Economic evaluations of health technologies in Dutch healthcare decision-making: a qualitative study of the current and potential use, barriers, and facilitators
Bibliographic record
Abstract
BACKGROUND: The use of economic evaluations in healthcare decision-making can potentially help decision-makers in allocating scarce resources as efficiently as possible. Over a decade ago, the use of such studies was found to be limited in Dutch healthcare decision-making, but their current use is unknown. Therefore, this study aimed to provide insight into the current and potential use of economic evaluations in Dutch healthcare decision-making and to identify barriers and facilitators to the use of such studies. METHODS: Interviews containing semi-structured and structured questions were conducted among Dutch healthcare decision-makers. Participants were purposefully selected and special efforts were made to include decision-makers working at the macro- (national), meso- (local/regional), and micro-level (patient setting). During the interviews, a topic list was used that was based on the research questions and a literature search, and was developed in consultation with the Dutch National Healthcare Institute. Responses to the semi-structured questions were analyzed using a constant comparative approach. As for the structured questions, participants' definitions of various economic evaluation concepts were scored as either being "correct" or "incorrect" by two researchers, and summary statistics were prepared. RESULTS: Sixteen healthcare decision-makers were interviewed and two health economists. Decision-makers' knowledge of economic evaluations was only modest, and their current use appeared to be limited. Nonetheless, decision-makers recognized the importance of economic evaluations and saw several opportunities for extending their use at the macro- and meso-level, but not at the micro-level. The disparity between the limited use and recognition of the importance of economic evaluations is likely due to the many barriers decision-makers experience preventing their use (e.g. lack of resources, lack of formal willingness-to-pay threshold). Possible facilitators for extending the use of economic evaluations include, amongst others, educating decision-makers and the general population about economic evaluations and presenting economic evaluation results in a clearer and more understandable way. CONCLUSIONS: This study demonstrated that the current use and impact of economic evaluations in Dutch healthcare decision-making is limited at best. Therefore, strategies are needed to overcome the barriers that currently prevent economic evaluations from being used extensively.
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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 | 0.050 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), 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".