Care transition from rehabilitation to home: A QI project using the RED Toolkit to decrease readmission rates
Bibliographic record
Abstract
Objective: This quality improvement (QI) project’s aim was to lower 30-day healthcare reutilization for patients aged 50 or older with hip fracture using an evidence-based discharge process method, the Re-Engineered Discharge (RED) Toolkit.Methods: The QI project of a revised patient discharge process to lower healthcare reutilization of Baton Rouge Rehabilitation Hospital (BRRH) hip fracture patients was implemented as an evidence-based quality improvement initiative. Inpatient and outpatient discharge process revisions were implemented at an inpatient rehabilitation facility (IRF) based on Re-Engineered Discharge (RED) Toolkit recommendations. Inpatient revisions included patient barrier identification with associated documentation changes to the IRF interdisciplinary team form. Outpatient modifications consisted of an After-Hospital Care Plan (AHCP), and two post-discharge Telephone Follow-Up (TFU) calls.Results: Healthcare reutilization and thirty-day hospital readmission for this project were measured at 8.5% and 5.7%, respectively. A decrease in healthcare reutilization of at least 1.6% was observed for the IRF. Most participants scored at a high level (88.6%) of “patient knowledge of self-management” post intervention. Out of participants who did not attend their first Primary Care Provider (PCP) appointment, 33.3% experienced healthcare reutilization. This result emphasized the importance of seeing one’s PCP post-discharge. Patient satisfaction increased by 5% and 6.73%, measured by Hospital Consumer Assessment of HealthCare Providers and Systems (HCAHP) scores for nursing care and physician care, respectively.Conclusions: Implementation of a RED Toolkit-based discharge process at an IRF positively impacted all three study outcomes and associated healthcare costs in lowering preventable readmissions.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".