《中国康复理论与实践》

《中国康复理论与实践》 ›› 2024, Vol. 30 ›› Issue (11): 1262-1271.doi: 10.3969/j.issn.1006-9771.2024.11.004

• 康复政策与发展 • 上一篇    下一篇

ICF框架下的康复大数据标准研究

田益凡1, 叶海燕1, 刘叶1, 程亚宁1, 殷瑞雪1, 吕雪丽1, 陈迪1,2()   

  1. 1.中国康复科学所康复信息研究部，北京市 100068
    2.世界卫生组织国际分类家族中国合作中心，北京市 100068
  • 收稿日期:2024-09-14 修回日期:2024-09-23 出版日期:2024-11-25 发布日期:2024-12-05
  • 通讯作者: 陈迪(1982-)，男，汉族，北京市人，博士，副研究员，主要研究方向：ICF、残疾研究、康复科学、康复大数据、康复信息。E-mail： chendi@crrc.com.cn
  • 作者简介:田益凡(1999-)，女，汉族，北京市人，硕士，主要研究方向：康复大数据、康复信息、康复科学。
  • 基金资助:
    中国康复科学所中央级公益性科研院所基本科研业务费项目(2021CZ-14)

Rehabilitation big data standards under ICF framework

TIAN Yifan1, YE Haiyan1, LIU Ye1, CHENG Yaning1, YIN Ruixue1, LÜ Xueli1, CHEN Di1,2()   

  1. 1. Rehabilitation Information Research Department, China Rehabilitation Science Institute, Beijing 100068, China
    2. WHO-FIC Collaborating Center in China, Beijing 100068, China
  • Received:2024-09-14 Revised:2024-09-23 Published:2024-11-25 Online:2024-12-05
  • Contact: CHEN Di, E-mail: chendi@crrc.com.cn
  • Supported by:
    The Fundamental Research Funds for Central Public Welfare Research Institutes, conducted by China Rehabilitation Science Institute(2021CZ-14)

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摘要：

目的 探讨康复数据标准。

方法 基于世界卫生组织(WHO)《国际功能、残疾和健康分类》(ICF)框架，讨论康复大数据的内涵与外延。参考《关于分析和使用常规卫生信息系统康复模块的指南》《健康服务体系中的康复：行动指南》《康复指标清单：康复监测和评估架构配套的工具(FRAME)》《数据质量保障：框架和指标》等文件，探讨康复大数据的来源，并结合ICF理论探讨康复数据模式、分类体系与编码标准，探索康复的元数据标准。根据《国家健康医疗大数据标准、安全和服务管理办法(试行)》讨论康复大数据标准的应用与管理。

结果 康复大数据包括康复服务数据和个人健康数据，来源主要包括基于人群和基于机构两大类，涵盖宏观、中观和微观3个层次；流程模式匹配康复服务全流程数据的交互和来源，以实现对康复大数据的有效组织和管理。分类体系包括对象类、对象特征类、参与角色类、关系类和活动与事件类，每个类别下又细分为多个子类，覆盖康复涉及的实体、特征、角色、关系和活动。元数据标准包含核心元数据、通用元数据和专用元数据3个层次，确保康复数据的规范管理、共享和互操作性。

结论 本研究深入探讨了基于ICF框架的康复大数据标准体系，涵盖康复大数据的内涵和外延、数据来源、数据模式、分类体系、编码标准及元数据标准等多个维度。康复大数据标准体系的构建涉及数据内容、数据结构、数据编码和元数据等多个方面的标准化工作，这些标准不仅遵循数据流程的规范性，也考虑了数据构成的复杂性。该标准体系与健康大数据的标准体系相互衔接，共同确保数据的一致性、准确性和互操作性，为不同数据源之间的有效交换和比较提供了基础。康复大数据标准体系的建立，不仅保障了康复大数据的标准化处理，而且为康复大数据与其他健康数据的有效交换以及康复大数据的广泛应用奠定了坚实基础，提升康复服务的质量和效率、确保患者获得适当的保健康复和支持，对于推动康复事业的发展具有重要的理论和实践意义。

关键词: 康复大数据, 数据标准化, 国际功能、残疾和健康分类, 数据质量, 数据管理

Abstract:

Objective To explore and organize the standards of rehabilitation big data.

Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning, Disability and Health (ICF) framework. Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module, Rehabilitation in health systems: guide for action, Rehabilitation indicator menu: a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation (FRAME), and Data quality assurance. Module 1. Framework and metrics, the sources, patterns, classification systems and coding standards were discussed under the ICF theory, and the metadata standards were explored. The application and management of rehabilitation big data standards were discussed according to National Health Medical Big Data Standards, Security and Service Management Measures (Trial).

Results The rehabilitation big data included rehabilitation service data and personal health data, coming from population-based and institution-based data, covering macro, meso and micro levels. The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service, to organize and manage rehabilitation big data. The classification system included object classes, object feature classes, participant role classes, relationship classes, and activity and event classes, each of which was further subdivided into subcategories to cover the entities, features, roles, relationships and activities involved in the rehabilitation process. The metadata standards included three levels: core, general and specialized metadata, ensuring standardized management, sharing and interoperability of rehabilitation data.

Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework, encompassing multiple dimensions such as the connotation and extension of rehabilitation big data, data sources, data models, classification systems, coding standards, and metadata standards. The construction of a rehabilitation big data standard system involves standardization efforts in various aspects, including data content, data structure, data coding, and metadata. These standards not only adhere to the norms of data flow, but also take into account the complexity of data composition. This system aligns with health big data standards, ensuring data consistency, accuracy, and interoperability, thus providing a foundation for effective exchange and comparison between different data sources. The establishment of a rehabilitation big data standard system not only ensures the standardized processing of rehabilitation big data, but also lays a solid foundation for effective exchange between rehabilitation big data and other health data, as well as for the widespread application of rehabilitation big data. This provides crucial support for improving the quality and efficiency of rehabilitation services, ensuring that patients receive appropriate care, rehabilitation and support. It holds significant theoretical and practical implications for promoting the development of the rehabilitation field.

Key words: rehabilitation big data, data standardization, International Classification of Functioning, Disability and Health, data quality, data management

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