Health Policy and Technology最新文献

{"title":"Quantifying pharmaceutical and medical device industry-physician financial ties: An analysis of honorarium payments to Japanese medical association leadership between 2019 and 2021","authors":"Akemi Hara ,&nbsp;Tetsuya Tanimoto ,&nbsp;Piotr Ozieranski ,&nbsp;James Larkin ,&nbsp;Michioki Endo ,&nbsp;Hiroaki Saito ,&nbsp;Akihiko Ozaki","doi":"10.1016/j.hlpt.2025.101081","DOIUrl":"10.1016/j.hlpt.2025.101081","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the extent and distribution of pharmaceutical and medical device industry honorarium payments to medical association leadership, enhancing our understanding of industry-physician financial ties in Japan.</div></div><div><h3>Methods</h3><div>We conducted a retrospective analysis of publicly disclosed payment data from pharmaceutical companies affiliated with the Japan Pharmaceutical Manufacturers Association and medical device companies affiliated with the Medical Devices Network. Data covered honorarium payments for speaking, writing, and consulting to board members of 18 major professional medical associations from 2019 to 2021.</div></div><div><h3>Results</h3><div>Of the 399 executive board members, 373 (93.5 %) received payments totaling $15.99 million. The median payment per member over the three years was $22,529, (interquartile range [IQR], $7230.8–$57,223.9). Payments were concentrated, with four professional medical associations—representing Internal Medicine ($2.97 million), Ophthalmology ($1.78 million), Dermatology ($1.78 million), and Urology ($1.87 million)—accounting for 52.5 % of the total. Surgical specialties received a higher proportion of payments from medical device companies, while non-surgical specialties – pharmaceutical companies. Payments declined in 2020, coinciding with the COVID-19 pandemic, recovering by 2021. None of the 18 associations' leadership publicly disclosed their board members' financial ties.</div></div><div><h3>Conclusions</h3><div>We found extensive and concentrated ties between industry and medical association leadership in Japan, with the pharmaceutical and medical device sectors each displaying distinctive payment patterns. The substantial scale of payments and limited transparency displayed by the association highlight the urgent need for legally mandated disclosure, including specialty-specific solutions.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101081"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guiding stakeholder involvement in health technology assessment for medical devices: A novel approach for clarifying stakeholders’ roles and contributions","authors":"Liliana Freitas ,&nbsp;Mónica D. Oliveira ,&nbsp;Ana C.L. Vieira","doi":"10.1016/j.hlpt.2025.101075","DOIUrl":"10.1016/j.hlpt.2025.101075","url":null,"abstract":"<div><h3>Objectives</h3><div>Stakeholder involvement is recognized as essential in Health Technology Assessment (HTA), yet engagement remains insufficient, particularly in medical device (MD) evaluations. Literature on how to systematically identify and integrate stakeholders remains scarce. This study proposes a reflective framework to support HTA practitioners think about stakeholder inclusion and applies it to explore perspectives within the medical device context in Portugal.</div></div><div><h3>Methods</h3><div>We adapted Ulrich’s Critical Systems Heuristics (CSH) as a conceptual lens to structure reflection on stakeholder roles and contributions in MD HTA. The framework is organized around four sources of influence (motivation, control, knowledge, and legitimacy) and was operationalized through 26 semi-structured interviews with experts from Portugal’s HTA agency, hospitals, patient associations, and industry. Interview data were analysed using directed content analysis and the Framework Method, allowing to contrast current ('is') and ideal ('ought') views within each source of influence. The common themes were then used to construct interpretative narratives that captured rationales for stakeholder inclusion.</div></div><div><h3>Results</h3><div>The application of the framework revealed context-specific insights into stakeholder engagement in MD evaluation. Findings show stakeholder roles in MD evaluations extend beyond traditional classifications. For each CSH source of influence, rationales and conditions for stakeholder engagement were identified. Under motivation, stakeholders identified diverse purposes and measures of success, ranging from improved patient access to innovation to system-wide resource optimization. Under control, providers, purchasers, and payers were seen as central decision-makers, yet ideal processes included multidisciplinary governance and clearer procedural support. Under knowledge, multiple actors (including patients) were valued as contributors of contextual expertise, but gaps were highlighted in methodological tools. Under legitimacy, patients and the public were underrepresented and called for stronger mechanisms for direct involvement and broader societal alignment. Across all sources of influence, significant gaps were found between current practices and stakeholder expectations to highlight areas for development.</div></div><div><h3>Conclusions</h3><div>Rather than prescribing fixed engagement procedures, the proposed reflective framework offers a structured lens to support HTA practitioners in reasoning through stakeholder roles, values, and contributions in MD evaluations. The framework is transferable to other decision-making contexts and fosters more transparent and inclusive deliberation.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101075"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in healthcare: Tailoring education to meet EU AI-Act standards","authors":"Elena Bignami ,&nbsp;Luigino Jalale Darhour ,&nbsp;Wolfgang Buhre ,&nbsp;Maurizio Cecconi ,&nbsp;Valentina Bellini","doi":"10.1016/j.hlpt.2025.101078","DOIUrl":"10.1016/j.hlpt.2025.101078","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) in Intensive Care Units (ICUs) has the potential to transform critical care by enhancing diagnosis, management, and clinical decision-making. Generative and Predictive AI technologies offer new opportunities for personalized care and risk stratification, but their implementation must prioritize ethical standards, patient safety, and the sustainability of care delivery. With the EU AI-Act entering into force in February 2025, a structured and responsible adoption of AI is now imperative. This article outlines a strategic framework for ICU AI integration, emphasizing the importance of a formal declaration of intent by each unit, detailing current AI-use, implementation plans, and governance strategies. Central to this approach is the development of tailored AI education programs adapted to four distinct professional profiles, ranging from experienced clinicians with limited AI knowledge to new intensivists with strong AI backgrounds but limited clinical experience. Training must foster critical thinking, contextual interpretation, and a balanced relationship between AI tools and human judgment. A multidisciplinary support team should oversee ethical AI-use and continuous performance monitoring. Ultimately, aligning regulatory compliance with targeted education and practical implementation could enable a safe, effective, and ethically grounded use of AI in intensive care. This balanced approach would support a culture of transparency and accountability, while preserving the central role of human clinical reasoning and improving the overall quality of ICU care.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101078"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-clinical direct-to-consumer genetic testing: a scoping review of regulatory frameworks and issues","authors":"Natasha A. Bujang ,&nbsp;Chandrani Ghosh ,&nbsp;Kavitha Palaniappan ,&nbsp;Silke Vogel ,&nbsp;John C.W. Lim ,&nbsp;Wei Wei Tiong ,&nbsp;Adrian Sim ,&nbsp;Beatrice Lee","doi":"10.1016/j.hlpt.2025.101106","DOIUrl":"10.1016/j.hlpt.2025.101106","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Introduction&lt;/h3&gt;&lt;div&gt;Direct-to-Consumer Genetic Testing (DTC-GT) provides consumers access to genetic tests without the mediation of healthcare professionals. This raises regulatory and ethical concerns including potential misinformation from company claims, especially in Singapore where DTC-GT is limited to non-clinical applications and is presently not regulated under health products regulations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This scoping review aims to map non-clinical DTC-GT regulatory guidelines and compare data protection laws for genetic data to understand the regulatory landscape.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Online databases (PubMed, EBSCO, Springer, ScienceDirect, Embase, Web of Science, and SCOPUS) were used to identify papers published from 2000 onwards along with grey literature like websites and reports from Google searches.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Based on the scoping review, 35 publications were identified, comprising 19 regulations and guidelines, and 16 relevant articles. Our findings indicate that the regulatory landscape of DTC-GT lacks uniformity, with most jurisdictions without specific regulations for non-clinical DTC-GT. 7 jurisdictions were identified to have data protection laws concerning genetic data privacy.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;The review concluded that non-clinical DTC-GT is generally perceived as low-risk, resulting in minimal regulatory scrutiny across the surveyed regions. Despite the fundamental roles of informed consent and anonymisation of genetic data within existing frameworks for genetic data privacy, the regulation of non-clinical DTC-GT remains either limited or entirely absent due to its low-risk classification. Consequently, there is a significant need to enhance consumer health literacy, ensuring individuals are well-informed about GT services and are aware of the limitations and implications of data privacy risks. This approach is essential for safeguarding consumer interests in the evolving genetic testing landscape, as accuracy and reliability of these tests can be questionable, often leading to misinformation.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Public Interest Summary&lt;/h3&gt;&lt;div&gt;This scoping review highlights that non-clinical DTC-GT often have minimal regulations because they are seen as low risk. However, the lack of specific regulations for how genetic data is collected, used, and shared poses privacy concerns. As genetic research technology advances, regulations should be adaptable and based on fundamental principles to keep up with these changes. It is also crucial to protect individuals from discrimination based on their genetic information. While there is no urgent need to regulate non-clinical DTC-GT that do not impact medical diagnoses, there is a growing concern about companies suggesting these tests have clinical importance without clear evidence. The best way forward is to implement strong consumer education programmes to help people understand","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101106"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From accuracy to comprehensibility: Evaluating large language models for myopia patient queries","authors":"Ezgi Karataş ,&nbsp;Ceren Durmaz Engin","doi":"10.1016/j.hlpt.2025.101073","DOIUrl":"10.1016/j.hlpt.2025.101073","url":null,"abstract":"<div><h3>Objectives</h3><div>This study evaluated the accuracy and comprehensibility of responses from three large language models (LLMs)—ChatGPT-4, Gemini, and Copilot—when addressing patient queries about myopia. Accurate, understandable information is crucial for effective patient education and management of this common refractive error.</div></div><div><h3>Methods</h3><div>Sixty questions across six categories (definition, etiology, symptoms and diagnosis, myopia control, correction, and new treatments) were presented to ChatGPT-4, Gemini, and Copilot. Responses were assessed for accuracy by two experienced ophthalmologists using a 3-point Likert scale. Quality and reliability were evaluated using the DISCERN and EQIP scales, while readability was measured with the Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Coleman-Liau Index. Statistical analyses were conducted using SPSS version 25.</div></div><div><h3>Results</h3><div>ChatGPT-4 provided the most accurate responses in the defsinition, symptoms, and diagnosis categories, with a 75 % overall success rate. Copilot had a similar success rate of 73.3 % but the highest inaccuracy rate (6.7 %). Gemini had a 71.7 % success rate. Copilot scored highest in reliability (DISCERN 76) and readability (Flesch Reading Ease 46.74), followed by ChatGPT-4 and Gemini. No significant differences in accuracy were found among the LLMs across categories.</div></div><div><h3>Conclusions</h3><div>All three LLMs performed well in providing myopia-related information. Copilot excelled in readability and reliability despite a higher inaccuracy rate. ChatGPT-4 and Copilot outperformed Gemini, likely due to their advanced architectures and training methodologies. These findings highlight the potential of LLMs in patient education and the need for ongoing improvements to ensure accurate, comprehensible AI-generated health information.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101073"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards equity-oriented topic selection in health technology assessment for orphan drugs in Iran: A commentary","authors":"Fatemeh Sadat Hosseini ,&nbsp;Mohammadreza Mobinizadeh","doi":"10.1016/j.hlpt.2025.101074","DOIUrl":"10.1016/j.hlpt.2025.101074","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101074"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contributing factors of sentinel events involving medical devices: A cross-sectional retrospective human factors analysis","authors":"Mees Casper Baartmans ,&nbsp;Steffie Marijke Van Schoten ,&nbsp;Cordula Wagner","doi":"10.1016/j.hlpt.2025.101068","DOIUrl":"10.1016/j.hlpt.2025.101068","url":null,"abstract":"<div><h3>Background and objective</h3><div>Although most medical device applications in hospitals are safe and effective, in a small number of cases devices are involved in patient safety events causing serious unintended patient harm. These so-called sentinel events are thoroughly investigated by hospitals, with detailed event descriptions filed in reports. Studying these reports may help fill the knowledge gap on the latent contributing factors of sentinel events involving medical devices. This study aims to identify the contributing factors of sentinel events involving medical devices and how these factors lead to unintended patient harm.</div></div><div><h3>Design</h3><div>A cross-sectional retrospective analysis of 20 sentinel event reports involving medical devices from Dutch general hospitals, using a human factors approach and specific classification system for medical device related events.</div></div><div><h3>Results</h3><div>A total of 105 contributing factors were identified. For most events, factors relating to the operator (e.g., flaws in setting up and checking devices before use), device (e.g., design issues), infrastructure (e.g., poor environmental ergonomics) and patient (e.g., complicating anatomy) mutually contributed and interacted. Jointly these factors triggered events causing unintended patient harm.</div></div><div><h3>Conclusions</h3><div>In-depth analysis of reports of sentinel events using a human factors approach, showed the underlying patterns of interacting contributing factors leading to unintended patient harm. Sentinel events involving medical devices are triggered by an interplay of factors related to the operator, device, infrastructure, and patient. To prevent future patient harm, an integral approach addressing all these elements is needed.</div></div><div><h3>Public Interest Summary</h3><div>Medical devices can be involved in events leading to unintended patient harm in hospitals. We know little about the underlying factors contributing to such events. Therefore, 20 reports of events involving a medical device that led to serious patient harm were analysed in depth. In most events, the patient harm was triggered by factors relating to the operator of the device, the device itself, the organisation and environment in which the device was applied, and the patient to whom the device was applied. Jointly, these factors prompted the events and led to patient harm. The insights from this study can be used to further improve the safe application of medical devices in hospitals.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101068"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review of modelling in target product profile development for medical innovation","authors":"Ying Jie Chong ,&nbsp;Nitirot Phasitthanaphak ,&nbsp;Teerawat Wiwatpanit ,&nbsp;Yot Teerawattananon ,&nbsp;Yi Wang","doi":"10.1016/j.hlpt.2025.101070","DOIUrl":"10.1016/j.hlpt.2025.101070","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Target Product Profiles (TPPs) serve as strategic tools for planning medical innovation development, facilitating communication with regulators, and developing market access strategies. Modelling can assist TPPs development and provide guidance on defining attributes. This scoping review aims to outline the general steps in employing models in developing or refining TPPs and to identify the common themes of the attributes informed by modelling.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;PRISMA-ScR checklist was used to guide this review. Literature search in PubMed, Scopus, Web of Science, Embase, and the World Health Organization Institutional Repository for Information Sharing (WHO IRIS) was conducted between August and September 2023. Subsequently, two researchers reviewed abstracts independently. Only full text articles used modelling to inform TPPs of health technologies with detailed modelling and methodology reported were included in this review. General information, technology-related information, TPP-related information, and model-related information were extracted from the articles and analysed thematically. EPIFORGE 2020 checklist was used to assess the reporting quality of modelling approach of the included articles.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;This review included 23 articles published from 2010 onward, reflecting a growing interest in using modelling to inform TPPs development. The studies covered diverse medical innovations, including drugs, vaccines, devices, procedures, and vector control tools. Commonly modelled attributes for devices included health impact, economic value, and efficacy. For non-device innovations, clinical efficacy, economic value, and dosage were the most frequently modelled attributes. The modelling process typically involved three steps: scoping, model development and validation, and analysis with recommendations. Limitations from the modelling process discussed across studies fell into three categories: evidence quality, modelling assumptions and structure, and the generalisability of findings. While some attributes, like clinical efficacy, are straightforward to model, certain attributes such as human factors require considering proxies such as compliance rates or capacity constraints.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;This review identified common product attributes in TPPs that were informed by modelling, outlined the modelling process, and highlighted key limitations. It provided recommendations to improve the modelling approach in TPPs development and highlighted the need for further research to standardise the modelling process.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Public Interest Summary&lt;/h3&gt;&lt;div&gt;Target Product Profiles (TPPs) are documents that outline targets for new medical innovations to effectively address specific public health needs. These documents are developed through multiple rounds of consultations with domain experts and stakeholders. We aimed to study how mathemati","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 6","pages":"Article 101070"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring policy and regulations of clinical AI systems: Views from patients with chronic diseases","authors":"Bijun Wang ,&nbsp;Onur Asan ,&nbsp;Turki Alelyani","doi":"10.1016/j.hlpt.2025.101035","DOIUrl":"10.1016/j.hlpt.2025.101035","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Artificial Intelligence (AI) has become a transformative force in healthcare, offering opportunities to enhance patient care, improve efficiency, and reduce costs. However, patients' perspectives, which greatly influence the acceptance and implementation of AI technologies, remain under-researched.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This study explores patients with chronic conditions’ perspectives on clinical AI systems, focusing on their concerns, government involvement, accountability for potential AI error, and preferences between AI and doctor recommendations. These insights are crucial for tailoring AI technologies to meet patients' needs and expectations and better engage patients in adopting new technologies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Method&lt;/h3&gt;&lt;div&gt;This study conducted an online open-ended survey with valid responses from 140 patients with chronic conditions, exploring four aspects of clinical AI perspectives. The data was systematically coded and analyzed using an inductive thematic analysis approach to identify emergent themes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Result&lt;/h3&gt;&lt;div&gt;The majority of participants expressed concerns about the implementation of AI in healthcare (92.86 %), with the top worries including lack of human touch (22.86 %), potential AI bias and fairness (16.43 %), and over-dependence on AI (16.43 %). Regarding responsibility for potential treatment damages, 37.14 % of participants believed that physicians should bear the responsibility, 16.43 % considered AI developers accountable, and 1.42 % viewed the government as the responsible party. Furthermore, 44.57 % suggested that responsibility should be shared among stakeholders. In terms of government role, 51.43 % saw regulation and monitoring as key responsibilities, while 8.57 % perceived no government role in AI healthcare. Finally, around 80 % of patients preferred treatment recommendations from care providers over AI.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;The findings suggest patients are looking for a balanced approach between technology and human involvement, with clear accountability and proper regulation. Though most prefer human doctors, an openness to AI's potential indicates an evolving perception. This underscores the need for a governance-inclusive and patient-centric strategy that addresses these aspects to ensure successful AI integration in healthcare.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Lay Summary&lt;/h3&gt;&lt;div&gt;This study explores the opinions of chronic patients on using AI in healthcare. It found that while patients appreciate the potential benefits of AI, they have concerns about losing the personal touch of human doctors, potential biases, and over-reliance on technology. They also believe that accountability for AI errors should be shared among doctors, developers, and the government. The findings highlight the need for careful integration of AI in healthcare, with clear regulations and a focus on patient safety to build trust and acceptance.&lt;/div&gt;&lt;/di","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101035"},"PeriodicalIF":3.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing economic evaluation guidelines for the Kingdom of Saudi Arabia: Engagement of local experts","authors":"Fatma Maraiki ,&nbsp;Tusneem Elhassan ,&nbsp;Shouki Bazarbashi ,&nbsp;Paul Scuffham ,&nbsp;Haitham Tuffaha","doi":"10.1016/j.hlpt.2025.101042","DOIUrl":"10.1016/j.hlpt.2025.101042","url":null,"abstract":"<div><h3>Objectives</h3><div>Economic evaluation has increased due to the emergence of national health technology assessment (HTA) agencies. This study aims to develop a country-specific guideline for conducting economic evaluation in the Kingdom of Saudi Arabia (KSA) as an HTA component to determine the value for money of new health interventions.</div></div><div><h3>Methods</h3><div>The study conducted a real-time Delphi survey using 17 items from the method component of the Consolidated Health Economic Evaluation Reporting Standards checklist as foundation for guidelines. Consensus was reached for the relevance of guideline recommendations for the KSA healthcare system. We set a threshold of 80 % for agreement and an interquartile range less than three on a nine-point Likert scale. Interim analysis provided feedback for recommendations of items if <em>no consensus</em> exists. A natural language processing (NLP) approach was employed to examine the relationship between experts’ comments and consensus decisions.</div></div><div><h3>Results</h3><div>The study recruited 78 % experts with an average response progress rate of 97.2 %. Interim analysis provided a 63 % adjustment rate for recommendations with the majority requiring further clarification (65 %). The guidelines concluded with a consensus on 76 % of recommendations, while four remained undetermined, namely, choice of discount rate, use of same rates for health benefits and costs, outcome selection, and gross costing. The NLP results supported the consensus decision.</div></div><div><h3>Conclusions</h3><div>Expert consensus contributed to the development of informative guidelines relevant to KSA. The guidelines serve as a reference case, thus providing a foundation for HTA practices, reimbursement decisions, and future research for the KSA and its neighboring countries.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101042"},"PeriodicalIF":3.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}