A SWOT analysis of the development of health technology assessment in Iran
Bibliographic record
Abstract
BACKGROUND: Health systems need to prioritize their services, ensuring efficiency and equitable health provision allocation and access. Alongside, health technology assessment (HTA) seeks to systematically evaluate various aspects of health technologies to be used by policy- and decision-makers. In the present study, we aim to identify strengths, weaknesses, opportunities, and threats in developing an HTA in Iran. METHOD: This qualitative study was conducted using 45 semi-structured interviews from September 2020 to March 2021. Participants were selected from key individuals involved in health and other health-related sectors. Based on the objectives of the study, we used purposive sampling (snowball sampling) to select individuals. The range of length of the interviews was between 45 to 75 minutes. Four authors of the present study carefully reviewed the transcripts of interviews. Meanwhile, the data were coded on the four domains of strengths, weaknesses, opportunities, and threats (SWOT). Transcribed interviews were then entered into the software and analyzed. Data management was performed using MAXQDA software, and also analyzed using directed content analysis. RESULTS: Participants identified eleven strengths for HTA in Iran, namely the establishment of an administrative unit for HTA within the Ministry of Health and Medical Education (MOHME); university-level courses and degrees for HTA; adapted approach of HTA models to the Iranian context; HTA is mentioned as a priority on the agenda in upstream documents and government strategic plans. On the other hand, sixteen weaknesses in developing HTA in Iran were identified: unavailability of a well-defined organizational position for using HTA graduates; HTA advantages and its basic concept are unfamiliar to many managers and decision-makers; weak inter-sectoral collaboration in HTA-related research and key stakeholders; and, failure to use HTA in primary health care. Also, participants identified opportunities for HTA development in Iran: support from the political side for reducing national health expenditures; commitment and planning to achieve universal health coverage (on behalf of the government and parliament); improved communication among all stakeholders engaged in the health system; decentralization and regionalization of decisions; and capacity building to use HTA in organizations outside the MOHME. High inflation and bad economic situation; poor transparency in decisions; lack of support from insurance companies; lack of sufficient data to conduct HTA research; rapid change of managers in the health system; and economic sanctions against Iran are threats to the developmental path of HTA in Iran. CONCLUSION: HTA can be properly developed in Iran if we use its strengths and opportunities, and address its weaknesses and threats.
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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 | 0.033 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".