Models & Researchmental healthdigital remote monitoringalgorithmic prediction
People with Psychosis Evaluate Algorithmic Relapse Prediction
5.7
Researchers conducted a qualitative study of people with psychosis to assess attitudes toward algorithm-based relapse prediction and data sharing. The study examines views on using digital remote monitoring (DRM) systems and algorithmic tools to prevent relapses, focusing on patient perspectives that can shape DRM design, consent, and data-sharing practices.
Key Points
- 1Patients describe attitudes toward algorithm-based relapse prediction and sharing personal monitoring data.
- 2Preventing psychosis relapse motivates development and testing of DRM systems for early detection.
- 3Patient views inform ethical DRM design, consent models, and acceptable data-sharing practices.
Scoring Rationale
Patient perspectives directly inform the design and ethical deployment of algorithmic relapse-prediction and DRM systems, making this study moderately important for practitioners building clinical monitoring tools.
Sources
Public references used for this report.
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