Health technology assessment in Switzerland: a descriptive analysis of “Coverage with Evidence Development” decisions from 1996 to 2013
Bibliographic record
Abstract
OBJECTIVES: To identify factors associated with the decisions of the Federal Department of Home Affairs concerning coverage with evidence development (CED) for contested novel medical technologies in Switzerland. DESIGN: Quantitative, retrospective, descriptive analysis of publicly available material and prospective, structured, qualitative interviews with key stakeholders. SETTING: All 152 controversial medical services decided on by the Federal Commission on Health Insurance Benefits within the framework of the new federal law on health insurance in Switzerland from 1997 to 2013, with focus on 33 technologies assigned initially to CED and 33 to evidence development without coverage. MAIN OUTCOME MEASURES: Factors associated with numbers and type of contested services assigned to CED per year, the duration and final outcome of the evaluations and perceptions of key stakeholders. RESULTS: The rate of CED decisions (82 total; median 1.5/year; range 0-9/year), the time to final decision (4.5 years median; 0.75 to +11 years) and the probability of a final 'yes' varied over time. In logistic regression models, the change of office of the commission provided the best explanation for the observed outcomes. Good intentions but absence of scientific criteria for decisions were reported as major comments by the stakeholders. CONCLUSIONS: The introduction of CED enabled access to some promising technologies early in their life cycle, and might have triggered establishment of registries and research. Impact on patients' outcome and costs remain unknown. The primary association of institutional changes with measured end points illustrates the need for evaluation of the current health technology assessment (HTA) system.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".