Project Grant K01DA059606

USING MACHINE LEARNING TO ANALYZE INTEGRATED CLINICAL AND GEO-SOCIAL DATA TO INFORM THE DESIGN OF A DECISION SUPPORT SYSTEM TO REDUCE THE RISK OF STIMULANT THERAPY - PROJECT SUMMARY/ABSTRACT STIMULANT USE DISORDER (STUD) AND OVERDOSE FATALITIES ARE DEVASTATING PUBLIC HEALTH PROBLEMS, STRAINING HEALTHCARE AND CRIMINAL JUSTICE SYSTEMS, AND SOCIETAL PRODUCTIVITY. ANNUALLY, APPROXIMATELY 55% OF STIMULANT PRESCRIPTIONS HAVE BEEN PRESCRIBED TO ADULTS, WITH NEARLY ONE-THIRD OF THEM REPORTING STIMULANT MISUSE OR STIMULANT-RELATED HARM. DESPITE GROWING CONCERN THAT STIMULANT MEDICATIONS MAY INCREASE THE RISK OF STUD AND OVERDOSE IN SOME ADULTS, RISK FACTORS FOR STIMULANT-RELATED HARM HAVE NOT BEEN WELL UNDERSTOOD, AND NO SCREENING TOOL IS AVAILABLE FOR ASSESSING THE RISK/BENEFIT OF STIMULANT THERAPY AT THE POINT OF CARE. THIS PROJECT HYPOTHESIZES THAT MACHINE LEARNING (ML) TECHNIQUES, APPLIED TO LARGE CLINICAL (AIM 1) AND LINKED CENSUS (AIM 2) DATASETS, CAN HELP IDENTIFY PERSONALIZED RISK FACTORS FOR STUD AND OVERDOSE AMONG ADULTS TREATED WITH STIMULANTS, AND ASSIST WITH THE DEVELOPMENT OF CLINICAL DECISION SUPPORT SYSTEM (CDSS) TOOL (AIM 3), WHICH, IN TURN, CAN HELP GUIDE CLINICAL DECISION MAKING WHEN CONSIDERING STIMULANT THERAPY AND DECREASE STIMULANT- RELATED RISK OF HARM. TO EVALUATE POTENTIAL DEMOGRAPHIC AND CLINICAL RISK FACTORS FOR STUD AND OVERDOSE, RETROSPECTIVE LONGITUDINAL ML-BASED ANALYSIS OF ELECTRONIC HEALTH RECORDS (EHR) FROM THE TRINETX RESEARCH NETWORK WILL BE CONDUCTED (AIM 1). BECAUSE EHRS DO NOT INCLUDE CHARACTERISTICS OF NEIGHBORHOODS WHERE PATIENTS RESIDE, AND SOCIAL DETERMINANTS OF HEALTH (SDOH) CAN CONTRIBUTE TO STUD AND OVERDOSE RISK, THE PREDICTIVE MODEL FROM AIM 1 WILL BE FURTHER ENHANCED BY GEOCODING AND LINKING THE LOCAL HEALTH SYSTEM'S EHR TO THE CENSUS BLOCK GROUP LEVEL NEIGHBORHOOD DATA FOR EACH PATIENT TO ACCOUNT FOR THE POTENTIAL IMPACT OF SDOH AND ENHANCE THE ACCURACY OF THE STUD AND OVERDOSE RISK MODELING (AIM 2). RISK FACTORS IDENTIFIED THROUGH AIM 1 AND AIM 2 ANALYSIS, ALONG WITH PRESCRIBING-CLINICIAN INPUT, WILL ENABLE THE DEVELOPMENT OF A CDSS TOOL TO AID CLINICIANS WITH RISK/BENEFIT ASSESSMENT OF STIMULANT THERAPY AT THE POINT-OF-CARE, BASED ON UNIQUE DEMOGRAPHIC, CLINICAL AND NEIGHBORHOOD CHARACTERISTICS OF EACH PATIENT (AIM 3). THE CDSS HAS BEEN INCREASINGLY UTILIZED IN DIGITALLY-DRIVEN HEALTHCARE TO IMPROVE TREATMENT SAFETY AND OUTCOMES, BUT THEIR APPLICATIONS TOWARD IMPROVING CARE FOR ADULTS TREATED WITH STIMULANTS OR TO MITIGATE ADDICTION AND OVERDOSE RISKS HAVE BEEN LAGGING. THE PROPOSED RESEARCH LEVERAGING BIG DATA AND ML HAS ENORMOUS POTENTIAL FOR UNDERSTANDING AND PREDICTING HEALTH RISKS OF STIMULANT THERAPY IN A PERSONALIZED WAY, AND WILL LAY FOUNDATION FOR FUTURE CLINICAL TRIALS EVALUATING THE EFFICACY OF CDSS-BASED INTERVENTION ON REDUCING THE RISK, AND, ULTIMATELY, IMPROVING HEALTH OF ADULTS CONSIDERED FOR STIMULANT TREATMENT. THIS MENTORED RESEARCH SCIENTIST DEVELOPMENT AWARD (K01) WILL ALSO ENABLE THE PI TO PURSUE ADDITIONAL ADVANCED TRAINING IN RELEVANT CLINICAL KNOWLEDGE, ANALYTICAL SKILLS, GRANTSMANSHIP, LEADERSHIP, AND DIVERSITY, EQUITY, AND INCLUSIVENESS TO SUPPORT HIS LONG-TERM GOAL OF BECOMING AN INDEPENDENT SCIENTIST CONTRIBUTING TO THE MISSION OF THE NATIONAL INSTITUTE ON DRUG ABUSE TO REDUCE THE BURDEN OF SUBSTANCE USE AND ADDICTION, PARTICULARLY WHEN IT INVOLVES STIMULANTS.