Effectiveness of quality improvement strategies for coordination of care to reduce use of health care services: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Frequent users of health care services are a relatively small group of patients who account for a disproportionately large amount of health care utilization. We conducted a meta-analysis of the effectiveness of interventions to improve the coordination of care to reduce health care utilization in this patient group. METHODS: We searched MEDLINE, Embase and the Cochrane Library from inception until May 2014 for randomized clinical trials (RCTs) assessing quality improvement strategies for the coordination of care of frequent users of the health care system. Articles were screened, and data abstracted and appraised for quality by 2 reviewers, independently. Random effects meta-analyses were conducted. RESULTS: We identified 36 RCTs and 14 companion reports (total 7494 patients). Significantly fewer patients in the intervention group than in the control group were admitted to hospital (relative risk [RR] 0.81, 95% confidence interval [CI] 0.72-0.91). In subgroup analyses, a similar effect was observed among patients with chronic medical conditions other than mental illness, but not among patients with mental illness. In addition, significantly fewer patients 65 years and older in the intervention group than in the control group visited emergency departments (RR 0.69, 95% CI 0.54-0.89). INTERPRETATION: We found that quality improvement strategies for coordination of care reduced hospital admissions among patients with chronic conditions other than mental illness and reduced emergency department visits among older patients. Our results may help clinicians and policy-makers reduce utilization through the use of strategies that target the system (team changes, case management) and the patient (promotion of self-management).
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".