Health technologies for the improvement of chronic disease management: a review of the Medical Advisory Secretariat evidence-based analyses between 2006 and 2011.
Bibliographic record
Abstract
BACKGROUND: As part of ongoing efforts to improve the Ontario health care system, a mega-analysis examining the optimization of chronic disease management in the community was conducted by Evidence Development and Standards, Health Quality Ontario (previously known as the Medical Advisory Secretariat [MAS]). OBJECTIVE: The purpose of this report was to identify health technologies previously evaluated by MAS that may be leveraged in efforts to optimize chronic disease management in the community. DATA SOURCES: The Ontario Health Technology Assessment Series and field evaluations conducted by MAS and its partners between January 1, 2006, and December 31, 2011. REVIEW METHODS: Technologies related to at least 1 of 7 disease areas of interest (type 2 diabetes, coronary artery disease, atrial fibrillation, chronic obstructive pulmonary disease, congestive heart failure, stroke, and chronic wounds) or that may greatly impact health services utilization were reviewed. Only technologies with a moderate to high quality of evidence and associated with a clinically or statistically significant improvement in disease management were included. Technologies related to other topics in the mega-analysis on chronic disease management were excluded. Evidence-based analyses were reviewed, and outcomes of interest were extracted. Outcomes of interest included hospital utilization, mortality, health-related quality of life, disease-specific measures, and economic analysis measures. RESULTS: Eleven analyses were included and summarized. Technologies fell into 3 categories: those with evidence for the cure of chronic disease, those with evidence for the prevention of chronic disease, and those with evidence for the management of chronic disease. CONCLUSIONS: The impact on patient outcomes and hospitalization rates of new health technologies in chronic disease management is often overlooked. This analysis demonstrates that health technologies can reduce the burden of illness; improve patient outcomes; reduce resource utilization intensity; be cost-effective; and be a viable contributing factor to chronic disease management in the community. PLAIN LANGUAGE SUMMARY: People with chronic diseases rely on the health care system to help manage their illness. Hospital use can be costly, so community-based alternatives are often preferred. Research published in the Ontario Health Technology Assessment Series between 2006 and 2011 was reviewed to identify health technologies that have been effective or cost-effective in helping to manage chronic disease in the community. All technologies identified led to better patient outcomes and less use of health services. Most were also cost-effective. Two technologies that can cure chronic disease and 1 that can prevent chronic disease were found. Eight technologies that can help manage chronic disease were also found. Health technologies should be considered an important part of chronic disease management in the community.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".