SCH: Improving Patient Health and Equity through the Digital Transformation of Diabetes Care Delivery

Stanford University, Stanford CA

Investigators

Abstract

This Smart and Connected Health (SCH) award will contribute to the advancement of the national health and welfare by developing the scientific foundation of using digital technology to improve the quality and reduce the cost of diabetes management. Increased adoption of remote activity trackers, continuous glucose monitors, other types of sensors, and telehealth (precipitated by the COVID-19 pandemic) have transformed diabetes care. The typical standard of care for diabetes revolves around a few “finger poke” glucose measurements per day and a few in-clinic visits with the care team per year, with many missed opportunities to detect and remedy deteriorating glucose management. The digitization of diabetes care holds the potential for as-needed measurement, monitoring, and personalized patient care. In a best case scenario, these technological advances promise to narrow gaps in health care access, by providing limited care resources to those most in need. Achieving this outcome requires novel scientific progress to leverage sensor data to better understand patients; to develop algorithmic techniques to allocate care resources efficiently to patients; and to help both providers and patients develop and implement care decisions together. This award specifically develops algorithms, platforms, and decision support tools to address these challenges in the context of care of type 1 diabetes in pediatric populations, by connecting engineers, computer scientists, and statisticians with clinicians in an interdisciplinary effort. A key component of the project involves deployment at two sites (Lucile Packard Children’s Hospital at Stanford, and Children’s Mercy Hospital at Kansas City) to capture the real-world constraints the theory must tackle and to validate the real-world efficacy of the scientific innovations developed. The research objectives are to: (1) develop methods to identify patient types from sensor data, informing the health care team's understanding of differentiated needs and behaviors across the patient population; (2) develop methods to allocate scarce provider resources to those patients most in need of care; and (3) design interpretable treatment planning recommendations to facilitate interactions between providers and patients, including both provider-facing and patient-facing “dashboards” that visualize progress and disease management, and guide discussion of interventions. These thrusts both leverage and advance the state of the art in time-series modeling and machine learning; behavioral psychology; and data-driven operations research. Data and real-world understanding from the clinical context serve to define the problems the theory must tackle and as a benchmark to validate the models and methods developed. The models developed will be deployed and evaluated in clinics caring for pediatric patients with type 1 diabetes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.