Background: Atrial fibrillation (AF) significantly impacts the clinical and financial burden of healthcare systems worldwide. Patient-reported outcomes (PROMs) are considered a core element of patient-centered care. However, the integration of PROMs into AF clinical pathways remains limited. Objectives: In this study, we aimed to develop a policy-oriented framework for integrating PROMs into AF management, building on QoL assessment and the AF-CARE pathway. Methods: We compared the selected instruments (AFEQT, SF-36, EQ-5D, and AFSS) to examine how different QoL domains are represented. We then considered how the missing domains relate to routine care and healthcare delivery. On this basis, we constructed a conceptual framework linking PROM domains with clinical and policy decisions, as well as with diagnostic and follow-up strategies in AF. Results: Existing PROMs capture symptom burden reasonably well, but they do not fully reflect several domains that shape the daily experience of patients with AF, including cognition, sleep quality, sexual health, and economic burden. The proposed framework places PROMs within the AF-CARE components and illustrates how they can be used in routine practice—not only for long-term monitoring, but also to improve clinical decision-making, and better align with value-based healthcare strategies at both the clinical and system levels. It also outlines a practical approach to incorporating PROMs into everyday care and into broader health-system decision-making. Conclusions: Integrating PROMs into AF-CARE requires a system-level redesign rather than isolated tool implementation. Within the proposed framework, the question is no longer whether PROMs should be used in AF care, but how to integrate them in a way that meaningfully influences clinical decisions and patient outcomes.

Background: Disease-modifying therapies (DMTs) for Alzheimer’s disease, including lecanemab and donanemab, have received regulatory approval in multiple jurisdictions. These therapies require complex diagnostic workup and safety monitoring, raising significant budget impact concerns for healthcare payers. No budget impact analysis specific to Ireland or comparable small European healthcare systems has been published. Objective: To estimate the 5-year budget impact of introducing DMTs for early-stage Alzheimer’s disease in Ireland from the Health Service Executive (HSE) payer perspective. Methods: A budget impact model was developed following International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines. The model incorporated Irish epidemiological data, published drug prices, and healthcare resource utilisation estimates. Three treatment uptake scenarios (conservative, base case, optimistic) were modelled over a 5-year time horizon. Sensitivity analyses examined parameter uncertainty. Results: From an estimated eligible population of 11,568 individuals with early-stage Alzheimer’s disease, annual budget impact in Year 5 ranged from €12.8 million (conservative: 3% uptake) to €89.6 million (optimistic: 20% uptake), with a base case estimate of €35.8 million (8% uptake). Cumulative 5-year budget impact ranged from €32.0 million to €224.0 million. Drug acquisition costs represented 61% of total expenditure, with diagnostic and monitoring costs comprising 24% and 15%, respectively. Sensitivity analysis identified drug price, eligible population size, and treatment uptake as the most influential parameters. Conclusions: Introduction of DMTs for Alzheimer’s disease will have a substantial but manageable budget impact on the Irish healthcare system, contingent on treatment uptake rates constrained by diagnostic capacity. Strategic investment in diagnostic infrastructure, phased implementation, and negotiated drug pricing could mitigate budgetary pressures while enabling patient access to these novel therapies. Key Points for Decision Makers: This is the first budget impact analysis of disease-modifying therapies (DMTs) for Alzheimer’s disease specific to Ireland, a small European healthcare system with constrained diagnostic capacity. Annual budget impact ranges from €12.8 million (conservative scenario) to €89.6 million (optimistic scenario) in Year 5, representing 0.05% to 0.33% of the total Health Service Executive budget. Drug acquisition costs account for 58-65% of total expenditure, with diagnostic workup and safety monitoring comprising substantial ancillary costs. Phased implementation aligned with diagnostic infrastructure expansion could enable budget-neutral introduction through efficiency gains in dementia care pathways.

Background: Health technology assessment (HTA) bodies across Europe are preparing to evaluate Alzheimer’s disease-modifying therapies (DMTs), yet no standardised framework exists for eliciting and comparing stakeholder preferences on DMT implementation across different healthcare systems. Existing discrete choice experiment (DCE) evidence on Alzheimer’s treatment preferences is limited to the US context and focuses narrowly on treatment characteristics, neglecting the implementation factors diagnostic pathways, monitoring requirements, geographic access, waiting times that are critical for European healthcare systems with capacity constraints. Objective: To present the PREFER-DMT (PREFerences for European Reform of Disease-Modifying Therapy) framework: a standardised DCE protocol designed for multi-country deployment across European healthcare systems, enabling cross-national comparison of stakeholder preferences for DMT implementation. Methods: The PREFER-DMT framework comprises: (1) a nine-attribute, three-domain DCE design covering treatment characteristics (efficacy, ARIA risk, administration, duration), implementation factors (diagnostics, monitoring, geographic access, waiting time), and cost; (2) a four-stakeholder sampling strategy (patients/caregivers, clinicians, HTA professionals, general public) with country-specific sample size calculations; (3) a Bayesian D-efficient experimental design (Ngene) with 36 choice tasks blocked into 3 versions of 12 tasks each; (4) a three-stage analytical pipeline (conditional logit, mixed logit, latent class); (5) country-specific attribute level calibration to reflect local healthcare system characteristics; and (6) an interactive R Shiny dashboard for results dissemination and stakeholder engagement. The framework was validated using Monte Carlo simulation calibrated to published clinical trial evidence and the DCE literature. Nine attributes were identified through systematic review (12 relevant studies), expert consultation, and alignment with published health system preparedness assessments. Results: Simulation-based validation demonstrates the framework’s analytical capability. Under calibrated coefficients, the nine-attribute design achieves a D-error of 0.0023, adequate for mixed logit estimation with preference heterogeneity. Key illustrative findings include: (a) implementation-related attributes collectively account for 42.5% of total preference importance, exceeding treatment characteristics alone (35.9%); (b) geographic access to treatment centres and diagnostic pathway choice are the most highly valued implementation factors (WTP: EUR 20,571 and EUR 18,571 per year, respectively); (c) latent class analysis identifies three distinct preference segments (efficacy-driven, access-prioritising, and risk-averse) with implications for patient-centred implementation. The framework is designed for adaptation to different European contexts through country-specific level calibration: for example, cost levels calibrated to national pharmaceutical pricing, travel distances calibrated to geographic dispersion of specialist centres, and waiting times calibrated to local healthcare system capacity. Conclusions: PREFER-DMT provides a standardised, validated, and immediately deployable DCE framework for multi-country preference elicitation on Alzheimer’s DMT implementation in Europe. By covering implementation factors alongside treatment characteristics, the framework generates evidence directly relevant to the system-level decisions that European HTA bodies must make. The framework is operationalised as an open-access interactive platform and is available for adoption by national DMT working groups, HTA agencies, and research consortia.

Background: Health technology assessment bodies evaluating Alzheimer’s disease-modifying therapies (DMTs) require preference-based evidence to support patient-centred decision-making. However, primary DCE data collection across multiple stakeholder groups and countries is resource-intensive and time-consuming. No published preference evidence currently exists for DMT implementation outside the United States. This paper presents a simulation-based DCE framework with an accompanying interactive decision support tool, designed to anticipate likely preference structures, demonstrate the analytical pipeline, and provide a ready-to-deploy platform for real-world data collection by national working groups. Methods: A complete DCE analytical framework was developed comprising nine attributes across three domains: treatment characteristics (efficacy, ARIA risk, mode of administration, treatment duration), implementation factors (diagnostic testing, safety monitoring, geographic access, waiting time), and cost. Monte Carlo simulation of DCE response data was calibrated to published clinical trial evidence, healthcare system data, and expert elicitation from the Alzheimer’s disease health technology assessment literature. Simulated choice data for four stakeholder groups (patients/caregivers, clinicians, HTA professionals, general public) were analysed using conditional logit, mixed logit, and latent class models. An interactive web-based decision support tool was developed to enable policymakers to explore preference estimates, simulate market scenarios, and evaluate budget impact under different implementation configurations. Results: Under calibrated simulation parameters, slowing of cognitive decline emerged as the most important attribute (relative importance 25.7%), followed by annual cost (17.6%), ARIA risk (15.4%), and travel to treatment centre (13.0%). Implementation-related attributes collectively accounted for 42.5% of total attribute importance, exceeding treatment characteristics alone (35.9%). Simulated willingness-to-pay for a 10-percentage-point improvement in cognitive decline slowing was €12,857 per year. Latent class analysis identified three illustrative preference segments: efficacy-driven (42%), access-prioritising (35%), and risk-averse (23%). Sensitivity analysis across a wide range of plausible parameter values confirmed the robustness of the finding that implementation factors rival treatment characteristics in importance. Conclusions: This simulation-based framework provides a methodologically rigorous, immediately deployable platform for preference elicitation in Alzheimer’s DMT implementation. The interactive dashboard enables national DMT working groups and HTA bodies to adopt the framework for real-world data collection, replacing simulated parameters with empirical estimates while retaining the validated analytical infrastructure. The framework is designed for direct adoption by national DMT working groups and HTA bodies globally.

Background: Disease-modifying therapies (DMTs) for Alzheimer’s disease, including lecanemab and donanemab, have secured regulatory approval in multiple jurisdictions, yet health technology assessment (HTA) bodies in small and medium healthcare systems face unique challenges in evaluating these therapies. Existing economic evaluation frameworks have been developed predominantly within large-market HTA contexts (e.g., NICE, ICER), leaving a critical methodological gap for smaller systems with constrained diagnostic infrastructure, limited specialist workforce capacity, and distinct epidemiological profiles. This study presents a transferable health economic evaluation framework and accompanying interactive decision-support platform designed to address this gap. Methods: A decision-analytic modelling framework was developed incorporating three interconnected components: (1) a five-state Markov cohort model for cost-effectiveness analysis (CEA) of lecanemab and donanemab versus standard of care; (2) a multi-treatment budget impact analysis (BIA) engine with seven cost layers, country-specific population cascades, and adjustable market share trajectories; and (3) a discrete choice experiment (DCE) module for eliciting stakeholder preferences regarding DMT implementation attributes. The framework was parameterised and validated for Ireland as the index country, representing a tax-funded EU small market with emerging DMT readiness challenges. An interactive R Shiny platform was developed and publicly deployed to enable real-time scenario analysis and threshold exploration. The modular architecture is designed for transferability to additional healthcare systems. Model validation included comparison against published HTA assessments from NICE, ICER, CADTH, and PBAC. Results: Base-case incremental cost-effectiveness ratios (ICERs) for lecanemab versus standard of care were €296,506 per QALY gained, and €371,299 per QALY for donanemab, from the HSE payer perspective under HIQA guidelines. Neither treatment met the €45,000/QALY willingness-to-pay threshold at current list prices. Five-year budget impact under the base-case uptake scenario was estimated at €48.7 million, with drug acquisition costs representing the largest cost category (42–58%) followed by diagnostic infrastructure (18%). The DCE module identified monitoring burden, out-of-pocket costs, and treatment location as dominant preference attributes. Probabilistic sensitivity analysis demonstrated that cost-effectiveness conclusions were most sensitive to drug price, treatment discontinuation rates, and the time horizon adopted. The R Shiny platform enabled identification of threshold drug prices at which DMTs become cost-effective, providing actionable evidence for pricing negotiations. Conclusions: This study provides a transferable health economic evaluation framework specifically designed for DMT assessment in small and medium healthcare systems, demonstrated through application to Ireland. The integrated CEA-BIA-DCE architecture, combined with the publicly accessible interactive platform, offers a replicable methodology that can be adapted to additional country contexts as DMTs progress through regulatory and reimbursement pathways. The framework directly addresses calls from recent service-readiness analyses for quantitative decision-support tools to complement qualitative implementation planning.

Background: Discrete choice experiments (DCEs) are the most widely used stated-preference methodology in health technology assessment (HTA), yet the analytical outputs of DCE studies coefficient estimates, willingness-to-pay values, market simulations, and budget impact projections are typically presented in static tables and figures that limit stakeholder engagement and scenario exploration. No publicly available, disease-agnostic interactive platform currently exists to enable HTA professionals, clinicians, and policymakers to explore DCE results dynamically, simulate alternative scenarios, and evaluate their economic implications without requiring statistical programming expertise. Objective: To develop, implement, and validate an open-access, interactive web-based platform for DCE analysis that is (a) disease-agnostic with fully customisable attributes and levels, (b) capable of accepting both simulated and empirical DCE coefficient estimates, (c) equipped with real-time market simulation and budget impact projection, and (d) designed for adoption by HTA bodies and clinical working groups globally. Methods: The platform was developed in R Shiny (R version 4.4) and deployed on shinyapps.io. The architecture comprises seven interactive modules: Study Design, Attribute Editor, Model Results, Willingness-to-Pay Analysis, Market Simulation, Economic Evaluation, and Upload/Export. The Attribute Editor enables users to define, add, remove, and modify attributes (continuous or categorical), their levels, domain classifications, coefficients, standard errors, and calibration sources with all downstream outputs updating reactively. The Upload module accepts CSV files of estimated coefficients from any mixed logit model, enabling transition from simulated to empirical data without code modification. The platform ships with a default preset for Alzheimer’s disease-modifying therapy (DMT) implementation (9 attributes, 3 domains) but is fully configurable for any disease area or healthcare intervention. Results: The platform was validated using a simulation-based DCE calibrated to published clinical trial evidence for Alzheimer’s DMTs. Under the default preset, all seven modules function as designed: the Attribute Editor correctly propagates changes to all downstream outputs; the Model Results module dynamically computes relative importance from any attribute configuration; the WTP module automatically detects the cost attribute and computes marginal rates of substitution; the Market Simulation module generates logit-based preference shares that update in real time as users adjust treatment profile sliders; and the Economic module projects 5-year budget impact under user-specified parameters. The Upload module successfully replaced simulated coefficients with uploaded values, with all outputs recalculating automatically. The platform is publicly accessible at https://sevinc.shinyapps.io/dce-dmt-platform/ with source code available upon request. Conclusions: This platform provides the first open-access, disease-agnostic, interactive tool for DCE analysis in HTA. By enabling attribute customisation, real-data integration, and real-time scenario simulation, it bridges the gap between econometric DCE analysis and stakeholder decision-making. The platform is freely available for adoption by HTA bodies, clinical working groups, and researchers conducting preference studies in any therapeutic area.