Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

Abstract

Objectives Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. Methods Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. Results The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. Conclusion A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

“…In general, such processes outside of FHIR can be accomplished on multiple platforms through merging datasets [ 159 ] with overlapping patient instances or concatenating data instances to an already existing data set. FHIR [ 160 ] directed EHR extraction to clinical outcome prediction pipelines [ 161 ] are incipient, and examples include predicting opioid use after spine surgery [ 162 ], outcomes and superiority of chronic disease treatment methods [ 163 ], and others [ 164 ]. Data extraction, or accession pipelines [ 162 ] far more complex than these, are also being explored and implemented to conduct clinical outcome predictions.…”

Personalised Dosing Using the CURATE.AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus

“…Applications in digital pathology and robotic surgery have been widely publicised [ 11 ]. In chronic disease, examples of AI applications include digital health programmes [ 11 ], conversational agents (e.g., chatbots) [ 12 ], games to promote physical activity [ 13 ], clinical decision support systems [ 14 ], wearables (i.e., to track disease management, physical activity, or disease exacerbations) [ 15 ], diagnostics, and prediction of chronic disease complications [ 16 ]. A novel dosing optimisation system—CURATE.AI—is the latest innovation that has the potential to improve chronic disease care.…”

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

“… Automate detection, e.g., automated acute myocardial infarction detection using ECGs from smartwatches [ 11 ]. Improved quality of care, e.g., using AI to improve chronic disease care for type 2 diabetes mellitus patients [ 12 ]. Reduced healthcare costs, e.g., predicting health and population well-being [ 13 ].…”

“…Improved quality of care, e.g., using AI to improve chronic disease care for type 2 diabetes mellitus patients [ 12 ].…”