OP93 Collaboration Between Health Technology Assessment And Procurement: A Rapid Mixed-Methods Study
Bibliographic record
Abstract
Introduction The Irish Health Service (HSE) Health Technology Assessment Group (HTAG) aims to maximise the impact of its work by collaborating with HSE Procurement, formalised through an evidence-based Memorandum of Understanding (MOU). This study aims to inform the MOU. Methods A sequential mixed-methods study design was used. A rapid review of the literature identified no substantive body of evidence on collaboration between independent national health technology assessment (HTA) and procurement bodies. Personnel involved in HTA or procurement were invited by email to complete a survey, take part in an interview, or both. The quantitative and qualitative data were analysed using descriptive statistics and thematic analysis, respectively. Findings were integrated using a conceptual framework that examined the complementarity of HTA and procurement processes relevant to an MOU. Results Thirteen surveys were completed (response rate was 13 percent). Eleven interviews (five Ireland, two Canada, three UK, one New Zealand) were conducted between August and November, 2017. No formalised collaboration between independent national HTA and procurement bodies was identified. However in New Zealand, HTA and procurement are an integrated function of the Pharmaceutical Management Agency (PHARMAC). In other jurisdictions, successful ad hoc collaborations occurred where there was a clear need expressed by Procurement for additional evidence required for decision-making, and where HTA personnel tailored their research approaches accordingly. Key themes to successful collaboration were relationships, communication, clear roles, rigorous research and ‘system support’. Good individual relationships and ready access/communication promoted successful outcomes. Successful outcomes included improved clinical practice, and major cost savings. Collaboration may be focussed on: innovative or established devices; specific types of HTA/research products; specific categories/specialties; or specific procurement departments. Conclusions All participants considered collaboration to be beneficial but requiring good relationships and ‘system support’. Furthermore, successful collaboration requires clarity regarding the purpose, parties involved, their roles, responsibilities, modes of communication, information to be shared, and the expected outcomes.
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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 | 0.086 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".