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BackgroundOpioid use disorder (OUD) is an addiction crisis in the United States. As recent as 2019, more than 10 million people have misused or abused prescription opioids, making OUD one of the leading causes of accidental death in the United States. Workforces that are physically demanding and laborious in the transportation, construction and extraction, and health care industries are prime targets for OUD due to high-risk occupational activities. Because of this high prevalence of OUD among working populations in the United States, elevated workers’ compensation and health insurance costs, absenteeism, and declined productivity in workplaces have been reported. ObjectiveWith the emergence of new smartphone technologies, health interventions can be widely used outside clinical settings via mobile health tools. The major objective of our pilot study was to develop a smartphone app that can track work-related risk factors leading to OUD with a specific focus on high-risk occupational groups. We used synthetic data analyzed by applying a machine learning algorithm to accomplish our objective. MethodsTo make the OUD assessment process more convenient and to motivate potential patients with OUD, we developed a smartphone-based app through a step-by-step process. First, an extensive literature survey was conducted to list a set of critical risk assessment questions that can capture high-risk behaviors leading to OUD. Next, a review panel short-listed 15 questions after careful evaluation with specific emphasis on physically demanding workforces—9 questions had two, 5 questions had five, and 1 question had three response options. Instead of human participant data, synthetic data were used as user responses. Finally, an artificial intelligence algorithm, naive Bayes, was used to predict the OUD risk, trained with the synthetic data collected. ResultsThe smartphone app we have developed is functional as tested with synthetic data. Using the naive Bayes algorithm on collected synthetic data, we successfully predicted the risk of OUD. This would eventually create a platform to test the functionality of the app further using human participant data. ConclusionsThe use of mobile health techniques, such as our mobile app, is highly promising in predicting and offering mitigation plans for disease detection and prevention. Using a naive Bayes algorithm model along with a representational state transfer (REST) application programming interface and cloud-based data encryption storage, respondents can guarantee their privacy and accuracy in estimating their risk. Our app offers a tailored mitigation strategy for specific workforces (eg, transportation and health care workers) that are most impacted by OUD. Despite the limitations of the study, we have developed a robust methodology and believe that our app has the potential to help reduce the opioid crisis. |
