Abstract WP325: Transitional Care Interventions Result in Statistically Significant Reduction in Stroke Readmission Rate
Bibliographic record
Abstract
Background: Readmissions can negatively impact patients, families and health care organizations. Readmission penalties have resulted in focused efforts around discharge planning, patient engagement and transitional care. Purpose: We hypothesized that development of a transitional care intervention 'bundle' could decrease our stroke readmission rate. Methods: A literature search related to readmission reduction strategies for stroke patients and a transitional care gap-analysis were completed. The literature review defined at-risk patients as over 80 years old with cardiovascular comorbidities, dysphagia, diabetes and high stroke severity. Additional variables included a lack of stroke-validated readmission predictive models, variable causes for readmissions and different insurance and databases used. A retrospective review of our 30-day readmitted stroke patient records from November 2012- October 2013, showed our patients were most often low stroke severity, low risk of mortality and readmission based on an internal risk assessment tool called PRISM, most often discharged home, younger than reported in the literature, and most often returned within a week from discharge. We noted there was vast variation between the literature findings and our stroke readmissions. The literature review included best practices for non-stroke related readmissions. The gap-analysis showed we were not doing many best practices at our organization related to transitional care. We created a set of stroke-specific interventions including: scheduled follow-up appointments based on PRISM score, new patient education and teach-back tools, then targeted patients discharged home. Implementation was complete in July 2014. Results: January 2014-June 2014 readmissions were 12%. July 2014- December 2014 readmissions decreased to 8%. Using a 2 sample t-test, a comparison of these time periods showed a significant improvement: P = 0.046. Conclusion: Standardizing transitional care by implementing strategies focusing on stroke patients discharged home has resulted in a statistically significant reduction in readmissions. The reason for the differences between the organization's readmission profile and that discussed in the literature remains unclear.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".