Leveraging financial incentives and behavioural economics to engage physicians in achieving quality-improvement process measures
Bibliographic record
Abstract
BACKGROUND: Dedicated quality-improvement (QI) initiatives within health care systems are of clear benefit, and physicians respond to financial incentivization. The Canadian health care system often lacks this lever, and many financially incentivized QI programs rely on traditional economic principles. We describe our evaluation of financial incentivization for the implementation of QI process metrics in a department of surgery at a Canadian academic hospital system and its impact over a 4-year period. METHODS: Quality-improvement processes informed by extant QI incentivization literature and guided by the principles of behavioural economics were implemented within our institution's Department of Surgery. Disbursement of supplemental government funding was modified to be contingent on the ability of divisions within the department to meet predefined QI metrics, including regular multidisciplinary meetings, morbidity and mortality rounds with documented feedback of systemic issues to division members, reviews of adverse events, and implementation of annual patient experience projects. We evaluated the effect of the QI processes from 2015/16 to 2018/19. RESULTS: < 0.01). The application of behavioural economics principles, such as reward versus penalty payoff, loss aversion, payment separation, aligning of values, and relative social ranking, was important to the outcome of the study. CONCLUSION: Incentivizing QI activities in the Canadian health care system is possible and led to improvement in QI processes as a whole in our department. This paper lays out a method of financial reimbursement to facilitate engagement of physicians and establishment of a foundation of important QI processes and measures within a department.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".