PP506 Health Technology Reassessment (HTR) Of A Non-Drug Technology: Methods Used By A Regional HTA Unit
Bibliographic record
Abstract
Introduction An environmental scan conducted by the Canadian Agency for Drugs and Technologies (CADTH-March-2019) revealed that several health technology assessment (HTA) organisations are currently developing standard health technology reassessment (HTR) processes. Here we present methods used to conduct an HTR of a prioritization programme for non-immediate life-threatening urgent surgeries implemented in 2017 at a tertiary referral hospital in (Quebec-Canada). This HTR initiative was conducted by a regional HTA unit to optimize the programme efficiency and resources utilization as well as to motivate change in the clinical community of other hospitals within its healthcare network. Patient and healthcare personnel satisfaction levels towards the programme were also considered. Methods In this case study, HTR methods and outputs were elaborated using elements presented in the CADTH environmental scan and relevant publications identified through PubMed and in the grey literature. Documents in English and French, published between January 2002 and March 2019 were considered. Key stakeholders were consulted to identify barriers of the programme implementation to other hospitals in regards to aspects related either to the local medical practice or organizational factors. Results The prioritization process was conducted using the same tool applied for HTA appraisal with the additional criterion that the HTR could facilitate the programme implementation. The research processes used in this HTR included: i) systematic review of the literature, ii) hospital database search (efficacy and resource utilization), iii) perceptions of healthcare teams and patients. HTR outputs consist of specific recommendations on implementation barriers and methods to monitor the impacts of the programme. Conclusions In this evolving field, sharing lessons from HTR methods provides information to develop standard adaptable processes to different contexts. Hence, this work applies HTR to a healthcare programme while most of the literature focuses on the HTR processes on drug and interventional medicine disinvestment. These elements represented HTR methods used from prioritization appraisal, research processes for evaluation and outputs used to plan the implementation and finally monitoring from a regional HTA unit. It also showcases that HTR being conducted as a structured evidence-based assessment adds value to a healthcare programme and could also facilitate its implementation.
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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 | 0.098 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".