OP109 The Conceptualization And Value Of A Disease Management Approach To HTA In Canada: Findings From A Qualitative Study
Bibliographic record
Abstract
Introduction Our objective was to conceptualize and assess the potential value of a disease management approach to health technology assessment (HTA) in Canada. Methods We conducted 18 semi-structured interviews between April 2022 and October 2022 to elicit informant views on potential opportunities for re-conceptualizing the decision-problem in HTA as a disease management problem versus a technology management problem. Participants were purposefully sampled from national and provincial HTA agencies and related organizations in Canada to achieve representation across the disease pathway including prevention, screening, and treatment, and the decision-making pathway including HTA organizations, expert committee members, and decision-makers. Data were analyzed using thematic organizations (based on the interview guide) and manual line by line coding of the data. Ethics approval was received from the Health Sciences Research Ethics Board at the University of Toronto. Results Three key features of a disease management approach to HTA (i.e., disease-based, multi-interventional, and dynamic) emerged from informants that differed from traditional HTA processes in Canada. The concept was generally not perceived to be a new idea – some informants indicating that it was implicit in the HTA analysis framework. There was general support for an explicit disease management approach to HTA if the impact of the approach could be demonstrated, if the assessment could be completed within an appropriate time frame, and if the assessment could include the equity, ethical and implementation domains of HTA. Informants indicated that the reconceptualization of HTA could lead to effective and efficient decision-making throughout a technology’s lifecycle, help breakdown system silos, and offer a platform for greater consideration of non-drug alternatives and upstream interventions. The impact of this approach was anticipated to contribute to a proactive health system that could improve population health, enhance the patient experience, and ensure appropriate stewardship of health care resources. Conclusions A disease management approach to HTA has international relevance as an approach that could promote integrated, proactive, sustainable, and resilient health systems.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".