Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Health

The work - and the opinions expressed herein - are those of the author, and do not necessarily reflect the views of CVS Health, Indiana University, or their affiliates, nor those of any of the author's previous affiliations or collaborators. Abstract Objective: Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often cooccur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate in a large statewide population whether we can use artificial intelligence (AI) techniques to identify clusters of patient trajectories within the broader diabetes population in order to create cost-effective, narrowly-focused case management intervention strategies to reduce development of complications. Methods: This approach combined data from: 1) claims, 2) case management notes, and 3) social determinants of health from 300,000 real patients between 2014 and 2016. We categorized complications as five types: Cardiovascular, Neuropathy, Opthalmic, Renal, and Other. Modeling was performed combining a variety of machine learning algorithms, including supervised classification, unsupervised clustering, natural language processing of unstructured care notes, and feature engineering. Results: The results showed that we can predict development of diabetes complications roughly 83.5% of the time using claims data or social determinants of health data. They also showed we can reveal meaningful clusters in the patient population related to complications and mental health that can be used to design a cost-effective screening program, reducing the number of patients to be screened down by 85%. Conclusion: This study outlines creation of an AI framework to develop protocols to better address mental health comorbidities that lead to complications development in the diabetes population.