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Journal Article

Citation

Stokes DC, Kishton R, McCalpin HJ, Pelullo AP, Meisel ZF, Beidas RS, Merchant RM. Psychiatr. Serv. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, American Psychiatric Association)

DOI

10.1176/appi.ps.202000267

PMID

unavailable

Abstract

OBJECTIVE: Previous studies indicate that patients' satisfaction with mental health care is correlated with both treatment outcomes and quality of life. The aims of this study were to describe online reviews of mental health treatment facilities, including key themes in review content, and to evaluate the correlation between narrative review themes, facility characteristics, and review ratings.

METHODS: United States National Mental Health Services Survey (N-MHSS) facilities were linked to corresponding Yelp pages, created between March 2007 and September 2019. Correlations between review ratings and both machine learning-generated latent Dirichlet allocation topics and N-MHSS-reported facility characteristics were measured by using Spearman's rank-order correlation coefficient. Significance was defined by a Bonferroni-adjusted p<0.001.

RESULTS: Of 10,191 unique mental health treatment facilities, 1,383 (13.6%) had relevant Yelp pages with 8,133 corresponding reviews. The number of newly reviewed facilities and the number of new reviews increased throughout the study period. Narrative topics positively correlated with review ratings included caring staff (Spearman's ρ=0.39) and nonpharmacologic treatment (ρ=0.16). Topics negatively correlated with review ratings included rude staff (ρ=-0.14) and safety and abuse (ρ=-0.14). Of 126 N-MHSS survey items, 11 were positively correlated with review rating, including "outpatient mental health facility" (ρ=0.13), and 33 were negatively correlated with review rating, including accepting Medicare (ρ=-0.21).

CONCLUSIONS: Narrative topics provide information beyond what is currently collected through the N-MHSS. Topics associated with positive and negative reviews, such as staff attitude toward patients, can guide improvement in patients' satisfaction and engagement with mental health care.


Language: en

Keywords

Natural language processing; Patient satisfaction; Quality of care

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