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Psychiatric Risk Assessment from the Clinician's Perspective: Lessons for the Future
Alex S Cohen1, Taylor Fedechko2, Elana K Schwartz2
1Department of Psychology, Louisiana State University, 236 Audubon Hall, Baton Rouge, LA, 70803, USA. acohen@lsu.edu.
Community Mental Health Journal
|June 3, 2019
Summary
Clinicians struggle with accurate risk assessment for serious mental illnesses (SMIs). They lack standardized methods and awareness of new biobehavioral technologies, hindering effective patient care and risk prediction.
Area of Science:
- Psychiatry and Behavioral Sciences
- Digital Health
- Clinical Psychology
Background:
- Accurate risk-state prediction in Serious Mental Illnesses (SMIs) is crucial for mitigating societal impact.
- Current risk assessment methods are often inaccurate and lack standardization among clinicians.
- Mobile technologies offer potential for novel biobehavioral data collection to improve assessments.
Purpose of the Study:
- To evaluate current risk assessment practices among clinicians serving SMI populations.
- To identify barriers and facilitators for improving risk assessment accuracy and efficiency.
- To gauge clinician awareness and potential adoption of emerging biobehavioral technologies.
Main Methods:
- Survey of 90 multi-disciplinary clinicians working with SMI patients across diverse settings.
- Assessment of current risk assessment procedures, use of standardized measures, and time allocation.
- Inquiry into clinician perspectives on improving risk assessment and awareness of technological advancements.
Main Results:
- Significant variability reported in clinicians' risk assessment procedures.
- Low endorsement and confidence in existing standardized risk measures.
- Clinicians reported spending insufficient time on assessments and lacked awareness of objective, ambulatory biobehavioral technologies.
Conclusions:
- Current risk assessment practices for SMIs are inconsistent and suboptimal.
- There is a critical need to educate clinicians on utilizing objective, technology-driven data for improved risk prediction.
- Integrating innovative biobehavioral monitoring could significantly enhance the accuracy and efficiency of SMI risk assessment.