From Data to Decisions: Ethnographic Evaluation of Electronic Health Record Use in Acute Care Surgery

Introduction: Despite advances in digital technology, acute care surgery (ACS) systems have yet to optimize electronic health record (EHR) workflows to reduce redundancy, enhance data visualization, and integrate structured and unstructured data for dynamic decision-making in high-stakes settings. We conducted an ethnographic evaluation of ACS surgeons’ EHR use, identifying opportunities and challenges for EHR-enabled, augmented decision-making.

Methods: Using novel ‘pair fieldwork’ by a surgeon and ethnographer, 10 in-depth interviews with ACS surgeons and 30 hours of field observations during handover were performed to assess how optimized EHR workflows may enhance decision-making.

Results: Surgeons mainly fell into two groups: those who accepted documentation and data overload as inherent to the EHR, relying on generic templates and standard attestations, while others viewed it as a problem to fix, actively correcting errors and composing individualized summaries. Unclear billing requirements were widely recognized as a driver of over-documentation, leading to excessive ‘note bloat’ and limiting access to high-yield information. EHR use during decision-making primarily focused on risk prediction, but challenges in identifying and navigating to critical data persisted. Artificial intelligence (AI)-driven predictions, automatically generated from live EHR data while minimizing alert fatigue, were seen as potential solutions. Predicting quality-of-life outcomes was also considered valuable for shared decision-making but underutilized.

Conclusion: In high-stakes ACS settings, surgeons employ a range of tactics to navigate EHR challenges and focus on high-value tasks. AI-driven risk prediction may offer sustainable workflow improvements, but solutions must integrate seamlessly with the EHR to provide rapid, automated outputs that prioritize patient outcomes in critical decisions.