INTRODUCTION Despite adoption of the emergency general surgery (EGS) service by hospitals nationally, quality improvement (QI) and research for this patient population are challenging because of the lack of population-specific registries. Past efforts have been limited by difficulties in identifying EGS patients within institutions and labor-intensive approaches to data capture. Thus, we created an automated electronic health record (EHR)–linked registry for EGS. METHODS We built a registry within the Epic EHR at University of California San Diego for the EGS service. Existing EHR labels that identified patients seen by the EGS team were used to create our automated inclusion rules. Registry validation was performed using a retrospective cohort of EGS patients in a 30-month period and a 1-month prospective cohort. We created quality metrics that are updated and reported back to clinical teams in real time and obtained aggregate data to identify QI and research opportunities. A key metric tracked is clinic schedule rate, as we care that discontinuity postdischarge for the EGS population remains a challenge. RESULTS Our registry captured 1,992 patient encounters with 1,717 unique patients in the 30-month period. It had a false-positive EGS detection rate of 1.8%. In our 1-month prospective cohort, it had a false-positive EGS detection rate of 0% and sensitivity of 85%. For quality metrics analysis, we found that EGS patients who were seen as consults had significantly lower clinic schedule rates on discharge compared with those who were admitted to the EGS service (85% vs. 60.7%, p
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