The Economic Impact of a Partnership-Measurement Model of Disease Management: Improving Cardiovascular Outcomes in Nova Scotia (ICONS)
Bibliographic record
Abstract
Improving Cardiovascular Outcomes in Nova Scotia (ICONS) was a five-year, community partnership-based disease-management project that sought, as a primary goal, to improve the care and outcomes of patients with heart disease in Nova Scotia. This program, based on a broad stakeholder partnership, provided repeated measurement and feedback on practices and outcomes as well as widespread communication and education among all partners. From a clinical viewpoint, ICONS was successful. For example, use of proven therapies for the target diseases improved and re-hospitalization rates decreased. Stakeholders also perceived a sense of satisfaction because of their involvement in the partnership. However, the universe of health stakeholders is large, and not many have had an experience similar to ICONS. These other health stakeholders, such as decision-makers concerned with the cost of care and determining the value for cost, might, nonetheless, benefit from knowledge of the ICONS concepts and results, particularly economic analyses, as they determine future health policy. Using budgetary data on actual dollars spent and a robust input-output methodology, we assessed the economic impact of ICONS, including trickle-down effects on the Canadian and Nova Scotian economies. The analysis revealed that the $6.22 million invested in Nova Scotia by the private sector donor generated an initial net increase in total Canadian wealth of $5.32 million and a global net increase in total Canadian wealth of $10.23 million, including $2.27 million returned to the different governments through direct and indirect taxes. Thus, the local, provincial and federal governments are important beneficiaries of health project investments such as ICONS. The various government levels benefit from the direct influx of private funds into the publicly funded healthcare sector, from direct and indirect tax revenues and from an increase in knowledge-related employment. This, of course, is in addition to the clinical benefits associated with the partnership-measurement disease-management model. Because of their uniquely simultaneous roles as beneficiary and major resource provider, the public payer can play an early and active role in such partnerships to enhance its efficiencies and increase the likelihood of sustainability if the original concepts are proven of value.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".