An environmental analysis of the evolution of readmission reduction strategies: A study of United States hospitals
Bibliographic record
Abstract
Objective: Environmental factors have changed the manner in which issues in the U.S. healthcare industry are addressed. One of these changes is in the area of quality improvement, specifically readmission reduction. The purpose of this paper is two-fold: (1) analyze macro-environmental segments (political, technological, economic, and socio-demographic); and (2) trace the historic evolution of readmission reduction programs to understand how macro-environmental factors have shaped the development of readmission reduction strategies.Methods: Scopus, PubMed, and ABI/Inform electronic databases were searched for articles on readmission reduction programs from 2000 to 2014. In addition, literature on macro-environment was retrieved from these sources for the same time period. Studies were identified using specific search terms and inclusion criteria. A total of 24 articles were selected for review. Data on the following variables were extracted: type of organization studied, type of quality improvement strategy used, type of patients studied, and results of the strategy. In addition, an examination of macro-environmental factors that may have affected the above variables was done. Finally, results were integrated and presented in a chronological order.Results: Findings suggest that macro-environmental factors have influenced the development of readmission reduction strategies over time. This paper informs healthcare managers about being cognizant of environmental trends when devising readmission reduction strategies within hospitals.Conclusions: Insights from this paper urge hospital administrators to forge collaborations with key stakeholders while developing new quality improvement strategies when facing an unstable and complex environment.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".