Systematic review of methods for evaluating healthcare research economic impact
Bibliographic record
Abstract
BACKGROUND: The economic benefits of healthcare research require study so that appropriate resources can be allocated to this research, particularly in developing countries. As a first step, we performed a systematic review to identify the methods used to assess the economic impact of healthcare research, and the outcomes. METHOD: An electronic search was conducted in relevant databases using a combination of specific keywords. In addition, 21 relevant Web sites were identified. RESULTS: The initial search yielded 8,416 articles. After studying titles, abstracts, and full texts, 18 articles were included in the analysis. Eleven other reports were found on Web sites. We found that the outcomes assessed as healthcare research payback included direct cost-savings, cost reductions in healthcare delivery systems, benefits from commercial advancement, and outcomes associated with improved health status. Two methods were used to study healthcare research payback: macro-economic studies, which examine the relationship between research studies and economic outcome at the aggregated level, and case studies, which examine specific research projects to assess economic impact. CONCLUSIONS: Our study shows that different methods and outcomes can be used to assess the economic impacts of healthcare research. There is no unique methodological approach for the economic evaluation of such research. In our systematic search we found no research that had evaluated the economic return of research in low and middle income countries. We therefore recommend a consensus on practical guidelines at international level on the basis of more comprehensive methodologies (such as Canadian Academic of Health Science and payback frameworks) in order to build capacity, arrange for necessary informative infrastructures and promote necessary skills for economic evaluation studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.489 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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; both teacher heads 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".