Quality Legislation: Lessons for Ontario from Abroad
Bibliographic record
Abstract
While the Excellent Care for All Act, 2010 (ECFA Act) provides a comprehensive approach to stimulating quality improvement in healthcare, there are other examples of legislations articulating strategies aimed at the same goal but proposing different approaches. This paper reviews quality of care legislations in the Netherlands, the United States, England and Australia, compares those pieces of legislation with the ECFA Act and suggests lessons for Ontario in planning the next stages of its healthcare quality strategy. Notable among the commonalities that the EFCA Act shares with the selected examples of legislation are mandatory reporting of performance results at an organizational level and furthering quality improvement, evidence generation and performance monitoring. However, the EFCA Act does not include any elements of restructuring or competition, unlike some of the other examples. Key to successful transformation of the Ontario healthcare system will be to propose a package of changes that will deal systematically with all aspects of transformation sought (including structural changes, payments systems and elements of competition), will garner support from all the actors, and will be implemented consistently and persistently. Benchmarking on the implementation and impact of reforms with the countries presented in this paper may be an additional important step. Quality of care is a key focus of health system reforms, and in recent years many countries in the Organisation of Economic Co-operation and Development (OECD), including Canada, have developed strategies aimed at improving healthcare quality and patient safety (OECD 2010). Øvretveit and Klazinga propose that national strategies for quality of care can be targeted at different types of health system stakeholders: professionals, healthcare organizations, medical products and technologies, patients and financers (World Health Organization Regional Office for Europe 2008). The generic elements of these strategies relate to legislation and regulation, monitoring and measurement; assuring and improving the quality and safety of individual healthcare services, and assuring and improving the quality of the healthcare system as a whole. Various combinations of quality improvement approaches (such as quality assessment, standards-based quality management, team problem solving, and patient and community participation) are suitable for these functions as part of the respective quality strategies.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".