M. C. Smith1, A. W. Maiga1, V. Tiwari2, S. McConnell3, K. A. Smith2, O. L. Gunter1 1Vanderbilt University Medical Center, Acute Care Surgery, Nashville, TN, USA 2Vanderbilt University Medical Center, Anesthesiology, Nashville, TN, USA 3Vanderbilt University Medical Center, Health Information Technology, Nashville, TN, USA
Introduction: While registrars support trauma programs, this resource is often lacking for Emergency General Surgery (EGS).?Institutional metrics underestimate EGS clinical activity, complicating resource allocation. Electronic medical record (EMR) systems facilitate patient identification with service lists. We hypothesized that institutional definitions of EGS are inaccurate, and that creating an automated EGS registry is possible using EMR order-based query.
Methods: We developed an EGS registry using Reports (Epic) with inclusion criteria order entry of “admit to primary team EGS” OR “add following team EGS” for consults. These reports were combined with Clarity (Epic) and uploaded to a web-accessible database (Tableau), which is continuously updated and available in real-time. We compared registry data using daily EGS service patient lists as gold standard. Institutional operative logs by service line were compared over a three-month period with registry data to determine institutional service definition accuracy. We performed a prospective validation over a two-week period. Descriptive statistics are reported for comparisons of accuracy and misclassification.?
Results: Institutional logs appropriately captured 170 of the 426 EGS operative cases (39.9%). Over the validation period, our registry identified 112 unique patient encounters. 54 were admitted to the EGS service, and 58 were seen in consultation. The registry had a sensitivity of 100% and a false positive rate of 0% with prospective validation against the EGS team list.
Conclusion: Institutional definitions of EGS are inaccurate and unreliable. It is feasible to create an accurate, automated EGS registry with existing EMR data and available in real time. This inside-out approach to defining the true volume of EGS is reproducible with EMR queries and could be critical for determining hospital resource allocation and quality improvement measures.