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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.
An automated electronic health record (EHR)-linked registry for emergency general surgery (EGS) effectively calculates risk scores, improving quality assessment and reducing manual data extraction burdens.
Area of Science:
- Surgical Quality Improvement
- Health Informatics
- Epidemiology
Background:
- Emergency general surgery (EGS) has high mortality and morbidity.
- Current risk scores require manual data extraction, which is time-consuming and expensive.
- Automated quality assessment tools are needed for EGS.
Purpose of the Study:
- To develop and implement an automated electronic health record (EHR)-linked registry for EGS.
- To calculate modified Emergency Surgery Score (mESS) and modified Predictive OpTimal Trees in Emergency Surgery Risk (mPOTTER) scores.
- To demonstrate the utility of these automated scores in benchmarking EGS outcomes.
Main Methods:
- Queried an EHR-linked EGS registry for patients undergoing emergent laparotomies (2018-2023).
- Captured demographics, diagnoses, procedures, vitals, and labs.
- Calculated mESS and mPOTTER, estimating subjective variables from diagnosis codes; validated against manual ESS and POTTER scores.
Main Results:
- Registry included 177 emergent laparotomies with 18% mortality and 45% 30-day complications.
- mESS and mPOTTER showed good agreement with manual ESS and POTTER for mortality prediction (86% and 76% within 10% difference, respectively).
- Observed:expected ratios for mortality were 1.45 (mESS) and 1.45 (mPOTTER), indicating effective risk prediction.
Conclusions:
- An automated EHR-linked EGS registry can effectively generate quality metrics.
- This system enhances standardization and assessment of EGS care.
- It mitigates the need for extensive human resources in quality assessment.
