Protocol for a Single-Arm Pilot Clinical Trial: Developing and Evaluating a Machine Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE).

Hong JJ, Wilson DL, Nguyen K, Gellad WF, Diiulio J, Militello L, Yan S, Harle CA, Nelson D, Rosenberg EI, Schmidt S, Chang CH, Cochran G, Wu Y, Staras SAS, Kuza C, Lo-Ciganic WH. Protocol for a Single-Arm Pilot Clinical Trial: Developing and Evaluating a Machine Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE). Journal of clinical medicine. 2025 Dec 1; 14(23):DOI: 10.3390/jcm14238522.

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Abstract:

< b > Background/Objectives < /b > : The Developing and Evaluating a Machine Learning Opioid Prediction and Risk-Stratification E-Platform (DEMONSTRATE) trial aims to assess the usability, acceptability, feasibility, and effectiveness of implementing a machine learning (ML)-based clinical decision support (CDS) tool-the Overdose Prevention Alert-which predicts a patient''s risk of opioid overdose within three months. < b > Methods < /b > : This single-arm study uses a pre-post implementation design with mixed-methods evaluation in 13 University of Florida Health, Gainesville, internal medicine and family medicine clinics. Eligible patients are aged ≥18 years, received an opioid prescription within the year prior to their upcoming primary care visit, are not receiving hospice care, do not have a malignant cancer diagnosis, and are identified by the ML algorithm as high risk for overdose. The Overdose Prevention Alert triggers when a primary care provider (PCP) signs an opioid order in electronic health records. We will evaluate effectiveness by comparing pre- and post-implementation outcomes using a composite patient-level measure defined by the presence of any of the following 6 favorable indicators: (1) evidence of naloxone access; (2) absence of opioid overdose diagnoses and naloxone administration; (3) absence of emergency department (ED) visits or hospitalizations due to opioid overdose or opioid use disorder (OUD); (4) absence of overlapping opioid and benzodiazepine use within a 7-day window; (5) absence of opioid use ≥50 morphine milligram equivalent daily average; (6) receipt of referrals to non-pharmacological pain management. Additional quantitative metrics will include alert penetration, usage patterns, and clinical actions taken. Usability and acceptability will be assessed using a 12-item questionnaire for PCPs and semi-structured interviews. < b > Expected Results < /b > : The trial will provide insights into real-world ML-driven CDS implementation and inform future strategies to reduce opioid-related harm.