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Supporting the Capture of Social Needs Through Natural Language Processing.

Lewis J Frey1, Chanita Hughes Halbert2, Christopher D Blasy2

  • 1From the Ralph H. Johnson Veterans Affairs Medical Center (VAMC), Charleston, SC (LJF, CDB); University of Southern California, Los Angeles, CA (CHH) lewis.frey@va.gov.

Journal of the American Board of Family Medicine : JABFM
|June 8, 2023
PubMed
Summary
This summary is machine-generated.

Physicians document social needs in clinical notes, but unstructured data hinders care. Natural language processing can extract social determinants of health from electronic health records, improving documentation consistency.

Keywords:
Natural Language ProcessingSocial Determinants of Health

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Area of Science:

  • Clinical Informatics
  • Public Health
  • Health Services Research

Background:

  • Social factors significantly influence patient morbidity and mortality.
  • Family physicians commonly document social needs in clinical notes.
  • Unstructured data on social factors in electronic health records (EHRs) impedes clinical action.

Purpose of the Study:

  • To propose and evaluate natural language processing (NLP) for identifying social needs within EHRs.
  • To enable structured and reproducible documentation of social determinants of health (SDOH).

Main Methods:

  • Utilizing NLP techniques to parse unstructured clinical notes.
  • Developing algorithms to extract predefined social needs categories.
  • Assessing the consistency and reproducibility of extracted information.

Main Results:

  • NLP successfully identified social needs from unstructured clinical text.
  • The proposed method facilitates structured data capture without increasing physician workload.
  • Potential for improved identification and addressing of patient social needs.

Conclusions:

  • NLP offers a viable solution to overcome unstructured data challenges in documenting social needs.
  • This approach can enhance clinical decision-making by providing structured SDOH data.
  • Automated social needs extraction can streamline documentation and improve patient care.