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Automating Anesthesiology Resident Case Logs Reduces Reporting Variability.

Michael S Douglas¹, Lan Leeper¹, Jiahao Peng¹

¹The following authors are in the Department of Anesthesiology at Loma Linda University School of Medicine, Loma Linda, CA: is a Clinical Instructor; is an Assistant Professor; , , and are Associate Professors; is a Professor. is a Senior Extract, Transform, and Loading Developer in the Department of Data Governance at Loma Linda University Medical Center, Loma Linda, CA. is a Research Analyst in the Center for Health Research, at Loma Linda University School of Public Health, Loma Linda, CA.

The Journal of Education in Perioperative Medicine : JEPM

|December 22, 2022

Summary

Automated anesthesiology resident case logs improve accuracy and reduce variability in reporting training experiences. This system enhances data aggregation from electronic health records for graduate medical education.

Keywords:

Anesthesiology training technology in education

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Area of Science:

Medical Education
Anesthesiology Training
Health Informatics

Background:

The Accreditation Council for Graduate Medical Education (ACGME) case log system for anesthesiology residents relies on subjective procedure categorization and lacks clear credit role guidelines.
This subjectivity leads to significant variability in resident reporting practices within and between institutions.
Current methods present challenges in accurately capturing the full scope of resident training experiences.

Purpose of the Study:

To develop a systematic process for generating automated case logs using data extracted from electronic health records.
To improve the accuracy and reduce the variability of anesthesiology resident case logging.
To test the hypothesis that automated case log reporting enhances accuracy and reduces reporting variability.

Main Methods:

Developed a systematic approach to automate anesthesiology resident case logs from electronic health records.
Utilized a discrete classification system for assigning credit roles and Anesthesia Current Procedure Terminology codes.
Compared the median number of cases performed between automated and resident-reported ACGME case logs.

Main Results:

Automated case log generation successfully extracted and visualized case log elements from electronic health records.
Automated reporting captured a median of 1226.5 cases, compared to 1134.5 reported by residents (P = .0014).
Automation decreased case count interquartile range, indicating reduced reporting variability and a distribution approaching normality.

Conclusions:

Automated case log reporting provides a uniform capture of resident training experiences and significantly reduces reporting variability.
This methodology offers a foundation for aggregating graduate medical education data from electronic health records.
The study advocates for the advancement and adoption of automated case logging systems in medical education.