Optimizing Healthcare at the Population Level: Results of the Improving Cardiovascular Outcomes in Nova Scotia Partnership
Bibliographic record
Abstract
Disease management is increasingly considered a valid strategy in the chronic care of our aging patient populations with multiple diseases. The Improving Cardiovascular Outcomes in Nova Scotia (ICONS) project examined whether a community-oriented health management partnership would lead to enhanced care and improved outcomes across an entire healthcare system. ICONS was a prospective cohort study, with baseline and repeated measurements of care and outcomes fed back to all project partners, along with other interventions aimed at optimizing care; preceding interval cohorts served as controls to post-intervention cohorts. The setting was the province of Nova Scotia, whose population is approximately 950,000. All 34,060 consecutive adult patients hospitalized in Nova Scotia with acute myocardial infarction (AMI), unstable angina (UA) or congestive heart failure (CHF) October 1997-March 2002 were included. Interventions were a combination of serial audits and feedbacks of practices and outcomes, web-based publication of findings, newsletter-based education and reminders, physician small-group workshops, pharmacy monitoring and compliance programs, care maps, algorithms, discharge forms and patient information cards. Rates of use of evidence-based marker therapies were the primary outcome measure. Secondary measures included one-year, all-cause mortality and re-hospitalization. Evidence-based prescription practices, for all target diseases, continuously and markedly improved over time. At the population level, there were no changes in one-year mortality for any disease state, although use of proven therapies predicted survival at the individual level throughout the five-year period for all disease states. Rates of re-hospitalization decreased significantly for all disease states over the course of ICONS; but most traditional positive and negative predictors of this outcome, like advanced age and use of proven therapies, respectively, were not predictive. ICONS demonstrated that provider prescribing patterns and patient re-hospitalization rates were continuously improved in three disease states and across an entire health system, through a community partnership model of disease management that was sustained over a long time. Further studies are needed to better understand the drivers and modifiers of patient outcomes at the population level.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".