The development of an Australian national classification system for social work practice in health care

Using natural language processing to classify social work interventions

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.

The why of practice: utilizing PIE to analyze social work practice in Australian hospitals

This research used a collaborative approach to gain a comprehensive, quantitative understanding of the breadth and depth of the social work role in health care. Data was collected from individual interviews with all employed hospital social workers (N = 120) across five Melbourne, Australia health networks about their most recently completed case. This data was coded using a revised version of the Karls and Wandrei (1994) Person-in-Environment (PIE) tool to retrospectively analyze the reasons for social work involvement over the course of the case. The findings demonstrate that the hospital social work role is multidimensional across a number of domains but centers predominantly on assisting clients and their significant others with issues of altered social roles and functioning; particularly in relation to role responsibility, dependency, and managing associated role-change losses. The findings of this study will assist hospital social workers, managers, and academics to better describe and effectively undertake this complex work. These findings will also assist in the development of professional training and education to up-skill social workers who operate within this complex setting.

Client satisfaction and empowerment through social work intervention

This study examines the utility of satisfaction questionnaires in gauging the effectiveness of social work services in a paediatric hospital setting. Participants completed an empowerment scale before seeing a social worker. Approximately four weeks later, participants completed the empowerment scale again, at which time they also completed a satisfaction questionnaire. The difference between the pre- and post-test empowerment scores was compared with the satisfaction scores, and the influence of some demographic and intervention variables was examined. The results indicated that there was no significant relationship between participants' reported level of satisfaction with the social work service provided and the change in participants' empowerment scores before and after intervention. Most demographic and intervention variables tested did not yield any significant associations with satisfaction or change in empowerment. However, it was found that those who received both counselling and practical assistance (rather than only one or the other) and those with a higher level of education were more likely to report an increase in their level of empowerment after receiving social work intervention. This study lends further support to the contention that satisfaction questionnaires alone may not provide reliable information with regard to the utility and effectiveness of paediatric hospital social work intervention.