Incorporating Resource Constraints in Health Economic Evaluations: Overview and Methodological Considerations
Bibliographic record
Abstract
It is well known that healthcare resource constraints influence the capacity to deliver care, affecting both the costs and outcomes of medical interventions. If these constraints are not adequately accounted for in economic evaluations, there may be a lack of understanding regarding the full impact of implementing health technologies, leading to decisions being made with suboptimal information. This paper offers an overview of the types of healthcare resource constraints and their potential effects, and introduces a framework grounded in operations research and health economics principles, outlining the methodological considerations for incorporating resource constraints into economic evaluations. Drawing from a literature review and advisory group feedback, three categories of resource constraints were identified: single-use resource constraints, reusable resource constraints and patient throughput constraints. The proposed framework outlines a comprehensive set of steps necessary for effectively incorporating constraints into health economic evaluations and details specific approaches and methodological considerations for each stage to ensure a more accurate and realistic assessment of health interventions. This paper also aims to raise awareness among payers and decision-makers with regards to the limitations of technology evaluations in a resource-constrained health system. Specifically, it suggests that health technology assessment agencies ought to offer guidance on incorporating constraints into the submissions they receive. Moreover, it advocates for a more comprehensive economic evaluation in economic assessments to fully capture an intervention's value.
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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 | 0.470 | 0.614 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".