ReviewJournal of graduate medical education2026
Automating Resident Case Logs: Narrative Review and Challenges Ahead.
Review in Journal of graduate medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: A surgical resident's logs should represent their operative experience. In practice, manually compiled logs are fraught with inaccuracies and incompleteness. Electronic health record (EHR) data may enable case log automation, potentially improving accuracy and reducing resident administrative burden. Objective: We examined and summarized the current literature on automated case logging systems to understand the current approaches, outcomes, and ongoing challenges. Methods: We performed a narrative review using MEDLINE, Scopus, and Embase databases from January 1946 to February 2025 using keywords associated with resident case and procedure logging. English language, peer-reviewed manuscripts evaluating automated or semiautomated case logging systems were included. Articles focusing on case log analysis without addressing automated logging were excluded. Extracted information included automation methods, integration with residency systems, and measured impacts on accuracy, completeness, or efficiency. Results: A total of 64 deduplicated articles were screened, yielding 8 semiautomated case logging systems used in emergency medicine, anesthesiology, general surgery, and ophthalmology. No fully automated end-to-end systems were identified. These systems typically increased number of cases logged as well as accuracy and completeness. Common methods included EHR data aggregation in dashboards, interfaces with logging applications, and machine learning-assisted decision support. Reported outcomes showed improved logging frequency, accuracy, and reduced variability. Studies consistently demonstrated efficiency gains and reduced resident administrative burdens. Conclusions: Automating resident case logging by leveraging EHR data can improve log accuracy and decrease administrative workload. Current implementations remain semiautomated and institution specific, highlighting challenges with data integration, coding consistency, and specialty-specific requirements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.