Health Policy and Technology最新文献

{"title":"The introduction of AI into OpenNotes and the prospects for dialogue","authors":"John W. Murphy,&nbsp;Caroline Irene-Alonso Miles","doi":"10.1016/j.hlpt.2025.101059","DOIUrl":"10.1016/j.hlpt.2025.101059","url":null,"abstract":"<div><h3>Objectives</h3><div>OpenNotes is a platform that allows patients and their caregivers to both review and modify case notes. Through this strategy the clinical process will be transparent and trust can be established between these persons. The aim of this paper is to shed some light on the problems that may arise from OpenNotes introducing AI into this process. Accompanying this change is the introduction of technologically mediated communication into the production of case notes.</div></div><div><h3>Methods</h3><div>An examination was conducted of technologically mediated communication and the impact of this mode of interaction on the collaborative relationship that OpenNotes strives to foster.</div></div><div><h3>Results</h3><div>Technologically mediated communication modifies significantly interaction, thereby altering the clinical setting. Specifically important is that clients may be intimidated by this technology, inadequate responses may be given to client inquiries, language use may be overly simplified, and automation may require standardization that distorts patient-caregiver dialogue.</div></div><div><h3>Conclusions</h3><div>The introduction of AI may undermine the trust that OpenNotes is designed to improve in the clinical setting. Patient-caregiver interaction may become transactional, if proponents of using in OpenNotes are not careful. The rush to adopting AI should be tempered, so that the impact of this technology on patient-caregiver dialogue can be critically assessed.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101059"},"PeriodicalIF":3.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596527","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":"Costs and psychosocial burden of tuberculosis to the treatment supporters in Ghana","authors":"Robert Bagngmen Bio ,&nbsp;Patricia Akweongo ,&nbsp;John Azaare ,&nbsp;Francis Adane ,&nbsp;Kasim Abdulai ,&nbsp;Richard Ali Laar ,&nbsp;Abraham Titiati","doi":"10.1016/j.hlpt.2025.101060","DOIUrl":"10.1016/j.hlpt.2025.101060","url":null,"abstract":"<div><h3>Objectives</h3><div>Tuberculosis treatment supporters contribute crucially to tuberculosis control and prevention without financial compensation. The World Health Organization recommends direct observation of treatment, involving supporters who incurred costs for frequent health facility visits and waiting times, potentially impacting their socio-economic status. This study aims to inform tuberculosis control and prevention policy by determining the costs and psychosocial burden associated with treatment support.</div></div><div><h3>Methods</h3><div>A cross-sectional cost-of-illness approach, data from 385 supporters were collected through validated questionnaires. Both direct and indirect costs were assessed, with psychosocial burden measured using the Zarit Burden Interview (ZBI) 12-item questionnaire.</div></div><div><h3>Results</h3><div>Results reveal that, on average, supporters spent GHS 122.4 (US$21.1) monthly, constituting 19 % of their income. A significant 77.1 % experienced a high burden on the ZBI scale, with females facing a greater burden than males. Socio-demographic factors such as education, household size, income, and district of residence influenced both direct and indirect costs.</div></div><div><h3>Conclusion</h3><div>In conclusion, the study underscores the substantial costs and psychosocial burden on tuberculosis treatment supporters and recommends extending the livelihood empowerment against poverty program in Ghana to cover treatment support costs.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101060"},"PeriodicalIF":3.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571210","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":"Global ChatGPT interest across healthcare and education access","authors":"Amrit Kirpalani","doi":"10.1016/j.hlpt.2025.101061","DOIUrl":"10.1016/j.hlpt.2025.101061","url":null,"abstract":"<div><h3>Objectives</h3><div>The rapid adoption of AI tools like ChatGPT has transformed information access, particularly in healthcare. However, engagement with AI may be influenced by factors such as healthcare accessibility and educational resources, with potential implications for misinformation in low-resource settings. This study investigates the relationship between physician density, tertiary education enrollment, and national interest in ChatGPT.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using global datasets. Physician density, tertiary education enrollment, GDP, and internet penetration were sourced from WHO, UNESCO, and the World Bank, respectively. The primary outcome, ChatGPT interest scores, was derived from Google Trends. Pearson correlation and multiple linear regression analyses were used to explore associations, controlling for GDP and internet penetration. Logistic regression was employed as a sensitivity analysis, categorizing variables into high and low groups.</div></div><div><h3>Results</h3><div>Data from 100 countries were analyzed. A significant negative correlation was observed between physician density and ChatGPT interest (<em>r</em> = -0.32, <em>p</em> = 0.012). Multiple linear regression confirmed that lower physician density was significantly associated with higher ChatGPT interest (β = -0.2857, <em>p</em> = 0.045). Tertiary education enrollment showed no significant association with ChatGPT interest. Logistic regression supported these findings, with higher physician density significantly reducing the likelihood of high ChatGPT interest (OR = 0.214, <em>p</em> = 0.001).</div></div><div><h3>Conclusion</h3><div>Our study suggests that regions with fewer healthcare professionals may engage more with AI tools like ChatGPT, highlighting the need for careful integration of AI into healthcare systems to prevent misinformation and support equitable access to reliable health information.</div></div><div><h3>Public Interest Summary</h3><div>It is well known that people who have difficulty in accessing healthcare may turn to the internet for medical advice, but it is not yet known if artificial intelligence, like ChatGPT, is being adopted by users for this same purpose. Given the widespread use of ChatGPT, this study explored whether ChatGPT interest in different countries was related to the number of physicians in those countries. We found that in countries with fewer doctors per capita, public interest in ChatGPT tends to be higher. While this does not confirm that people are using ChatGPT specifically for medical advice, it raises important questions about how AI may be filling gaps in access to healthcare. Given the potential for AI to spread inaccurate information, these findings highlight the need for careful regulation to ensure AI tools are used responsibly and do not contribute to misinformation in healthcare.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101061"},"PeriodicalIF":3.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338968","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":"Narrative review and bibliometric analysis on infodemics and health misinformation: A trending global issue","authors":"Muhammad Iqhrammullah ,&nbsp;Naufal Gusti ,&nbsp;Asyraf Muzaffar ,&nbsp;Yousef Khader ,&nbsp;Sidik Maulana ,&nbsp;Marius Rademaker ,&nbsp;Asnawi Abdullah","doi":"10.1016/j.hlpt.2025.101058","DOIUrl":"10.1016/j.hlpt.2025.101058","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic exposed how infodemics undermine public health efforts, which subsequently led to the promotion of harmful behaviors. This review aimed to examine major sources of misinformation and explore how demographic and socioeconomic factors affect digital and health literacy, shaping vulnerability to infodemics.</div></div><div><h3>Methods</h3><div>A narrative review was conducted to synthesize evidence on the pathways, sources, and social determinants of health misinformation. Additionally, a bibliometric analysis was performed using Scopus data from 1997 to 2024, analyzed via Bibliometrix and VOSviewer. The analysis focused on publications related to infodemics and health misinformation on digital platforms, mapping thematic clusters, trends, and keyword co-occurrences.</div></div><div><h3>Results</h3><div>Mainstream news media, social media, and scientific journals each play a role in disseminating misinformation, exacerbated by time pressure, algorithmic amplification, and inadequate validation processes. Factors attributable to low digital and health literacy include age, education, income, and internet access, which increase vulnerability to misinformation. The bibliometric analysis revealed exponential growth in related research, peaking during the COVID-19 pandemic. Eight dominant research clusters were identified: Health communication and social media; Infodemiology and data analysis; COVID-19 and misinformation; Public and digital health; Vaccine hesitancy; Risk and infodemic management; Conspiracy theories in social media; and Crisis communication.</div></div><div><h3>Conclusion</h3><div>Infodemics are driven by multi-source digital misinformation and disproportionately affect those with limited literacy. Fact-checking as a mitigation effort can be developed by leveraging artificial intelligence, machine learning, and natural language processing, yet strengthening digital and health literacy remains critical.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101058"},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480932","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":"Integrating internationally educated nurses into the nursing faculty workforce: a new policy for nursing regulators","authors":"Houssem Eddine Ben-Ahmed ,&nbsp;Intissar Souli ,&nbsp;Emmanuel Akwasi Marfo ,&nbsp;Abir Rebhi","doi":"10.1016/j.hlpt.2025.101057","DOIUrl":"10.1016/j.hlpt.2025.101057","url":null,"abstract":"<div><div>Nursing faculty shortages received less attention in the literature and media outlets compared to registered clinical nursing staff shortages. One may question whether we do not have enough nursing faculty to teach and train students, who will take that responsibility? This critical question should be addressed by nursing leaders, researchers, and key system partners to develop innovative and sustainable policies that reduce nursing faculty shortages. Otherwise, the nursing faculty shortage would negatively affect the quality of nursing education and lead to a declining number of nursing seats, which should be avoided as we need more nurses in the upcoming years. This paper suggested developing a new policy for nursing regulators, titled “Non-clinical Academic Registration Category”, to support internationally educated nurses (IENs) with master's or doctoral degrees who wish to contribute to the nursing faculty workforce. To better understand the context of this policy and its benefits, the paper described the challenges of the registration process experienced by three IENs and the implications of integrating them into the workforce. Through collective and innovative policies, we can empower the future nursing faculty workforce and rationally respond to the ongoing crisis.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101057"},"PeriodicalIF":3.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321507","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":"The emergence and future of precision public health: a scoping review","authors":"Ms Rebecca Bosward,&nbsp;Annette Braunack-Mayer,&nbsp;Ms Emma Frost,&nbsp;Stacy Carter","doi":"10.1016/j.hlpt.2025.101056","DOIUrl":"10.1016/j.hlpt.2025.101056","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;Rapid uptake of big data and technologies in healthcare are transforming methodological capabilities in medicine and public health, giving rise to new fields such as precision public health. We conceptualised precision public health as an emerging technology to understand the emergence of this term and its associated characteristics.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We undertook a scoping review to collate and analyse existing literature on precision public health. Documents in English that mentioned the exact phrase “precision public health” were searched for in CINAHL, Medline, PubMed, Scopus, Web of Science and Google Scholar. A descriptive statistical analysis was performed on resulting documents to generate an account of precision public health terminology and definitions as well as author and funder characteristics of articles. Data were analysed through a sociotechnical lens, which is an approach for understanding how technologies emerge and disrupt existing systems.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Precision public health was ill-defined at first but is now stabilising. Using an emerging technology conceptual framework, we identified characteristics of precision public health including rapid growth, incoherence, uncertainty about future impacts and outcomes, and ambiguity about use of terminology. Novelty was contested.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Definitions of precision public health are continuously changing, and terms have different meanings and uses. Lack of consensus on definitions and terms for precision public health may impact progress of resarch. A single definitions of precision public health is not achievable; however, definitions should be negotiable among stakeholders, acknowledge similarities and differences between stakeholder values and expectations, and reflect research and policy objectives.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Public interest summary&lt;/h3&gt;&lt;div&gt;Precision public health is an emerging field which often relies on data-centric approaches, including artificial intelligence and machine learning, to improve population health outcomes, which potentially disrupt traditional evidence-based research methods and practice. We conducted a scoping review of current literature, and conceptualised precision public health as an emerging technology to understand how it impacts evidence-based practice and how terms and definitions of precision public health have changed over time.&lt;/div&gt;&lt;div&gt;There is currently no consensus around terms and definitions most appropriate for the field and the absence of empirical evidence makes it difficult to evaluate potential future impacts. If precision public health is going to deliver on its promises, researchers and practitioners must be transparent about reporting potential uncertainties, benefits and harms. Definitions should also be open and negotiable among stakeholders in precision public health, and reflect research and policy objectives.&lt;/div&gt;&lt;/div","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101056"},"PeriodicalIF":3.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291212","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":"How has Aggregated Mobility Data-informed public health research?","authors":"Jennifer Turnnidge ,&nbsp;Oluwatoyosi Kuforiji ,&nbsp;Sina Sayyad ,&nbsp;Sarah Greco ,&nbsp;Sawmmiya Kirupaharan ,&nbsp;Angélique Roy ,&nbsp;Nancy Dalgarno ,&nbsp;Angel Zhou ,&nbsp;Mir Sanim Al Mamun ,&nbsp;Hiroshi Mamiya ,&nbsp;Khai Hoan Tram ,&nbsp;Sahar Saeed","doi":"10.1016/j.hlpt.2025.101055","DOIUrl":"10.1016/j.hlpt.2025.101055","url":null,"abstract":"<div><h3>Objective</h3><div>The widespread adoption of smartphones has enabled the collection and analysis of population-level mobility patterns through Aggregated Mobility Data. This type of data, which is derived from both operator and crowdsourced sources, presents opportunities and challenges for public health research. This data derived from both operator and crowdsourced sources, presents opportunities and challenges for public health research. We explore how this novel data source has been used in public health studies, its benefits, limitations, and ethical considerations.</div></div><div><h3>Methods</h3><div>We conducted a narrative review of Aggregated Mobility Data applications in public health research, critically examining its potential and challenges. A systematic search of Embase and Google Scholar identified 645 peer-reviewed primary research articles. We included English peer-reviewed and primary research published between 2010–2024 where aggregated mobility data was being used to evaluate a public health outcome. After applying inclusion criteria, 95 studies were included for narrative synthesis and descriptive quantitative analysis.</div></div><div><h3>Results</h3><div>We found the majority of studies to date using Aggregated Mobility Data were related to COVID-19. Reporting of ethical and privacy considerations varied widely, with some studies undergoing formal ethics review and others citing exemptions based on the use of anonymized or aggregate data. Key limitations of Aggregated Mobility Data included restricted access to data sources and challenges associated with small population sizes.</div></div><div><h3>Conclusion</h3><div>This review underscores the potential of Aggregated Mobility Data in public health research and highlights key considerations for researchers and policymakers. Future studies should address ethical standardization, data accessibility, and broader applications beyond infectious disease surveillance to fully leverage the utility of Aggregated Mobility Data in public health decision-making.</div></div><div><h3>Public Interest Summary</h3><div>With the rise of smartphones, researchers can now track population movement using Aggregated Mobility Data from mobile devices. This data has been widely used in public health, especially during COVID-19, to understand how people move and how that impacts disease spread. However, access to this data is often restricted, and ethical considerations like privacy protections vary across studies. Our review examined 95 studies to assess the applications in public health research. While this data offers valuable insights, future research should focus on standardizing ethical guidelines, improving data access, and expanding its use beyond infectious disease tracking to other public health challenges.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101055"},"PeriodicalIF":3.4,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563636","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":"2025-06-06","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}

{"title":"AI in personalized medicine: Bridging ethical and regulatory gaps in resource-limited settings","authors":"Aliasgar Shahiwala","doi":"10.1016/j.hlpt.2025.101052","DOIUrl":"10.1016/j.hlpt.2025.101052","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101052"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271476","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":"Dynamic decision system for ENT surgery waiting list prioritization using M-Score and TOPSIS methodology","authors":"Fabián Silva-Aravena,&nbsp;Jenny Morales","doi":"10.1016/j.hlpt.2025.101036","DOIUrl":"10.1016/j.hlpt.2025.101036","url":null,"abstract":"<div><div>Objective: This study aims to develop and evaluate a dynamic prioritization system to improve surgical waiting list management for otorhinolaryngology (ENT) patients in a high-complexity public hospital in Chile. The proposed model aims to reduce waiting times and improve equity and clinical outcomes by dynamically incorporating changes in patient condition. Methods: We implemented a dynamic scoring system (M-Score), updated weekly using multidimensional biopsychosocial criteria, and integrated it with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize patients. The evaluation was carried out using Monte Carlo simulations over a 52-week horizon, simulating patient inflows and outflows via a balanced flow model. The stability and performance of the proposed model were compared with a static model and a traditional first-come, first-served (FCFS) protocol. Results: The proposed approach reduced the average waiting time from 130 to 91 days compared to the static model (a 30 % relative and absolute decrease of 39 days) and from 157 to 91 days compared to FCFS (a 42 % relative and absolute reduction of 66 days). The greatest improvements were observed among high-risk patients, whose prioritization was adapted in real time to worsening clinical conditions. Conclusions: Our adaptive prioritization model demonstrates significant improvements in waiting time management, particularly for clinically vulnerable patients. Although the findings support its feasibility, further prospective validation is necessary before clinical implementation. Future research should focus on real-time integration with electronic medical records, scalability between specialties, and evaluation of impacts on patient satisfaction and health outcomes. Lay Summary: ENT patients in public hospitals often face long waiting times that increase health risks. This study introduces a weekly update to the prioritization model using social and health factors of the patient. The system reduced average waiting times by up to 66 days in simulation. High-risk patients were prioritized as their conditions worsened. This approach offers a promising data-driven strategy for improving waitlist management and resource allocation in public healthcare.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101036"},"PeriodicalIF":3.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330586","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}