Coverage with evidence development: applications and issues

Copyright © Cambridge University Press, 2010OBJECTIVES: The aim of this study was to describe the current issues surrounding Coverage with Evidence Development (CED). CED is characterized by restricted coverage for a new technology in parallel with targeted research when the stated goal of the research or data collection is to provide definitive evidence for the clinical or cost-effectiveness impact of the new technology. METHODS: Presented here is information summarized and interpreted from presentations and discussions at the 2008 Health Technology Assessment International (HTAi) meeting and additional information from the medical literature. This study describes the differences between CED and other conditional coverage agreements, provides a brief history of CED, describes real-world examples of CED, describes the areas of consensus between the stakeholders, discusses the areas for future negotiation between stakeholders, and proposes criteria to assist stakeholders in determining when CED could be appropriate. RESULTS: Payers could interpret the evidence obtained from a CED program either positively or negatively, and a range of possible changes to the reimbursement status of the new technology may result. Striking an appropriate balance between the demands for prompt access to new technology and acknowledging that some degree of uncertainty will always exist is a critical challenge to the uptake of this innovative form of conditional coverage. CONCLUSIONS: When used selectively for innovative procedures, pharmaceuticals, or devices in the appropriate disease areas, CED may provide patients access to promising medicines or technologies while data to minimize uncertainty are collected.The development of the manuscript was funded by Medicines Australi

Development of a self-report measure of capability wellbeing for adults: the ICECAP-A

Purpose The benefits of health and social care are not confined to patient health alone and therefore broader measures of wellbeing may be useful for economic evaluation.\ud This paper reports the development of a simple measure of capability wellbeing for adults (ICECAP-A).\ud Methods In-depth, informant-led, interviews to identify the attributes of capability wellbeing were conducted with 36 adults in the UK. Eighteen semi-structured, repeat interviews were carried out to develop a capability-based descriptive system for the measure. Informants were purposively selected to ensure variation in socio-economic status, age, sex, ethnicity and health. Data analysis was carried out inductively and iteratively alongside interviews, and findings were used to shape the questions in later interviews.\ud Results Five over-arching attributes of capability wellbeing were identified for the measure: ‘‘stability’’,‘‘attachment’’, ‘‘achievement’’, ‘‘autonomy’’ and ‘‘enjoyment’’. One item, with four response categories, was developed for each attribute for the ICECAP-A descriptive system.\ud Conclusions The ICECAP-A capability measure represents a departure from traditional health economics outcome measures, by treating health status as an influence over broader attributes of capability wellbeing. Further work is required to value and validate the attributes and test the sensitivity of the ICECAP-A to healthcare interventions

Best practice considerations on the assessment of robotic assisted surgical systems:results from an international consensus expert panel

Background Health technology assessments (HTAs) of robotic assisted surgery (RAS) face several challenges in assessing the value of robotic surgical platforms. As a result of using different assessment methods, previous HTAs have reached different conclusions when evaluating RAS. While the number of available systems and surgical procedures is rapidly growing, existing frameworks for assessing MedTech provide a starting point, but specific considerations are needed for HTAs of RAS to ensure consistent results. This work aimed to discuss different approaches and produce guidance on evaluating RAS. Methods A consensus conference research methodology was adopted. A panel of 14 experts was assembled with international experience and representing relevant stakeholders: clinicians, health economists, HTA practitioners, policy makers, and industry. A review of previous HTAs was performed and seven key themes were extracted from the literature for consideration. Over five meetings, the panel discussed the key themes and formulated consensus statements. Results A total of ninety-eight previous HTAs were identified from twenty-five total countries. The seven key themes were evidence inclusion and exclusion, patient- and clinician-reported outcomes, the learning curve, allocation of costs, appropriate time horizons, economic analysis methods, and robotic ecosystem/wider benefits. Conclusions Robotic surgical platforms are tools, not therapies. Their value varies according to context and should be considered across therapeutic areas and stakeholders. The principles set out in this paper should help HTA bodies at all levels to evaluate RAS. This work may serve as a case study for rapidly developing areas in MedTech that require particular consideration for HTAs.</p

Evidence-informed capacity building for setting health priorities in low- and middle-income countries: : A framework and recommendations for further research

Priority-setting in health is risky and challenging, particularly in resource-constrained settings. It is not simply a narrow technical exercise, and involves the mobilisation of a wide range of capacities among stakeholders – not only the technical capacity to “do” research in economic evaluations. Using the Individuals, Nodes, Networks and Environment (INNE) framework, we identify those stakeholders, whose capacity needs will vary along the evidence-to-policy continuum. Policymakers and healthcare managers require the capacity to commission and use relevant evidence (including evidence of clinical and cost-effectiveness, and of social values); academics need to understand and respond to decision-makers’ needs to produce relevant research. The health system at all levels will need institutional capacity building to incentivise routine generation and use of evidence. Knowledge brokers, including priority-setting agencies (such as England’s National Institute for Health and Care Excellence, and Health Interventions and Technology Assessment Program, Thailand) and the media can play an important role in facilitating engagement and knowledge transfer between the various actors. Especially at the outset but at every step, it is critical that patients and the public understand that trade-offs are inherent in priority-setting, and careful efforts should be made to engage them, and to hear their views throughout the process. There is thus no single approach to capacity building; rather a spectrum of activities that recognises the roles and skills of all stakeholders. A range of methods, including formal and informal training, networking and engagement, and support through collaboration on projects, should be flexibly employed (and tailored to specific needs of each country) to support institutionalisation of evidence-informed priority-setting. Finally, capacity building should be a two-way process; those who build capacity should also attend to their own capacity development in order to sustain and improve impact

Prioritizing solutions to incorporate Prosthetics and Orthotics services into Iranian health benefits package: Using an analytic hierarchy process

Introduction: Health benefits package (HBP) is regarded as one of the main dimensions of health financing strategy. Even with increasing demands for prosthetics and orthotics (P&O) services to approximately 0.5% of the world’s population, only 15% of vulnerable groups have the chance to make use of such benefits. Inadequate coverage of P&O services in the HBP is accordingly one of the leading reasons for this situation in many countries, including Iran. Aims: The main objective of this study was to find and prioritize solutions in order to facilitate and promote P&O services in the Iranian HBP. Study design: A mixed-methods (qualitative-quantitative) research design was employed in this study. Methods: This study was conducted in two phases. First, semi-structured interviews were undertaken to retrieve potential solutions. Then an analytic hierarchy process (AHP) reflecting on seven criteria of acceptability, effectiveness, time, cost, feasibility, burden of disease, and fairness was performed to prioritize them. Results: In total, 26 individuals participated in semi-structured interviews and several policy solutions were proposed. Following the AHP, preventive interventions, infant-specific interventions, inpatient interventions, interventions until 6 years of age, and emergency interventions gained the highest priority to incorporate in the Iranian HBP. Conclusion: A number of policy solutions were explored and prioritized for P&O services in the Iranian HBP. Our findings provide a framework for decision- and policy-makers in Iran and other countries aiming to curb the financial burdens of P&O users, especially in vulnerable group

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI)

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