Hospitalist time‐motion studies: A systematic review
Bibliographic record
Abstract
BACKGROUND: Hospitalist workflows have evolved significantly, yet optimal workflows and workloads remain ill-defined. Time and motion studies (TMSs) offer insights into hospitalist activities but face methodological challenges, including variability and lack of standardization. OBJECTIVES: We aimed to systematically review TMSs of hospitalist workflows, assess trends in direct and indirect patient care, and develop a novel quality assessment tool for evaluating TMS studies. METHODS: We conducted a comprehensive search of Ovid MEDLINE (1946-October 2024), Embase (1947-October 2024), and Web of Science (1974-October 2024) in August 2023 and updated October 7, 2024. We included studies that employed observational or quantitative TMS methods focused on attending hospitalists in US general adult inpatient settings and reported the proportion of time spent in direct and indirect patient care. We assessed study quality using a quality assessment tool adapted from the Newcastle-Ottawa scale. RESULTS: Seven studies met the inclusion criteria. Direct patient care accounted for a mean of 18% (range: 13%-25%) of observed time. We identified high variability in study quality, with scores ranging from 2 to 5 out of eight stars. Significant study variability precluded statistical analysis of trends, though a narrative synthesis was possible. Few studies represented diverse settings or shifts. CONCLUSIONS: This review utilizes a novel quality assessment tool and highlights the need for standardized TMS methodologies to enable longitudinal comparisons and more accurate assessments of hospitalist workflows. Future studies should integrate validated tools, consider multitasking, and explore emerging metrics beyond productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".