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目的 探讨国内建立区域性临床研究整合平台的可行方案,为研究者发起的临床研究(investigator initiated trial,IIT)提供数据支持。方法 总结国内外各类临床研究平台建设现状,以此为改进依据介绍上海交通大学医学院人工智能临床研究平台建设情况。结果 人工智能临床研究平台涵盖医学院附属医院的业务库文字及影像专病数据,支持回顾性与前瞻性临床研究,但在数据使用的伦理考量与共享机制等管理方面仍需要实践探索。结论 合规的区域性临床研究整合平台可支持科研人员发现临床问题、探索或验证临床假设,为开展多中心、大规模的IIT打下良好的数据基础。 相似文献
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针对新型冠状病毒感染疫情采取的各项隔离措施在有效切断病毒传播的同时,也影响了第三方在中医临床研究中的现场监察,因此远程数据质量控制成为临床研究过程中亟需解决的问题。本文依据“十三五”中医药防治重大传染病专项及新型冠状病毒感染期间的远程数据质量控制经验,结合国内外临床研究中的相关法规要求,按照临床研究质量可溯源性(attributable)、清晰性(legible)、同时性(cotemporaneous)、原始性(original)和准确性(accurate)的ALCOA标准和完整性(complete)、一致性(consistent)、持久性(enduring)、可用性(available)的CCEA标准,从实践出发,详述了数据核查的计划、要点和方式,以及两种数据质量评估方法。介绍了集约电子数据平台的优势,以及如何利用电子数据平台远程控制试验进度和数据质量的管理方法,以期日后临床研究者可以通过电子数据平台和批量化的电子数据处理,减少现场接触,为有效提高数据质量提供保障。 相似文献
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随着健康医疗大数据和人工智能技术的飞速发展,如何利用基于临床诊疗产生的海量医疗数据成为临床研究领域一个亟待解决的重要课题。临床诊疗数据是健康医疗大数据的重要组成部分,也是健康医疗大数据研究的主要领域,随着信息化的不断深入和广泛开展,医院积累了大量以患者为中心的临床诊疗数据,通过大数据技术对这些数据进行深入挖掘和分析,可对疾病的精准诊疗和规范化防控提供参考。然而开展相关研究依然面临较多难点与堵点问题,例如,数据泄露或滥用的风险增加、知情同意的难以实施等。为安全、合法、高效地利用临床诊疗数据开展临床研究,充分挖掘这些宝贵医疗资源的价值,北京某三甲医院建设了科研大数据平台,并制定了相关制度,以有效解决丰富的临床资源更好地应用于临床研究的堵点与难点问题,提升医疗机构服务质量和科研成果转化率。通过介绍基于科研大数据平台开展的临床研究实施过程中要点与管理方法,分析探讨存在的问题以及改进措施,以期为高质量、高效率开展健康医疗大数据临床研究提供理论依据与制度参考,为医学科研管理工作的不断完善起到启发和推动的作用,促进医药卫生科技创新的发展。 相似文献
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张嵬 《复旦学报(医学版)》2017,44(1):122-126
临床研究中产生的大量医学数据使临床科研人员逐渐认识到数据长期保存和共享的重要性,进而在实践中形成了研究数据管理的理念和方法。临床研究数据管理,可以促进临床研究的准确和高效进行,满足研究人员、机构、研究资助者的预期和要求。本文通过单个临床研究数据管理和机构临床研究数据管理两个层面,讨论了临床研究数据管理的关键事项及相关策略,帮助临床科研人员及科研管理人员了解临床研究数据管理流程,促进临床科研活动规范执行和机构数据管理的发展。 相似文献
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为促进中医药事业发展,创新中医临床科研模式,实现中医临床研究数据共享,为广大医护、研究人员提供海量信息支撑,构建中医临床研究数据中心是实现创新型中医临床研究的重要手段之一。对中医临床研究数据中心构建模式进行了探讨,从中医临床研究数据的分层管理模式出发,阐述了国家数据中心与医院数据中心的关系;介绍了医院数据中心的构建方式,并探讨医院数据中心与医院信息中心的数据交互方式,以及协作单位的数据整合方式等;通过国家与医院数据中心的互联,实现了中医临床研究数据跨区域共享:通过对数据存取流程的分析,阐述了中医临床数据中心数据的传输方式。 相似文献
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Brief communication: pSCANNER: patient-centered Scalable National Network for Effectiveness Research
Lucila Ohno-Machado Zia Agha Douglas S Bell Lisa Dahm Michele E Day Jason N Doctor Davera Gabriel Maninder K Kahlon Katherine K Kim Michael Hogarth Michael E Matheny Daniella Meeker Jonathan R Nebeker the pSCANNER team 《J Am Med Inform Assoc》2014,21(4):621-626
This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration''s 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses. 相似文献
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Rainu Kaushal George Hripcsak Deborah D Ascheim Toby Bloom Thomas R Campion Jr Arthur L Caplan Brian P Currie Thomas Check Emme Levin Deland Marc N Gourevitch Raffaella Hart Carol R Horowitz Isaac Kastenbaum Arthur Aaron Levin Alexander F H Low Paul Meissner Parsa Mirhaji Harold A Pincus Charles Scaglione Donna Shelley Jonathan N Tobin 《J Am Med Inform Assoc》2014,21(4):587-590
The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants’ experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools. We strive to ensure that our project is patient-centered; nurtures collaboration among all stakeholders; develops scalable solutions facilitating growth and connections; chooses simple, elegant solutions wherever possible; and explores ways to streamline the administrative and regulatory approval process across sites. 相似文献
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Craig Barnes Binam Bajracharya Matthew Cannalte Zakir Gowani Will Haley Taha Kass-Hout Kyle Hernandez Michael Ingram Hara Prasad Juvvala Gina Kuffel Plamen Martinov J Montgomery Maxwell John McCann Ankit Malhotra Noah Metoki-Shlubsky Chris Meyer Andre Paredes Jawad Qureshi Xenia Ritter Philip Schumm Mingfei Shao Urvi Sheth Trevar Simmons Alexander VanTol Zhenyu Zhang Robert L Grossman 《J Am Med Inform Assoc》2022,29(4):619
ObjectiveThe objective was to develop and operate a cloud-based federated system for managing, analyzing, and sharing patient data for research purposes, while allowing each resource sharing patient data to operate their component based upon their own governance rules. The federated system is called the Biomedical Research Hub (BRH).Materials and MethodsThe BRH is a cloud-based federated system built over a core set of software services called framework services. BRH framework services include authentication and authorization, services for generating and assessing findable, accessible, interoperable, and reusable (FAIR) data, and services for importing and exporting bulk clinical data. The BRH includes data resources providing data operated by different entities and workspaces that can access and analyze data from one or more of the data resources in the BRH.ResultsThe BRH contains multiple data commons that in aggregate provide access to over 6 PB of research data from over 400 000 research participants.Discussion and conclusionWith the growing acceptance of using public cloud computing platforms for biomedical research, and the growing use of opaque persistent digital identifiers for datasets, data objects, and other entities, there is now a foundation for systems that federate data from multiple independently operated data resources that expose FAIR application programming interfaces, each using a separate data model. Applications can be built that access data from one or more of the data resources. 相似文献
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随着越来越多的医疗机构开始应用电子健康档案系统(Electronic Health Records,EHR)来管理患者资料,基于在临床研究工作对患者资料的需求,各研究机构也开始以电子健康档案系统作为临床研究的数据来源。EHRCR(Electronic Health Records/Clinical Research)项目是在2006年12月由HL7技术委员会(Health Level Seven Technical Committee,HL7TC)和欧洲健康档案研究所(European Institute for Health Records,EuroRec)发起,旨在研究未来可以支持临床研究的电子健康档案系统应具有的功能,以及与此相关的系统、网络和业务流程。因此,对该项目的最新研究成果加以介绍,作为我国电子健康档案行业发展的参考。 相似文献