Impact of Reengineered Discharge Toolkit on Patients Undergoing Total Joint Surgeries
Bibliographic record
Abstract
ABSTRACT: Poorly coordinated care transitions account for nearly one fifth of Medicare hospital readmissions within 30 days postdischarge. The primary aim of this pilot project was to determine the impact of the Reengineered Discharge (RED) Toolkit on patient knowledge for self-management, satisfaction with the discharge process, readiness for discharge, discharge time, and 30-day readmission rate following hip or knee joint replacement or revision surgeries. Staff adherence with the RED Toolkit was also measured.Thirty adult patients received the intervention of the RED Toolkit. Patient knowledge for self-management ranged from 85.2% to 92.6%; satisfaction with the discharge process scores increased from 33% to 59.2%; patient readiness for discharge scores increased from 2% to 64%. Discharge times decreased. On average, patients left the unit 5.67 (±2.52) hours after the written discharge order. The all-cause 30-day readmission rate was reduced to 3.3%. Staff achieved a RED Toolkit adherence rate of 86.8%. Findings provide a basis for developing a coordinated discharge planning process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".