Balancing costs and benefits at different stages of medical innovation: a systematic review of Multi-criteria decision analysis (MCDA)
Bibliographic record
Abstract
BACKGROUND: The diffusion of health technologies from translational research to reimbursement depends on several factors included the results of health economic analysis. Recent research identified several flaws in health economic concepts. Additionally, the heterogeneous viewpoints of participating stakeholders are rarely systematically addressed in current decision-making. Multi-criteria Decision Analysis (MCDA) provides an opportunity to tackle these issues. The objective of this study was to review applications of MCDA methods in decisions addressing the trade-off between costs and benefits. METHODS: Using basic steps of the PRISMA guidelines, a systematic review of the healthcare literature was performed to identify original research articles from January 1990 to April 2014. Medline, PubMed, Springer Link and specific journals were searched. Using predefined categories, bibliographic records were systematically extracted regarding the type of policy applications, MCDA methodology, criteria used and their definitions. RESULTS: 22 studies were included in the analysis. 15 studies (68 %) used direct MCDA approaches and seven studies (32 %) used preference elicitation approaches. Four studies (19 %) focused on technologies in the early innovation process. The majority (18 studies - 81 %) examined reimbursement decisions. Decision criteria used in studies were obtained from the literature research and context-specific studies, expert opinions, and group discussions. The number of criteria ranged between three up to 15. The most frequently used criteria were health outcomes (73 %), disease impact (59 %), and implementation of the intervention (40 %). Economic criteria included cost-effectiveness criteria (14 studies, 64 %), and total costs/budget impact of an intervention (eight studies, 36 %). The process of including economic aspects is very different among studies. Some studies directly compare costs with other criteria while some include economic consideration in a second step. CONCLUSIONS: In early innovation processes, MCDA can provide information about stakeholder preferences as well as evidence needs in further development. However, only a minority of these studies include economic features due to the limited evidence. The most important economic criterion cost-effectiveness should not be included from a technical perspective as it is already a composite of costs and benefit. There is a significant lack of consensus in methodology employed by the various studies which highlights the need for guidance on application of MCDA at specific phases of an innovation.
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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 | 0.120 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".