Caption: A 2022 review of mental health-related calls for service (CFS) in a rural Virginia region, reveals a 10% increase in call volume over three years, with the sharpest growth (47.6%) in rural townships. The review highlights the need for comprehensive local data systems mapping, noting that over 30% of mental health crisis calls were not reported in call type search.
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There is a significant gap in real-world evidence (RWE) supporting the effectiveness of many public sector and community-based programs. As a result, critical frontline initiatives often lag in building a data-driven evidence base. To fully realize the benefits of real-world evidence, agencies must prioritize end-to-end compliance and ethical data management.
CAPTION: AI-POWERED INSIGHTS FOR PUBLIC SAFETY
Justice-involved individuals should receive the appropriate level of care, provided in the least restrictive way possible, facing mental or other behavioral health crises. Prompt identification of risks and needs, such as access to public documents, housing security, family and social support challenges, and peer associations, can be critical to ensuring the appropriate level of care is provided. Here, we explore ways AI can assist in the facilitation of risk and needs-based assessment in the field to promote positive outcomes.
Tracking the law enforcement response to mental health crises using standardized data fields is crucial improve the hidden costs associated with crisis and in supporting transformative research and evaluation of the effectiveness of interventions. Robust methods for measuring and monitoring law enforcement response often involve a combination of data collected in response to 9-1-1 calls for service, specialized field-based performance tracking, and program evaluation. General industry guidance emphasizes the importance of maintaining clearly defined and standardized data collection protocol.
