REVIEW OF NURSE-LED INTERVENTIONS IN REDUCING HOSPITAL READMISSION RATES AMONG ELDERLY PATIENTS WITH CHRONIC ILLNESSES: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Hospital readmissions among elderly patients with chronic illnesses remain a major global healthcare concern, leading to increased morbidity, reduced quality of life, and escalating healthcare costs. Nurse-led interventions have emerged as a promising approach to improve care continuity and reduce preventable readmissions. However, existing studies show variability in design, outcomes, and intervention types, necessitating a systematic synthesis of evidence to establish their effectiveness. Objective: This systematic review aims to evaluate the impact of nurse-led interventions on hospital readmission rates among elderly patients with chronic illnesses, assessing their effectiveness compared to standard or physician-led care. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for studies published between 2015 and 2025. Eligible studies included randomized controlled trials, quasi-experimental, and observational designs focusing on nurse-led interventions for adults aged ≥65 with chronic conditions. Data extraction and quality appraisal were performed independently by two reviewers using standardized tools, including the Cochrane Risk of Bias 2 and Newcastle-Ottawa Scale. A narrative synthesis and meta-analysis using a random-effects model were performed to summarize outcomes. Results: Eight studies involving 8,945 participants were included. Nurse-led interventions—such as transitional care, home visits, telehealth follow-up, and education—significantly reduced hospital readmission rates (RR = 0.67; 95% CI 0.49–0.92; p = 0.01). Secondary outcomes demonstrated improved self-management, medication adherence, and quality of life. Heterogeneity was moderate (I² = 58%), and the overall risk of bias was low to moderate. Conclusion: Nurse-led interventions effectively reduce hospital readmissions and improve overall care outcomes among elderly patients with chronic diseases. These findings highlight the critical role of nurses in transitional and chronic care management. Nonetheless, further large-scale, standardized RCTs are required to confirm long-term effectiveness and cost-efficiency across diverse healthcare systems.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".