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Updated: Jun 3, 2025
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
Published on: May 20, 2018
Automating excellence: A breakthrough in emergency general surgery quality benchmarking
Louis A Perkins1, Zongyang Mou, Jessica Masch
1From the Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, Department of Surgery (L.A.P., Z.M., J.M., B.H., T.W.C., L.N.H., A.B., L.A., J.J.D., J.E.S.), UC San Diego School of Medicine, San Diego, California; and Division of Acute Care Surgery, Department of Surgery (A.E.L.), University of Missouri School of Medicine, Columbia, Missouri.
Background:
Given the high mortality and morbidity of emergency general surgery (EGS), designing and implementing effective quality assessment tools is imperative. Currently accepted EGS risk scores are limited by the need for manual extraction, which is time-intensive and costly. We developed an automated institutional electronic health record (EHR)-linked EGS registry that calculates a modified Emergency Surgery Score (mESS) and a modified Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) score and demonstrated their use in benchmarking outcomes.
Methods:
The EHR-linked EGS registry was queried for patients undergoing emergent laparotomies from 2018 to 2023. Data captured included demographics, admission and discharge data, diagnoses, procedures, vitals, and laboratories. The mESS and modified POTTER (mPOTTER) were calculated based off previously defined variables, with estimation of subjective variables using diagnosis codes and other abstracted treatment variables. This was validated against ESS and the POTTER risk calculators by chart review. Observed versus expected (O:E) 30-day mortality and complication ratios were generated.
Results:
The EGS registry captured 177 emergent laparotomies. There were 32 deaths (18%) and 79 complications (45%) within 30 days of surgery. For mortality, the mean difference between the mESS and ESS risk predictions for mortality was 3% (SD, 10%) with 86% of mESS predictions within 10% of ESS. The mean difference between the mPOTTER and POTTER was -2% (SD, 11%) with 76% of mPOTTER predictions within 10% of POTTER. Observed versus expected ratios by mESS and ESS were 1.45 and 1.86, respectively, and for mPOTTER and POTTER, they were 1.45 and 1.30, respectively. There was similarly good agreement between automated and manual risk scores in predicting complications.
Conclusion:
Our study highlights the effective implementation of an institutional EHR-linked EGS registry equipped to generate automated quality metrics. This demonstrates potential in enhancing the standardization and assessment of EGS care while mitigating the need for extensive human resources investment.
Level Of Evidence:
Prognostic and Epidemiologic Study; Level IV.
