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Updated: May 28, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among
Myrthe van den Broek1,2, M Claire Greene3, Anthony F Guevara1
1Research and Development, War Child Alliance, Amsterdam, The Netherlands.
Abstract:
This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6-18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. This mixed-methods study was conducted from January to May 2023 in Palorinya refugee settlement in Uganda. We evaluated (1) the added value of the CCDT+ in improving the accuracy of detection and mental health service utilization compared to standard CCDT, and (2) implementation outcomes of the CCDT+. Of the 1026 children detected, 801 (78%) sought help, with 656 needing mental health care (PPV = 0.82; 95% CI: 0.79, 0.84). The CCDT+ significantly increased detection accuracy, with 2.34 times higher odds compared to standard CCDT (95% CI: 1.41, 3.83). Additionally, areas using the CCDT+ had a 2.05-fold increase in mental health service utilization (95% CI: 1.09, 3.83). The CCDT+ shows promise as an embedded quality-optimization process for the detection of mental health problems among children and enhance help-seeking, potentially leading to more efficient use of mental health care resources.
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