Improving Graduate Medical Education by Aggregating Data Across the Medical Education Continuum

Area of Science:

• Medical Education Research
• Health Informatics
• Data Science in Healthcare

Background:

• Current graduate medical education (GME) improvements often stem from small trials lacking rigorous outcome measures and generalizability.
• A significant barrier to advancing GME is the limited access to large-scale data for empirical evidence generation.

Purpose of the Study:

• To examine the potential of a national GME data infrastructure for enhancing medical education.
• To review findings from national workshops on improving GME through data.
• To propose a strategic pathway for establishing such a data infrastructure.

Main Methods:

• Analysis of outputs from two National Academies of Sciences, Engineering, and Medicine workshops.
• Conceptualization of a future data infrastructure integrating premedical, undergraduate medical, GME, and practicing physician data.
• Proposal of next steps including data inventory, pilot sharing, and framework development.

Main Results:

• Broad consensus exists on the value of a longitudinal data infrastructure for GME improvement.
• Significant obstacles to data infrastructure development were identified.
• A multi-institutional, longitudinal data system using common standards and unique identifiers is envisioned.

Conclusions:

• A national GME data infrastructure is essential for evidence-based decision-making and optimizing resident education.
• Harnessing big data in GME represents a critical step toward advancing physician education.
• Key next steps involve data inventory, pilot sharing initiatives, and establishing technical/governance frameworks.