Co‐construction of health technology assessment recommendations with patients: An example with cardiac defibrillator replacement
Bibliographic record
Abstract
CONTEXT: The National Institute of Excellence in Health and Social Services (INESSS), which functions as the Québec health technology assessment (HTA) agency, tested a new way to engage patients along with health-care professionals in the co-construction of recommendations regarding implantable cardioverter-defibrillator replacement. OBJECTIVE: The objective of this article was to describe the process of co-construction of recommendations and to propose methods of building best practices for patient involvement (PI) in HTA. DESIGN: Throughout the process, documents were collected and participant observations were made. Individual interviews were conducted with patients, health-care professionals and the INESSS scientific team, from January to March 2018. RESULTS: Three committees were established: an expert patient committee to reflect on patient experience literature; an expert health professional committee to reflect on medical literature; and a co-construction committee through which both patients and health-care professionals contributed to develop the recommendations. The expert patients validated and contextualized a literature review produced by the scientific team. This allowed the scientists to consider aspects related to the patient experience and to integrate the feedback from patients into HTA recommendations. The most important factor contributing to a positive PI experience was the structured methodology for selecting patient participants, and a key factor that inhibited the process was a lack of training in PI on the part of the scientific team. CONCLUSIONS: This experience demonstrates that it is possible to co-construct recommendations, even for technically complex HTA subjects, through a more democratic process than usual which led to more patient-focused guidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".