Moving from intervention management to disease management: a qualitative study exploring a systems approach to health technology assessment in Canada
Bibliographic record
Abstract
OBJECTIVES: Health technology assessment (HTA) traditionally informs decision making for single health technologies, which could lead to ill-informed decisions, suboptimal care, and system inefficiencies. We explored opportunities for conceptualizing the decision space in HTA as a disease management question versus an intervention management question. METHODS: Semistructured interviews were conducted between April 2022 and October 2022 with purposefully selected individuals from national and provincial HTA agencies and related organizations in Canada. We conducted manual line by line coding of data informed by our interview guide and sensitizing concepts from the literature. One author coded the data, and findings were independently verified by a second author who coded a subset of transcripts. RESULTS: Twenty-four invitations were distributed, and eighteen individuals agreed to participate. A disease management approach to HTA was differentiated from traditional approaches as being disease-based, multi-interventional, and dynamic. There was general support for an explicit care pathway approach to HTA by informing discussions around patient choice and suboptimal care, creating a space where decision makers can collaborate on shared objectives, and in setting up a platform for open dialogue about managing high-cost and high-severity diseases. There are opportunities for a care pathway approach to be implemented that build on the strengths of the existing HTA system in Canada. CONCLUSIONS: A disease management approach may enhance the impact of HTA by supporting dynamic decision making that could better inform a proactive, resilient, and sustainable healthcare system in Canada.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".