Implementation of an enhanced discharge teaching protocol to reduce 30-day hospital readmissions in adults diagnosed with Sickle Cell Disease
Bibliographic record
Abstract
Background and objectives: Sickle Cell Disease is a painful genetic disorder characterized by abnormal hemoglobin that affects 1-in-365 African American births. Individuals with Sickle Cell Disease often experience frequent hospitalizations, resulting in 30-day readmission rates 2.5 times higher than those without Sickle Cell Disease. Discharge education programs have shown to improve 30-day readmissions and improve health outcomes.Methods: This advanced practice nurse-led initiative incorporated components of the Re-Engineered Discharge (Project RED) protocol, which included patient and medication education, specific discharge teaching, and post-discharge follow-up phone calls over a 12-week period. A pre/posttest design was used to evaluate 30-day readmission. Analyses included descriptive statistics and Fisher’s exact test.Results: Of the (N = 10) participants, eight (80%) were female, with a mean age of 31.08 (± 4.33) and all were African Americans. Although not statistically significant, there was a 48% reduction in 30-day readmissions rates between the pre/post intervention periods.Conclusions: The utilization of an enhanced discharge teaching protocol is a practical solution to increase patients’ readiness for discharge and to reduce 30-day readmission rates. Project RED discharge teaching protocol is an effective strategy for nurses to employ to improve patient outcomes and quality of care for persons with Sickle Cell Disease.
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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 | 0.009 | 0.024 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".