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This study developed a robust method to identify patients receiving radiation therapy using health records. This approach enhances research into radiation treatment outcomes and patient cohorts.

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Area of Science:

  • Medical Informatics
  • Oncology Research
  • Health Data Science

Background:

  • Identifying patients who received radiation therapy is crucial for outcomes research.
  • Existing methods may not fully capture all patients undergoing radiation oncology care.
  • Structured and unstructured health data offer potential for improved patient cohort identification.

Purpose of the Study:

  • To develop and validate a methodology for identifying patients who received radiation therapy.
  • To leverage structured and semistructured health data for precise patient phenotyping.
  • To facilitate future research on radiation therapy treatment outcomes.

Main Methods:

  • Retrospective cohort study of Veterans from 2014-2023.
  • Utilized administrative codes for referrals, encounters, and billing for radiation oncology care.
  • Employed keyword searches in clinical notes for unstructured data analysis.
  • Validated the algorithm against a chart-reviewed cohort.

Main Results:

  • Identified a cohort of 589,318 Veterans with radiation oncology care.
  • 355,276 Veterans had specific radiation therapy delivery codes.
  • Common treatments included IGRT, 3D-CRT, IMRT, and SRS/SBRT.
  • Algorithm demonstrated strong concordance with chart review validation.

Conclusions:

  • Automated extraction from medical records can effectively identify radiation therapy patient cohorts.
  • This algorithm enables precise phenotyping of radiation therapy cases.
  • Enhanced identification will significantly improve understanding of patient outcomes in radiation oncology.