Cost and effects of integrated care: a systematic literature review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Health and care services are becoming increasingly strained and healthcare authorities worldwide are investing in integrated care in the hope of delivering higher-quality services while containing costs. The cost-effectiveness of integrated care, however, remains unclear. This systematic review and meta-analysis aims to appraise current economic evaluations of integrated care and assesses the impact on outcomes and costs. METHODS: CINAHL, DARE, EMBASE, Medline/PubMed, NHS EED, OECD Library, Scopus, Web of Science, and WHOLIS databases from inception to 31 December 2019 were searched to identify studies assessing the cost-effectiveness of integrated care. Study quality was assessed using an adapted CHEERS checklist and used as weight in a random-effects meta-analysis to estimate mean cost and mean outcomes of integrated care. RESULTS: Selected studies achieved a relatively low average quality score of 65.0% (± 18.7%). Overall meta-analyses from 34 studies showed a significant decrease in costs (0.94; CI 0.90-0.99) and a statistically significant improvement in outcomes (1.06; CI 1.05-1.08) associated with integrated care compared to the control. There is substantial heterogeneity in both costs and outcomes across subgroups. Results were significant in studies lasting over 12 months (12 studies), with both a decrease in cost (0.87; CI 0.80-0.94) and improvement in outcomes (1.15; 95% CI 1.11-1.18) for integrated care interventions; whereas, these associations were not significant in studies with follow-up less than a year. CONCLUSION: Our findings suggest that integrated care is likely to reduce cost and improve outcome. However, existing evidence varies largely and is of moderate quality. Future economic evaluation should target methodological issues to aid policy decisions with more robust evidence on the cost-effectiveness of integrated care.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.064 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".