基于ICF的康复大数据理论架构研究

doi:10.3969/j.issn.1006-9771.2024.09.007

《中国康复理论与实践》 ›› 2024, Vol. 30 ›› Issue (9): 1043-1052.doi: 10.3969/j.issn.1006-9771.2024.09.007

基于ICF的康复大数据理论架构研究

田益凡¹, 陈迪¹^,²(), 程亚宁¹, 叶海燕¹, 刘叶¹, 张赢心¹, 吕雪丽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:2024-07-21 出版日期:2024-09-25 发布日期:2024-10-15
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，研究实习员，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)

Theoretical framework of rehabilitation big data based on ICF

TIAN Yifan¹, CHEN Di¹^,²(), CHENG Yaning¹, YE Haiyan¹, LIU Ye¹, ZHANG Yingxin¹, LÜ Xueli¹

1. Rehabilitation Information Research Department, China Rehabilitation Science Institute, Beijing 100068, China
2. WHO-FIC Collaborating Center in China, Beijing 100068, China

Received:2024-07-21 Published:2024-09-25 Online:2024-10-15
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)

摘要/Abstract

摘要：

目的基于《国际功能、残疾和健康分类》(ICF)，构建康复大数据的理论架构。
方法基于国际康复政策文件，包括世界卫生组织《健康服务体系中的康复》《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》《康复现状系统评估配套工具(STARS)：康复信息收集模板(TRIC)》和《国家卫生信息系统框架和标准》等分析康复大数据的构成与功能；探讨基于世界卫生组织国际健康分类家族(WHO-FICs)的康复大数据领域的内容架构；基于卫生健康计量网络框架与大数据层级分类探讨康复大数据生成模式。
结果在健康服务体系的6大要素中，信息系统要素涵盖了康复大数据这一重要分支。康复大数据的核心构成包括健康状况、健康决定因素和健康服务3大范畴，其内容架构基于WHO-FICs框架，涵盖了健康与功能，疾病、功能与健康，疾病、功能与干预3个维度。依据大数据架构建设要求，构建了康复服务中康复大数据全面的数据生成和应用模式。其中康复大数据的数据来源包括人口统计、民事登记、人群调查、资源、服务和个人记录，其结果链覆盖投入、过程、产出、结果和影响5大流程。康复大数据加工与利用流程包含数据的采集、存储、管理、分析和应用等环节。
结论基于ICF理论构建了康复大数据的理论架构。

关键词: 国际功能、残疾和健康分类, 康复大数据, 卫生健康计量网络, 康复服务

Abstract:

Objective To construct the theoretical framework of rehabilitation big data based on International Classification of Functioning, Disability and Health (ICF).
Methods Drawing upon international rehabilitation policy documents, such as the World Health Organization's Rehabilitation in health systems; Rehabilitation in health systems: guide for action; Rehabilitation indicator menu: a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation (‎FRAME); Template for Rehabilitation Information Collection (TRIC): a tool accompanying the Systematic Assessment of Rehabilitation Situation (STARS); and Framework and Standards for Country Health Information Systems; this study examined the composition and function of rehabilitation big data. The content structure of the rehabilitation big data domain was analyzed using the World Health Organization Family of International Classifications (WHO-FICs). Furthermore, the generation patterns of rehabilitation big data was constructed drawing on the Health Metrics Network and big data hierarchical classification.
Results Within the six primary elements of the health service system, the information system element was particularly significant, encompassing a substantial branch known as rehabilitation big data. There were three components of rehabilitation big data: health condition, health-related factors and health services. The content framework for this data was derived from the WHO-FICs framework, which covered three dimensions: health and function, disease and function, and disease, function and intervention. A comprehensive model for generating and applying rehabilitation big data in rehabilitation services was developed in line with the requirements for constructing big data architectures. The sources of this data included population censuses, social registration information, population surveys, resources, services and personal records. The result chain of rehabilitation big data encompassed five major processes: input, process, output, outcome and impact. The processing and utilization of this data involved collection, storage, management, analysis and application.
Conclusion A theoretical framework for rehabilitation big data has been constructed based on the ICF theory.

Key words: International Classification of Functioning, Disability and Health, rehabilitation big data, health metrics network, rehabilitation services

中图分类号:

田益凡, 陈迪, 程亚宁, 叶海燕, 刘叶, 张赢心, 吕雪丽. 基于ICF的康复大数据理论架构研究[J]. 《中国康复理论与实践》, 2024, 30(9): 1043-1052.

TIAN Yifan, CHEN Di, CHENG Yaning, YE Haiyan, LIU Ye, ZHANG Yingxin, LÜ Xueli. Theoretical framework of rehabilitation big data based on ICF[J]. Chinese Journal of Rehabilitation Theory and Practice, 2024, 30(9): 1043-1052.

图/表 4

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基于ICF的康复大数据理论架构研究

Theoretical framework of rehabilitation big data based on ICF

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