic Health Record (EHR) and improve data quality using natural language processing (NLP) and prostate-specific antigen (PSA) based algorithms. Materials and Methods: We built a data pipeline to transform UTMB Epic EHR data from 2010 to 2021 into OMOP Common Data Model (CDM) v5.4. Data quality was assessed by comparing the OMOP-standardized data with Galveston Cancer Registry data using availability agreement, Cohen's kappa, and Intraclass Correlation Coefficient. NLP was used to extract PSA, Gleason score, and cancer stage from clinical text, and PSA-based algorithms were used to identify missing treatment and biochemical recurrence. Results: We extracted 815 analytic cases from UTMB EHR. Among them, 700, or 85.9%, were complete and concordant with the cancer registry. PSA showed excellent value agreement. Structured Gleason score and stage data were sparse, with fewer than 20 cases, but NLP greatly improved capture. Treatment agreement was good compared with the cancer registry and improved slightly for radical prostatectomy after applying a PSA-based algorithm. Using PSA trajectories, we identified 60 cases of biochemical recurrence. Discussion: The OMOP-standardized data from UTMB showed good agreement with the cancer registry. However, structured EHR fields incompletely captured diagnosis, pathology, and treatment details. NLP and PSA-based algorithms substantially improved data capture. Manual review also revealed errors in registry data, showing that OMOP-standardized EHR data can complement and help improve cancer registry quality. Conclusion: OMOP standardization combined with NLP and PSA-based algorithms improved prostate cancer data quality and research readiness.