HTA System in Switzerland: Current State, Gaps, and Prospects for Improvement
Bibliographic record
Abstract
Abstract Health technology assessment (HTA) is critical for funding decisions in solidarity-based healthcare systems. Switzerland's intricate healthcare system merges regulated competition and corporatism within a decentralized structure, inspired by direct democracy. This study aims to analyze the national HTA system, identify challenges, and propose insights to strengthen HTA, contributing to future research and supporting Universal Health Coverage efforts. A cross-sectional mixed-methods approach was employed, combining nine institutional surveys and eight in-depth interviews with participants from various health system levels in Switzerland. The study examined HTA practices in nine Swiss institutions, comprising 67% academic, 22% public, and 11% non-governmental sectors. Findings revealed structured processes (67%) with the Federal Office of Public Health as a central agency (67%). While 56% reported substantial funding, challenges included a lack of government financing. Primary HTA use was for reimbursement decisions (56%) and clinical guidance support (44%). Impediments included awareness gaps, political support deficiencies, and institutional capacity issues. The research highlights the importance of HTA in healthcare decision-making and resource allocation in Switzerland, revealing an effective grasp of HTA, but also identifying policy gaps. Enhancing awareness and institutional strengthening were identified as crucial for advancing HTA in the country. The study suggests that Switzerland could benefit from establishing a national HTA agency. Key messages • Establishing a unified and integrated national HTA agency in Switzerland is priority. • HTA system in Switzerland experianced a signaficit transformation with an urgent need to address the existing gasp such as integration, standardization, and resources and capacities.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".