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Automatically Identifying Financial Stress Information from Clinical Notes for Patients with Prostate Cancer.

V Zhu1, L Lenert1, B Bunnell1

  • 1Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, United States.

Cancer Research and Reports
|February 6, 2024
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Summary
This summary is machine-generated.

Financial stress is common in cancer patients but often undocumented. Natural language processing (NLP) can accurately identify financial stress from clinical notes, improving patient care and support.

Keywords:
Electronic health recordFinancial stressNatural language processingProstate cancer

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Area of Science:

  • Oncology
  • Health Informatics
  • Social Determinants of Health

Background:

  • Financial stress is a significant social determinant impacting cancer patients due to high treatment costs.
  • The National Academy of Medicine recommends discussing cost concerns to improve shared decision-making and identify financial aid.
  • Financial stress is under-documented in clinical practice and research, hindering patient support.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) approach for identifying financial stress in prostate cancer patients' clinical narratives.
  • To assess the feasibility of using NLP to extract financial stress data from electronic health records (EHRs).

Main Methods:

  • Developed and iteratively refined an NLP algorithm and financial stress lexicon using a training dataset of 3,138 prostate cancer patients.
  • Utilized a separate test dataset of 1,057 patients to evaluate the NLP algorithm's performance, including negation detection.
  • Employed domain experts for manual review of NLP-identified cases to validate accuracy.

Main Results:

  • The NLP algorithm identified financial stress in 6.0% of the training sample and 6.2% of the test sample.
  • Performance metrics included a precision of 0.86, recall of 1.0, and F-score of 0.92 when validated by domain experts.
  • Key terms identified included 'financial concerns,' 'unable to afford,' and 'insurance issue.'

Conclusions:

  • Financial stress is infrequently documented in EHRs, both in structured data and clinical narratives.
  • NLP demonstrates high accuracy in extracting financial stress information from available clinical notes.
  • This technology can help uncover and address financial stress among cancer patients, facilitating better support and care.