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Summary
This summary is machine-generated.

We developed EHRsupport, an electronic health record (EHR) tool, to identify social support in breast cancer patients. This algorithm accurately extracts crucial social support data from clinical notes, aiding in identifying those needing assistance.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Social Sciences

Background:

  • Social support is crucial for breast cancer management.
  • Electronic Health Records (EHRs) contain valuable patient data.
  • Quantifying social support from EHRs presents a challenge.

Purpose of the Study:

  • To develop EHRsupport, a novel EHR-based measure of social support.
  • To create structured variables (modules) from EHR data for social support assessment.
  • To establish a clinical tool for identifying breast cancer patients with inadequate social support.

Main Methods:

  • A natural language processing (NLP) algorithm was developed using clinical notes from 7,989 invasive breast cancer patients.
  • The NLP algorithm processed 565,258 EHR notes and 68,760 patient messages.
  • Modules were developed from unstructured and structured EHR data, tuned, updated, and tested against chart reviews.

Main Results:

  • Eleven modules were identified and developed from unstructured EHR data, covering aspects like partner status, living situation, and social isolation.
  • Module data availability varied significantly, from 1.4% (social isolation) to 92.0% (spouse/partner status).
  • The developed modules demonstrated high accuracy (0.81-0.95) and performance metrics (F1 scores, precision, recall) for available data.

Conclusions:

  • The EHRsupport algorithm effectively identifies social support information within EHR data.
  • This facilitates the development of a clinical tool to pinpoint breast cancer patients experiencing low social support.
  • The tool has the potential to improve patient care by enabling timely interventions.