Development of a decisional flowchart for meaningful patient involvement in Health Technology Assessment
Bibliographic record
Abstract
INTRODUCTION: This paper aims to describe the development of a flowchart to guide the decisions of researchers in the Spanish Network for Health Technology Assessment of the National Health System (RedETS) regarding patient involvement (PI) in Health Technology Assessment (HTA). By doing so, it reflects on current methodological challenges in PI in the HTA field: how best to combine PI methods and what is the role of patient-based evidence. METHODS: A decisional flowchart for PI in HTA was developed between March and April 2019 following an iterative process, reviewed by the members of the PI Interest Group and other RedETS members and validated during an online deliberative meeting. The development of the flowchart was based on a previous methodological framework assessed in a pilot study. RESULTS: The guidelines on how to involve patients in HTA in the RedETS were graphically represented in a flowchart. PI must be included in all HTA reports, except those that assess technologies with no relevant impact on patients' experiences, values, and preferences. Patient organizations or expert patients related to the topic of the HTA report must be identified and invited. These patients can participate in protocol development, outcomes' identification, assessment process, and report review. When the technology assessed affects in a relevant way patient experiences, values, and preferences, patient-based evidence should be included through a systematic literature review or a primary study. CONCLUSIONS: The decisional flowchart for PI in HTA contributes to the current methodological challenges by proposing a combination of direct involvement and patient-based evidence.
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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 | 0.177 | 0.250 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".