INTRODUCTION:Economic evaluation supports public funding decisions about the use of health technologies within the Portuguese National Health System (NHS). The methods guide for economic evaluation in Portugal serves both companies preparing economic evaluation submissions and the independent commission appraising the evidence submitted. METHODS:This article presents the revised methods guide for economic evaluation in Portugal. The revisions reflect advances in economic evaluation, updates to regulatory policies, and responses to the evolving economic context. The paper highlights the most significant changes to the guidance, comparing the new Portuguese guidelines to those from the United Kingdom and Canada. The discussion is framed around key comments received during public consultation. RESULTS:The updated guidelines recommend cost-effectiveness analyses based on quality-adjusted life years and advocate for long-term modelling, a 4 percent discount rate, and a focus on NHS costs. New features include guidance on the identification and management of uncertainty within a dynamic appraisal process with regular contract negotiations (which can trigger reappraisals). The guide also covers how cost-effectiveness models, typically centrally developed, should be adapted to the Portuguese context. It highlights the key role of structured expert elicitation to address uncertainties in evidence, including those related to model adaptation. CONCLUSIONS:The revision was developed through stakeholder consultations and aligns with international best practices, offering more explicit and transparent methods to support health resource allocation decisions.

Background:Whilst strong compression is effective in treating venous leg ulcers, compression systems vary in their effects on ulcer healing and wider outcomes. Four-layer bandage systems (4LB) and two-layer hosiery (2LH) are recognised effective compression systems in practice. The relative effectiveness and cost-effectiveness of two-layer bandage systems (2LB, a commonly used system), compression wraps (CW, a newer option), and short-stretch bandages (SSB) were uncertain. This paper aims to compare the effectiveness on time to venous ulcer healing and cost-effectiveness of 4LB and 2LH (or a choice of these), with 2LB, SSB, and CW. Methods:We updated the Ovid MEDLINE search of the Cochrane review on compression systems to identify new-up to 02 February 2026-relevant randomised controlled trials (RCTs), in addition to the individual patient data from VenUS 6-a recent large RCT comparing a choice arm of 4LB and 2LH, with 2LB and CW. We identified RCTs evaluating any type of compression systems (bandage or stockings). Study's risk of bias was assessed using the Cochrane Collaboration tool. We used network meta-analyses to estimate relative treatment effects on time to ulcer healing, and used a Markov model to compare the cost-effectiveness of interventions over a lifetime horizon and from a UK NHS perspective. The primary outcome for the network meta-analysis was the hazard ratio (HR) of time to ulcer healing for alternative compression systems. For the cost-effectiveness analysis, total costs, total quality-adjusted life years (QALYs), and Incremental Cost-Effectiveness Ratios (ICERs) were estimated. Findings:Overall, 21 trials with 2934 participants were included, of which 5 (24%) and 4 (19%) studies had a moderate and a low risk of bias, respectively. Due to the sparsity of data, fixed-effects models were used. Heterogeneity, transitivity and consistency were explored by comparison of population characteristics in included studies, comparison of fixed- and random-effects models, and assessment of consistency matrix. The HRs of time to ulcer healing compared with 4LB/2LH, when including all studies, were 0.972 (95% CI 0.851-1.107) for SSB, 1.044 (95% CI 0.859-1.255) for 2LB, 0.927 (95% CI 0.745-1.136) for CW. The HRs compared with 4LB/2LH, when including only studies with low and moderate risk of bias, were 0.954 (95% CI 0.823-1.099) for SSB, 1.004 (95% CI 0.817-1.213) for 2LB, 0.901 (95% CI 0.719-1.110) for CW. The HRs compared with 4LB/2LH, when including only studies with low risk of bias, were 1.009 (95% CI 0.856-1.187) for SSB, 10.995 (95% CI 0.792-1.233) for 2LB, 0.872 (95% CI 0.695-1.085) for CW. The cost-effectiveness analysis (when using the NMA results including all studies) showed small differences in total costs and total QALYs across treatments. Compared with 4LB/2LH, the total costs for SSB, 2LB, and CW were higher, of £390 (95% CI: 41-985), £345 (95% CI: -102 to 1127), and £348 (95% CI: -133 to 1118), respectively. Total QALYs differences versus 4LB/2LH were -0.0015 (95% CI: -0.0110 to 0.0064) for SSB, 0.0022 (95% CI: -0.0085 to 0.0100) for 2LB, and -0.0044 (95% CI: -0.0207 to 0.0079) for CW. SSB and CW were dominated by 4LB/2LH. The ICER of 2LB versus 4LB/2LH was £159,614/QALY gain. Interpretation:These findings suggest potential small differences in the clinical effectiveness of 4LB/2LH, 2LB, SSB, and CW, but there is uncertainty in the estimates. In the UK, 4LB/2LH emerges as cost-effective. Funding:National Institute for Health and Care Research (NIHR) Health Technology Assessment Programme (Project Reference: 128625).

Real-world data on patients with ROS1-positive advanced non-small cell lung cancer (NSCLC) remain scarce. In this descriptive observational retrospective cohort study, we describe characteristics, treatments, and real-world progression-free survival (rwPFS) and overall survival (OS) among patients with ROS1-positive advanced NSCLC (de novo or recurrent) using secondary data pooled from clinical sites in Canada, France, Germany, Portugal, and Spain as part of the Oncology Evidence Network. Site-specific patient inclusion periods occurred between 2009 and 2023, with follow-up to 2024, allowing ≥1 year of potential follow-up at each site. In total, 108 patients were included, with most (n = 105; 97.2%) having a de novo diagnosis of advanced NSCLC. 103 patients (95.4%) received ≥1 line of systemic anticancer therapy (SACT), of which 65 (63.1%) received first-line targeted therapy, mostly crizotinib monotherapy (n = 45) or crizotinib-based regimens (n = 10), with a median (95% CI) rwPFS and OS of 14.0 (8.3-19.8) and 47.9 (27.3-not estimable) months, respectively. Thirty-eight of the 103 SACT-treated patients (36.9%) received first-line non-targeted therapy, mostly platinum-based chemotherapy (n = 26); median (95% CI) rwPFS and OS were 9.0 (7.5-11.0) and 29.3 (17.7-65.7) months, respectively. Results from this study indicated a tendency for longer survival using currently available ROS1-targeted versus non-targeted therapy for patients with ROS1-positive advanced NSCLC. Nevertheless, survival outcomes were limited, highlighting the importance of more effective emerging treatments for ROS1-positive disease.

BACKGROUND:Strong compression is a recommended first line venous leg ulcer treatment. With limited research comparing the clinical effectiveness of compression wraps (CW) and two-layer compression bandage treatments with evidence-based compression (EBC) (four-layer compression bandages and two-layer compression hosiery), this study aimed to evaluate their clinical effectiveness on time to venous leg ulcer healing. METHODS AND FINDINGS:A pragmatic, three-arm, randomised controlled trial in 33 United Kingdom primary, community and hospital sites between 03.02.2021 and 31.08.2024. Adults with a venous leg ulcer appropriate for compression therapy were randomised 1:1:1 to be offered CW, two-layer bandage, or EBC (two-layer hosiery or four-layer bandage). Participants and clinical staff were not blinded. The primary outcome was time to blind assessed ulcer healing (date of ulcer healing: date of earliest photograph showing healing). Analyses included a noninferiority comparison of two-layer bandage and EBC (handling key intercurrent events under hypothetical and treatment policy strategies), and superiority comparisons of CW with both EBC and two-layer bandage (handling key intercurrent events under a treatment policy strategy). Healing times were analysed using Cox proportional hazards regression adjusted for fixed effects (treatment allocation, baseline ulcer area and duration, participant age, and mobility status), and shared frailties (recruitment site). The trial was pre-registered: ISRCTN67321719. 637 participants were randomised to be offered CW (n = 213), two-layer bandage (n = 211) or EBC (n = 213). Mean age was 70.3 (range 24.6 to 97.0) years, 55% (n = 351) were male, and the majority (n = 606, 95%) were white. 633 participants contributed time at risk of healing and were included in the analysis. Using a treatment policy strategy to handle key intercurrent events (modified intention-to-treat analysis), the estimated hazard ratio (HR) for the noninferiority comparison (EBC and two-layer bandage) was 1.01 (95% CI [0.79, 1.28]), meeting the pre-specified noninferiority margin of 1.33. The corresponding hypothetical strategy analysis gave a HR of 1.16 (95% CI [0.86, 1.58]), which did not demonstrate noninferiority. For the superiority comparisons, healing was slower in the CW group than in the EBC group (HR 0.78, 95% CI [0.61, 1.00]; p = 0.046). Results were similar for the two-layer bandage group (HR 0.79, 95% CI [0.61, 1.01]; p = 0.056), although this did not reach statistical significance. Both comparisons showed considerable statistical uncertainty, with confidence intervals ranging from a 39% reduction in the hazard of healing to little or no difference between groups. Nine serious adverse events occurred; one potentially related to treatment (cause of death could not be ascertained). Departures from allocated compression treatment were common, which limits generalisability to settings with different adherence patterns. These departures, lower than expected ulcer healing incidence rates and slight under-recruitment, resulted in the number of healing events being smaller than the number required for 80% power. CONCLUSION:CW is unlikely to reduce the time to venous leg ulcer healing compared to two-layer bandage or EBC, although confidence intervals included treatment effects indicating little or no difference between groups. Despite remaining uncertainty, these findings may not support CW as a first line strong compression treatment for venous leg ulcers. TRIAL REGISTRATION:ISRCTN - reference 67321719.

Background: Lung carcinoids—typical and atypical—are rare neuroendocrine tumors (NETs) representing 1–2% of lung cancers. Despite clinicopathological differences, their clinical management often mirrors lung cancer protocols rather than NET-specific recommendations. Objectives: Portray a 12-year real-world experience with lung carcinoids at a Comprehensive Cancer Center, identifying gaps in diagnostic work-up, treatment decision-making, and follow-up. Methods: Retrospective observational cohort study of adult patients with histologically confirmed lung carcinoids diagnosed at IPO Porto between January 2013 and December 2024. Demographic, clinical, imaging, and treatment data were collected from electronic patient records. Analyses were descriptive. Results: Among 179 identified cases, 129 met eligibility criteria. Median age was 62 years (range 18–84); 53.6% were women and 53.5% were non-smokers; 84.5% had ECOG-PS 0–1. The most frequent presentation was respiratory symptoms (34.1%), followed by incidental findings (43.4%, of which ~20% were during staging or surveillance of other cancers). Typical carcinoids accounted for 49.6% and atypical for 43.4%. FDG-PET/CT was requested in 70.9% of cases, including many with typical carcinoid, and SSTR-PET/CT in 64.6% (dual PET in 38.8%). Most patients (65.1%) presented with stage I disease; 17.1% were stage IV. Mean time-to-first treatment was 83 days (range 1–259). Surgery was the first treatment option for 78.3% of patients. Conclusions: This real-world series highlights heterogeneity in diagnostic pathways, excessive FDG-PET use in typical carcinoids, and non-standardized follow-up. Dedicated multidisciplinary lung-NET boards and national reference centers are needed to homogenize and streamline patient management.

BACKGROUND:Lung cancer (LC) is the most common cause of cancer death in the UK and worldwide, but screening with low-dose CT (LDCT) reduces LC deaths. The UK National Screening Committee has recommended nationwide roll-out of LDCT screening, but the optimal risk thresholds for eligibility remain uncertain. METHODS:We conducted a cost-effectiveness analysis in the Yorkshire Lung Screening Trial (YLST) population comparing three eligibility criteria: US Preventive Services Task Force (USPSTF)2013, Prostate Lung Colorectal and Ovarian study (PLCO)M2012 ≥1.51% and Liverpool Lung Project model (LLP)v2 ≥5%. A Markov model estimated a no-screening counterfactual. Scenario analyses assessed how increasing PLCOM2012 (1.51%-7%) and LLPv2 (5%-9%) thresholds affected LC detection, costs and quality-adjusted life years (QALYs). Payouts per detected LC were calculated using mortality and utility estimates from the literature and cost data from the trial. RESULTS:Incremental cost-effectiveness ratios (ICERs) versus no screening were £3949 (USPSTF2013), £3797 (LLPv2≥5%) and £4013 (PLCOM2012≥1.51%). PLCOM2012 yielded the largest numbers screened and LCs detected, most QALYs gained and highest incremental net monetary benefit. Raising LLPv2 and PLCOM2012 thresholds reduced both ICERs and QALYs gained. CONCLUSION:All three screening eligibility criteria are cost-effective according to the UK's willingness to pay threshold of £20 000/QALY. Within the range of thresholds observed in YLST, PLCOM2012 thresholds between ≥1.51% and ≥4% offered the most efficient cost-benefit trade-offs. Evidence suggests that lowering thresholds further would detect more LC cases while remaining cost-effective. These findings support the criteria implemented in YLST, but do not by themselves identify the optimal screening threshold for the wider UK population. TRIAL REGISTRATION NUMBER:ISRCTN42704678.

BACKGROUND:Multi-indication cancer drugs receive licensing extensions to include additional indications, as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting health technology assessment (HTA). METHODS:We applied meta-analytic methods to randomized trial data on bevacizumab, to share information across oncology indications on the treatment effect on overall survival (OS) or progression-free survival (PFS) and on the surrogate relationship between effects on PFS and OS. Common or random indication-level parameters were used to facilitate information sharing, and the further flexibility of mixture models was also explored. RESULTS:Treatment effects on OS lacked precision when pooling data available at present day within each indication separately, particularly for indications with few trials. There was no suggestion of heterogeneity across indications. Sharing information across indications provided more precise estimates of treatment effects and surrogacy parameters, with the strength of sharing depending on the model. When a surrogate relationship was used to predict treatment effects on OS, uncertainty was reduced only when sharing effects on PFS in addition to surrogacy parameters. Corresponding analyses using the earlier, sparser (within and across indications) evidence available for particular HTAs showed that sharing on both surrogacy and PFS effects did not notably reduce uncertainty in OS predictions. Little heterogeneity across indications meant limited added value of the mixture models. CONCLUSIONS:Meta-analysis methods can be usefully applied to share information on treatment effectiveness across indications in an HTA context, to increase the precision of target indication estimates. Sharing on surrogate relationships requires caution, as meaningful precision gains in predictions will likely require a substantial evidence base and clear support for surrogacy from other indications. HIGHLIGHTS:We investigated how sharing information across indications can strengthen inferences on the effectiveness of multi-indication treatments in the context of health technology assessment (HTA).Multi-indication meta-analysis methods can provide more precise estimates of an effect on a final outcome or of the parameters describing the relationship between effects on a surrogate endpoint and a final outcome.Precision of the predicted effect on the final outcome based on an effect on the surrogate endpoint will depend on the precision of the effect on the surrogate endpoint and the strength of evidence of a surrogate relationship across indications.Multi-indication meta-analysis methods can be usefully applied to predict an effect on the final outcome, particularly where there is limited evidence in the indication of interest.

A growing number of oncology treatments, such as bevacizumab, are used across multiple indications. However, in health technology assessment (HTA), their clinical and cost-effectiveness are typically appraised within a single target indication. This approach excludes a broader evidence base across other indications. To address this, we explored multi-indication meta-analysis methods that share evidence across indications.We conducted a simulation study to evaluate alternative multi-indication synthesis models. This included univariate (mixture and non-mixture) methods synthesizing overall survival (OS) data and bivariate surrogacy models jointly modeling treatment effects on progression-free survival (PFS) and OS, pooling surrogacy parameters across indications. Simulated datasets were generated using a multistate disease progression model under various scenarios, including different levels of heterogeneity within and between indications, outlier indications, and varying data on OS for the target indication. We evaluated the performance of the synthesis models applied to the simulated datasets in terms of their ability to predict OS in a target indication.The results showed univariate multi-indication methods could reduce uncertainty without increasing bias, particularly when OS data were available in the target indication. Compared with univariate methods, mixture models did not significantly improve performance and are not recommended for HTA. In scenarios where OS data in the target indication is absent and there are also outlier indications, bivariate surrogacy models showed promise in correcting bias relative to univariate models, though further research under realistic conditions is needed.Multi-indication methods are more complex than traditional approaches but can potentially reduce uncertainty in HTA decisions.

Limited evidence on relative effectiveness is common in Health Technology Assessment (HTA), often due to sparse evidence on the population of interest or study-design constraints. When evidence directly relating to the policy decision is limited, the evidence base could be extended to incorporate indirectly related evidence. For instance, a sparse evidence base in children could borrow strength from evidence in adults to improve estimation and reduce uncertainty. In HTA, indirect evidence has typically been either disregarded (‘splitting’; no information-sharing) or included without considering any differences (‘lumping’; full information-sharing). However, sophisticated methods that impose moderate degrees of information-sharing have been proposed. We describe and implement multiple information-sharing methods in a case-study evaluating the effectiveness, cost-effectiveness and value of further research of intravenous immunoglobulin for severe sepsis and septic shock. We also provide metrics to determine the degree of information-sharing. Results indicate that method choice can have significant impact. Across information-sharing models, odds ratio estimates ranged between 0.55 and 0.90 and incremental cost-effectiveness ratios between £16,000–52,000 per quality-adjusted life year gained. The need for a future trial also differed by information-sharing model. Heterogeneity in the indirect evidence should also be carefully considered, as it may significantly impact estimates. We conclude that when indirect evidence is relevant to an assessment of effectiveness, the full range of information-sharing methods should be considered. The final selection should be based on a deliberative process that considers not only the plausibility of the methods’ assumptions but also the imposed degree of information-sharing.

Early cancer detection through minimally invasive methods is key for improving patient outcomes. We aimed to assess the performance of a novel blood‐based test leveraging DNA methylation patterns for simultaneous detection of lung (LC), breast (BrC), colorectal (CRC), and prostate (PCa) cancer. Using The Cancer Genome Atlas (TCGA) methylation data, we identified shared hypermethylated gene promoters ( ADCY4 , MIR129‐2 , NID2 , and MAGI2 ) among those four cancers. Validation was performed using online datasets, an in‐house tissue set ( N = 179), and plasma samples ( N = 485) using droplet digital PCR (ddPCR). The test showed sensitivities of 81.82% (lung), 45% (breast), 69.23% (colorectal), and 44.14% (prostate), with 91.04% specificity. Overall, the PanCancer panel achieved 60.1% sensitivity and 87.4% specificity in detecting these four cancers. In early‐stage cancers, sensitivities were slightly lower but followed a similar trend. Additionally, the test detected nine other cancer types in plasma. This proof‐of‐concept study demonstrates the feasibility of a single methylation‐targeted blood test for multi‐cancer detection, offering potential as an affordable and scalable screening tool for early cancer detection.

Economic evaluation of antimicrobial resistance (AMR) interventions is complicated by the multisectoral, inter-temporal and international aspects of the problem, further hindered by a lack of available data and theoretical understanding of the emergence and transmission of AMR. Despite the substantial global focus on the problem, there is a lack of comprehensive economic evaluation literature on AMR policies. The goal of this work is to review the available literature on the economic evaluation of AMR interventions focusing on methods used to quantify the effects on AMR and the associated health consequences and costs. The studies included in the review were identified by a previous study by Painter et al. that included all full economic evaluations of AMR policies in the peer-reviewed and grey literature published between 2000 and 2020. The current review extracted additional information to (1) summarise the types and the key features of the AMR intervention economic evaluation literature available; (2) systemise the types of intervention effects on AMR quantified and describe these across the dimensions of AMR burden: time, space, wider pathogen pool and different sectors (One Health framework); and (3) categorise the methods used to derive these outcomes and how were these linked to health consequences and costs. Thirty-one studies were included within this review, of which 18 evaluated interventions that aimed to reduce infection rates and 11 evaluated interventions that aimed to optimise antimicrobial use. Almost all were conducted with a high-income and/or upper-middle income country perspective and focused on human health. Thirteen of 31 studies were cost-utility analyses. Fifteen of 31 and 7/31 studies estimated the AMR effects through decision tree and/or Markov models and transmission models, respectively. Transmission models and linkage of AMR outcomes to quality-adjusted life-years and costs were more common in evaluations of interventions aimed at reducing infection rates. Most of the included studies restricted the scope of evaluation to a short time horizon and a narrow geographical scope and did not consider the wider impact on other pathogens and other settings, potentially resulting in an incomplete capture of the effects of interventions. This review found limited available literature that mainly focused on high-income countries and infection prevention/reduction strategies. Most evaluations used a narrow study scope, which might have prevented the full capture of the costs and outcomes associated with interventions. Finally, despite the known complexities associated with quantifying AMR effects, and the corresponding methodological challenges, the implications of these choices were rarely discussed explicitly.

In a landscape of accelerated approvals and a less mature evidence base, constrained health systems make reimbursement decisions based on uncertain evidence about the expected clinical and economic value of a health technology. Uncertain decisions require expert judgments, and there has recently been a drive to improve the accountability and transparency in the way these judgments are collected and reported. Structured expert elicitation (SEE) refers to formal methods to quantify experts' judgments. Protocols for conducting SEE exist; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation. This article describes the development of Structured Expert Elicitation Resources (STEER), a collection of open access resources based on a published protocol for SEE specific to the health care decision-making (HCDM) setting. The resources cover the entire SEE process from design to reporting. The resources include an overview and a practical guide for conducting SEE in this setting, adaptable tools for building bespoke SEE exercises, training materials for experts taking part in SEE, resources used in previous SEE exercises, and examples of published SEE in HCDM. The materials cover practical considerations such as timelines team skills requirements, and administrative requirements such as contracting. The use of off-the-shelf resources can streamline the SEE process in HCDM while maintaining robustness.HighlightsThere is a drive to improve accountability and transparency in the way expert judgments are used in health care decision making; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation.Structured Expert Elicitation Resources (STEER) is a collection of open access resources for conducting SEE in health care decision making, based on a published methods protocol for SEE specific to this setting.The use of off-the-shelf resources can streamline the SEE process in health care decision making while maintaining robustness.

Background: Evidence maps have been used in healthcare to understand existing evidence and to support decision-making. In oncology they have been used to summarise evidence within a disease area but have not been used to compare evidence across different diseases. As an increasing number of oncology drugs are licensed for multiple indications, visualising the accumulation of evidence across all indications can help inform policy-makers, support evidence synthesis approaches, or to guide expert elicitation on appropriate cross-indication assumptions. Methods: The multi-indication oncology therapy bevacizumab was selected as a case-study. We used visualisation methods including timeline, ridgeline and split-violin plots to display evidence across seven licensed cancer types, focusing on the evolution of evidence on overall and progression-free survival over time as well as the quality of the evidence available. Results: Evidence maps for bevacizumab allow for visualisation of patterns in study-level evidence, which can be updated as evidence accumulates over time. The developed tools display the observed data and synthesised evidence across- and within-indications. Limitations: The effectiveness of the plots produced are limited by the lack of complete and consistent reporting of evidence in trial reports. Trade-offs were necessary when deciding the level of detail that could be shown while keeping the plots coherent. Conclusions: Clear graphical representations of the evolution and accumulation of evidence can provide a better understanding of the entire evidence base which can inform judgements regarding the appropriate use of data within and across indications. Implications: Improved visualisations of evidence can help the development of multi-indication evidence synthesis. The proposed evidence displays can lead to the efficient use of information for health technology assessment.

Objectives This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To conduct a component network meta‐analysis (CNMA) comparing different SEP combinations to identify the most clinically effective exercise prescription for people with IC. To conduct an economic evaluation comparing different SEP combinations for people with IC to identify which are cost‐effective.

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

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.