OBJECTIVES: Medicaid managed care organizations are developing comprehensive strategies to reduce the impact of opioid use disorder (OUD) among their members. The goals of this study were to develop and validate a predictive model of OUD and to predict future OUD diagnosis, resulting in proactive, person-centered outreach. STUDY DESIGN: We utilized machine learning methodology to select a multivariate logistic regression and identify predictors. METHODS: Using 2016-2018 data, we used a staged approach to test and validate the predictive accuracy of our model. We identified OUD, the dependent variable, using an industry-standard definition. We included a series of patient demographic, chronic condition, social determinants of health (SDOH), opioid-related, and health utilization indicators captured in administrative data. RESULTS: Caucasian (odds ratio [OR], 1.65), male (OR, 1.57), and younger (aged 40-64 years compared with 18-39 years: OR, 0.75) members had greater odds of being diagnosed with an OUD. Members with an SDOH vulnerability had 26% higher odds than those without a documented issue. From a prescribing perspective, we found that having an opioid dose of 120 morphine milligram equivalents and contiguous 5-day supply increased odds of OUD by 1.87 times, and an opioid supply of 30 days or longer increased the odds of OUD by 1.56 times. CONCLUSIONS: We built the necessary machine learning infrastructure to identify members with greater than 50% probability of developing OUD. The generated list strategically informs and guides person-centered care and interventions. Through application of these results, we strive to proactively reduce OUD-related structural barriers and prevent OUD from occurring.
OBJECTIVES: Medicaid managed care organizations are developing comprehensive strategies to reduce the impact of opioid use disorder (OUD) among their members. The goals of this study were to develop and validate a predictive model of OUD and to predict future OUD diagnosis, resulting in proactive, person-centered outreach. STUDY DESIGN: We utilized machine learning methodology to select a multivariate logistic regression and identify predictors. METHODS: Using 2016-2018 data, we used a staged approach to test and validate the predictive accuracy of our model. We identified OUD, the dependent variable, using an industry-standard definition. We included a series of patient demographic, chronic condition, social determinants of health (SDOH), opioid-related, and health utilization indicators captured in administrative data. RESULTS: Caucasian (odds ratio [OR], 1.65), male (OR, 1.57), and younger (aged 40-64 years compared with 18-39 years: OR, 0.75) members had greater odds of being diagnosed with an OUD. Members with an SDOH vulnerability had 26% higher odds than those without a documented issue. From a prescribing perspective, we found that having an opioid dose of 120 morphine milligram equivalents and contiguous 5-day supply increased odds of OUD by 1.87 times, and an opioid supply of 30 days or longer increased the odds of OUD by 1.56 times. CONCLUSIONS: We built the necessary machine learning infrastructure to identify members with greater than 50% probability of developing OUD. The generated list strategically informs and guides person-centered care and interventions. Through application of these results, we strive to proactively reduce OUD-related structural barriers and prevent OUD from occurring.
Authors: Sajjad Fouladvand; Jeffery Talbert; Linda P Dwoskin; Heather Bush; Amy Lynn Meadows; Lars E Peterson; Steve K Roggenkamp; Ramakanth Kavuluru; Jin Chen Journal: AMIA Annu Symp Proc Date: 2022-02-21