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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
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Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience
James M Gold1, Philip R Corlett2, Gregory P Strauss3
1Department of Psychiatry and Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD.
Schizophrenia Bulletin
|July 11, 2020
Summary
New behavioral measures may improve early psychosis risk detection. This approach enhances specificity and accessibility for identifying individuals at clinical high risk (CHR) for psychosis, potentially improving illness outcomes.
Area of Science:
- Clinical Neuroscience
- Cognitive Neuroscience
- Psychiatry
Background:
- Early identification and intervention in clinical high risk (CHR) for psychosis can improve illness course.
- Current CHR identification relies on interviews with limited specificity (15-30% conversion rate) and accessibility.
Purpose of the Study:
- Introduce a novel CHR assessment approach using advanced behavioral measures.
- Enhance the specificity and accessibility of psychosis risk assessment.
- Develop more accurate predictors of psychosis conversion.
Main Methods:
- Utilize new behavioral measures derived from clinical and computational cognitive neuroscience.
- Assay cognitive mechanisms and neural systems underlying psychosis symptoms.
- Develop internet-deployable, low-cost assessments.
Main Results:
- Hypothesize enhanced sensitivity and specificity compared to interview methods.
- Expect improved positive predictive value for psychosis conversion.
- Anticipate increased accessibility of CHR assessments.
Conclusions:
- Novel behavioral measures offer a promising avenue for improving CHR assessment.
- This approach addresses limitations of current interview-based methods.
- Potential for widespread, cost-effective implementation to aid early psychosis intervention.
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