Using natural language processing to classify social work interventions.

Journal: The American journal of managed care
PMID: 33471465

Abstract

OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms.

Authors

  • Abdulaziz Tijjani Bako
    Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University-Purdue University Indianapolis, 1050 Wishard Blvd, Indianapolis, IN 46202. Email: atbako@iu.edu.
  • Heather L Taylor
  • Kevin Wiley
  • Jiaping Zheng
  • Heather Walter-McCabe
  • Suranga N Kasthurirathne
    Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.
  • Joshua R Vest
    Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.

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