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Updated: Jun 26, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident

Jiyoun Song1, Maxim Topaz2, Aviv Y Landau3

  • 1Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, PA, USA.

Journal of the American Medical Directors Association
|May 16, 2024
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Summary

An NLP algorithm effectively identifies home care patients lacking advance directives or surrogates (INEADS). This tool aids agencies in proactive advance care planning for at-risk individuals.

Keywords:
Advance care planninghealthcare proxyhome health carenatural language processingsocial isolationunbefriended

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Geriatric Care

Background:

  • Home health care patients at risk of incapacitation without advance directives or surrogates (INEADS) require timely intervention.
  • Advance care planning is crucial for patients who may lose decisional capacity.

Purpose of the Study:

  • To develop and validate natural language processing (NLP) algorithms.
  • To identify home care patients lacking advance directives, family, or social contacts for surrogate decision-making.

Main Methods:

  • Cross-sectional study utilizing electronic health records from a large NYC home health agency.
  • Developed NLP algorithms to analyze free-text clinical notes for relationship and advance directive information.
  • Validated algorithm performance against clinician review (precision, recall, F-score).

Main Results:

  • The NLP algorithm demonstrated excellent performance (average F-score = 0.91).
  • High accuracy in identifying close relationships (F-score = 0.99) and advance directives (F-score = 0.86).
  • Algorithm flagged relevant text in 22% of notes and over 98% of patient episodes.

Conclusions:

  • Feasibility of an automated screening algorithm for identifying at-risk patients (INEADS) is established.
  • This tool can support home health agencies in advance care planning.
  • Facilitates clinician support for patients needing advance care planning.