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
DOI:
https://doi.org/10.30953/thmt.v9.506Keywords:
AI, artificial intelligence, community behavioral health, digital mental health, health equity, Measurement-Based Care, MBC, pediatric trauma, TQIntelligenceAbstract
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.
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Copyright (c) 2024 Yared Alemu, PhD, Elizabeth Cárdenas Bautista, PhD, Sarah Vinson, MD, Patrick Ohiomoba MS, Nakia Melecio PhD, Hermon Amare MD, Jaya Vivian, Harshitha Karippara
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