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