Be part of the SCDM 2026 China Conference
Embracing the data-intelligent future.
人工智能与医药产业的融合持续深化，从辅助诊断、药物研发到上市后监测，智能化技术在医疗各环节的应用探索不断拓展。临床数据管理作为临床试验中确保数据质量与完整性的重要环节，正逐步探索人工智能在数据采集、建库、清理、核查及锁库等流程中的潜在价值与应用路径。同时，eCOA等数字化数据来源日益普及，以及数据安全合规要求的持续强化，对数据治理、审计轨迹管理与风险管控提出了新的要求。行业需要在技术应用与合规实践之间寻求稳妥平衡，推动临床数据管理向更高效、更规范的方向发展。
The convergence of AI and the pharmaceutical industry continues to deepen, with intelligent technologies expanding their footprint across the healthcare spectrum – from diagnostic assistance and drug discovery through post-market surveillance. Clinical data management, pivotal to ensuring data quality and integrity in clinical trials, is increasingly exploring AI’s potential value and implementation pathways across data collection, database build, cleaning, verification, and lock processes. Meanwhile, the growing prevalence of digital data sources such as eCOA, alongside escalating data security and compliance requirements, is driving new imperatives for data governance, audit trail management, and risk mitigation. The industry needs to navigate a measured balance between technological innovation and regulatory compliance to advance clinical data management toward greater efficiency and standardization.
人工智能与医药产业的融合持续深化，从辅助诊断、药物研发到上市后监测，智能化技术在医疗各环节的应用探索不断拓展。临床数据管理作为临床试验中确保数据质量与完整性的重要环节，正逐步探索人工智能在数据采集、建库、清理、核查及锁库等流程中的潜在价值与应用路径。同时，eCOA等数字化数据来源日益普及，以及数据安全合规要求的持续强化，对数据治理、审计轨迹管理与风险管控提出了新的要求。行业需要在技术应用与合规实践之间寻求稳妥平衡，推动临床数据管理向更高效、更规范的方向发展。
The convergence of AI and the pharmaceutical industry continues to deepen, with intelligent technologies expanding their footprint across the healthcare spectrum – from diagnostic assistance and drug discovery through post-market surveillance. Clinical data management, pivotal to ensuring data quality and integrity in clinical trials, is increasingly exploring AI’s potential value and implementation pathways across data collection, database build, cleaning, verification, and lock processes. Meanwhile, the growing prevalence of digital data sources such as eCOA, alongside escalating data security and compliance requirements, is driving new imperatives for data governance, audit trail management, and risk mitigation. The industry needs to navigate a measured balance between technological innovation and regulatory compliance to advance clinical data management toward greater efficiency and standardization.
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.
下午：会前培训和研讨专题介绍
PM：PRE-CONFERENCE WORKSHOP SESSIONS
Track A (全天): AI驱动的临床数据管理：从自动化实践到智能化变革
随着人工智能技术的迅猛发展，临床数据管理正站在一个范式转变的关键节点。从传统的手工操作到自动化流程，再到AI智能体驱动的端到端解决方案，数据管理者的角色正在从”被动清洗”走向”主动洞察”，从”工具使用者”演进为”智能协作者”。本研讨会聚焦AI与自动化在临床数据管理中的前沿应用，汇聚制药企业、技术公司及行业机构的一线实践者，分享真实落地经验。上午聚焦顶层视野与框架构建，从AI在健康与临床试验领域的发展趋势、监管法规要求，到数据管理中的AI应用框架与临床开发全链路的AI探索路径，构建系统性认知；下午通过”问题—方案—成效”框架，深入探讨智能化建库与数据处理、电子化流程与智能问答、AI深度应用三大主题。不设空泛理论，每场分享均源于真实项目痛点与突破，为数据管理从业者、临床运营专家及AI技术探索者提供可落地的思路与方案，共同见证AI如何重新定义临床数据管理的效率与质量边界 。
AI-Driven Clinical Data Management: From Automation in Practice to Intelligent Transformation
As AI continues to advance at a remarkable pace, CDM stands at a pivotal turning point of paradigm shift. From traditional manual operations to automated workflows, and now to end-to-end solutions powered by AI agents, the role of data managers is evolving — from “reactive data cleaning” to “proactive insight generation,” and from “tool users” to “intelligent collaborators.” This workshop brings together frontline practitioners from pharmaceutical companies, technology firms, and industry organizations for in-depth sharing and discussion on real-world applications of AI and automation in clinical data management. The morning session focuses on strategic vision and framework building — covering development trends of AI in healthcare and clinical trials, regulatory requirements, AI application frameworks in data management, and AI exploration across the full clinical development lifecycle, helping attendees build a systematic understanding of the complete landscape of AI empowered data management. The afternoon session shifts to hands-on case studies and advanced applications, following a unified presentation format of “Problem — Solution — Outcome,” organized around three core topics: Intelligent Database Build and Data Processing, Electronic Workflows and Intelligent Q&A and Advanced AI Application Scenarios. Every presentation is rooted in real pain points and breakthroughs from actual projects, offering actionable insights and reusable solutions for data management professionals, clinical operations experts, and AI technology explorers, to jointly witness how AI is redefining the boundaries of efficiency and quality in clinical data management.
Track B （半天）: 生成式AI x 低代码：重构临床数据管理工作的智能化黄金组合
随着临床研究设计日益复杂，临床数据管理团队正面临着在更短时间内、以更少资源交付更高质量数据的持续压力。高度依赖人工、以文档为中心、并严重依赖个人经验的传统DM工作模式，已难以满足当前和未来的业务需求。本场实操型工作坊将展示如何将生成式AI、AI Agents以及低代码平台真正应用到DM核心工作流程中，帮助DM团队构建更智能、更高效、且可规模化扩展的工作模式。讲者将现场Demo，并手把手带领参会者一步步完成实操练习。内容将围绕以下两个真实DM工作场景展开：(1) SSU – Setup阶段: 基于研究方案（Protocol）、eCRF以及知识图谱，生成标准化、可复核的edit check specs及对应代码初稿; (2) Study Conduct – Data Review阶段: 执行Data Review、生成高质量query文本，并将query自动提交至EDC，在提升一致性与数据质量的同时显著减少人工工作量。本专题重实践、轻理论，聚焦真实DM场景、可复用的设计模式、动手实操以及设计思维，帮助参会者将所学内容直接迁移并落地到自身的研究项目与组织中。
GenAI x Low-Code: The Intelligent Golden Duo Redefining Clinical Data Management
As clinical studies evolve toward greater complexity, Data Management (DM) teams are under increasing pressure to deliver high quality data faster, more consistently, and with fewer resources. Traditional DM operating models—highly manual, document driven, and experience dependent—are no longer sufficient. This hands-on workshop demonstrates how Generative AI, AI Agents, and Low-Code Platform can be practically applied to core DM workflows, transforming them into a smarter, more efficient, and scalable operating model. Presenters will conduct live demos, and walk the audience through exercises in two real DM scenarios: (1) SSU—Setup: Generating standardized good draft edit check specifications and corresponding code based on protocol, eCRF and knowledge graph; (2) Study Conduct—Data Review: Performing data review, generating query text, and posting query to EDC, reducing manual effort while improving consistency and quality. This workshop emphasizes practical application over theory, focusing on real DM scenarios, reusable patterns, hands-on practice, and design thinking that attendees can directly adapt to their own studies and organizations.
主旨演讲环节
Keynote Session
专题1：临床试验相关数据管理与数据保护最新法规要求
临床试验是一个高法规管控的行业，密切关注法规要求，第一时间把法规要求落实到工作细节中，才能确保临床试验数据的可靠性。ICH E6 R3正式落地，中国也发布了包含数据治理和计算机化系统要求的法规要求，国际递交带来的数据出境和数据安全关注也是我们必须确保的工作内容。本专题主要讨论法规更新对我们数据领域的影响，分享最佳应对方式，找到保护受试者隐私与促进国际多中心临床顺畅开展的平衡点。
Session 1: New Regulation Requirements in Clinical Data Management and Data Protection
Clinical trial is a highly regulated industry. Closely monitoring regulatory requirements and implementing them promptly are essential to ensuring the reliability of clinical trial data. With the official release of ICH E6 R3, China has also issued regulatory requirements related to data governance and computerized systems. Additionally, attention to cross-border data transfers and data security for international submissions has become a critical aspect of our responsibilities. This session aims to discuss the impact of these regulatory updates on our field, share best practices for addressing them, and find a balance between protecting subject privacy and facilitating the smooth conduct of international multi-center trials.
专题2：从eCOA到数据治理：构建可信、可用的临床数据体系
随着 eCOA 及其他数字化数据来源在临床试验中的广泛应用，如何确保数据的完整性、可追溯性和合规性，已成为临床数据管理的重要议题。本专题旨在邀请业内人士共同探讨eCOA与临床数据治理的最新实践与思考，重点关注eCOA数据全生命周期治理实践、审计轨迹与元数据管理、基于风险的eCOA数据治理、跨系统eCOA数据流转与一致性管理，以及新型试验模式下的eCOA数据治理挑战.。
Session 2: From eCOA to Data Governance: Ensuring Trustworthy and Fit for Purpose Clinical Data
With the increasing adoption of eCOA and other digital data sources in clinical trials, ensuring data integrity, traceability, and regulatory readiness has become a key focus in clinical data management. This session aims to bring together industry professionals to share the latest updates and practical insights at the intersection of eCOA and clinical data governance. The discussion will focus on eCOA data lifecycle governance, audit trail and metadata management, risk based eCOA data governance, cross system eCOA data flow and consistency, and data governance challenges in emerging trial models.
专题3：数智跃迁: AI赋能临床数据管理
随着人工智能（AI）在生命科学领域的快速发展，临床数据管理（CDM）正进入全面升级的新阶段。本专题将重点探讨AI如何提升临床试验数据的采集、建库、清理、核查、查询管理及锁库等核心环节的效率与质量。通过来自行业前线的实践案例，参会者将了解AI如何显著缩短数据库上线与锁库周期、减少人工重复操作、提升数据可追溯性与合规性。本专题旨在帮助CDM专业人士了解AI驱动的流程重塑趋势，为构建更高效、更可靠、更具前瞻性的临床数据管理体系提供洞见与方法论。
Session 3: AI Powered Clinical Data Management
With the rapid advancement of artificial intelligence (AI) in the life sciences, Clinical Data Management (CDM) is entering a new phase of comprehensive transformation. This session will focus on how AI can enhance the efficiency and quality of key clinical trial data management activities, including data collection, database build, data cleaning, validation, query management, and database lock. Through real-world use cases from the industry pioneers, participants will gain insights into how AI can significantly shorten database go-live and lock timelines, reduce repetitive manual work, and improve data traceability and regulatory compliance. This session aims to help CDM professionals understand AI-driven process reengineering trends and to provide insights and methodologies for building more efficient, reliable, and forward-looking clinical data management systems.
专题4：基于RBQM的法规要求的临床试验质量管理
随着ICH E6 R(3)在中国的正式实施，质量源于设计(QbD)与RBQM的理念已经贯穿并影响着临床试验执行的每个环节，从临床研发策略、试验方案的设计、临床运营、医学监察、数据管理及生物统计都离不开基于风险的质量管理，本专题将以RBQM的基本概念为主线，从质量管理体系建立、临床运营、数据管理等方面深入探讨临床试验的质量管理以确保临床结果的可靠性。
Session 4: Clinical Trial Quality Management Based on the regulatory Requirement of RBQM
With the official implementation of ICH E6 R(3) in China, the concepts of Quality by Design (QbD) and RBQM have permeated and influenced every aspect of clinical trial execution, from clinical development strategies, trial protocol design, clinical operations, medical monitoring, data management, and biostatistics, all of which cannot do without risk-based quality management. This session will take the basic concepts of RBQM as the main line and deeply explore the quality management of clinical trials from aspects such as development of quality management system, clinical operations, and data management to ensure reliability of trial results.
专题5：自动化、标准化与可视化：下一代临床数据管理的良好实践
近年来，临床数据管理的节奏持续加快，项目数量增多，数据规模扩大，质疑核查的压力亦与日俱增。与此同时，人工智能、数据中台等新概念密集落地，促使行业思考：日常工作的未来形态将如何演变？从业者应如何规划职业发展路径？本专题旨在探讨上述现实议题, 将聚焦自动化、标准化、可视化三项技术方向，分析其对临床数据管理日常工作的改善路径：人工智能辅助采集能否降低重复性劳动？全流程标准化能否优化数据流转效率？可视化质控能否实现问题的早期识别？来自制药企业、合同研究组织及技术公司的业界同仁将分享其实践探索，期望为数据管理从业者搭建务实的交流平台，助力把握技术进展，梳理发展方向，推动工作实践持续优化。
Session 5: Automation, Standardization and Visualization: Good Practices for Next-Generation Clinical Data Management
In recent years, the pace of clinical data management has accelerated considerably, with increasing project volumes, expanding data scales, and mounting pressures for data query and validation. Concurrently, emerging concepts such as AI and data middle platforms have been rapidly implemented, prompting the industry to reflect upon: how will the future landscape of daily operations evolve? How should practitioners chart their professional development trajectories?
This session is designed to address these pertinent issues. It will concentrate on three technological directions—automation, standardization, and visualization—and analyze their potential to enhance routine clinical data management practices: Can AI-assisted collection reduce repetitive workloads? Can end-to-end standardization optimize data flow efficiency? Can visualization-enabled quality control facilitate earlier issue detection? Industry professionals from pharmaceutical companies, CROs, and technology firms will share their practical explorations, with the objective of establishing a substantive exchange platform for data management practitioners and supporting professionals in grasping technological advancements, clarifying development directions, and driving continuous refinement of operational practices.
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. ]