Supporting electronic health record data usage in research for teams with varying data science and clinical knowledge: a food service analogy approach.

Abstract

To guide research data services (RDS) teams in managing researcher variability (eg, differing deadlines, funding, expertise) when honest-brokering data, we present a framework based on operations management principles and a food service analogy. Our framework describes 4 data service offerings with different levels of efficiency and service customization: vending machine, fast food, custom meal, and personal chef. We describe examples from 2 institutions. Vending machine and fast food are efficient but less customizable, making them better-suited for researchers with limited funding or time. Custom meal and personal chef are less efficient but more customized, making them well suited for better-resourced researchers. Efficiency and service tradeoffs should be balanced to align with demand and institutional goals. RDS teams can overcome such tradeoffs through uncompromised reduction or low-cost accommodation approaches. Our framework can be applied by RDS teams in their design and implementation of data services.