Using natural language processing to classify social work interventions

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.

Study design: Secondary data analysis of a longitudinal cohort.

Methods: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme.

Results: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92).

Conclusions: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs.