A quality improvement initiative aimed at reducing service strain and improving physician wellness in internal medicine
Bibliographic record
Abstract
INTRODUCTION: Hospital strain has been shown to negatively impact physician wellness, educational experience, and patient care. To address rising service demands, a non-academic hospitalist service was implemented to reduce daily clinical teaching unit (CTU) census by approximately 30%. Secondary aims were to evaluate physician and trainee wellness on CTU as well as assess unintended adverse patient outcomes. METHODS: A two-phase intervention was implemented at one of two academic hospital campuses in January and April 2023. Mean daily census, mortality, 30-day readmissions, and length of stay (LOS) were obtained from an administrative database for the pre-study (October to December 2022) and study (January to December 2023) periods. The Mini-Z physician wellness survey was administered in March, June and December 2023. Data were analyzed by quarters using descriptive statistics as well as parametric and non-parametric testing, and a reflexive thematic analysis was undertaken. RESULTS: A CTU census trough of 71.3 was briefly attained in the second quarter of 2023 but increased to 78.6 in the fourth quarter of 2023, while remaining below pre-intervention levels. The proportion of attendings and residents reporting burnout was significantly different at the intervention (65.2%, n = 15/23) versus non-intervention site (94.1%, n = 16/17) in Q4 2023 (p = 0.033). Burnout was positively correlated with daily CTU census across both sites (r = 0.906). There were no differences in proportion of in-hospital mortality (p = 0.854), 30-day readmissions (p = 0.262), or LOS (p = 0.977) between the pre- and post-implementation periods. Qualitative analysis identified the hospitalist program as beneficial, but inadequate to address workload, education challenges, and patient safety concerns. CONCLUSION: The addition of a non-academic hospitalist service reduced CTU census numbers and improved burnout, but the improvement in service strain was limited by rising admissions. Multifaceted approaches to wellness are needed, but this study supports ongoing endeavors aimed at reducing clinical workload to optimize the clinical teaching environment.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".