Using a Health Economic Framework to Prioritize Quality Indicators: An Example With Smoking Cessation in Chronic Obstructive Pulmonary Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background. Health care performance monitoring is a major focus of the modern quality movement, resulting in widespread development of quality indicators and making prioritizations an increasing focus. Currently, few prioritization methods of performance measurements give serious consideration to the association of performance with expected health benefits and costs. We demonstrate a proof-of-concept application of using a health economic framework to prioritize quality indicators by expected variations in population health and costs, using smoking cessation in chronic obstructive pulmonary disease (COPD) as an example. Methods. We developed a health state transition, microsimulation model to represent smoking cessation practices for adults with COPD from the health care payer perspective in Ontario, Canada. Variations in life years, quality-adjusted life years (QALYs), and lifetime costs were associated with changes in performance. Incremental net health benefit (INHB) was used to represent the joint variation in mortality, morbidity, and costs associated with the performance of each quality indicator. Results. Using a value threshold of $50,000/QALY, the indicators monitoring assessment of smoking status and smoking cessation interventions were associated with the largest INHBs. Combined performance variations among groups of indicators showed that 81% of the maximum potential INHB could be represented by three out of the six process indicators. Conclusions. A health economic framework can be used to bring dimensions of population health and costs into explicit consideration when prioritizing quality indicators. However, this should not preclude policymakers from considering other dimensions of quality that are not part of this framework.
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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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it