Using Re-inforcement Learning to Automatically Adapt a Remote Therapy Intervention (RTI) for Reducing Adolescent Violence Involvement

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Youth violence is a key public health problem. Homicide is a leading cause of death among adolescents (age:14- 20) and disproportionately impacts African-American populations. Urban EDs are a critical opportunity for violence prevention, especially with >600,000 adolescents/year seeking treatment for violence-related injuries. In our longitudinal study of violently-injured adolescents in urban EDs, we found that within 2-years, 37% returned for a repeat violent injury, 59% experienced firearm violence, 38% were arrested, and 1% died. Despite the importance of the problem, strategies to decrease repeat violence after an ED visit have not been well studied. Given our prior work demonstrating that theoretically-based single session ED interventions are efficacious reducing violence among lower risk adolescents, the application of this therapy, expanded to address greater problem severity over multiple sessions and enhanced by including care management, represents a potentially efficacious approach for altering risk trajectories of higher-risk violently-injured adolescents. Our recent pilot of this approach (S-RTI) was well received and addressed problems identified in prior multisession interventions (e.g., transportation) with the addition of remote therapy delivery (e.g., phone). While innovative and promising, this S-RTI approach is resource intensive and does not address heterogeneity in treatment responses. By contrast, adaptive treatment strategies allow for “just-in-time” tailoring that provides a balance between too much and not enough intervention and enhances outcomes while reducing the use of costly resources. Reinforcement learning is an artificial intelligence domain that allows computer systems to “learn” from the success of prior treatments and is a promising approach to constructing adaptive “just-in-time” interventions. For this study, we propose to test two versions of our RTI, a standard RTI condition (S-RTI) comprised of a single ED session followed by 8 remote therapy sessions, and an adaptive RTI version (AI-RTI) optimized by reinforcement learning to step up or down the intensity of treatment between three levels (i.e., remote therapy sessions, automated two- way text messaging, assessment only) based on patient response to daily text message assessments. The specific aims are: 1) To refine and adapt our RTI for delivery using two packages (S-RTI; AI-RTI); 2) To conduct a 3-arm RCT enrolling 900 violently-injured adolescents seeking ED care (age:14-20) to compare the efficacy of S-RTI (n=300), AI-RTI (n=400), and a control condition (n=200); and, 3) To evaluate adaptability of the AI-RTI RL algorithm by comparing the first 50% of enrollees to the second 50% on process variables (e.g., engagement, helpfulness/likability). Primary outcomes (assessed at 4-, 8-, and 12-months) include aggression, victimization, and ED recidivism for violent injury. Secondary outcomes include substance use, mental health symptoms, and criminal justice involvement. As a secondary aim, we will compare resource utilization (i.e., costs/event averted) for the active intervention conditions. Given elevated rates of violence among socio-disadvantaged youth with disparities in access to services, the proposed study has the potential for significant public health impact.