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Automated methods can effectively identify heritable cancer risk factors in electronic health records (EHRs), reducing the chance of missed information. This approach offers a rapid review of patient histories, improving genetic risk evaluation.

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Area of Science:

  • Medical Informatics
  • Genetics
  • Oncology

Background:

  • Clinicians may miss critical patient information within extensive electronic health records (EHRs).
  • Unrecognized EHR risk factor information, such as heritable cancer predispositions, poses a diagnostic challenge.
  • A prior study revealed half of eligible women with EHR risk factors were not referred for genetic counseling.

Purpose of the Study:

  • To compare automated methods (OCR and NLP) against manual review for identifying heritable breast and ovarian cancer risk factors in EHRs.
  • To assess the efficacy of automated tools in detecting genetic risk information often overlooked by clinicians.

Main Methods:

  • Evaluated automated methods including Amazon Textract, CLAMP, and a custom Java application.
  • Compared automated tool accuracy against a criterion standard of physician chart review.
  • Focused on identifying risk factors for heritable breast and ovarian cancer within scanned EHR documents.

Main Results:

  • Automated methods successfully identified the majority of cancer risk factor information missed by manual clinician review.
  • The automated approach demonstrated high accuracy in detecting significant risk factors within EHRs.

Conclusions:

  • Automated EHR analysis offers an accurate and rapid way to review patient histories for heritable cancer risk factors.
  • Enhancements in analyzing handwritten notes, tables, and informal language could further improve automated detection.
  • Implementing automated methods can improve the identification of patients eligible for genetic counseling and risk evaluation.