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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.

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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.