Implementing the patient care collaborative model in three general internal medicine units: a mixed-methods healthcare improvement initiative
Bibliographic record
Abstract
Background As part of the scale-up of the Patient Care Collaborative (PCC) at our institution, we explored staff perceptions and patient outcomes at different levels of model implementation in three general internal medicine units. Methods We conducted a mixed-methods embedded experimental healthcare improvement initiative. In the qualitative strand, we conducted five focus group discussions. In the quantitative strand, we used hospital administrative data to compare outcomes (falls per 1000, median length of stay in days and resource use measured as resource intensity weights (RIW), before and after the implementation of the PCC, using χ2 tests, Wilcoxon’s rank sum tests and interrupted time series analyses. Results Staff showed considerable knowledge and acceptance of the PCC but expressed mixed feelings with regards to patient safety, workload, communication and teamwork. Staff perceptions varied by level of implementation of the PCC. A number of falls (overall) in the full implementation phase were not significantly different from the preimplementation phase (227 per 1000 vs 200 per 1000; p=0.449), but the number of moderate to severe falls dropped (12 vs 2 per 1000); p<0.001). Median length of stay (5 vs 6 days; p<0.001) and resource use were lower (0.1 vs 0.4; p<0.001) in the full implementation phase compared with the preimplementation phase. The trend analyses showed differences across units. Conclusions The PCC was moderately well adopted. Perceptions of the PCC among staff and patient outcomes are likely linked to the levels of implementation. The PCC resulted in improved safety, shorter hospital stays and lower costs of care.
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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 | 0.082 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".