Early Rehabilitation Bundle in a Canadian PICU: Cost Analysis of Implementation in 2018–2020
Bibliographic record
Abstract
OBJECTIVES: First, to determine the resources and costs required to implement an early rehabilitation (ABCDEF) bundle. Second, to compare the impact of the bundle on costs pre- and post-implementation. DESIGN AND SETTING: Cost analysis was conducted as part of an implementation study at McMaster Children's Hospital PICU in 2018-2020. MEASUREMENTS AND MAIN RESULTS: Resource estimates for all implementation activities from 2018 to 2020 were calculated from material costs and hours spent by personnel multiplied by wages. PICU and patient-level costs before (from January 2019 to March 2019) and after bundle implementation (from January 2020 to March 2020) were compared using case-costing data. Linear regression was used to analyze log-transformed costs adjusted for age, sex, and severity of illness score. Costs are reported in Canadian dollars (CAD). A total of 907 hours were spent over a 2-year implementation period, at an estimated cost of CAD 50,813. Physicians contributed the most hours, followed by the nurse educator and pharmacist. Material costs were CAD 860. There were 141 patients pre-implementation and 84 patients post-implementation in the analyses. Adjusted mean PICU cost per patient was CAD 17,342 and CAD 20,310, pre- to post-implementation, respectively; mean difference (95% CI) between post- and pre-implementation was 17% higher (95% CI, from 6.3% lower to 46% higher). Adjusted mean pharmacy cost per patient was CAD 834 pre-implementation and CAD 827 post-implementation; mean difference of 0.8% lower post-implementation (95% CI, from 27% lower to 35% higher). CONCLUSIONS: Implementation of the ABCDEF bundle requires significant time and collaboration of key stakeholders. There was no impact on PICU or patient costs following bundle implementation, but the period of observation was limited by COVID-19. Future studies should include cost analyses that incorporate longer-term, patient-centered health outcomes to determine whether this intervention is cost-effective.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".