The Effectiveness and Efficiency of Disease Management Programs for Patients with Chronic Diseases
Bibliographic record
Abstract
OBJECTIVE: Disease management (DM) approach is increasingly advocated as a means of improving effectiveness and efficiency of healthcare for chronic diseases. To evaluate the evidence on effectiveness and efficiency of DM, evidence synthesis was carried out. METHODS: To locate eligible meta-analyses and systematic reviews, we searched Medline, EMBASE, the Cochrane Library, SCI-EXPANDED, SSCI, A&HCI, DARE, HTA and NHS EED from 1995 to 2010. Two reviewers independently extracted data and assessed a study quality. RESULTS: Twenty-eight meta-analyses and systematic reviews were included for synthesizing evidence. The proportion of articles which observed improvement with a reasonable amount of evidence was the highest at process (69%), followed by health services (63%), QOL (57%), health outcomes (51%), satisfaction (50%), costs (38%) and so on. As to mortality, statistically significant results were observed only in coronary heart disease. Important components in DM, such as a multidisciplinary approach, were identified. CONCLUSION: The evidence synthesized shows considerable evidence in the effectiveness and efficiency of DM programs in process, health services, QOL and so on. The question is no longer whether DM programs work, but rather which type or component of DM programs works best and efficiently in the context of each healthcare system or country.
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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 | 0.000 |
| 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".