Quality and Innovation: Redesigning a Coordinated and Connected Health System
Bibliographic record
Abstract
Nova Scotia's consolidated health system was launched on April 1, 2015. This new approach to organizing health administration and services in the province arose out of necessity. When planning began, Nova Scotia was spending 41% of its annual budget on health services. In comparison to other provinces and territories, our per capita health-related spending was among the highest in the country, we had one of Canada's oldest populations and we had some of the worst health outcomes. Clearly, we could not continue to do the same things and expect different results. Both the life sciences and technology are changing at breakneck speed, while design of healthcare delivery has barely moved beyond a mid-twentieth century paternalistic provider-centric model. Nova Scotia's transformation journey was facilitated by a major policy effort 20 years earlier that had integrated emergency health services across the province. Our aim was to build on that foundation by integrating administration in order to build primary care networks with enhanced regional specialty services, with tertiary services located in Halifax. The goal of health system innovation in Nova Scotia was - and is - based firmly on the dimensions of quality: safe care that avoids harming patients; effective care that is based on levels of evidence to achieve scalability; access to care that is focused on individuals; efficient care that reduces waste, time, energy and supplies; and equitable care that ensures a system is in place that mitigates differences in geography and social economic status. The author offers a sketch of the principal initiatives, challenges, considerations, approaches and lessons involved in this multi-factorial, multi-stakeholder innovation process.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".