Intelligent Graduate Medical Education Dashboard (IGMED) to Enhance Trainee Oversight During the COVID-19 Pandemic

Abstract

Background: Medical residents comprise a large, but unique, subset of the physician workforce. They serve as front-line staff, but are trainees, regulated by duty hour and supervision restrictions. Heightened oversight is necessary to ensure resident supervision and safety whilst mobilizing this important workforce during crisis. This manuscript describes the development and implementation of an institutional crisis dashboard to facilitate timely information gathering and decision-making regarding resident activities during the COVID-19 pandemic. Objective: The purpose of the study was to develop an intelligent graduate medical education dashboard to centralize and integrate data to support accurate, timely decisions in several areas: (1) track redeployment; (2) ensure adherence to supervision and duty hour regulations; and (3) monitor infection control and safety measures. Methods: The dashboard is a live Microsoft Excel database saved to a shared fileserver. All existing databases within the education department were reviewed to form a foundational template. A monitoring section provides at-a-glance information on trainee assignments. Embedded color-coded flags attached to specific responses immediately highlight areas of deficiency. Results: The dashboard facilitated the efficient mobilization of trainees, while ensuring assignments followed education regulations. It allowed the education office to centralize and proactively arrange mass scrubs orders, facemask fitting, and personal protective equipment training. It was a monitoring system that enabled recognition of safety concerns in real-time, including identifying areas where residents were most exposed or infected. Conclusion: The dashboard improved the efficiency of crisis response, while prioritizing resident safety. It is feasible, low cost and easily accessible, even during Internet disruption. It can be used by hospitals worldwide, including low resource settings.