Abstract: Harnessing Natural Language Processing to Measure Person-Centered Care in Behavioral Health Settings (Society for Social Work and Research 27th Annual Conference - Social Work Science and Complex Problems: Battling Inequities + Building Solutions)

All in-person and virtual presentations are in Mountain Standard Time Zone (MST).

SSWR 2023 Poster Gallery: as a registered in-person and virtual attendee, you have access to the virtual Poster Gallery which includes only the posters that elected to present virtually. The rest of the posters are presented in-person in the Poster/Exhibit Hall located in Phoenix A/B, 3rd floor. The access to the Poster Gallery will be available via the virtual conference platform the week of January 9. You will receive an email with instructions how to access the virtual conference platform.

Harnessing Natural Language Processing to Measure Person-Centered Care in Behavioral Health Settings

Friday, January 13, 2023
Camelback A, 2nd Level (Sheraton Phoenix Downtown)
* noted as presenting author
Victoria Stanhope, PhD, Associate Professor, New York University, New York, NY
Gahwan Yoo, MA, PhD Student, New York University, NY
Daniel Baslock, MSW, PhD Student, New York University, New York, NY
Background and Purpose: While receiving widespread endorsement both in medical and behavioral health care settings, operationalizing and documenting person-centered care has been a challenge. Most Person-Centered Care (PCC) measures rely on self-report which can be unreliable, prompting the need for objective and feasible ways to document PCC. Clinical narrative notes provide a rich, but relatively untapped, source of unstructured data that can be an important indicator of PCC. Natural language processing (NLP) is an artificial intelligence method that has the capability to convert clinical narratives embedded within the EHR into structured data. The purpose of this study was to utilize NLP to develop a measure of PCC for behavioral health care settings.

Methods: We utilized a supervised ontology-based information extraction approach, an NLP method which has been widely used in medical settings. The dataset (or corpus) is clinic visit notes completed by providers in a community mental health center. These notes are narrative documents completed when the provider has face-to-face in person contact with a service user to develop, revise or document progress on the service plan. Following the Noy & McGuiness ontology development 101 procedure, we developed an ontology to capture the domains of person-centered care. Ontology structures were developed using Protégé software and frequency analysis and validation were performed using Python 3.0.

Results: The ontology development process involved the following steps 1) we grounded the domains of PCC in the items of the Person-Centered Care Planning Assessment Measure and the Person-Centered Care Planning intervention, and the broader person-centered care literature, 2) we enumerated the terms under the high-level concepts according to the sources, 3) we determined hierarchies and relationships between PCC concepts and language, 4) we supplemented the list of terms that could belong to each concept through term frequency-inverse document frequency analysis and bag of bigrams model of the corpus of clinical visit notes, 5) we mapped identified terms to their concepts using the ontology, and 6) we validated the ontology by examining the degree of representability on our collected clinical visit notes.

Conclusions and Implications: This study demonstrated that complex and abstract practices such as PCC can be amenable to NLP methods to generate objective measures. This deductive ontological approach has the potential to provide a scalable method that mines clinically rich unstructured data documenting service processes to measure aspects of service quality in behavioral health settings, and can be utilized both in research and quality improvement efforts.