Time-motion studies of internal medicine residents' duty hours: a systematic review and meta-analysis
Bibliographic record
Abstract
Cameron W Leafloor,1 Heather A Lochnan,2,3,6 Catherine Code,2,4 Erin J Keely,2,3,6 Deanna M Rothwell,5,6 Alan J Forster,2,4–6 Allen R Huang2,6,7 1Faculty of Medicine, 2Department of Medicine, 3Division of Endocrinology and Metabolism, 4Division of General Internal Medicine, 5Performance Measurement and Innovation, 6Ottawa Hospital Research Institute, 7Division of Geriatric Medicine, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada Background: Since the mid-1980s, medical residents' long duty hours have been under scrutiny as a factor affecting patient safety and the work environment for the residents. After several mandated changes in duty hours, it is important to understand how residents spend their time before proposing and implementing future changes. Time-motion methodology may provide reliable information on what residents do while on duty.Purpose: The purpose of this study is to review all available literature pertaining to time-motion studies of internal medicine residents while on a medicine service and to understand how much of their time is apportioned to various categories of tasks, and also to determine the effects of the Accreditation Council for Graduate Medical Education (ACGME)-mandated duty hour changes on resident workflow in North America.Methods: Electronic bibliographic databases were searched for articles in English between 1941 and April 2013 reporting time-motion studies of internal medicine residents rotating through a general medicine service.Results: Eight articles were included. Residents spent 41.8% of time in patient care activities, 18.1% communicating, 13.8% in educational activities, 19.7% in personal/other, and 6.6% in transit. North American data showed the following changes after the implementation of the ACGME 2003 duty hours standard: patient care activities from 41.8% to 40.8%, communication activities from 19.0% to 22.3%, educational activities from 17.7% to 11.6%, and personal/other activities from 21.5% to 17.1%.Conclusion: There was a paucity of time-motion data. There was great variability in the operational definitions of task categories reported in the studies. Implementation of the ACGME duty hour standards did not have a significant effect on the percentage of time spent in particular tasks. There are conflicting reports on how duty hour changes have affected patient safety. A low proportion of time spent in educational activities deserves further study and may point to a review of the educational models used.Keywords: time-motion studies, residents’ duty hours, workflow, systematic review
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.026 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".