The current opioid crisis is significantly impacting millions of lives, healthcare, social welfare, and the economy. Patient interactions with the treatment system coupled with results of completed studies create a wealth of data stored in disparate electronic sources. Therapists and health care providers with limited time and resources face challenges to access, integrate and monitor this vast data set for novel opportunities to improve care. Significant advances would include predicting when a patient will relapse out of a program, and also suggesting optimal personalized care strategies to reengage patients before this negative event occurs. Survive-OUD will solve these challenges by providing a web-based therapist interface integrating survivor model artificial intelligence (AI) strategies. Based on multiple input data domains leveraged from existing electronic medical record sources, survivor recurrent neural networks will be trained to recognize when patients are likely to relapse or drop out of an OUD program. Examples of data domains that can be input to the network include patient demographics, medical and prescription data, engagement with therapy paradigms, and compliance with logistical program tasks. Furthermore, once a patient is noted as high risk, a second layer of algorithms will be developed to recommend a specific and personalized care strategy for retention based on existing best practices in the literature and clinical trials. Therefore, the Survive-OUD platform will also integrate with common literature database and clinical trial repositories. Utilizing an existing AI platform for searching, tagging, and extracting data from database sources, the innovative platform will close the loop on actionable results by recommending updated care options based on potential outcomes learned from best practices in existing literature. The AI architecture developed will greatly improve success rates in opioid addition programs and expand high quality healthcare. While the commercialized Survive-OUD platform will integrate all features above, Phase I will target feasibility of data aggregation and AI algorithms to detect relapse and recommend intervention strategies. The innovative technical challenge in Phase I is to develop and validate targeted AI tools using data already being captured in patient workflow to allow early prediction of patient retention issues. More specifically, a prototype therapist interface and data network infrastructure will be developed to source personalized patient data as well as literature and clinical trial sources. Once the platform architecture has passed verification testing, it will be deployed in a field data collection study to determine usability and also provide a rich set of de-identified data for algorithm development. Collected data will then be used to train and test AI algorithms for early detection of patient dropout/relapse and appropriate treatment recommendation.

The objective is to design, develop, and demonstrate feasibility of Survive-OUD, an artificial intelligence driven platform to integrate complex data sources, predict patient relapse, and recommend intervention strategies for individuals impacted by opioid use disorder. Currently millions of Americans suffer from an opioid use disorder (OUD) and program relapse rates are extremely high. Therefore, an advanced, bioinformatics platform that accurately predicts when OUD patients will drop out of programs and offers personalized prevention strategies would provide a novel clinical tool to help combat the opioid crisis in the U.S.