2026-06-24 マサチューセッツ大学アマースト校
This illustrates the timeline of two surgeries performed by the same surgeon. The blue area represents the duration of the actual surgery itself, while the white blocks represent pre- and post-surgery activity. Surgeon gap time is the time interval between two operations where the surgeon is not actively working.
＜関連情報＞
- https://www.umass.edu/news/article/scheduling-surgeons-umass-amherst-researchers-identify-factors-could-influence
- https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocag081/8700062
電子カルテデータを用いて外科医の業務量を特徴づけ、手術間隔と術後ケア提供までの時間を予測する Characterizing surgeon workload with electronic health record data to predict time interval between surgeries and postoperative care delivery
Jonathan Akhagbosu, BS ,Muge Capan, PhD ,Hari Balasubramanian, PhD ,Tovy H Kamine, MD, MBA
Journal of the American Medical Informatics Association Published:02 June 2026
DOI:https://doi.org/10.1093/jamia/ocag081
Abstract
Objectives
This study positions surgeon gap time, defined as the interval between consecutive surgeries performed by the same surgeon, as a surgeon-level metric of efficiency. Understanding gap time requires accounting for a surgeon’s operative workload, yet no objective electronic health record (EHR)-derived measure exists. We conceptualize surgical case demand as an EHR-derived surrogate for operative workload and examine its association with surgeon gap time.
Materials and Methods
We analyzed 86 480 surgeries in 14 specialties performed between 2020 and 2023 at a US Medical Center. Surgical case demand was operationalized using patient and surgery features and clustered into demand types. Clinical implications were assessed by comparing postoperative care location and length of stay (LOS) across demand types. A classification tree was developed to assign demand types for future cases. Regression models were used to identify predictors of gap time.
Results
Clustering identified 3 demand types with distinct postoperative profiles. High-level recovery was required for 5.9%, 6.5%, and 17.2% of demand types 1, 2, and 3 cases, respectively (P < .001), with median LOS of 0.09, 0.97, and 1.80 days (P < .001). Demand type, case priority, surgical specialty, and surgical care location were significant predictors of gap time.
Discussion
Surgical case demand serves as an EHR-derived surrogate for operative workload, enabling structured analysis of surgeon gap time and the factors associated with it.
Conclusion
Surgeon gap time is an objective metric that can be operationalized from EHR data, providing insights for scheduling, resource allocation, and overall health system efficiency.
