Positive Variants: Learning from Exceptional Performers for Electronic Health Record Modernization

Abstract

This paper presents a conceptual review of three distinct but related research streams—exceptional performers, stars, and positive variants—and their application to Electronic Health Record Modernization (EHRM) efforts in the Department of Veterans Affairs (VA). Exceptional performers and stars, well-studied in fields such as transportation security and organizational management, provide foundational insights into identifying and learning from individuals who consistently excel. The Positive Deviance approach, widely used in health care, focuses on high performers who succeed despite shared resource constraints. We describe a mixed-methods study underway at VA that adapts these approaches to identify high-performing clinical units using a newly implemented Federal Electronic Health Record (EHR). Positive variants are identified through analysis of user experience (UX) survey results, focusing on units scoring highest in measures of EHR confidence and the EHR’s impact on efficiency. Follow-up interviews aim to uncover local initiatives that enhance UX and inform scalable change management strategies. Our approach modifies traditional Positive Deviance methods by prioritizing unit-level over individual performance and leveraging subjective UX data. This methodology minimizes clinician burden while maximizing insights into operational best practices. Findings will contribute to EHRM improvements within VA and offer human factors practitioners broader guidance for studying and applying lessons from exceptional performers across healthcare and other complex systems.