Selecting effective incentive structures in health care: A decision framework to support health care purchasers in finding the right incentives to drive performance
Bibliographic record
Abstract
BACKGROUND: The Ontario health care system is devolving planning and funding authority to community based organizations and moving from steering through rules and regulations to steering on performance. As part of this transformation, the Ontario Ministry of Health and Long-Term Care (MOHLTC) are interested in using incentives as a strategy to ensure alignment - that is, health service providers' goals are in accord with the goals of the health system. The objective of the study was to develop a decision framework to assist policymakers in choosing and designing effective incentive systems. METHODS: The first part of the study was an extensive review of the literature to identify incentives models that are used in the various health care systems and their effectiveness. The second part was the development of policy principles to ensure that the used incentive models are congruent with the values of the Ontario health care system. The principles were developed by reviewing the Ontario policy documents and through discussions with policymakers. The validation of the principles and the suggested incentive models for use in Ontario took place at two meetings. The first meeting was with experts from the research and policy community, the second with senior policymakers from the MOHLTC. Based on the outcome of those two meetings, the researchers built a decision framework for incentives. The framework was send to the participants of both meetings and four additional experts for validation. RESULTS: We identified several models that have proven, with a varying degree of evidence, to be effective in changing or enabling a health provider's performance. Overall, the literature suggests that there is no single best approach to create incentives yet and the ability of financial and non-financial incentives to achieve results depends on a number of contextual elements. After assessing the initial set of incentive models on their congruence with the four policy principles we defined nine incentive models to be appropriate for use in Ontario and potentially other health care systems that want to introduce incentives to improve performance. Subsequently, the models were incorporated in the resulting decision framework. CONCLUSION: The design of an incentive must reflect the values and goals of the health care system, be well matched to the performance objectives and reflect a range of contextual factors that can influence the effectiveness of even well-designed incentives. As a consequence, a single policy recommendation around incentives is inappropriate. The decision framework provides health care policymakers and purchasers with a tool to support the selection of an incentive model that is the most appropriate to improve the targeted performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".