Assessing value in health care: using an interpretive classification system to understand existing practices based on a systematic review
Bibliographic record
Abstract
BACKGROUND: Implementing adequate strategies to assess the value of health services plays a central role in the effort to deal with the financial pressures faced by health care systems worldwide. This study aimed to understand which approaches to value assessment have been used in developed countries. METHODS: We conducted a rapid review and a gray literature search to identify value assessment frameworks. A two-stage screening process was utilized to identify existing approaches and cluster similar frameworks. In addition, we developed an interpretive classification system to make sense of existing approaches. RESULTS: One thousand one hundred seventy-six references were identified and 38 papers were selected for full-review. Among these 38 articles, 22 distinct approaches to assess value of health care interventions were identified and classified according to four points: 1) use of single or multiple considerations to base value estimates; 2) use of disease-specific or generic criteria; 3) reliance on process-based or outcomes-based consideration; and 4) type of input and evidence considered. CONCLUSIONS: The contextual nature of value assessment in health care becomes evident with the diversity of existing approaches. Despite the predominance of cases relying on the Incremental cost-effectiveness ratio as the measure of value, this approach has not been sufficient to meet the needs of decision-makers. The use of multiple criteria has become more and more important, as well as the consideration of patient-reported measures. Considerations of costs are not always explicit and consistent.
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 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 | 0.373 | 0.625 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.099 | 0.053 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".