ICONS: Managing Care and Costs: The Sustained Cost Impact of Reduced Hospitalizations in a Partnership-Measurement Model of Disease Management
Bibliographic record
Abstract
Improving Cardiovascular Outcomes in Nova Scotia (ICONS) was a multidisciplinary-partnership, measurement-driven disease management project designed to improve the care and outcomes of patients with acute and chronic heart diseases in Nova Scotia. Previous analyses demonstrated beneficial clinical and macroeconomic end points at the population and system levels, including heightened awareness of the value of team care, increased use of proven therapies, decreased re-hospitalizations and a positive dollar return on investment for the economies of Nova Scotia and Canada. This article analyzes the additional cost-reduction benefits resulting from the reduced re-hospitalizations that occurred among patient populations with heart attacks and heart failure. Over the five-year course of ICONS, one-year readmissions and readmission rates fell continuously for both index disease states. Despite a general inflationary rise in real hospital costs, the per-event cost of readmissions expressed in constant 2002 dollars also decreased: from $10,377 in 1997 to $9,022 in 2002 for the heart attack patient population; and from $9,020 to $8,697 for patients with heart failure. Total real yearly costs for heart attack readmissions fell from $7.4 million in 1997 to $6.4 million in 2002, a 14% decrease; for heart failure, yearly costs decreased by 26%, from $9.2 million to $6.8 million. These microeconomic data supplement the previously reported improvements in patient care and the positive macroeconomic impact of ICONS. Overall, ICONS demonstrated that quality and cost of healthcare could be simultaneously and successfully managed over a sustained period of time for whole patient populations in a real-world setting. ICONS offers strong evidence of the value of the partnership-measurement model of disease management and prevention as a reproducible and desirable template for next-generation healthcare in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".