bluuebunny/crossref_metadata_2025_split huggingface dataset:
OD28 Towards Implementing New Payment Models For The Reimbursement Of High-Cost, Curative Therapies: Insights From Semi-Structured Interviews
Authors: Desmet Thomas , Michelsen Sissel , den Brande Elena Van , Dyck Walter Van , Simoens Steven , Huys Isabelle 
Published: January 2025

IntroductionIn response to the intricate challenges posed by high-cost, one-shot curative therapies, this study explores what hinders the wide implementation of innovative payment schemes across Europe. Drawing insights from the Belgian social healthcare system, this study focused on defining the necessary and sufficient conditions for implementing outcome-based spread payments in the context of market access to advanced medicinal productsMethods
Semi-structured interviews (n=33) were conducted with physicians (n=2), hospital pharmacists (n=4), hospital managers (n=2), patient representatives (n=3), industry representatives (n=5), Belgian policymakers (n=6), sickness fund representatives (n=4), legislative experts (n=2), and accounting experts (n=5) to elicit opinions and insights on stakeholders’ responsibilities and roles, and identify the necessary and sufficient conditions to establish outcome-based spread payments for the reimbursement of innovative therapies. The interviews took place between July 2020 and October 2020. The framework method analysis was performed using NVivo software (version 20.4.1.851). Statements were allocated into six main topics: payment structure, spread payments, outcome-based agreements, governance, transparency, and regulation.Results
Interviewees across stakeholder groups endorsed the idea of implementing outcome-based spread payments. However, opinions varied on practical and legal feasibility, especially regarding long-term follow-up for patients, data collection burden on physicians, and implications on the financing flow of health technology developers, hospitals, and the government. Concerns were also raised regarding the potential need for new governance structures, enhanced transparency on agreements and pricing mechanisms, as well as defining data requirements to address uncertainties often seen with this type of therapy. All interviewees emphasized the importance of increasing stakeholders’ understanding of these agreements to foster broader acceptance and successful implementation.ConclusionsThe effective implementation of outcome-based spread payments falls behind because consensus on how this reimbursement method can be a sustainable solution is missing. Leveraging the concepts of necessary and sufficient conditions from organizational research, this study provides guidance on resolving challenges and defines stakeholders’ roles for successfully implementing this reimbursement approach.
OD30 Clinician-Driven Health Technology Assessment: National Cancer Medicines Review For Off-Label Uses And On-Label Off-Patent Uses In NHSScotland
Authors: O’Connell Richard , Doherty Louis , Andrews Pamela , Craig Louise , Drinkell James , Dalrymple Heather , Clive Sally 
Published: January 2025

Introduction
Publicly funded cancer services face significant financial and capacity challenges. It is estimated that 40 percent of medicines used to treat cancer are outside their marketing authorization or “off-label.” These uses are usually outside the remit of health technology assessment (HTA) groups. Accessing emerging off-label uses is mostly through individual patient requests, which are resource intensive, delay patient treatment, and produce inequity.MethodsA program providing national HTA review of off-label and off-patent cancer medicine uses has been established by Healthcare Improvement Scotland. Processes include horizon scanning, support for proposing clinicians, and engaging patient groups. Relevant published and unpublished clinical and cost-effectiveness information—identified through systematic literature searches, engagement with pharmaceutical companies, academic and health service data groups—supports independent appraisal and decision-making. Where cost-effectiveness information is unavailable, a value-judgment framework, including magnitude of clinical benefit, uncaptured benefits, and budget and service impact, is utilized to standardize review. The decision-making Council includes public partners, and advice is shared across NHSScotland.ResultsFrom July 2022 to October 2023, the program has published advice on nine proposals—eight off-label uses and one on-label off-patent use. Health economic models from a pharmaceutical company and an academic group supported decision-making on two proposals, value-judgment frameworks for two proposals, and real-world evidence for one proposal. Eight proposals were supported, and one was not supported. Each supported proposal slowed cancer, prolonged life, or reduced toxicity compared to standard treatment options. Four were cost-saving and three had a low medicines budget impact. Three were service-saving and three had no significant impact on services.ConclusionsNovel HTA programs can address gaps in medicines governance to improve patient outcomes and support sustainability. Clinical connections, patient group engagement, health economic collaborations and linkage to national cancer data teams and academics have facilitated bespoke approaches to evidence-gathering despite limited resources. Our agile and adaptive approach has enabled robust review and decision-making on varied and impactful proposals.
OD33 Assessment Tool For MHealth Apps To Manage Depression (EvalDepApps Project)
Authors: Carrion Carme , Davins-Riu Meritxell , Duarte-Díaz Andrea , Ferreira-Cavalcanti Janaina , Fuster-Casanovas Aïna , Gelabert Estel , Gómez-Calderón Corpus , Moretó-Melero Sònia , Pérez-Navarro Antoni , Robles Noemí , Vidal-Alaball Josep 
Published: January 2025

IntroductionThe use of apps represents a revolution in mental health. Fast, versatile, and manageable, mHealth apps allow empowerment of patients and professionals and can even reduce stigmatization. There is not yet a standardized method to assess their effectiveness and safety. The objective of the EvalDepApps project is to develop an assessment tool for apps that have management of depression as the main goal.MethodsThe EvalDepApps project follows several stages:(i) Systematic review and meta-analysis following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, to identify evidence about effectiveness and safety of mHealth interventions to manage depressive symptoms among adults, and criteria to be included in the app assessment tools. The primary outcome was the reduction of depressive symptoms, and only randomized controlled trials (RCTs) were included.(ii) Delphi process with 30 participants (patients and healthcare professionals) to reach consensus about the criteria to be included in the tool.(iii) Co-creation workshops with 12 healthcare professionals and 12 patients to co-design the EvalDepApps tool.Results
Twenty-nine RCTs were included. The most common elements were psychoeducation, goal setting, and gamification. Significant effect for mHealth interventions in reducing depressive symptoms compared with non-active control (95% confidence interval: −0.87, −0.37; I2=87%) was identified. Hybrid interventions combining mHealth with face-to-face sessions were the most effective. Any study-related adverse events were reported. Response rate was 59 percent (26/44) in round one and 52 percent (23/44) in round two. Twenty-eight out of 51 criteria (54.9%) were accepted by consensus. Proposals were received about the look and feel of the content, usability aspects, sections, and main features of the EvalDepApps tool.ConclusionsmHealth interventions, particularly hybrid ones, can be effective in reducing depressive symptoms. There is a need for personalized approaches. It is important to prioritize evidence-based principles and standardized evaluation tools. A set of 25 criteria will be included in the EvalDepApps tool that will be co-created thanks to the input given by healthcare professionals and people diagnosed with depression.
OD34 Consideration Of Medical Device Characteristics In Health Technology Assessment: A Gap In The Methods Guidelines And In Practice
Authors: Basu Rituparna , Eggington Simon , Hallas Natalie , Strachan Liesl 
Published: January 2025

IntroductionIt is well accepted that medical devices (MDs) and procedures have several unique characteristics compared to pharmaceuticals, such as learning curve (LC), incremental innovation (II), dynamic pricing (DP), and organizational impact (OI). The objective of this study was to determine the extent to which these MD characteristics are routinely assessed by health technology assessment (HTA) agencies and incorporated in their guidelines and reports.MethodsThree approaches were taken. First, a review of the most recent HTA methods guidelines from 13 selected HTA agencies and five HTA networks was undertaken. Next, HTA reports from these agencies were reviewed for inclusion of MD-specific characteristics for 16 selected MDs, and finally, a narrative literature review on this topic was conducted.ResultsTwelve of thirteen included HTA organizations and some HTA networks (2/5) have either published general or MD-specific method guidelines, while several addressed MD-specific characteristics. The National Institute for Health and Care Excellence (NICE) included all four MD characteristics in their guidelines, but this did not equate to their inclusion in published HTA evaluations. EuNetHTA described the inclusion of LC (within patient safety) and OI within their guidance. The results highlight a lack of consistency among HTA organizations. For the narrative review, 10/149 articles were reviewed. Most provided recommendations on challenges faced by HTAs, proposed steps to address uncertainties around MD characteristics and reported a lack of methodological guidance for evaluating MDs.ConclusionsA lack of inclusion of MD characteristics in HTA is a complex interplay of several important factors. For these characteristics to become a formal part of HTA of MDs in the future, clear guidance and frameworks are required to enable manufacturers to develop appropriate evidence and for HTA practitioners to assess their impact more broadly.
OD48 Improving Public Understanding Of Health Technology Assessment Decisions
Authors: Russell Kate , McCormack Jackie , Dickson Jennifer , Cairns Daniel 
Published: January 2025

IntroductionSince 2018, the Scottish Medicines Consortium (SMC) has published a plain English summary for each health technology assessment (HTA) decision called Decision Explained (DE). This provides clear information to patients, Patient Group Partners (PGPs), and the public about what SMC’s decisions mean for them. The DE document was evaluated and updated in 2023 to ensure it continues to meet this purpose.MethodsThe evaluation consisted of two components. All (186) registered PGPs were invited to complete an online five-question survey focusing on the use of the DE document. This included readership, language, and SMC decision-making transparency. A separate focus group for public representatives took place, discussing design, accessibility, and content. Identical sample DE documents were used for both groups for consistency. Survey responses were collated and analyzed. Focus group responses were analyzed using thematic analysis. The DE document was revised, and the new version was considered for implementation by SMC’s Public Involvement Network Advisory Group.Results
Survey respondents (n=20) found the DE document helpful or very helpful in improving understanding of SMC advice. Some commented on complex language and information about how the medicine works being irrelevant. The focus group commented on excess information and favored simplified content and structure. Analysis of both sets of research data resulted in several recommended changes. These included the decision statement being moved to the start of the document, the language being simplified, and the section on how a medicine works being removed. Revised documents including these changes were prepared and were reviewed and approved by the SMC’s Public Involvement Network Advisory Group.ConclusionsPublished plain English explanations are helpful for improving patient and public understanding of HTA decisions for new medicines. It is important this information is concise, relevant, and aligned to accessibility good practice. The recent review of the SMC DE documents led to changes that help to ensure they meet the needs of stakeholders.
PP07 One Bad Apple Can Spoil The Barrel: Are We Effectively Evaluating Software As A Medical Device?
Authors: Curtis-Gretton Luc , Malcolm Robert 
Published: January 2025

IntroductionThere are many differences between medical devices, pharmaceuticals, and Software as a Medical Device (SaMD). This should impact the way SaMDs are evaluated in health technology assessment (HTA). SaMD technologies often target multiple indications, are regularly updated, and often result in non-quantifiable benefits. The objective of this research was to identify problems and potential solutions when evaluating SaMDs in England.MethodsThis research took the perspective of the HTA process in England. We conducted a pragmatic review of publicly available grey literature, such as National Institute for Health and Care Excellence (NICE) guidelines and processes, government schemes, funding mechanisms, and other published reports and opinion pieces, to summarize how SaMDs are currently being evaluated. This included an overview of the current systems and funding structures (inclusive of recent developments), where potential issues may lie, and what is currently being done to address these issues. We concluded by making recommendations to improve the evaluation of these technologies.ResultsDifficulty quantifying outcomes of SaMD technologies, alongside the preference of decision-makers to evaluate technologies for single indications, causes a bottleneck of unevaluated technologies to build. HTA bodies then group many non-identical technologies into single appraisals, resulting in a range of SaMD technologies with varying quality being implemented through managed access agreements. Some schemes and funding mechanisms led by public bodies in England aim to improve efficiency and encourage technological development. However, the HTA process in England remains characterized by long evaluation processes and high clinical evidence requirements, which many SaMD providers find difficult to navigate.ConclusionsAlthough progress has been made, there is clear incentive to improve the way in which SaMD technologies are assessed in HTA. We recommend that a more rapid mixed-method approach be implemented. This should draw on quantitative economic analysis supplemented with qualitative clinical, patient, and expert opinion. SaMDs should be evaluated either individually or within smaller groups than current evaluation systems.
PP19 Time And Cost Savings Of Machine Learning And Artificial Intelligence (AI) In Systematic Reviews: A Case Study
Authors: Littlewood Anne , McCool Rachael , Edwards Mary , di Ruffano Lavinia Ferrante 
Published: January 2025

IntroductionConducting a systematic review (SR) of clinical trials is labor-intensive and expensive. However, existing open-source content can be used to develop custom machine learning tools suited to the workflow of individual organizations. This case study details the potential of a bespoke tool developed by York Health Economics Consortium (YHEC) for reducing the time and cost involved in producing an SR.MethodsRESbot is a flexible, stand-alone machine learning tool created using an extensively tested open-source dataset developed by Cochrane. The tool identifies randomized controlled trials (RCTs) from a large corpus of records. It has a user interface and inputs/outputs to fit into the company’s existing workflow at any stage. RESbot has two settings. The “sensitive” setting identifies a higher number of possible RCTs with a lower risk of missing eligible studies, while the “precise” setting is more focused. For both settings, we estimated the reduction in resources required for record screening in two examples of RCT-only reviews.ResultsScoping searches in MEDLINE were conducted for SRs of RCTs in femoropopliteal artery diseases (FAD) and postpartum depression (PD). The results were run through RESbot. For the FAD SR, 1,444 references were retrieved, with the sensitive and precise RESbot settings reducing the record set by 38 percent and 64 percent, respectively. For the PD SR, a record set of 2,153 records was reduced by 25 percent and 41 percent, respectively. Resource savings offered by RESbot vary depending on subject but may reduce the time taken to screen records by up to 64 percent, with a subsequent reduction in cost to the organization commissioning the SR.ConclusionsThe use of bespoke machine learning tools in SR production has the potential to reduce the time and staff costs involved in producing a review. This case study tested the effect on a small number of records, but for larger reviews retrieving tens of thousands of records, reductions in time and costs can be very significant.
PP22 A Dashboard To Support The Value Assessment Of Digital Health Technologies For Health Technology Assessment Agencies
Authors: Sandhu Gurmit , Mohamed Ghazali Izzuna Mudla , Laurenzi Emanuele , Miho Enkelejda 
Published: January 2025

IntroductionThere needs to be more awareness and agreement on value criteria important to digital health stakeholders (DHSs), including health technology assessment agencies, especially in low- and middle-income countries (LMICs). To overcome these challenges, we focused on the feasibility of a dashboard to improve low confidence and assessment readiness of digital health technologies (DHTs) among DHSs in Malaysia.Methods“Artificial Intelligence Embedded X-ray for Lung Cancer Screening” from the Malaysian Health Technology Assessment Section (MaHTAS) was selected as a DHT use case. Representatives from MaHTAS, health informatics, research and innovation agencies, the digital health industry, patient advocates, and bioethicists attended an information briefing session and workshops. Participants completed a value assessment checklist questionnaire on the completeness of the information for the use case. Data and insights from these online workshops were incorporated into the development of Figma dashboard mock-up. Feedback from participants, other Malaysian DHSs, additional LMICs, and high-income countries was incorporated into the mock-up.ResultsFor the use case, average ratings from participants on the completeness of information on the seven value domains (VDs) were 3.4 and 3.1 for “healthcare system challenges and current use of technology” and “effectiveness” out of a maximum rating of 4 for “sufficient.” Both VDs were classified as “partially sufficient.” Ratings for the remaining VDs were 2.9 on “technical product information and use,” 2.8 on “safety,” 2.5 on “cost and economic impact,” 2.2 for “usability and accessibility,” and “ethical aspects.” These VDs were classified as “not sufficient.” The dashboard and its use were evaluated positively by 17 of 22 DHSs, including participants.ConclusionsIt was feasible to develop a useful dashboard applying a value assessment framework for DHTs on a use case with clear visualizations and a systematic approach. Our results have potential for applications in early scientific advice. It is to be considered that workshop participants’ profiles may create a bias regarding their subjectivity in rating completeness of information on a DHT.
PP25 Artificial Intelligence In Healthcare Decision-Making: Addressing Challenges, Ethical Considerations, And Bias
Authors: Bergemann Rito , Sharma Sugandh , Vanderpuye-Orle Jackie , Poirrier Jean-Etienne 
Published: January 2025

IntroductionArtificial intelligence (AI) is transforming healthcare decision-making, particularly in evidence evaluation and health technology assessment (HTA). This research explores challenges and ethical considerations associated with AI implementation, and biases. It highlights the need for diverse stakeholder perspectives and collaboration to ensure responsible AI use. Through transparency, accountability, and bias mitigation, AI has the potential to revolutionize decision-making and improve patient care while promoting equitable outcomes.MethodsLiterature research was conducted, including peer-reviewed studies and grey literature, using the PEARL search strategy. Relevant articles from various databases and sources were screened and selected based on their alignment with the research objectives. The selected articles were then analyzed to identify key findings and insights related to the integration of AI in healthcare decision-making, ethical considerations, bias mitigation, and stakeholder perspectives.ResultsThe literature research revealed that AI in healthcare decision-making holds great promise. AI algorithms can efficiently analyze diverse healthcare data sources, improve evidence evaluation, and streamline decision-making processes. Ethical considerations, patient privacy and transparency are crucial. Bias in AI algorithms emerged as a significant challenge, requiring diverse and representative data, bias testing, and explainable AI. Stakeholder engagement plays a vital role in responsible AI implementation. Strategies for ongoing monitoring, collaboration, and training were identified to ensure fair and ethical decision-making in healthcare. The results emphasize the need for a balanced approach to harness the potential of AI while addressing its challenges.ConclusionsIntegration of AI in healthcare decision-making offers promising opportunities but also presents challenges that need to be carefully navigated. By addressing ethical considerations and mitigating bias, AI can revolutionize decision-making, improve patient outcomes, and ensure the responsible and ethical use of AI in healthcare. The results provide valuable insights and recommendations for researchers working in the field of HTA.