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Area of Science:

  • Oncology
  • Health Informatics
  • Data Science

Background:

  • Transition to electronic health records (EHRs) in oncology necessitates efficient data extraction for research.
  • Accurate capture of comprehensive cancer treatment history from EHRs is crucial for clinical research.

Purpose of the Study:

  • Develop and validate a data-agnostic algorithm to identify systemic therapy regimens.
  • Enable accurate data retrieval for patients with breast, colorectal, and lung cancer.

Main Methods:

  • Iterative algorithm development and validation using a cohort of cancer patients.
  • Performance assessment across multiple patient groups to ensure robustness.

Main Results:

  • Achieved high sensitivity (97.2%-100%) with 0% false-positive rate for first-course therapy.
  • Algorithm accurately matched systemic therapy courses and regimens 88%-100% of the time with EHR data.

Conclusions:

  • The validated algorithm facilitates characterization of complete systemic therapy treatment.
  • Enables comparative effectiveness studies on novel and standard regimens in real-world settings.