Health Policy and Technology最新文献

{"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}

{"title":"Spillover effects of pain medication tapering in chronic pain patients: a systematic review and consequences for health economic evaluation studies","authors":"Frenn Bultinck ,&nbsp;Nick Verhaeghe ,&nbsp;Max Lelie ,&nbsp;Bo Vandenbulcke ,&nbsp;Elke Wuyts ,&nbsp;Cleo L. Crunelle ,&nbsp;Lisa Goudman ,&nbsp;Maarten Moens ,&nbsp;Koen Putman","doi":"10.1016/j.hlpt.2025.101037","DOIUrl":"10.1016/j.hlpt.2025.101037","url":null,"abstract":"<div><h3>Background</h3><div>Spillover effects of pain medication tapering (PMT) programs in patients with chronic pain (CP) are underexplored. This systematic review presents current research on the study of spillover effects of PMT in patients with CP and provides suggestions for examination of spillover effects in health economic research of PMT. Understanding spillover effects enable wide-ranging assessment of interventions, including its broader impacts.</div></div><div><h3>Methods</h3><div>Literature was searched up to September 2023 in Web of Science, PubMed, Scopus, Embase, PsychINFO, APA PsychNet, Cochrane library, Econlit, and grey literature sources including Google Scholar, CADTH, Mednar and the WHO website. QualSyst was used for Risk of bias assessment. The study protocol was registered prospectively in PROSPERO (CRD42023461763). Results were classified into five domains and incorporated into the expanded impact inventory framework. No funding was obtained.</div></div><div><h3>Results</h3><div>Of 2099 records initially identified, six qualitative studies of varying quality were included. In the healthcare domain, additional demands on healthcare delivery, patients switching between healthcare providers and psychosocial impacts for healthcare providers were key findings. Scientific spillovers entailed evidence-based recommendations, enhanced PMT awareness and knowledge dissemination. Sociological effects encompassed bias affecting underrepresented groups and community-level benefits. No spillovers were found in other categories. Future research should extend beyond patient-centered outcomes to comprehensively assess PMT’s societal impact and reveal indirect benefits currently underrepresented in the literature.</div></div><div><h3>Conclusions</h3><div>Spillover effects of PMT in patients with CP were identified. Considering spillovers can allow policymakers to optimize healthcare policies and resource allocation in healthcare. Inclusion of only six studies is a limitation of this study.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101037"},"PeriodicalIF":3.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254395","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":"Using ChatGPT in ethical dilemmas and policy-related complex decision making: Are we ready yet?","authors":"Orna Tal ,&nbsp;Yaron Connelly","doi":"10.1016/j.hlpt.2025.101041","DOIUrl":"10.1016/j.hlpt.2025.101041","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Artificial intelligence (AI) algorithms using language models have emerged as valuable tools in medicine. While AI has demonstrated its ability to address clinical questions, its application in ethical dilemmas remains debated. Some argue that AI can synthesize diverse information to form a comprehensive perspective, while others caution against premature reliance. This study explored the potential of AI in addressing ethical medical dilemmas faced by physicians, transitioning from theoretical discussions to practical solutions.</div></div><div><h3>Methods</h3><div>ChatGPT-3.5 was presented with three socio-ethical dilemmas relevant to national health policy decisions, and its responses were compared to those of physicians and real-world decisions. The dilemmas included questions on (1) criteria for allocation of technologies when resources are limited (2) personalized treatment, and (3) conflicts between patient requests and health organizations' strategy.</div></div><div><h3>Results</h3><div>ChatGPT-3.5 aligned with physicians' views on budget allocation but diverged on age-related criteria. It struggled to resolve conflicts between patient preferences and organizational strategies. Its responses reflected physician paternalism and a private market perspective, emphasizing system-wide benefit (utilitarian approach), likely due to familiarity with private healthcare systems.</div></div><div><h3>Conclusions</h3><div>ChatGPT-3.5 demonstrated an evolving capacity to engage with complex medico-ethical dilemmas but also revealed biases and limitations. Policymakers must carefully integrate AI tools, incorporating broader economic and social insights while ensuring adaptability to diverse scenarios. The academic community and clinicians must remain vigilant and regulate the rapid implementation of AI in the increasingly uncertain and evolving healthcare landscape.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101041"},"PeriodicalIF":3.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549377","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":"2025-05-29","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":"Health equity in the digital age: Exploring health policy and inclusive digital care","authors":"Jessica A. Coetzer ,&nbsp;Nicole S. Goedhart ,&nbsp;Tjerk Jan Schuitmaker-Warnaar ,&nbsp;Christine Dedding ,&nbsp;Teun Zuiderent-Jerak","doi":"10.1016/j.hlpt.2025.101039","DOIUrl":"10.1016/j.hlpt.2025.101039","url":null,"abstract":"<div><h3>Objectives</h3><div>The digitalisation of care, whilst beneficial for some, also risks exacerbating health inequities if existing health (and social) disparities are not considered. Literature has indicated the broad, systemic causes of digital health inequities could be addressed through policy. This article aims to explore how health inequities are rendered (in)visible in and by digital care policies.</div></div><div><h3>Methods</h3><div>We inductively analysed sixteen Dutch health policy documents focusing on digital care. Employing a constructivist grounded theory approach, we analysed documents to determine how health equity is addressed in relation to digital care.</div></div><div><h3>Results</h3><div>Although Dutch health policies do consider health inequities, it is not always shown in policies as a concept related to digital care. Health policies portray digital care as progressive and innovative, being able to shape healthcare in several positive ways. The risks of digital care are attended to less, with focus being placed mostly on privacy and data-security rather than also paying attention to digital health inequities.</div></div><div><h3>Conclusions</h3><div>Policies either ignore digital health equity entirely or present digital health equity in ways that risk overlooking how digital care may subtly aggravate health inequities. This creates a blind spot in which technological deterministic narratives can be disguised. Current policies could unintentionally perpetuate exclusion by not highlighting the role of digital health inequities as a part of the health equity landscape. Policy needs to allow for digital health inequities to be better recognised, allowing digital care to drive, rather than limit, the possibilities for a more equitable future.</div></div><div><h3>Lay Summary</h3><div>Digital care is increasing in popularity, but risks excluding a significant number of people who usually already experience health inequities. Although Dutch health policy does consider health inequities, it is not shown in policies as a concept related to digital care. As a result, health equity risks being forgotten in the development of digital care. Policies portray digital care as being able to shape healthcare in a number of positive ways but do not address the risks it may pose in widening health inequities. Instead, issues like ensuring privacy receive more attention. By being overly optimistic about technology without being cautious about its other social consequences, achieving aims such as affordable and accessible care could be negatively impacted. Policy needs to allow for digital health inequities to be better recognised, allowing digital care to drive, rather than limit, the possibilities for a more equitable future.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101039"},"PeriodicalIF":3.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212820","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":"Rural digitalization and health outcomes of older adults in China","authors":"Kunkun Duan ,&nbsp;Jing Li","doi":"10.1016/j.hlpt.2025.101038","DOIUrl":"10.1016/j.hlpt.2025.101038","url":null,"abstract":"<div><div>This study investigates the relationship between rural digitalization and older adults' health conditions. Drawing on the China Longitudinal Aging Social Survey 2020 data, using ordinary least squares (OLS) regression analysis, instrumental variable (IV) methods, and propensity score matching (PSM), the present study finds that rural digitalization significantly improves both physical health (β = 0.295, <em>p</em> &lt; 0.001) and reduces depression propensity score (β = -1.540, <em>p</em> &lt; 0.001). Moreover, the impact of rural digitalization development on older adults' health exhibits differences: older adults (80+) and those using the internet gain more benefits; there is more remarkable support for the physical health of less educated older adults, while mental health support is more pronounced for those with higher education levels. The findings underscore the potential of rural digitalization to mitigate health disparities and advocate for inclusive digital policies tailored to vulnerable older populations.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 5","pages":"Article 101038"},"PeriodicalIF":3.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147257","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":"Facilitating acceptance, trust, and ethical integration of socially assistive robots among nurses: A quasi-experimental study","authors":"Izidor Mlakar (Dr.) ,&nbsp;Igor Robert Roj ,&nbsp;Vojko Flis (Dr.) ,&nbsp;Valentino Šafran ,&nbsp;Urška Smrke (Dr.) ,&nbsp;Nejc Plohl (Dr.)","doi":"10.1016/j.hlpt.2025.101034","DOIUrl":"10.1016/j.hlpt.2025.101034","url":null,"abstract":"<div><div>Objectives: To evaluate the impact of different types of demonstrations (no demonstration, video demonstration, and face-to-face demonstration) on nurses’ acceptance, trust, and ethical considerations regarding socially assistive robots.</div><div>Methods: The study employed a quasi-experimental design involving 312 nurses: 201 with no exposure to socially assistive robots, 97 exposed via video demonstrations, and 14 exposed through live face-to-face demonstrations in a hospital room. Participants completed self-report measures assessing their perceptions of ethical acceptability, trust, and acceptance of socially assistive robots.</div><div>Results: Participants exposed to any kind of demonstration reported significantly higher perceptions of ethical acceptability compared to those with no exposure. Among demonstration types, live face-to-face demonstrations resulted in higher overall ethical acceptability, satisfaction, and acceptance compared to video demonstrations.</div><div>Conclusions: Demonstrations, particularly face-to-face interactions, play a crucial role in fostering ethical acceptability and overall acceptance of socially assistive robots. These findings highlight the importance of incorporating live demonstrations in strategies to improve healthcare professionals’ trust and acceptance of robotic technology.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 3","pages":"Article 101034"},"PeriodicalIF":3.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137686","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":"S.A.R.A.H. and the decline of trust in health information: a case study","authors":"Federico Germani,&nbsp;Giovanni Spitale,&nbsp;Nikola Biller-Andorno","doi":"10.1016/j.hlpt.2025.101032","DOIUrl":"10.1016/j.hlpt.2025.101032","url":null,"abstract":"<div><div>This case study critically examines S.A.R.A.H. (Smart AI Resource Assistant for Health) as an element of the World Health Organization's (WHO) digital health strategy, focusing on its design and generated content. Launched in April 2024 to provide accessible health information and combat misinformation, S.A.R.A.H. utilizes generative AI to engage users across diverse health topics. Despite its purported empathetic design, concerns arise regarding its operational functionality and empathetic capabilities. By critically analyzing S.A.R.A.H.'s operational limitations and discussing the implications for trust, this paper highlights the tool's potential to erode public trust in WHO as a reliable health information source. It also identifies potential strategies for the development and release of similar tools. The paper underscores the importance of ethical considerations and operational effectiveness in deploying digital health initiatives, aiming to inform future strategies in AI integration within public health. Ultimately, it emphasizes the critical need to uphold trust and credibility in global health institutions.</div></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":"14 3","pages":"Article 101032"},"PeriodicalIF":3.4,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134768","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}