Working Towards the Development and Implementation of Precision Mental Healthcare: An Example

Leonard Bickman’s (2020) Festschrift paper in the special issue “The Future of Children’s Mental Health Services” on improving mental health services is an impressive reflection of his career, highlighting his major insights and the development of mental health services research as a whole. Five maj...

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Published in:Administration and policy in mental health and mental health services research 2020-09, Vol.47 (5), p.856-861
Main Authors: Lutz, Wolfgang, Schwartz, Brian, Martín Gómez Penedo, Juan, Boyle, Kaitlyn, Deisenhofer, Anne-Katharina
Format: Article
Language:English
Subjects:
Artificial intelligence
Child & adolescent mental health
Clinical Psychology
Clinical research
Clinical trials
Cognitive development
Commentary
Dropping out
Ecological momentary assessment
Evidence based research
Feedback
Health Administration
Health Informatics
Health research
Health services
Measurement
Medical research
Medicine
Medicine & Public Health
Mental health
Mental health care
Mental health services
Psychiatry
Public Health
Treatment methods
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Summary:Leonard Bickman’s (2020) Festschrift paper in the special issue “The Future of Children’s Mental Health Services” on improving mental health services is an impressive reflection of his career, highlighting his major insights and the development of mental health services research as a whole. Five major difficulties in this field’s current research and practice are attentively delineated: poor diagnostics, measurement problems, disadvantages of randomized controlled trials (RCTs), lack of feedback and personalized treatments. Dr. Bickman recommends possible solutions based on his extensive experience and empirical findings. We agree with his thoughts and illustrate how we, challenged with the same problems, have attempted to develop clinically informed research and evidence-based clinical practice. A comprehensive feedback system that deals with the aforementioned problems is briefly described. It includes pre-treatment recommendations for treatment strategies and an empirically informed dropout prediction based on a variety of data sources. In addition to treatment recommendations, continuous feedback as well as individualized treatment adaptation tools are provided during ongoing therapy. New projects are being implemented to further improve the system by including new data assessment strategies and sources, e.g., ecological momentary assessment (EMA) and automated video analysis.
ISSN:0894-587X
1573-3289
DOI:10.1007/s10488-020-01053-y