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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".