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Objectives <p>We evaluate the predictive accuracy of machine learning algorithms that forecast individual-level risk of behavioral health-involved encounters with police (BHIP) among community members with prior contact with first responders.</p> Methods <p>We linked arrest and ambulance data from one city (May 2016-October 2017) for 199,679 unique individuals. The first 12 months provided predictive features; the subsequent 6 months served as the outcome window. We compared machine learning models utilizing 283 features to a baseline Prior High-User (PHU) approach based solely on prior BHIP counts.</p> Results <p>Machine learning achieved positive predictive values (PPV) of 50.8%, 20.2%, and 7.5% when identifying the top 0.1% (n=199), 1% (n=1,996), and 5% (n=9,983) highest-risk individuals, representing improvements of 20%, 57%, and 44% over PHU. Critically, 28% of individuals in the ML-identified top 1% had no prior BHIP during the identification period, enabling true prevention. ML models demonstrated superior sensitivity, identifying 26.7% of all future BHIP events when targeting the top 1%, versus 17.1% for PHU. A simplified four-feature model using ambulance data maintained comparable performance. ML’s improved PPV increases statistical power for intervention studies: a 200-person trial using ML-identified participants could detect a 38% reduction in BHIP, while achieving equivalent power with PHU would require 310 participants (55% larger sample).</p> Conclusions <p>Machine learning substantially improves identification of individuals at BHIP risk, including those without prior events. This enhanced PPV can enable more efficient targeting of preventive services and make rigorous intervention research more feasible by increasing statistical power. Simplified models using only medical-services data suggest practical implementation pathways, though findings should be validated in other jurisdictions to ensure generalizability.</p>

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Predicting behavioral health-involved police encounters: A machine learning approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Zubin Jelveh,
  • Andrea Tentner,
  • Xander Beberman,
  • Harold Pollack

摘要

Objectives

We evaluate the predictive accuracy of machine learning algorithms that forecast individual-level risk of behavioral health-involved encounters with police (BHIP) among community members with prior contact with first responders.

Methods

We linked arrest and ambulance data from one city (May 2016-October 2017) for 199,679 unique individuals. The first 12 months provided predictive features; the subsequent 6 months served as the outcome window. We compared machine learning models utilizing 283 features to a baseline Prior High-User (PHU) approach based solely on prior BHIP counts.

Results

Machine learning achieved positive predictive values (PPV) of 50.8%, 20.2%, and 7.5% when identifying the top 0.1% (n=199), 1% (n=1,996), and 5% (n=9,983) highest-risk individuals, representing improvements of 20%, 57%, and 44% over PHU. Critically, 28% of individuals in the ML-identified top 1% had no prior BHIP during the identification period, enabling true prevention. ML models demonstrated superior sensitivity, identifying 26.7% of all future BHIP events when targeting the top 1%, versus 17.1% for PHU. A simplified four-feature model using ambulance data maintained comparable performance. ML’s improved PPV increases statistical power for intervention studies: a 200-person trial using ML-identified participants could detect a 38% reduction in BHIP, while achieving equivalent power with PHU would require 310 participants (55% larger sample).

Conclusions

Machine learning substantially improves identification of individuals at BHIP risk, including those without prior events. This enhanced PPV can enable more efficient targeting of preventive services and make rigorous intervention research more feasible by increasing statistical power. Simplified models using only medical-services data suggest practical implementation pathways, though findings should be validated in other jurisdictions to ensure generalizability.