Topic identification, selection, and prioritization for health technology assessment in selected countries: a mixed study design
Bibliographic record
Abstract
BACKGROUND: There is limited evidence-informed guidance on TISP processes for countries where health technology assessment (HTA) is in a nascent phase. We aimed to explore the range of topic identification, selection and prioritization (TISP) processes and practices for HTA in selected countries and identify aspects relevant to emerging HTA systems. METHODS: This mixed design study included a systematic literature review, an electronic survey, and individual interviews. We conducted a systematic literature review with criteria that were developed a priori to identify countries deemed to have a recently formalized HTA system. Based on the literature review, a twenty-three item online survey was shared with the identified countries, we completed follow-up interviews with ten participants who have experience with HTA. We analyzed documents, survey responses and interview transcripts thematically to identify lessons related to TISP processes and practices. RESULTS: The literature review identified 29 nine candidate countries as having a "potential" recently formalized HTA system. Twenty-one survey responses were analyzed and supplemented with ten individual interviews. We found variation in countries' approaches to TISP - particularly between pharmaceutical and non-pharmaceutical interventions. Results indicate that TISP is heavily driven by policy makers, expert involvement, and to a lesser extent, relevant stakeholders. The use of horizon-scanning and early warning systems is uncommon. Interviewee participants provided further insight to the survey data, reporting that political awareness and an institutional framework were important to support TISP. TISP can be optimized by stronger national regulations and legislative structures, in addition to education and advocacy about HTA among politicians and decision-makers. In some settings regional networks have been useful, particularly in the development of TISP guidelines and methodologies. Additionally, the technical capacity to conduct TISP, and access to relevant local data were factors limiting TISP in national settings. Increased network collaboration and capacity building were reported as future needs. CONCLUSIONS: This study provides current insights into a topic where there is limited published peer reviewed literature. TISP is an important first step of HTA, and topics should be selected and prioritized based on local need and relevance. The limited capacity for TISP in settings where HTA is emerging may be supported by local and international collaboration to increase capacity and knowledge. To succeed, both TISP and HTA need to be embedded within national health care priority setting and decision-making. More in-depth understanding of where countries are situtated in formalizing the TISP process may help others to overcome factors that facilitate or hinder progress.
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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 | 0.155 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".