Tewodros Eguale, François Bastardot, Wenyu Song, Daniel Motta-Calderon, Yasmin Elsobky, Angela Rui, Marlika Marceau, Clark Davis, Sandya Ganesan, Ava Alsubai, Michele Matthews, Lynn A Volk, David W Bates, Ronen Rozenblum
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引用次数: 0
Abstract
Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. The performance of a ML application to alert clinicians of a patient’s risk of OUD, was evaluated by comparing it to a structured chart review by clinicians. Objective: To assess the clinical validity of an ML-based application designed to identify and alert clinicians of different levels of patients’ OUD risk. Methods: The ML-application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010–2013. A random sample of 60 patients was selected from each of 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured chart review and came to consensus on a patient’s OUD risk level which was then compared to the ML-application’s risk assignments. Results: 78,587 non-cancer patients with at least 1 opioid prescription were identified as: Not High Risk (64.1%), High Risk (21.2%), and Suspected OUD/OUD (14.7%). The sample of 180 patients was representative of the total population in age, sex, and race. The inter-rater reliability between the ML-application and clinicians had a weighted kappa coefficient (95% Cl) of 0.62 (0.53, 0.71), indicating good agreement. Combining the High Risk and Suspected OUD/OUD categories and using the chart review as a ‘gold standard’, the ML application had a corrected sensitivity (95% CI) of 56.6% (48.7%, 64.5%) and the corrected specificity of 94.2% (90.3%, 98.1%). The positive and negative predictive value (95% CI) was 93.3% (88.2%, 96.3%) and 60.0% (50.4%, 68.9%), respectively. Key themes for disagreements between the ML-application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML system and clinicians for OUD risk identification. The ML-application generated clinically valid and useful alerts about patients’ different risk levels of OUD. ML-applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.