Benefits of Information Technology–Enabled Diabetes Management
Bibliographic record
Abstract
OBJECTIVE: To determine the financial and clinical benefits of implementing information technology (IT)-enabled disease management systems. RESEARCH DESIGN AND METHODS: A computer model was created to project the impact of IT-enabled disease management on care processes, clinical outcomes, and medical costs for patients with type 2 diabetes aged >25 years in the U.S. Several ITs were modeled (e.g., diabetes registries, computerized decision support, remote monitoring, patient self-management systems, and payer-based systems). Estimates of care process improvements were derived from published literature. Simulations projected outcomes for both payer and provider organizations, scaled to the national level. The primary outcome was medical cost savings, in 2004 U.S. dollars discounted at 5%. Secondary measures include reduction of cardiovascular, cerebrovascular, neuropathy, nephropathy, and retinopathy clinical outcomes. RESULTS: All forms of IT-enabled disease management improved the health of patients with diabetes and reduced health care expenditures. Over 10 years, diabetes registries saved $14.5 billion, computerized decision support saved $10.7 billion, payer-centered technologies saved $7.10 billion, remote monitoring saved $326 million, self-management saved $285 million, and integrated provider-patient systems saved $16.9 billion. CONCLUSIONS: IT-enabled diabetes management has the potential to improve care processes, delay diabetes complications, and save health care dollars. Of existing systems, provider-centered technologies such as diabetes registries currently show the most potential for benefit. Fully integrated provider-patient systems would have even greater potential for benefit. These benefits must be weighed against the implementation costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".