“…The following recommendations could be considered for HTA of precision medicine technologies, including those advanced by the ACEMID consortium to deal with specific issues: - Clarification of the intended position (or positions) of the diagnostic test in the clinical pathway, and assessment of the cost‐effectiveness of each position (eg, triage, add‐on, replacement), with estimates of the proportionate use in each position.
- Use of “base case” models that are updated with test performance characteristics (eg, sensitivity and specificity) as they learn and develop. These models could be created during AI algorithm testing, with preliminary inputs from software developers.
- Use of value of information analysis 24 to determine whether meta‐analyses could reduce the uncertainty in economic models associated with small denominators of subpopulations.
- Use of observational cohorts, indirect evidence comparisons, and registry data to assess comparative effectiveness where randomised trials are not possible.
- Consideration of distributional cost‐effectiveness analysis to provide an equity weighting, with a higher willingness to pay threshold if the technology can reduce inequities in access to dermatology or other specialist services and improve early detection among disadvantaged populations.
- Incorporation of patient and clinician preferences for imaging, biomarker or AI‐assisted diagnoses, assessed through quantitative methods such as discrete choice experiments.
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