Interventions aimed at addressing unplanned hospital readmissions in the U.S.: A systematic review
Bibliographic record
Abstract
One of the policy mechanisms aimed at improving population health through health care delivery is the Hospital Readmissions Reduction Program (HRRP) as outlined in the Affordable Care Act. Although numerous procedural and behavioral interventions have been implemented, the empirical evidence of the efficacy of these interventions is mixed and specific to certain patient segments. This review aimed to systematically assess studies of hospital interventions to reduce 30-day readmissions for specific diseases and populations. Following the PRISMA review checklist, searches were conducted from January 2000 to August 2018 in the MEDLINE and EMBASE databases using terms such as “patient readmission”, “readmit” and “re-hospitalization” in conjunction with disease terms such as “asthma”, “chronic obstructive pulmonary disease (COPD)” and “pneumonia”. Of 3,806 articles identified, 45 were included after a 3-step inclusion process. The age group most frequently considered among the studies was the 65 age cohort. Multidisciplinary collaborative interventions were most frequently effective for the subset of elderly, female, Caucasian, and heart failure patients. Interventions involving patient or family education delivered before and after care were most effective for racial minority, elderly, COPD, and heart failure patients. Telephone follow-up, tele-homecare, and medication reconciliation were largely found to be successful in reducing readmissions. Major gaps exist in identifying successful interventions for reducing 30-day readmissions among patients who sought treatment for sepsis, stroke, and replacement of the hip or knee. Our findings indicate an opportunity for researchers to further study, and for healthcare organizations to implement, more well-informed interventional strategies to reduce 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".