Roy Thomas, Bertaux Aurélie, Labbani Narsis Ouassila, Didier Jean-Pierre, Laroche Davy
Collaborative Research Network STARTER (Innovative Strategies and Artificial Intelligence for Motor Function Rehabilitation and Autonomy Preservation), France; CIAD UR 7533, Université Bourgogne Europe, F-21000 Dijon, France.
Collaborative Research Network STARTER (Innovative Strategies and Artificial Intelligence for Motor Function Rehabilitation and Autonomy Preservation), France; CIAD UR 7533, Université Bourgogne Europe, F-21000 Dijon, France.
Int J Med Inform. 2025 Jul;199:105882. doi: 10.1016/j.ijmedinf.2025.105882. Epub 2025 Mar 21.
Effective telerehabilitation requires robust, standardized models to ensure comprehensive and continuous patient monitoring. However, existing rehabilitation models often lack integration, failing to cover the entire care continuum and its interdisciplinary aspects. This gap limits their applicability in real-world settings.
This study introduces a semi-formal, Unified Modeling Language (UML)-based framework that provides a holistic, patient-centered representation of the rehabilitation pathway. The model is designed to bridge gaps in care coordination, aligning with recent scientific advances and healthcare policies emphasizing patient empowerment and interdisciplinary collaboration.
Using a professional didactics approach, we conducted a literature review, field observations, and expert consultations (questionnaires, interviews) to map rehabilitation pathways across diverse conditions and settings. The model was iteratively refined based on expert feedback to ensure its accuracy and usability.
Our findings reveal significant fragmentation in rehabilitation pathways, driven by diverse clinical practices and discontinuities in care. To address this, the proposed UML-based model integrates medical, functional, psychosocial, and organizational data, ensuring a cohesive, capability-driven approach. The structured design enhances communication between stakeholders and improves interoperability across healthcare systems.
The proposed model provides a scalable foundation for digital telerehabilitation solutions, adaptable to various healthcare environments. By facilitating data integration and standardization, it supports better patient monitoring, decision-making, and personalized rehabilitation strategies. Future research will focus on refining the model to incorporate specialized rehabilitation fields and enhance interoperability with existing medical information systems.
有效的远程康复需要强大、标准化的模型，以确保对患者进行全面且持续的监测。然而，现有的康复模型往往缺乏整合，无法涵盖整个护理连续过程及其跨学科方面。这一差距限制了它们在现实环境中的适用性。
本研究引入了一个基于统一建模语言（UML）的半正式框架，该框架以患者为中心，全面呈现康复路径。该模型旨在弥合护理协调方面的差距，与强调患者赋权和跨学科合作的最新科学进展及医疗政策保持一致。
我们采用专业教学方法，进行了文献综述、实地观察以及专家咨询（问卷调查、访谈），以梳理不同病情和环境下的康复路径。根据专家反馈对模型进行迭代完善，以确保其准确性和可用性。
我们的研究结果显示，由于临床实践多样以及护理过程中的不连续性，康复路径存在显著碎片化现象。为解决这一问题，所提出的基于UML的模型整合了医疗、功能、心理社会和组织数据，确保采用一种连贯的、以能力为驱动的方法。结构化设计加强了利益相关者之间的沟通，并提高了医疗系统之间的互操作性。
所提出的模型为数字远程康复解决方案提供了一个可扩展的基础，适用于各种医疗环境。通过促进数据整合和标准化，它支持更好的患者监测、决策制定以及个性化康复策略。未来的研究将集中于完善该模型，以纳入专业康复领域，并增强与现有医疗信息系统的互操作性。
