OP30 From Framework To Action: Implementing Patient Engagement
Bibliographic record
Abstract
Introduction: This session will share lessons learned from implementing a comprehensive patient and public engagement framework (developed by winners of the 2017 Egon Jonsson Award) in one government agency's health technology assessment (HTA) process. The presentation will share strategic and operational considerations for successful implementation, and the early effects of patient involvement activities on the agency's HTA recommendations. Methods: This presentation used a case study approach to understand the application of the framework described above. Results: The comprehensive framework by Abelson and colleagues describes many different public and patient engagement activities that could be conducted at each stage of an HTA process. Health Quality Ontario has chosen to focus on engaging patients to: prioritize topics; develop an additional evidence stream on patient preferences and values; serve on a committee that reviews the HTA, deliberates, and makes recommendations; and provide feedback on draft recommendations. Strategic considerations for these decisions include: aligning engagement activities to an evidence-focused organizational culture, and investing in engagement activities earlier in the HTA process to allow for sufficient consideration of the patient voice in developing recommendations. These activities have impacted the agency's organizational culture, and evidence suggests they have also influenced recommendations for what should be publicly funded. Patient engagement activities have also led to increased feedback from the public and patients for some HTAs and the associated draft recommendations. Conclusions: Public agencies must make strategic decisions about how and when to invest scarce resources in patient and public engagement. Investing in direct patient engagement as an additional stream of evidence and supporting the involvement of health system users in decision-making has had a significant impact on HTA deliberations and recommendations. For some HTAs, these activities have facilitated greater public engagement as well.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".