Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records
Bibliographic record
Abstract
BACKGROUND: Computerized decision support systems (CDSSs) are believed to enhance patient care and reduce healthcare costs; however the current evidence is limited and the cost-effectiveness remains unknown. OBJECTIVE: To estimate the long-term cost-effectiveness of a CDSS linked to evidence-based treatment recommendations for type 2 diabetes. METHODS: Using the Ontario Diabetes Economic Model, changes in factors (eg, HbA1c) from a randomized controlled trial were used to estimate cost-effectiveness. The cost of implementation, development, and maintenance of the core dataset, and projected diabetes-related complications were included. The base case assumed a 1-year treatment effect, 5% discount rate, and 40-year time horizon. Univariate, one-way sensitivity analyses were carried out by altering different parameter values. The perspective was the Ontario Ministry of Health and costs were in 2010 Canadian dollars. RESULTS: The cost of implementing the intervention was $483,699. The one-year intervention reduced HbA1c by 0.2 and systolic blood pressure by 3.95 mm Hg, but increased body mass index by 0.02 kg/m², resulting in a relative risk reduction of 14% in the occurrence of amputation. The model estimated that the intervention resulted in an additional 0.0117 quality-adjusted life year; the incremental cost-effectiveness ratio was $160,845 per quality-adjusted life-year. CONCLUSION: The web-based prototype decision support system slightly improved short-term risk factors. The model predicted moderate improvements in long-term health outcomes. This disease management program will need to develop considerable efficiencies in terms of costs and processes or improved effectiveness to be considered a cost-effective intervention for treating patients with type 2 diabetes.
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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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".