Be part of the SCDM China Conference 2025
Data Science – Collaboration Shaping the Future
在医疗健康产业数智化升级的进程中，临床数据管理正迎来前所未有的变革与发展契机。随着人工智能、大数据和数字化技术的深度融入，数据管理的智能化水平不断提升，同时，行业对数据质量、隐私保护和法规合规的要求也愈发严格。这种趋势不仅推动了临床数据管理从向高效、精准、规范化的方向发展，也催生了新的技术应用场景和业务模式，为从业者和企业带来了全新的挑战与机遇。
In the process of digital and intelligent transformation of the healthcare industry, clinical data management is facing unprecedented changes and opportunities. With further integration of artificial intelligence, big data, and digital technologies, the level of intelligence in data management is improving continuously. At the same time, the industry’s requirements for data quality, privacy protection, and regulatory compliance are becoming increasingly stringent. This trend not only drives clinical data management towards higher efficiency, precision, and standardization but also gives rise to new technological application scenarios and business models, bringing new challenges and opportunities for CDM professionals and their enterprises.
在医疗健康产业数智化升级的进程中，临床数据管理正迎来前所未有的变革与发展契机。随着人工智能、大数据和数字化技术的深度融入，数据管理的智能化水平不断提升，同时，行业对数据质量、隐私保护和法规合规的要求也愈发严格。这种趋势不仅推动了临床数据管理从向高效、精准、规范化的方向发展，也催生了新的技术应用场景和业务模式，为从业者和企业带来了全新的挑战与机遇。
In the process of digital and intelligent transformation of the healthcare industry, clinical data management is facing unprecedented changes and opportunities. With further integration of artificial intelligence, big data, and digital technologies, the level of intelligence in data management is improving continuously. At the same time, the industry’s requirements for data quality, privacy protection, and regulatory compliance are becoming increasingly stringent. This trend not only drives clinical data management towards higher efficiency, precision, and standardization but also gives rise to new technological application scenarios and business models, bringing new challenges and opportunities for CDM professionals and their enterprises.
Program
大会语言以中文为主，会议内容将持续更新，会议日程将以大会公布为准
The conference language is Chinese. For the most updated conference agenda, please refer to the notice from the organizer.
上午：临床数据管理领导力论坛
AM：Clinical Data Management Leadership Forum 求
论坛主题：智领未来-人工智能与临床数据管理的融合与创新
随着人工智能技术的迅猛发展，临床数据管理领域正迎来前所未有的变革与机遇。本场临床数据领导力论坛以“智领未来：人工智能与临床数据管理的融合与创新”为主题，旨在汇聚临床数据管理领军人员，共同探讨人工智能在临床数据管理中的应用与实践，深入分析其对数据管理员职业发展的深远影响与挑战。通过主题演讲、圆桌讨论与案例分享，与会者将获得前沿洞察，推动行业创新与个人职业成长。
IntelliFuture: The Convergence and Innovation of AI in Clinical Data Management
With the rapid development of artificial intelligence (AI) technology, the field of clinical data management is facing unprecedented changes and opportunities. Themed “Leading the Future: Integration and Innovation of AI and Clinical Data Management,” this Clinical Data Leadership Forum aims to bring together industry leaders in clinical data management to explore the application and practice of AI in this field, and to deeply analyze its far-reaching impact and challenges on the career development of data managers. Through keynote speeches, roundtable discussions, and case sharing, participants will gain cutting-edge insights to drive industry innovation and personal career growth.
下午：会前培训和研讨专题介绍
PM：PRE-CONFERENCE WORKSHOP SESSIONS
Track A: 随机化与盲态数据的管理
本次研讨会将深入探讨随机化与盲态管理的核心概念、实施方法及其在数据管理中的重要性。内容包括随机化的基本原理、不同随机化技术（如简单随机化、分层随机化、动态随机化等）的应用，以及盲态设计的类型和操作流程。此外，还将重点讲解如何在数据分析和研究过程中维持盲态，以及在必要时进行揭盲的规范操作。通过本次研讨会，参与者将能够掌握随机化与盲态管理的最佳实践方法，确保临床试验数据的完整性和可靠性，同时满足监管要求。培训结合理论讲解与实际案例分析，帮助学员更好地理解和应用这些关键技能，提升数据管理的专业水平。
The Randomization and Blinded Data Management
This workshop will delve into the core concepts, implementation methods, and significance of randomization and blinding management in data management. The content includes the basic principles of randomization, the application of different randomization techniques (such as simple randomization, stratified randomization, and dynamic randomization), as well as the types and operational procedures of blinding designs. Additionally, the workshop will focus on how to maintain blinding during data analysis and research processes, and the standardized procedures for unblinding when necessary. Through this workshop, participants will be able to master the best practices of randomization and blinding management, ensuring the integrity and reliability of clinical trial data while meeting regulatory requirements. The workshop combines theoretical explanations with case analyses to help participants better understand and apply these key skills, thereby enhancing their professional level of data management.
Track B: eCRF标准化建设与高效EDC建库实践—提升临床数据质量与效率
本次研讨会旨在通过系统化的课程设计与实战演练，帮助参与者深入掌握临床数据管理标准化建设的关键技能。培训将结合理论讲解与实际操作，涵盖eCRF设计原则、EDC建库效率提升策略以及质量保障方法。参与者将学习国、内外数据管理规范要求，掌握自动化建库工具的使用，并通过小组讨论探讨实际工作中的痛点与解决方案。培训将有助于快速提升临床数据管理的效率与质量。
Standardization of eCRF and Efficient EDC Database Building—Enhancing Clinical Data Quality and Efficiency
This workshop is designed to empower clinical data managers with critical skills in establishing standardized clinical data management systems through systematic curriculum design and hands-on practice. The workshop integrates theoretical instruction with practical exercises, covering principles of eCRF (electronic Case Report Form) design, strategies to optimize EDC (Electronic Data Capture) database development efficiency, and robust quality assurance methodologies. Participants will gain in-depth knowledge of domestic and international regulatory requirements for data management, master the use of automated database development tools, and collaborate in group discussions to address real-world challenges and solutions. The workshop aims to rapidly improve both the efficiency and quality of clinical data management practices.
主旨演讲环节
主旨演讲：临床数据管理效能提升的创新实践 —— 从6年量化分析看标准化、自动化与人工智能的作用
通过对 2019 年至 2024 年数据管理工时的量化分析中发现的数据管理的难点与痛点，我们借助技术手段与方法解决了这些问题，实现了临床数据管理效能的跃升。本次演讲将介绍数据管理计划（DMP）、数据管理审核计划（DMRP）、数据验证计划（DVS）、eCRF填写指南（CCG）以及外部数据传输协议（DTA）等文档的自动生成；通过建立标准电子病例报告表（eCRF）库和标准逻辑核查（EC）库，并与方案流程图相结合，我们实现了电子数据采集系统（EDC）的快速建库，从而推动了建库效率与数据清理效率的提升。此外，利用临床数据全流程管理系统（CDTMS）以及与其他电子化系统（如EDC， 医学编码， 外部数据管理， 随机化与药品管理系统）的集成，实现了数据管理过程的全程自动记录与数据管理文件从生成、在线审核到在线签署的自动化，实现了 “随时准备接受稽查（Inspection Readiness Anytime）” 的目标。本演讲还将探讨利用人工智能技术在临床数据管理流程中的应用场景，以提高工作效率，实现进一步的降本增效。
Keynote Session
Keynote speech: Innovative Practices for the Clinical Data Management Efficiency Improvement – Insights into the Roles of Standardization, Automation, and Artificial Intelligence from a Six-year Matrix Analysis
Through quantitative analysis of the DM matrix from 2019 to 2024, we have targeted the challenges and pain points in the data management and resolved these issues with technical means and methods，achieving a leap in the clinical data management efficiency. This presentation will share the automatic generation of documents such as the Data Management Plan (DMP), Data Management Review Plan (DMRP), Data Validation Plan (DVS), eCRF Completion Guidelines (CCG), and Data Transfer Agreement (DTA). By establishing a standard eCRF library and edit check library, and integrating them with protocol flowchart, we have achieved the rapid EDC database building, thereby promoting an increase in both database setup efficiency and data cleaning efficiency. In addition, by using the Clinical Data Total Management System (CDTMS) and integrating it with other electronic systems (such as EDC, medical coding, external data management system, and randomization and drug management systems), we have achieved full automation in data management process recording and automation of data management documents from generation, online review to online signature, thus achieving the goal of “Inspection Readiness Anytime”. This presentation will also explore how to utilize artificial intelligence technology to optimize the clinical data management process, in order to improve the quality and efficiency, and further reduce costs and increase efficiency.
专题1：临床试验数据法规新进展
临床试验数据法规是国际递交的要求，与时俱进的关注法规更新，严格按照法规要求精准完成临床试验数据管理和分析支持非常关键。本专题将介绍最新法规更新内容，着重于电子化数据和数据平台以及AI相关的信息分享。
Session 1: Regulation Progress in Clinical Trial Data Handling
Clinical trial data handling regulations are requirements for international submissions. It is crucial to keep abreast of regulatory updates and strictly adhere to regulatory requirements to precisely execute clinical trial data management and analytical support. This session will introduce the latest regulatory updates, focusing on electronic data and data platforms, as well as AI-related information.
专题2：eCOA最新进展与未来趋势
随着全球临床研究的不断推进和医疗科技的快速发展，电子临床结果评估（eCOA）在临床数据管理中扮演着越来越重要的角色。本专题旨在共同分享eCOA领域的最新信息、动态及洞见。我们将深入探讨eCOA技术的最新进展，包括其在提高数据收集质量和效率、优化患者体验以及推动临床研究创新方面的应用。同时，我们也将关注eCOA在数字化转型、远程临床研究以及智能化数据管理等方面的未来趋势。
Session 2： Exploring the Latest Advances and Future Trends of eCOA
With the continuous advancement of global clinical research and the rapid development of medical technology, electronic Clinical Outcome Assessment (eCOA) is playing an increasingly important role in clinical data management. This session aims to share the latest information, developments, and insights in the field of eCOA. We will delve into the latest progress of eCOA technology, including its applications in improving the quality and efficiency of data collection, optimizing patient experience, and driving innovation in clinical research. We will also focus on the future trends of eCOA in digital transformation, remote clinical research, and intelligent data management.
专题3：GenAI 在临床数据管理中的应用
生成式 AI （GenAI） 的快速发展为包括医疗保健在内的各个行业开辟了新的领域。在临床数据管理方面，GenAI 通过提高数据准确性、简化流程和实现更具洞察力的数据分析，提供了变革潜力。本专题旨在探讨 GenAI 在临床数据管理中的创新应用，重点介绍实际实施、挑战和未来前景。
Session 3: The Application of GenAI in Clinical Data Management
The rapid advancements in Generative AI (GenAI) have opened new frontiers in various industries, including healthcare. In clinical data management, GenAI offers transformative potential by enhancing data accuracy, streamlining processes, and enabling more insightful data analysis. This session aims to explore the innovative applications of GenAI in clinical data management, highlighting real-world implementations, challenges, and future prospects.
专题4：DM的未来发展
本专题旨在通过以下方面探讨临床数据管理的未来发展:
- 风险预测与质量控制：探讨CDS工作内容，例如如何建立风险预测模型，提前预警数据质量问题，受试者脱离分析等
- 跨学科知识体系构建：探讨DM需要具备哪些跨学科知识，如何通过培训体系，培养具有综合素养的DM人才等
- 工作流变革：探讨AI时代中，如何变革DM工作流适应新工作模型，例如自动化技术怎样提高数据管理效率，以及这些变化对DM工作内容和职责的改变
Session 4: The future development of Data Management
This session will discuss and explore the DM future developments from the following perspectives:
- Risk Prediction and Quality Control: Discuss the work content of Clinical Data Science (CDS), such as how to establish risk prediction models, provide early warnings for data quality issues, and conduct dropout analysis.
- Interdisciplinary Knowledge System Building: Explore the interdisciplinary knowledge that Data Managers (DMs) need to possess and how training systems can be used to cultivate DMs with comprehensive literacy.
- Workflow Transformation: Examine how Data Management (DM) workflows can be transformed in the AI era to adapt to new working models, such as how automation technology can improve data management efficiency and the changes these advancements bring to the content and responsibilities of DM work.
专题5：疫苗研发中数据核查要点
疫苗研发中的数据核查因其面向健康人群预防性使用的特殊性，需在研发科学性、合规性与上市后监测做更多考虑。一方面，由于疫苗主要应用于健康群体（尤其是儿童及易感人群），对安全性数据的完整性与长期追踪（如罕见不良反应监测）要求严苛，同时需验证免疫原性数据与临床保护效力的关联性；另一方面，生产工艺复杂性和冷链物流依赖性（如mRNA疫苗的超低温存储）要求多批次一致性核查与全链条温度追溯，而大规模多中心试验的数据整合压力与紧急审批场景下的时效挑战，则需通过动态风险管理。此外，由于大规模健康人使用，需考虑借助数字化技术（区块链溯源、AI分析）提升全生命周期数据管理效率，最终为疫苗的安全性与有效性筑牢证据基石。本专题会从围绕疫苗研发数据核查要点展开讨论，助力疫苗企业“研发-生产-上市后”的全生命周期数据管理。
Session 5: Key Points for Data Verification in Vaccine Development
Data verification in vaccine development requires additional considerations in terms of scientific rigor, compliance, and post-market monitoring due to its specific nature of being used preventively in healthy populations. On one hand, vaccines are primarily administered to healthy individuals, especially children and susceptible populations, necessitating stringent requirements for the completeness of safety data and long-term follow-up (such as monitoring for rare adverse reactions). At the same time, it is crucial to validate the correlation between immunogenicity data and clinical protective efficacy. On the other hand, the complexity of production processes and the reliance on cold chain logistics (such as ultra-low temperature storage for mRNA vaccines) necessitate verification of batch-to-batch consistency and full-chain temperature traceability. Furthermore, the challenges posed by data integration from large-scale, multicenter trials and the timeliness demands in scenarios of emergency approval necessitate dynamic risk management. Additionally, given the use of vaccines in large-scale healthy populations, it is advisable to leverage digital technologies (such as blockchain tracing and AI analysis) to enhance data management efficiency throughout the entire lifecycle, ultimately fortifying the evidentiary foundation for vaccine safety and effectiveness. This session discusses the key points for data verification in vaccine development, aiming to support vaccine enterprises in managing data throughout the “development-production-post-market” lifecycle.
专题6：电子医疗数据的流通利用前景与探讨
2024年首个医院的专病数据库在上海数据交易所被挂牌交易。这对行业带来了巨大的冲击，这需要制定数据流通利用规范标准，基本完成卫生健康大数据平台基础构架功能及历史数据融合治理，从而促进卫生健康数据安全合规流通应用。电子医疗记录能为药企、研究机构和医院提供了快速获取高质量、结构化数据的渠道，减少了数据收集的时间和成本，通过整合多源数据，研究人员可以访问更大规模的患者样本，获取真实世界的证据，以补充传统临床试验的不足，帮助研发人员更好地理解药物在实际临床环境中的效果和安全性。但如何打破医疗机构之间的数据壁垒，促进跨机构、跨地域的数据共享与合作并使数据符合临床研究的标准和规范；确保数据流通利用中数据隐私保护、患者知情同意及合规性是一个重要挑战。
Session 6: Discussion on the circulation and utilization prospect of electronic medical records
In 2024, the first hospital special disease database was listed on the Shanghai Data Exchange. This has brought a huge impact on the industry, which needs to develop the standards for data circulation and utilization, basically complete the health big data platform infrastructure functions and historical data integration governance, so as to promote the security and compliance of health data circulation and application. The medical database listing transaction can provide pharmaceutical companies, research institutions and hospitals with fast access to high-quality, structured data, reducing the time and cost of data collection. By integrating multi-source data, researchers can access a larger sample of patients and obtain real-world evidence to supplement the shortcomings of traditional clinical trials. To help developers better understand the efficacy and safety of drugs in real-world clinical Settings. However, how to break the data barriers between medical institutions, promote cross-institutional and cross-regional data sharing and cooperation, and make data conform to the standards and norms of clinical research; Ensuring data privacy protection, patient informed consent and compliance in electronic medical records’ circulation and utilization is an important challenge.
组委会 | PROGRAM COMMITTEE
Introducing the leading minds behind the #SCDM24 programme
颜崇超 Charles Yan
[SCDM中国指导委员会主席、上海盛迪医药有限公司临床数据科学中心副总经理 | SCDM China Steering Committee Chair; Vice President, Clinical Data Science Center, Shanghai Shengdi Medicine Co., Ltd. ]
田正隆 Zhenglong Tian
[SCDM中国指导委员会副主席、上海医药集团中央研究院常务副院长 | SCDM China Steering Committee Vice Chair; Executive Deputy Dean, Central Research Institute, Shanghai Pharma Group ]
张薇 Wei Zhang
[SCDM中国指导委员会前任主席、葛兰素史克（上海）医药研发有限公司医学开发部数据管理部门负责人 | SCDM China Steering Committee Past Chair; Head of Data Management, GSK China R&D]
张玥 Carrie Zhang
[SCDM中国指导委员会委员、上海复宏汉霖生物技术股份有限公司副总裁、全球产品开发部数据科学中心负责人 | DM China Steering Committee Member; Vice President, Data Science Center, Global Product Development, Shanghai Henlius Biotech, Inc. ]
邓亚中 Yazhong Deng
[SCDM中国指导委员会委员、北京信立达医药科技有限公司总经理 | SCDM China Steering Committee Member; General Manager, Beijing Trust Medical Consulting Co., Ltd. ]
黎婉珊 Joyce Lai
[SCDM中国指导委员会委员、默沙东研发（中国）有限公司全球临床数据管理中心亚太区高级总监 | SCDM China Steering Committee Member; Senior Regional Director, Clinical Data Management, Global Data Management & Standards, MSD R&D (China) Co., Ltd. ]
孙华龙 Hualong Sun
[SCDM中国指导委员会委员、苏州科林利康医药科技有限公司首席战略官 | SCDM China Steering Committee Member; Chief Strategy Officer, Clinical Service Center]
张蕴 Sally Zhang
[SCDM中国指导委员会委员、艾昆纬医药科技（上海）有限公司数据管理部门资深总监 | SCDM China Steering Committee Member; Sr Director, Head of Great China and Japan GDM, IQVIA ]
倪丽萍Annie Ni
[SCDM中国指导委员会委员、辉瑞（中国）研究开发有限公司中国临床数据科学部临床数据汇集与处理业务负责人，总监 | SCDM China Steering Committee Member; Director, Program Lead, Clinical Data Acquisition, China Clinical Data Sciences, Pfizer (China) Research and Development Co., Ltd. ]