Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)

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Project Summary/Abstract An unprecedented rise in opioid overdose and opioid use disorder (OUD) has become a public health crisis in the US. In response, health systems, payers, and policy makers have developed or adopted measures and programs to target individuals at high-risk for overdose or OUD. However, significant gaps exist in the current approaches to identify individuals at high-risk for overdose or OUD. First, the definition of ‘high-risk’ currently used by payers and health systems varies widely (ranging from high opioid dose to the number of pharmacies or prescribers a patient has visited). Second, little is known about how accurately these measures truly identify patients with overdose or OUD, and there is some evidence showing they perform poorly, missing 70% to 90% of individuals with an actual OUD diagnosis or overdose. Third, our NIDA-funded work (R01DA044985) using national Medicare and Pennsylvania Medicaid claims data has shown that machine-learning algorithms can achieve better performance for risk prediction for opioid overdose and OUD. Thus, the immediate next step is to expand our algorithms to other data sources (e.g., electronic health records [EHR]), as well as to apply state-of- the-art longitudinal neural networks and natural language processing (NLP) to further improve prediction accuracy. In addition, we aim to translate these risk scores into a clinical decision tool to be used by health care systems to automatically analyze and visualize the relevant information regarding risk prediction and stratification for opioid overdose or OUD, using either claims data, EHR data, or both in real time. Leveraging our NIDA-funded work on developing machine-learning algorithms to predict opioid overdose and OUD, we propose to “develop and evaluate a machine-learning opioid prediction & risk-stratification e- platform (DEMONSTRATE)” that can be used by health care systems to identify patients at high risk for opioid overdose and OUD. We have 3 specific aims. Aim 1 will refine and validate prediction algorithms to identify patients at risk for opioid overdose/OUD using 3 different datasets (i.e., 2011-2020 Florida all-payer EHR, Florida Medicaid claims, and Florida Medicaid claims linked with EHR data) from the OneFlorida Clinical Research Consortium. We will expand our current algorithms by applying state-of-the-art methods (e.g., NLP) to improve prediction. In Aim 2, we will design and prototype a DEMONSTRATE clinical decision support tool to incorporate the best prediction algorithms to provide automatic warnings to primary care providers of patients at high risk of overdose/OUD. An iterative user-centered design approach will be used to enhance DEMONSTRATE’s functionality and usability. In Aim 3, we will integrate DEMONSTRATE into the University of Florida Health’s EHR system, and deploy and pilot test DEMONSTRATE in three primary care clinics. We will assess DEMONSTRATE’s usability, acceptability, and feasibility. Our proposed research is highly innovative in its expansion, translation, and application of a promising NIDA-funded machine-learning opioid prediction and risk stratification tool into a software platform to better inform clinical practice for improving safety of opioid use.