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Using natural language processing to classify social work interventions.

Abdulaziz Tijjani Bako1, Heather L Taylor, Kevin Wiley

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Area of Science:

  • Health Informatics
  • Social Work in Healthcare
  • Natural Language Processing

Background:

  • Healthcare organizations increasingly employ social workers to address patient social needs.
  • Social work activities are often documented as unstructured text in electronic health records (EHRs), hindering analysis.
  • Automated methods are needed to efficiently process and analyze social work interventions within EHRs.

Purpose of the Study:

  • To develop and apply natural language processing (NLP) and machine learning (ML) algorithms for extracting and classifying social work interventions from EHR notes.
  • To categorize social work interventions based on a derived 10-category classification scheme.
  • To enable better measurement and analysis of social work activities in healthcare settings.

Main Methods:

  • Secondary data analysis of 815 social work encounter notes from a federally qualified health center's EHR system.
  • Development of a 10-category classification scheme for social work interventions based on literature review.
  • Application of NLP and ML algorithms (including SVM, logistic regression, and naive Bayes) to classify interventions.

Main Results:

  • 73.4% of social work notes contained at least one intervention.
  • The most frequent interventions were care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services (17.1%), and supportive counseling (15.3%).
  • High-performing classification algorithms achieved accuracies up to 0.97 (kernelized SVM).

Conclusions:

  • NLP and ML offer a viable approach for automated identification and classification of social work interventions in EHRs.
  • Healthcare administrators can use this automated approach to understand patient social needs and inform resource allocation and staffing decisions.
  • Improved insights into social interventions can lead to better management of social work services and patient care.