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OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.
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BACKGROUND: The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. METHODS: We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. RESULTS: In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). CONCLUSIONS: MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.
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Técnica Delphi , Política de Saúde , Modelos Econômicos , Avaliação da Tecnologia Biomédica , Humanos , Inquéritos e Questionários , Tomada de Decisões , Economia Médica , ConsensoRESUMO
Introduction: Meaningful patient involvement in health technology assessment (HTA) is essential in ensuring that the interests of the affected patient population, their families, and the general public are accurately reflected in coverage and reimbursement decisions. Central and Eastern European (CEE) countries are generally at less advanced stages of implementing HTA, which is particularly true for patient involvement activities. As part of the Horizon2020 HTx project, this research aimed to form recommendations for critical barriers to patient involvement in HTA in CEE countries. Methods: Built on previous research findings on potential barriers, a prioritisation survey was conducted online with CEE stakeholders. Recommendations for prioritised barriers were formed through a face-to-face workshop by CEE stakeholders and HTx experts. Results: A total of 105 stakeholders from 13 CEE countries completed the prioritisation survey and identified 12 of the 22 potential barriers as highly important. The workshop had 36 participants representing 9 CEE countries, and 5 Western European countries coming together to discuss solutions in order to form recommendations based on best practices, real-life experience, and transferability aspects. Stakeholder groups involved in both phases included HTA organisation representatives, payers, patients, caregivers, patient organisation representatives, patient experts, health care providers, academic and non-academic researchers, health care consultants and health technology manufacturers/providers. As a result, 12 recommendations were formed specified to the CEE region's context, but potentially useful for a broader geographic audience. Conclusion: In this paper, we present 12 recommendations for meaningful, systematic, and sustainable patient involvement in HTA in CEE countries. Our hope is that engaging more than a hundred CEE stakeholders in the study helped to spread awareness of the importance and potential of patient involvement and that the resulting recommendations provide tangible steps for the way forward. Future studies shall focus on country-specific case studies of the implemented recommendations.
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Participação do Paciente , Avaliação da Tecnologia Biomédica , Humanos , Avaliação da Tecnologia Biomédica/métodos , Europa (Continente)RESUMO
OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to reduce the risk of cardiovascular complications, which largely drive diabetes' health and economic burdens. Trial results indicated that SGLT2i are cost effective. However, these findings may not be generalizable to the real-world target population. This study aims to evaluate the cost effectiveness of SGLT2i in a routine care type 2 diabetes population that meets Dutch reimbursement criteria using the MICADO model. METHODS: Individuals from the Hoorn Diabetes Care System cohort (N = 15,392) were filtered to satisfy trial inclusion criteria (including EMPA-REG, CANVAS, and DECLARE-TIMI58) or satisfy the current Dutch reimbursement criteria for SGLT2i. We validated a health economic model (MICADO) by comparing simulated and observed outcomes regarding the relative risks of events in the intervention and comparator arm from three trials, and used the validated model to evaluate the long-term health outcomes using the filtered cohorts' baseline characteristics and treatment effects from trials and a review of observational studies. The incremental cost-effectiveness ratio (ICER) of SGLT2i, compared with care-as-usual, was assessed from a third-party payer perspective, measured in euros (2021 price level), using a discount rate of 4% for costs and 1.5% for effects. RESULTS: From Dutch individuals with diabetes in routine care, 15.8% qualify for the current Dutch reimbursement criteria for SGLT2i. Their characteristics were significantly different (lower HbA1c, higher age, and generally more preexisting complications) than trial populations. After validating the MICADO model, we found that lifetime ICERs of SGLT2i, when compared with usual care, were favorable (< 20,000/QALY) for all filtered cohorts, resulting in an ICER of 5440/QALY using trial-based treatment effect estimates in reimbursed population. Several pragmatic scenarios were tested, the ICERs remained favorable. CONCLUSIONS: Although the Dutch reimbursement indications led to a target group that deviates from trial populations, SGLT2i are likely to be cost effective when compared with usual care.
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Diabetes Mellitus Tipo 2 , Inibidores do Transportador 2 de Sódio-Glicose , Humanos , Análise Custo-Benefício , Diabetes Mellitus Tipo 2/complicações , Inibidores do Transportador 2 de Sódio-Glicose/uso terapêuticoRESUMO
Background: Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries. Methods: We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report. Results: Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure. Conclusion: In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.
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Inteligência Artificial , Avaliação da Tecnologia Biomédica , Humanos , Avaliação da Tecnologia Biomédica/métodos , Europa (Continente) , Política de Saúde , Gerenciamento de DadosRESUMO
OBJECTIVES: To develop best-practice guidance for health technology assessment (HTA) agencies when appraising diagnostic tests for SARS-CoV-2 and treatments for COVID-19. METHODS: We used a policy sandbox approach to develop best-practice guidance for HTA agencies to approach known challenges associated with assessing tests and treatments for COVID-19. The guidance was developed by a multi-stakeholder workshop of twenty-one participants representing HTA agencies, clinical and patient experts, academia, industry, and a payer, from across Europe and North America. The workshop was supported by extensive background work to identify the key challenges, including: targeted reviews of existing COVID-related methods guidance for assessing interventions and clinical guidelines, engagement with clinical experts, a survey and workshop of HTA agencies, a systematic review of published economic evaluations, and a workshop of health economic modelers. RESULTS: We suggest HTA agencies should consider using other types of evidence (e.g., real world) where high-quality randomized controlled trials may be lacking and healthcare systems would value timely HTA outputs. A "living" HTA approach may be useful, given the context of an evolving disease, scientific understanding and evidence base, allowing for decisions to be efficiently revisited in response to new information; particularly, if supported by a common "disease model" for COVID-19. Innovative ways of engaging with the public and clinicians, and early engagement with regulators and payers, are recommended. CONCLUSIONS: HTA agencies should consider the elements of this guidance that are most suited to their existing processes to enable them to assess the effectiveness and value of interventions for COVID-19.
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COVID-19 , SARS-CoV-2 , Humanos , Avaliação da Tecnologia Biomédica , Atenção à Saúde , Europa (Continente)RESUMO
Aim: Real-world data and real-world evidence (RWE) are becoming more important for healthcare decision making and health technology assessment. We aimed to propose solutions to overcome barriers preventing Central and Eastern European (CEE) countries from using RWE generated in Western Europe. Materials & methods: To achieve this, following a scoping review and a webinar, the most important barriers were selected through a survey. A workshop was held with CEE experts to discuss proposed solutions. Results: Based on survey results, we selected the nine most important barriers. Multiple solutions were proposed, for example, the need for a European consensus, and building trust in using RWE. Conclusion: Through collaboration with regional stakeholders, we proposed a list of solutions to overcome barriers on transferring RWE from Western Europe to CEE countries.
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Política de Saúde , Avaliação da Tecnologia Biomédica , Humanos , Europa (Continente) , Confiança , Tomada de DecisõesRESUMO
INTRODUCTION: Drug reimbursement decisions are often made based on a price set by the manufacturer. In some cases, this price leads to public and scientific debates about whether its level can be justified in relation to its costs, including those related to research and development (R&D) and manufacturing. Such considerations could enter the decision process in collectively financed health care systems. This paper investigates whether manufacturers' costs in relation to drug prices, or profit margins, are explicitly mentioned and considered by health technology assessment (HTA) organisations. METHOD: An analysis of reimbursement reports for cancer drugs was performed. All relevant Dutch HTA-reports, published between 2017 and 2019, were selected and matched with HTA-reports from three other jurisdictions (England, Canada, Australia). Information was extracted. Additionally, reimbursement reports for three cases of expensive non-oncolytic orphan drugs prominent in pricing debates in the Netherlands were investigated in depth to examine consideration of profit margins. RESULTS: A total of 66 HTA-reports concerning 15 cancer drugs were included. None of these reports contained information on manufacturer's costs or profit margins. Some reports contained general considerations of the HTA organisation which related prices to manufacturers' costs: six contained a statement on the lack of price setting transparency, one mentioned recouping R&D costs as a potential argument to justify a high price. For the case studies, 21 HTA-reports were selected. One contained a cost-based price justification provided by the manufacturer. None of the other reports contained information on manufacturer's costs or profit margins. Six reports contained a discussion about lack of transparency. Reports from two jurisdictions contained invitations to justify high prices by demonstrating high costs. CONCLUSION: Despite the attention given to manufacturers' costs in relation to price in public debates and in the literature, this issue does not seem to get explicit systematic consideration in the reimbursement reports of expensive drugs.
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OBJECTIVES: This study aimed to investigate whether the profit margins of pharmaceuticals would influence the outcome of reimbursement decisions within the Dutch policy context. METHODS: We conducted a discrete choice experiment among 58 Dutch decision makers. In 20 choice sets, we asked respondents to indicate which of 2 pharmaceutical treatment options they would select for reimbursement. Options were described using 5 attributes (disease severity, incremental costs per quality-adjusted life-year, health gain, budget impact, and profit margin) with 3 levels each. Additionally, cognitive debriefing questions were presented, and for validation debriefing, interviews were conducted. Choice data were analyzed using mixed logit models, also to calculate marginal effects and choice probabilities. RESULTS: Results indicated that the specified levels of profit margins significantly influenced choices made. Decision makers were less likely to reimburse a product with a higher profit margin. The relative importance of profit margins was lower than that of the included traditional health technology assessment criteria, but not negligible. When asked directly, 61% of respondents indicated that profit margin should play a role in reimbursement decision making, although concerns about feasibility and the connection to price negotiations were voiced. CONCLUSIONS: Our results suggest that if available to decision makers the profit margin of pharmaceutical products would influence reimbursement decisions within the Dutch policy context. Higher profit margins would reduce the likelihood of reimbursement. Whether adding profit margin as an additional, explicit criterion to the health technology assessment decision framework would be feasible and desirable is open to further exploration.
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Tomada de Decisões , Custos de Medicamentos , Preparações Farmacêuticas/economia , Mecanismo de Reembolso , Adulto , Idoso , Orçamentos , Análise Custo-Benefício , Feminino , Política de Saúde , Humanos , Masculino , Pessoa de Meia-Idade , Países Baixos , Anos de Vida Ajustados por Qualidade de Vida , Índice de Gravidade de Doença , Avaliação da Tecnologia BiomédicaRESUMO
The aim of this letter to the editor is to provide a comprehensive summary of uncertainty assessment in Health Technology Assessment, with a focus on transferability to the setting of rare diseases. The authors of "TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases" presented recommendations for reducing uncertainty in rare diseases. Their article is of great importance but unfortunately suffers from a lack of references to the wider uncertainty in Health Technology Assessment and research prioritisation literature and consequently fails to provide a trusted framework for decision-making in rare diseases. In this letter to the editor we critique the authors' tool and provide pointers as to how their proposal can be strengthened. We present references to the literature, including our own tool for uncertainty assessment (TRUST; unrelated to the authors' research), apply TRUST to two assessments of orphan drugs in rare diseases and provide a broader perspective on uncertainty and risk management in rare diseases, including a detailed research agenda.
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Produção de Droga sem Interesse Comercial , Doenças Raras , Humanos , Doenças Raras/tratamento farmacológico , Avaliação da Tecnologia Biomédica , IncertezaRESUMO
Currently, reimbursement decisions based on health technology assessments (HTA) in the Netherlands mostly concern outpatient pharmaceuticals. The Dutch government aspires to broaden the systematic application of full HTA towards other types of health care in order to optimise the content of the basic benefit package. This paper identifies important challenges for broadening the scope of full HTA to other types of health care. Based on a description of the Dutch reimbursement decision-making process, five important characteristics of outpatient pharmaceuticals were identified, which are all relevant to the successful application of HTA: (i) closed reimbursement system, (ii) absence of alternative policy measures, (iii) existence of marketing authorisation, (iv) identifiable and accountable counterparty, and (v) product characteristics. For a selection of other types of health care, which may be subject to HTA more frequently in the future, deviations from these characteristics of outpatient pharmaceuticals are discussed. The implications of such deviations for performing HTA and the decision-making process are highlighted. It is concluded that broadening the application of HTA will require policy makers to meet both important policy-related and methodological challenges. These challenges differ per health care domain, which may inform policy makers which expansions of the current use of HTA are most feasible.
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Pessoal Administrativo , Avaliação da Tecnologia Biomédica , Política de Saúde , Humanos , Países BaixosRESUMO
Deterministic sensitivity analyses (DSA) remain important to interpret the effect of uncertainties in individual parameters on results of cost-effectiveness analyses. Classic DSA methodologies may lead to wrong conclusions due to a lack of or misleading information regarding marginal effects, non-linearity, likelihood and correlations. In addition, tornado diagrams are misleading in some situations. Recent advances in DSA methods have the potential to provide decision makers with more reliable information regarding the effects of uncertainties in individual parameters. This practical application discusses advances to classic DSA methods and their implications. Three methods are discussed: stepwise DSA, distributional DSA and probabilistic DSA. For each method, the technical specifications, options for presenting results, and its implications for decision making are discussed. Options for visualizing DSA results in incremental cost-effectiveness ratios and in incremental net benefits are presented. The use of stepwise DSA increases interpretability of marginal effects and non-linearities in the model, which is especially relevant when arbitrary ranges are implemented. Using the probability distribution of each parameter in distributional DSA provides insight on the likelihood of model outcomes while probabilistic DSA also includes the effects of correlations between parameters.Probabilistic DSA, preferably expressed in incremental net benefit, is the most appropriate method for providing insight on the effect of uncertainty in individual parameters on the estimate of cost effectiveness. However, the opportunities provided by probabilistic DSA may not always be needed for decision making. Other DSA methods, in particular distributional DSA, can sometimes be sufficient depending on model features. Decision makers must determine to which extent they will accept and implement these new and improved DSA methodologies and adjust guidelines accordingly.
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Análise Custo-Benefício/métodos , Incerteza , Medicina Baseada em Evidências/estatística & dados numéricos , Humanos , ProbabilidadeRESUMO
The allocation of healthcare resources among competing priorities requires an assessment of the expected costs and health effects of investing resources in the activities and of the opportunity cost of the expenditure. To date, much effort has been devoted to assessing the expected costs and health effects, but there remains an important need to also reflect the consequences of uncertainty in resource allocation decisions and the value of further research to reduce uncertainty. Decision making with uncertainty may turn out to be suboptimal, resulting in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to better inform resource decisions. This value can be quantified using value of information (VOI) analysis. This report from the ISPOR VOI Task Force describes methods for computing 4 VOI measures: the expected value of perfect information, expected value of partial perfect information (EVPPI), expected value of sample information (EVSI), and expected net benefit of sampling (ENBS). Several methods exist for computing EVPPI and EVSI, and this report provides guidance on selecting the most appropriate method based on the features of the decision problem. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing VOI analyses. The software needed to compute VOI is discussed, and areas for future research are highlighted.
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Técnicas de Apoio para a Decisão , Custos de Cuidados de Saúde , Alocação de Recursos para a Atenção à Saúde/economia , Prioridades em Saúde/economia , Necessidades e Demandas de Serviços de Saúde/economia , Modelos Estatísticos , Avaliação das Necessidades/economia , Avaliação da Tecnologia Biomédica/economia , Consenso , Análise Custo-Benefício , Custos de Cuidados de Saúde/estatística & dados numéricos , Alocação de Recursos para a Atenção à Saúde/estatística & dados numéricos , Prioridades em Saúde/estatística & dados numéricos , Necessidades e Demandas de Serviços de Saúde/estatística & dados numéricos , Humanos , Avaliação das Necessidades/estatística & dados numéricos , Probabilidade , Avaliação da Tecnologia Biomédica/estatística & dados numéricos , IncertezaRESUMO
Healthcare resource allocation decisions made under conditions of uncertainty may turn out to be suboptimal. In a resource constrained system in which there is a fixed budget, these suboptimal decisions will result in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to make better resource allocation decisions. This value can be quantified using a value of information (VOI) analysis. This report, from the ISPOR VOI Task Force, introduces VOI analysis, defines key concepts and terminology, and outlines the role of VOI for supporting decision making, including the steps involved in undertaking and interpreting VOI analyses. The report is specifically aimed at those tasked with making decisions about the adoption of healthcare or the funding of healthcare research. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing the results of VOI analyses.
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Orçamentos , Tomada de Decisões , Técnicas de Apoio para a Decisão , Custos de Medicamentos , Desenvolvimento de Medicamentos/economia , Alocação de Recursos para a Atenção à Saúde/economia , Pesquisa sobre Serviços de Saúde/economia , Avaliação da Tecnologia Biomédica/economia , Redução de Custos , Análise Custo-Benefício , Humanos , Reembolso de Seguro de Saúde/economia , Modelos Econômicos , Modelos Estatísticos , Formulação de Políticas , Seguro de Saúde Baseado em Valor/economia , Aquisição Baseada em Valor/economiaRESUMO
BACKGROUND: An increasing number of technologies are obtaining marketing authorisation based on sparse evidence, which causes growing uncertainty and risk within health technology reimbursement decision making. To ensure that uncertainty is considered and addressed within health technology assessment (HTA) recommendations, uncertainties need to be identified, included in health economic models, and reported. OBJECTIVE: Our objective was to develop the TRansparent Uncertainty ASsessmenT (TRUST) tool for systematically identifying, assessing, and reporting uncertainties in decision models, with the aim of making uncertainties and their impact on cost effectiveness more explicit and transparent. METHODS: TRUST was developed by drawing on the uncertainty and risk assessment literature. To develop and validate this tool, we conducted HTA stakeholder discussion meetings and interviews and applied it in six real-world HTA case studies in the Netherlands and the UK. RESULTS: The TRUST tool enables the identification and categorisation of uncertainty according to its source (transparency issues, methodology issues, and issues with evidence: imprecision, bias and indirectness, and unavailability) in each model aspect. The source of uncertainty determines the appropriate analysis. The impact of uncertainties on cost effectiveness is also assessed. Stakeholders found using the tool to be feasible and of value for transparent uncertainty assessment. TRUST can be used during model development and/or model review. CONCLUSION: The TRUST tool enables systematic identification, assessment, and reporting of uncertainties in health economic models and may contribute to more informed and transparent decision making in the face of uncertainty.
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Técnicas de Apoio para a Decisão , Economia Médica , Modelos Econômicos , Avaliação da Tecnologia Biomédica/economia , Análise Custo-Benefício , Humanos , Países Baixos , Incerteza , Reino UnidoRESUMO
RATIONALE, AIMS, AND OBJECTIVES: In recent years, several expensive new health technologies have been introduced. The availability of those technologies intensifies the discussion regarding the affordability of these technologies at different decision-making levels. On the meso level, both hospitals and clinicians are facing budget constraints resulting in a tension to balance between different patients' interests. As such, it is crucial to make optimal use of the available resources. Different strategies are in place to deal with this problem, but decisions on a macro level on what to fund or not can limit the role and freedom of clinicians in their decisions on a micro level. At the same time, without central guidance regarding such decisions, micro level decisions may lead to inequities and undesirable treatment variation between clinicians and hospitals. The challenge is to find instruments that can balance both levels of decision making. DISCUSSION: Clinicians are becoming increasingly aware that their decisions to spend more resources (like time and budget) on 1 particular patient group reduce the resources available to other patients. Involving clinicians in thinking about the optimal use of limited resources, also in an attempt to bridge the world of economic reasoning and clinical practice, is crucial therefore. We argue that clinical guidelines may prove a clear vehicle for this by including both clinical and economic evidence to support the recommendations made. The development of such guidelines requires cooperation of clinicians, and health economists are cooperating with each other. CONCLUSION: The development of clinical guidelines which combine economic and clinical evidence should be stimulated, to balance central guidance and uniformity while maintaining necessary decentralized freedom. This is an opportunity to combine the reality of budgets and opportunity costs with clinical practice. Missing this opportunity risks either variation and inequity or central and necessarily crude measures.
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Tecnologia Biomédica , Tomada de Decisão Clínica , Medicina Baseada em Evidências/métodos , Assistência ao Paciente , Tecnologia Biomédica/economia , Tecnologia Biomédica/tendências , Tomada de Decisão Clínica/ética , Tomada de Decisão Clínica/métodos , Custos e Análise de Custo , Economia Médica/ética , Economia Médica/organização & administração , Economia Médica/normas , Custos de Cuidados de Saúde , Alocação de Recursos para a Atenção à Saúde/métodos , Humanos , Assistência ao Paciente/economia , Assistência ao Paciente/ética , Assistência ao Paciente/psicologia , Guias de Prática Clínica como AssuntoRESUMO
BACKGROUND: When proven effective, decision making regarding reimbursement of new health technology typically involves ethical, social, legal, and health economic aspects and constraints. Nevertheless, when applying standard value of information (VOI) analysis, the value of collecting additional evidence is typically estimated assuming that only cost-effectiveness outcomes guide such decisions. OBJECTIVES: To illustrate how decision makers' constraints can be incorporated into VOI analyses and how these may influence VOI outcomes. METHODS: A simulation study was performed to estimate the cost-effectiveness of a new hypothetical technology compared with usual care. Constraints were defined for the new technology on 1) the maximum acceptable rate of complications and 2) the maximum acceptable additional budget. The expected value of perfect information (EVPI) for the new technology was estimated in various scenarios, both with and without incorporating these constraints. RESULTS: For a willingness-to-pay threshold of 20,000 per quality-adjusted life-year, the probability that the new technology was cost-effective equaled 57%, with an EVPI of 1868 per patient. Applying the complication rate constraint reduced the EVPI to 1137. Similarly, the EVPI reduced to 770 when applying the budget constraint. Applying both constraints simultaneously further reduced the EVPI to 318. CONCLUSIONS: When decision makers explicitly apply additional constraints, beyond a willingness-to-pay threshold, to reimbursement decisions, these constraints can and should be incorporated into VOI analysis as well, because they may influence VOI outcomes. This requires continuous interaction between VOI analysts and decision makers and is expected to improve both the relevance and the acceptance of VOI outcomes.
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Tecnologia Biomédica/economia , Análise Custo-Benefício/métodos , Tomada de Decisões , Anos de Vida Ajustados por Qualidade de Vida , Avaliação da Tecnologia Biomédica/economia , Técnicas de Apoio para a Decisão , HumanosRESUMO
BACKGROUND: Value of information (VOI) is a tool that can be used to inform decisions concerning additional research in healthcare. VOI estimates the value of obtaining additional information and indicates the optimal design for additional research. Although it is recognized as good practice in handling uncertainty, it is still hardly used in decision making in the Netherlands. OBJECTIVE: This paper aims to examine the potential value of VOI, barriers and facilitators and the way forward with the use of VOI in the decision-making process for reimbursement of pharmaceuticals in the Netherlands. METHODS: Three focus group interviews were conducted with researchers, policy makers, and representatives of pharmaceutical companies. RESULTS: The results revealed that although all stakeholders recognize the relevance of VOI, it is hardly used and many barriers to the performance and use of VOI were identified. One of these barriers is that not all uncertainties are easily incorporated in VOI, and the results may be biased if structural uncertainties are ignored. Furthermore, not all research designs indicated by VOI may be feasible in practice. CONCLUSIONS: To fully embed VOI into current decision-making processes, a threshold incremental cost-effectiveness ratio and guidelines that clarify when and how VOI should be performed are needed. In addition, it should be clear to all stakeholders how the results of VOI are used in decision making.