bluuebunny/crossref_metadata_2025_split huggingface dataset:
OP21 A Critical Review Of Existing Health Inequality And Health Inequity Frameworks In Evidence Synthesis
Authors: Kunonga Patience , Hanratty Barbara , Bower Pete , Craig Dawn 
Published: December 2022

IntroductionIn recent years, there has been a growing recognition that health equity and health inequalities should be a consideration in all aspects of research. Since the Commission on Social Determinants of Health by the World Health Organization was established in 2005, there has been a growing interest in tackling systemic differences in health outcomes, including expanding the scope to health research including evidence synthesis and health technology assessments (HTA). This analysis aims to identify health inequality and health inequity frameworks that exist to help structure and plan research methods in evidence synthesis.MethodsA critical analysis of the existing frameworks used in evidence synthesis to address health inequality and/or inequity was undertaken. Comprehensive, systematic searching of seven social science electronic databases and grey literature was undertaken based on the Behavior/phenomenon of interest, Health context and Model/Theory (BeHEMoTh) model, from 1990 to May 2022 to identify all relevant studies. A narrative synthesis approach was used to critically appraise the existing frameworks.ResultsSixty-two reviews published between 2008 and 2022 reporting on using a framework to stratify health opportunities and outcomes met the inclusion criteria. Frameworks identified included the PROGRESS (place of residence, race or ethnicity, occupation, gender, religion, educational level, socioeconomic status, and social capital), PROGRESS-Plus (plus age, disability and sexual orientation) and Preferred Reporting Items for Systematic Reviews and Mata Analysis (PRISMA) – Equity checklist.ConclusionsCurrently, there does not seem to be consensus in how evidence of inequality or inequity in evidence synthesis or HTA are reported. As research interests in health inequality and inequity continue to grow, there is a need to develop a framework that provides an in-depth understanding of how inequalities in health and inequities in health should be considered within evidence synthesis and HTA. This will allow researchers to analyze not just the effects of interventions, but also how healthcare outcomes are impacted by inequalities or inequities.
OP30

M

odel for

AS

sessing The Value Of

AI

In Medical Imaging (MAS-AI)
Authors: Fasterholdt Iben , Kjølhede Tue , Naghavi-Behzad Mohammad , Schmidt Thomas , Rautalammi Quinnie , Kidholm Kristian , Hildebrandt Malene , Gerdes Anne , Barkler Astrid , Rasmussen Benjamin 
Published: December 2022

IntroductionArtificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, assessment of the value of these new technologies is still unclear and no agreed international HTA-based guideline exists. Therefore, a Model for ASsessing the value of AI (MAS-AI) in medical imaging was developed by a multidisciplinary group of experts and patient representatives.MethodsThe MAS-AI guideline is based on four steps. First a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging (5,890 studies were assessed with 86 studies included in the scoping review). Next, interviews with leading researchers in AI in Denmark. The third step was two workshops where decision-makers, patient organizations and researchers discussed crucial topics when evaluating AI. Between workshops, the multidisciplinary team revised the model according to comments from workshop-participants. Last step is a validation workshop in Canada.ResultsThe MAS-AI guideline has three parts. There are two steps covering nine domains and then advises for the evaluation process. Step 1 contains a description of patients, how the AI-model was developed, and initial ethical and legal considerations. Finishing the four domains in Step 1 is a prerequisite for moving to step 2. In step 2, a multidisciplinary assessment of outcomes of the AI-application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects and patient aspects. The last part, is five advices to facilitate a good evaluation process.ConclusionsWe have developed an HTA based framework to support the prospective phase while introducing novel AI technologies into healthcare in medical imaging. MAS-AI can assist HTA organizations (and companies) in selecting the relevant domains and outcome measures in the assessment of AI applications. It is important to ensure uniform and valid decisions regarding the adoption of AI technology with a structured process and tool. MAS-AI can help support these decisions and provide greater transparency for all parties involved.
OP31 Assessment Of AI Supported Health Technologies - How To Move Forward?
Authors: Daugbjerg Signe , Di Bidino Rossella , Cicchetti Americo 
Published: December 2022

IntroductionArtificial intelligence (AI)-supported technologies are rapidly developing and have the potential to improve healthcare quality at reduced cost. However, few examples exist of successfully deployed AI-technologies in a real-world context that have been adequately assessed. Therefore, the objective of this research is to: (i) identify existing health technology assessment (HTA) methods developed or adapted to assess AI-supported health technologies, (ii) identify new assessment topics or domains relevant for AI-technology uptake, and (iii) take the first step in developing a framework applicable for new challenges that emerge with the introduction of AI.MethodsA systematic literature review of studies describing methods or frameworks to assess AI-supported health technologies was performed on PubMed from January 2010 until February 2021. Furthermore, a web page search of international HTA agencies and international organizations such as the World Health Organization, Organziation for Economic and Co-ordination and Development, and the European Commission was performed to identify important aspects to consider when implementing and assessing AI technologies.ResultsNo assessment frameworks for AI technologies were identified from the systematic literature review or web page searches of international HTA agencies. Reports from international organizations highlight limitations or inability of most AI technologies to ‘explain’ their decision-making process (black box issue), leading to lack of trust in the technology that affects its adoption. It is recommended to put more emphasis on assessing transparency and ‘explainability’ of the AI solution as well as aspects of safety, ethical, legal, and social issues related to implementation and the development/training phase of the AI technology.ConclusionsThe results from this study uncover key gaps in frameworks posed for performing a systematic and holistic assessment of AI in a real-world context of health care. However, valuable information on relevant assessment aspects for AI-supported technologies have been identified.The results will form the basis for the development of a framework to assist decision-makers in assessing AI-supported technologies in a holistic manner for a responsible deployment – the HTA AI Framework.
OP36 A Lifecycle Approach In Evaluating Medical Technologies: Insights From The National Institute For Health And Care Excellence Guidance Review Process
Authors: Maslyankov Ivan , Wang Ying-Ying , Bowie Dionne , Heaney Joanne , Chalkidou Anastasia 
Published: December 2022

IntroductionHealth Technology Reassessment (HTR) is emerging, as the focus of health technology assessment agencies shifts from traditional methods of technology adoption to managing technologies throughout their lifecycle. The National Institute for Health and Care Excellence (NICE) evaluates devices, digital and diagnostic technologies by producing medical technologies guidance, which could recommend for adoption, no adoption, or further research. The desire to move to a lifecycle approach in the evaluation of medical technologies is reflected in the guidance review process, which involves review of the technology every three years or upon notification of significant new evidence. The outcomes of the guidance review can be to amend, update, withdraw, or leave the guidance unchanged.MethodsInformation on all technologies which have undergone guidance review since the commencement of the process was collected, including the recommendation before and after review and the basis for this recommendation. The proportion of guidances which were not changed, amended, updated, and withdrawn was calculated and the trends, including the bases for recommendation change were analyzed.ResultsIn total, 34 medical technology guidance reviews have been performed. During the process, 15 (44%) were amended to reflect minor changes in the economic or clinical evidence, which did not change the recommendation. Ten (29%) were not changed, while three (9%) were updated respectively. Three (9%) were withdrawn. Another three (9%) represent special cases, which entered guidance review, but were paused due to external reasons. Among the guidances that progressed to update, two out of three had a cost increase, whereas one was broadened to reflect evidence for a larger population.ConclusionsHTR is an important mechanism to improve patient care and system efficiency. In NICE’s evaluation of medical technologies, changes in the recommendation stemmed from changes in the technology’s (or standard care’s) cost, the evidence for clinical effectiveness, or the safety profile.
OP41 Facilitating Dialogue Of Real-World Evidence Use In Health Technology Assessment: Taxonomy Of Question/Data Source Pairings To Support A Registry Of Studies
Authors: Akehurst Ron L , Murphy Linda A , Mestre-Ferrandiz Jorge , Solà-Morales Oriol , de Pouvourville Gérard , Cunningham David , Corry Sorcha , Franklin Matthew , Chapman Ann-Marie 
Published: December 2022

IntroductionThis paper reports the results of the collaboration within the European initiative of new Reimbursement and aCCess Approaches (EUreccA) which is concerned with the use of real-world evidence (RWE) in health technology assessment (HTA) decision-making. The work grew from the observation of a large, very experienced group of HTA practitioners which found that the use of RWE varied depending on the type of question asked and the particulars of the data source(s) used. We set out to examine how RWE is used in HTA decision-making and to make proposals on its facilitation.MethodsLiterature reviews covering earlier reviews of RWE use, academic papers, and HTA agency websites were combined with case studies involving interviews with decision-makers in four countries (England, France, Italy, Sweden) to identify the circumstances of breakdown of RWE use and to build a categorization of the uses of RWE and associated difficulties. This evidence supported the creation of a taxonomy of pairings of data sources and the questions they were used to address. The face validity of the approach was tested at an advisory board of senior HTA practitioners.ResultsIn total, 27 questions were identified and 10 types of data source, giving 270 pairings. These pairings were linked to relevant methods guidance and to examples of their use, itemizing HTA issues and decisions made. Reports are being prepared for publication, covering the detail of the methods of the literature searches; methods of the country case studies; a description of the taxonomy; and guidance on governance.ConclusionsWhen using RWE in HTA decision-making, the detail of the particular data sources and question addressed matter. Recently, both the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Real-World Transparency Initiative have argued for a registry of the uses of RWE. The work described here offers a starting classification of the material that should be held in such a registry, and which in itself could be developed by the stakeholders, both agencies and companies, that use it, furthering trust and confidence.
OP50 Health Equity Considerations In HTA: A Case Example Of Prenatal Screening
Authors: Shaw Beth , King Valerie , Morse Josiah , Robalino Shannon , Harrod Curtis 
Published: December 2022

IntroductionThe Washington Health Technology Assessment (HTA) program has been guiding coverage decisions in the state since 2007. The Center for Evidence-based Policy works with the program to develop evidence-based HTA reports. In 2020, we presented an HTA on cell-free DNA prenatal screening for chromosomal aneuploidies. In the committee’s discussion, questions around access to screening tests and to prenatal care more generally were raised. We present a case study of how health equities were considered in the development of coverage criteria.MethodsWe conducted an HTA using standard systematic review methodologies. Outcomes focused on test accuracy and the impact of screening. We did not look for evidence on access to prenatal screening tests or people’s experience of prenatal screening. We reviewed the meeting transcript to identify issues of health equity and how they influenced the final decision.ResultsDuring the discussion of the evidence, the committee raised concerns around equitable access to cell-free DNA tests and prenatal screening, including: direct costs to the person; access to the full range of prenatal screening, including ultrasound; and, uptake of prenatal screening. Based on the findings from the evidence report, expert testimony, and public comment, the committee voted to cover cell-free DNA prenatal screening for chromosomal aneuploidies unconditionally.ConclusionsHealth equity is increasingly important in healthcare decision-making. Decision makers should consider how a decision may reduce health inequities and how it may inadvertently increase existing health inequities. Decision makers also need to understand the context within which the decision will be implemented. Consideration of health equity can be addressed in a number of ways, for example through systematic review of equity issues or patient experience or through the use of contextual knowledge from expert and public testimony. Regardless of the method, decision makers should remain transparent in how health equity considerations influenced their final determination.
OP57 The Identification Of Technological Innovations To Address The Challenge Of Antimicrobial Resistance Using Horizon Scanning Approaches
Authors: Barrass Lucy , Roberts Abigail , Arisa Oluwatomi , Robertson Emily , Oliver Aoife , Sharma Oshin , Kinnersley Janet , Craig Dawn , Oyewole Anne 
Published: December 2022

IntroductionInappropriate prescribing of antibiotics is a significant driver of antimicrobial resistance (AMR) which is a global health challenge. Technological innovations present an opportunity to reduce demand for antimicrobials through infection prevention, detection, and management. The National Institute for Health and Care Research (NIHR) Innovation Observatory (IO) has developed horizon scanning methods to identify promising innovations (devices/diagnostics/digital) and anticipate technological trends. Together these insights build a comprehensive landscape and presents a significant opportunity for decision-makers and HTAs to consider the clinical, financial, infrastructural, and logistical provisions to improve preparedness for the potential adoption of these future innovations.MethodsThe IO developed a detailed dataset of technologies by formulating search strategies for AMR, based on a comprehensive list of terms and input from expert panels. Primary and secondary sources were systematically scanned using a combination of traditional scanning methods, automated and novel artificial intelligence (AI)/machine learning techniques. Sources included clinical trial registries, MedTech news, academic sources, funding agencies, commercial sites, and regulatory authorities.ResultsOur global dataset identified over 3000 innovative preventative, detection, and monitoring technologies mapped across AMR clinical pathways (including sepsis, respiratory tract infections). Development activity largely concentrated in the United States of America and United Kingdom. Emerging trends included the application of novel materials to prevent infections (e.g., catheter coatings) and novel analytical techniques (e.g., biosensors, microfluidics, breath analysis) to support optimal patient treatment. Data analysis revealed a high proportion of technologies were diagnostic innovations addressing unmet needs such as rapid and accurate detection (including drug-resistant infections).ConclusionsThe rapid development and application of technological interventions presents an opportunity to strengthen national AMR strategies worldwide, through the adoption of new innovations. Improvements in exiting technologies, along with technological advancements have the potential to support appropriate prescribing of antimicrobials and thus address the rise in AMR.
OP59 The Tunisian Guidelines For Pharmacoeconomic Analysis: What We Need To Know
Authors: Harzallah Nabil , Chemli Jaafar , Grati Hela , Jebali Marie , Jameleddine Mouna , Hamouda Chokri 
Published: December 2022

IntroductionAs a new milestone in health technology assessment (HTA) implementation in Tunisia, L’Instance Nationale de l’Evaluation et de l’Accréditation en Santé (INEAS)—the Tunisian HTA body—published a set of methodological guidelines to support HTA dossier submission by the pharmaceutical industry. Including, ‘guide for submitting clinical data for an HTA at INEAS’, ‘methodological choices guide for pharmacoeconomic analysis at INEAS’, and ‘methodological choices guide for budget impact analysis at INEAS’. We aim to report the major methodological recommendations of the pharmacoeconomic analysis guideline.MethodsThe ‘methodological choices for pharmacoeconomic analysis at INEAS’ guideline was reviewed and the major recommendations were retrieved and reported.ResultsThe reference analysis required by INEAS is the cost-utility analysis systematically combined with a cost-effectiveness analysis (cost per life-year gained) from the public payers’ perspective. The choice of any other type of analysis must be duly justified. Comparators should include alternative treatments which are considered to be ‘the standard of care’ (i.e., interventions routinely used in Tunisia for the same indication) and in which public resources are invested. The time horizon should be sufficiently long to reflect all differences in costs and outcomes. Additionally, a discount rate of 5 percent per year is recommended. The best available evidence for efficacy, safety and quality of life is required. An indirect measure of patient preference, through a validated measurement instrument is preferred for utility calculation. Cost inputs should be identified from Tunisian sources. Health resource utilization should reflect the care pathway in Tunisia. INEAS favors the use of a recognized model. Uncertainty and impact of the input parameters on the results should be assessed and reported through probabilistic and deterministic sensitivity analyses. Model validation tests to assess face validity and internal validity should be performed, and a discussion of the methods used provided. Demonstration of external validity is required. Results should be presented in incremental cost-utility and cost-effectiveness ratios.ConclusionsThe recommendations of ‘methodological choices for pharmacoeconomic evaluation at INEAS’ is an important step to facilitate and harmonize pharmaceutical companies’ submissions and to enhance the use of these analyses in decision-making.
OP76 “Thunderbirds Are Go!” Rapid Response HTA Outputs For COVID-19
Authors: Hyseni Lirije , Islam Elizabeth , Bowie Dionne , Dimmock Paul 
Published: December 2022

IntroductionThe COVID-19 pandemic has highlighted the need for rapid assessment of potential health technologies that can improve health outcomes in COVID-19 patients, as well as helping pressurized health service provision. Medical technologies play a key role in the COVID-19 pandemic, especially diagnostic tests and respiratory technologies. This study evaluates the rapid response work that the medical technology evaluation programme (MTEP) at the National Institute for Health and Care Excellence (NICE) has done in response to the COVID-19 pandemic.MethodsCompanies routinely submit medical technologies for evaluation by NICE through HealthTech Connect, which is an online portal for devices, diagnostics and digital technologies intended for use in the NHS or wider United Kingdom health and care system. During the COVID-19 pandemic, companies were able to use a designated email address if they perceived their technology may benefit the healthcare system regarding the COVID-19 pandemic. This new system bypassed the usual full registration and data submission. All technologies were reviewed that were submitted via HealthTech connect and email between March 2020 and June 2021.ResultsDuring this period, 20 technologies were submitted to MTEP. Most of these technologies were submitted via email. These technologies consisted of a mix of digital, diagnostic, and respiratory technologies. Seven technologies were selected for a rapid COVID-19 MedTech innovation briefing (MIB), with one specifically addressing issues around waiting lists because of knock-on effects of COVID-19 restricting normal clinical work. A further six technologies were not selected because of limited evidence, while one was not selected because it was not perceived as innovative. The other five technologies were progressed as normal MIBs as there was not enough evidence of potential benefits related to COVID-19 to expedite to a rapid COVID-19 MIB. In total, two technologies were selected for medical technology guidance (myCOPD and Anaconda) and are currently in development.ConclusionsMTEP has responded to the COVID-19 pandemic by prioritising and producing rapid COVID-19 MIBs on technologies to improve health and social care.
OP89 The CE-Signal, A New Simplified Health Technology Assessment Method To Determine Whether Interventions Are Cost-Effective
Authors: Huisman Atse , Knies Saskia , Timmers Lonneke , Enzing Joost , Vreman Rick , Vrinzen Terry , Tran Ly 
Published: December 2022

IntroductionConducting a cost-effectiveness analysis (CEA) is resource consuming, and therefore the Dutch National Health Care Institute (ZIN) only performs those for interventions with a high budget impact. Sometimes, cost-effectiveness (CE) estimates are clearly below or far above reference values, which makes full cost-effectiveness assessments less vital. The objective of this study was to develop an efficient and simplified method to identify interventions that are clearly cost-(in)effective.MethodsThe method makes use of headroom analysis. Several HTA experts and other relevant stakeholders have been asked to provide feedback on a preliminary version of the CE signal.ResultsThe method consists of five steps. In the first step (i) the relevant willingness-to-pay threshold is determined. Reference values are used by ZIN for the maximum willingness-to-pay per incremental quality-adjusted life year (QALY), depending on burden of disease.In next step (ii) the health gain that can realistically be obtained with the new treatment is estimated. Hereby the effect of the intervention on the clinical outcomes, quality of life and gained life years is determined to estimate the number of QALYs gained, including uncertainty. Then (iii) the societal cost maximum (SCM) of the new treatment is calculated by multiplying step 2 with step 1. In step four (iv) the incremental treatment costs are estimated looking at both the costs and savings for both treatments options for the average patient. In the final step (v) the incremental treatment costs are compared to the SCM to determine if the intervention is probably cost effective, probably not cost-effective or if a conclusion cannot be drawn.ConclusionsThis method has proven to be feasible and could be a valuable addition to the current cost-effectiveness assessment toolbox. The CE-signal is being validated against performing a full cost-effectiveness analysis.