Recommendations on the Use of Structured Expert Elicitation Protocols for Healthcare Decision Making: A Good Practices Report of an ISPOR Task Force
Bibliographic record
Abstract
Healthcare decision making, including regulatory and reimbursement decisions, is based on uncertain assessments of clinical and economic value. This arises from the evidence supporting those assessments being uncertain, incomplete, or even absent. Qualitative, structured expert elicitation (SEE) is a valuable tool for extracting expert knowledge about an uncertain quantity and formulating that knowledge as a probability distribution. This creates a useful input to decision modeling and support, particularly in areas with limited evidence, such as advanced therapy products, precision medicine, rare diagnoses, and other areas with high uncertainty. Structured SEE protocols are used to improve the transparency, accuracy, and consistency of quantitative judgments from experts, limiting the effect of heuristics and biases. This task force report introduces 5 commonly used protocols for SEE (Sheffield elicitation framework; modified Delphi method; Cooke's classical method; investigate, discuss, estimate, aggregate protocol; and the Medical Research Council reference protocol). It describes the common elements of SEE, discusses how these protocols differ in their implementation of those elements and illustrates the use of the protocols. The report then reviews the relevant constraints on implementing SEE within the context of healthcare decision making and considers the strengths and weaknesses of these protocols in light of those considerations. Because this is an introductory report on an emerging topic, specific recommendations on practice are not made. However, there are broad recommendations based on the suitability of the different protocols in various decision contexts. The report concludes with recommendations for further research to better guide future practice.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".