A framework for precision “dosing” of mental healthcare services: algorithm development and clinical pilot

International Journal of Mental Health Systems

Research

Macayla L. Donegan¹  Jonathan Knights¹  Victoria Bangieva¹  Jacob Shen¹  Justin Baker¹  Michela Passoni¹  Audrey Klein¹  Holly DuBois¹ 

[1] Mindstrong, Inc., 101 Jefferson Drive, Suite 228, 94025, Menlo Park, CA, USA;

关键词: Mental healthcare;    Systems dynamics;    Indirect response models;    Digital healthcare;    Telehealth;    Learning healthcare system;    Measurement-based care;    Precision medicine;   

DOI  :  10.1186/s13033-023-00581-y

 received in 2022-12-19, accepted in 2023-05-18,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOne in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.MethodsHistorical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as “session dosing”: 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.ResultsThe developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.ConclusionsIt is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

【 授权许可】

CC BY   
© The Author(s) 2023

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