Evaluation of the impact of patient involvement in health technology assessments: A scoping review
Bibliographic record
Abstract
OBJECTIVES: While involving patients in health technology assessment (HTA) has become increasingly common and important around the world, little is known about the optimal methods of evaluating patients' involvement (PI) in HTA. This scoping review was undertaken to provide an overview of currently available methods for the evaluation of PI, specifically the impact of PI on HTA recommendations. METHODS: A literature search was conducted using nine databases as well as a grey literature search of the websites of 26 organizations related to the conduct, practice or research of HTA to identify articles, reports and abstracts related to the evaluation of PI impact in HTA. RESULTS: We identified 1,248 unique citations, six of which met our eligibility criteria. These six records (five articles, and one report) were all published after 2012. Four assessed the impact of patient experience submissions on final HTA recommendations; one evaluated the impact of direct involvement on HTA committees, and one assessed impact of multiple forms of involvement. Methods of evaluation included quantitative analyses of reimbursement decisions, qualitative interviews with those directly involved in an assessment, surveys of patient groups and committee members, and the review of HTA reports. CONCLUSIONS: Quantitative evaluation of PI based on associations with funding decisions may not be feasible or fully capture the relevant impact of PI in the assessment of health technologies. Rather, a combination of both qualitative and quantitative strategies may allow for the most comprehensive assessment of the impact of PI on HTA recommendations when possible.
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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 | 0.324 | 0.623 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".