Neurosurgery · 2026
Automated Case Logging for Surgical Residency Training Using the Electronic Health Record: A Pilot Study.
СтатьяНазвание и аннотация: Перевод в очереди
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Owolo Edwin, Mazurek Mercy H, Shankar Ganesh, Stapleton Christopher J, Jones Pamela S, Coumans Jean-Valery, Nanda Pranav, Tan Amanda, Roche Katie, Frerichs Kai U, Curry William T, Co John Patrick T, Berkowitz Lori, Landman Adam, Buliga Christopher, Ricci Michael, Nahed Brian V
Аннотация
BACKGROUND AND OBJECTIVES: Case logging is essential for competency-based evaluation in residency but requires manual entry, which is susceptible to error, leading to delayed submission and underreporting. We present a novel, fully automated electronic health record (EHR)-integrated platform that extracts data from clinical documentation and logs it into the Accreditation Council for Graduate Medical Education (ACGME) case log system. We aim to evaluate the feasibility and effectiveness of an automated EHR extraction and case-logging system linked to the ACGME.
METHODS: We evaluated case logs from 20 residents (post-graduate year 1-7) at an academic neurosurgery department from June to September 2025. Automated case logs were extracted from our digital EHR data warehouse, current procedural terminology codes were mapped to procedure code IDs, and residents verified automated logs before ACGME transmission. Manual case logs were extracted from operative reports in Epic by 2 staff members for comparison. Accuracy, precision, and sensitivity were assessed by comparing automated logs against manual logs. Time savings was estimated by comparing manual and automated per case-logging time across clinical residency years.
RESULTS: A total of 1977 cases were automatically pulled compared with 2041 cases pulled from manual chart review. After review by each resident, 1955 of the automatically pulled cases were correctly logged and categorized when compared with manual cases (94.8% accuracy), with a 98.9% precision and 95.8% sensitivity. Traditional self-reported case logging took, on average, 199.9 ± 99.1 seconds per case. In comparison, reviewing and confirming automated case logs required an average of 68.4 ± 75.1 seconds per case. Per ACGME case minimums, automated logging reduced time by 66%, saving a neurosurgical resident 58 hours over the course of 5 operative, clinical years.
CONCLUSION: Automated case logs provide an accurate, time-effective tool that can reduce administrative burden. Future studies should evaluate how to further improve and broaden the application of automated case logs to other specialties.
METHODS: We evaluated case logs from 20 residents (post-graduate year 1-7) at an academic neurosurgery department from June to September 2025. Automated case logs were extracted from our digital EHR data warehouse, current procedural terminology codes were mapped to procedure code IDs, and residents verified automated logs before ACGME transmission. Manual case logs were extracted from operative reports in Epic by 2 staff members for comparison. Accuracy, precision, and sensitivity were assessed by comparing automated logs against manual logs. Time savings was estimated by comparing manual and automated per case-logging time across clinical residency years.
RESULTS: A total of 1977 cases were automatically pulled compared with 2041 cases pulled from manual chart review. After review by each resident, 1955 of the automatically pulled cases were correctly logged and categorized when compared with manual cases (94.8% accuracy), with a 98.9% precision and 95.8% sensitivity. Traditional self-reported case logging took, on average, 199.9 ± 99.1 seconds per case. In comparison, reviewing and confirming automated case logs required an average of 68.4 ± 75.1 seconds per case. Per ACGME case minimums, automated logging reduced time by 66%, saving a neurosurgical resident 58 hours over the course of 5 operative, clinical years.
CONCLUSION: Automated case logs provide an accurate, time-effective tool that can reduce administrative burden. Future studies should evaluate how to further improve and broaden the application of automated case logs to other specialties.