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Original Clinical Research

Implementing and Analyzing the Advantages of Voice AI as Measurement-Based Care (MBC) to Address Behavioral Health Treatment Disparities among Youth in Economically Disadvantaged Communities

By
Yared Alemu Orcid logo ,
Yared Alemu

TQIntelligence, Inc. , Atlanta, Georgia , United States

Elizabeth Cárdenas Bautista Orcid logo ,
Elizabeth Cárdenas Bautista

Morehouse Healthcare , Atlanta, Georgia , United States

Sarah Vinson Orcid logo ,
Sarah Vinson

Department of Psychiatry & Behavioral Sciences,Child and Adolescent Psychiatry Fellowship Program, Morehouse School of Medicine , Atlanta , United States

Patrick Ohiomoba Orcid logo ,
Patrick Ohiomoba

Harvard John A. Paulson School of Engineering and Applied Sciences , Cambridge, Massachusetts , United States

Nakia Melecio Orcid logo ,
Nakia Melecio

Mental Health Advocate , Addis Ababa , Ethiopia

Hermon Amare Orcid logo ,
Hermon Amare

Quarry Lane School , San Ramon, California , United States

Jaya Vivian Orcid logo ,
Jaya Vivian
Harshitha Karippara Orcid logo
Harshitha Karippara

Abstract

Only 20% of behavioral health providers use measurement-based care [MBC].1 Two reasons for MBC’s low uptake outcomes are a need for stronger consensus regarding optimal use (in both frequency and consistency) and the absence of a widely utilized data analytics infrastructure. TQIntelligence has built and implemented a measurement-based system for community behavioral health providers, which includes the use of a novel AI-enabled voice algorithm designed to provide psychiatric decision and triaging support to pediatric populations.  The success of the implementation and related outcomes varied depending on the organization and the therapist's involvement in the pilot.2 This paper will contribute to the literature on measurement care and its effectiveness. It also challenges the dominant narrative that such systems are too complicated and ineffective in community behavioral health that serve children and adolescents from low-income communities.

References

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Caulley D, Alemu Y, Burson S, Cárdenas.
2.
Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I, et al. An AI-based Decision Support System for Predicting Mental Health Disorders. Information Systems Frontiers. 2023;25(3):1261–76.
3.
Roberts AL, Gilman SE, Breslau J, Breslau N, Koenen KC. Race/ethnic differences in exposure to traumatic events, development of post-traumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States. Psychological Medicine. 2011;41(1):71–83.
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Rariden C, SmithBattle L, Yoo JH, Cibulka N, Loman D. Screening for Adverse Childhood Experiences: Literature Review and Practice Implications. The Journal for Nurse Practitioners. 2021;17(1):98–104.
5.
Hughes K, Bellis MA, Hardcastle KA, Sethi D, Butchart A, Mikton C, et al. The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. The Lancet Public Health. 2017;2(8):e356–66.

Citation

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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