Ontario case costing: A catalyst for transforming Ontario’s health system into a value-based model
Bibliographic record
Abstract
IntroductionCase costing is the only source of integrated financial, clinical, and utilization data at the patient level in Ontario. This accounting tool improves funding and performance management processes and tracks health care service costs for informed decision making based on quality and value. Objectives and ApproachThe only source of integrated financial, clinical, and utilization data at the patient level, case costing provides a clear picture of each patient in terms of where the patient received care, who was the provider, what type of care was provided, and the total cost to treat each patient. Case costing is a critical tool for system-wide planning by providing a province-wide view of cost and clinical variations. Health organizations can compare costs associated with service utilization to identify organizational variances among peers. Identifying the source of these variances drives effective resource utilization and management while delivering high-quality care. ResultsCase costing provides a range of benefits and uses: Improved benchmarking and performance measurement processes by comparing utilization and costs among various clinical practices with similar clinical outcomes and measuring performance internally and externally to understand efficiencies and outcomes against peers. Providing critical information for innovative care initiatives, such as the Bundled Care Model. The bundled care model improves efficiency through more integrated use of resources, reduced variation in access to services, and improved patient outcomes through seamless transitions across the care continuum. Case costing informs funding allocations in the hospital sector with expected expansion to home care funding. More sophisticated planning and forecasting, such as impact analysis by modeling the adoption of new physicians or technologies within facilities to determine cost and volume impact. Conclusion/ImplicationsAdministrators, clinicians, and health service organizations use case costing data for informing best practices and improving efficiency. By helping users to understand the cost of care at patient and provider levels across the patient journey, case costing has been a catalyst for enabling value-based care.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".