Real-World Data Linkage Research Platform
This study aims to address the lack of intelligent governance tools in clinical data management to promote efficient governance and secure sharing of real-world health data. To achieve this, a self-adaptive, automated governance intelligent agent will be developed based on a High-Order Programming (HOP) architecture, integrating Large Language Models (LLMs) and deep learning techniques. The agent will continuously monitor and correct data quality issues in real time, improving data accuracy and usability.
In parallel, the project will establish a trusted data-sharing framework by integrating AI Confidential Computing (AICC) with Trusted Data Matrix (TDM) technologies. This framework will enable secure, real-time cross-institutional data exchange and collaborative computation while protecting sensitive information.
Overall, the study aims to transform fragmented clinical data into high-quality, standardized, and securely accessible resources, thereby facilitating the circulation of data value and advancing collaborative medical research.
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Descripción detallada
This multicenter, observational cohort study aims to integrate longitudinal health data from China, including routine health examinations, electronic medical records, and disease registries. The platform is designed to address key data challenges in the medical domain, particularly in chronic diseases and suboptimal health status. It is driven by two primary objectives:
- Intelligent and automated data governance To ensure high data quality, the platform will engineer a self-adaptive, automated governance intelligent agent. Integrating Large Language Models (LLMs) and High-Order Programming (HOP), this agent actively monitors and corrects real-world data issues, such as missing values, redundancies, and formatting inconsistencies. Through deep learning, the agent continuously optimizes its governance rules to adapt to complex medical data environments.
- Trusted and secure data sharing To facilitate multicenter collaborative research, the study will establish a secure and trusted data-sharing framework. By integrating AI confidential computation (AICC) with Trusted Data Matrix (TDM) technologies, the platform provides hardware-level security guarantees. This ensures that real-time, cross-institutional data exchange and collaborative computation without exposing sensitive patient information.
Overall Objective The platform aims to transform heterogeneous clinical data into standardized, high-quality, and securely accessible resources, thereby enabling efficient data utilization and promoting the value circulation of medical data for real-world evidence research.
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Yuanyuan Kong, PhD
- Número de teléfono: +86 1063139362 +86 15810026760
- Correo electrónico: kongyy@ccmu.edu.cn
Copia de seguridad de contactos de estudio
- Nombre: Hao Wang, PhD
- Número de teléfono: +86 1063139363 +86 18301250922
- Correo electrónico: hao.wang@mail.ccmu.edu.cn
Ubicaciones de estudio
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Beijing Municipality
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Beijing, Beijing Municipality, Porcelana, 100050
- Beijing Friendship Hospital, Capital Medical University.No. 95, Yongan Road, Xicheng District, Beijing, 100050, China
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
This study establishes a multicenter, observational real-world data platform integrating longitudinal health data from multiple sources across China, including routine health examinations, electronic medical records, and disease registries. The platform is designed to support population-level research without restriction to specific diseases or conditions, enabling inclusive and continuous assessment of health status, disease risk, progression, and outcomes in real-world settings.
All available individuals with usable health-related data are eligible for inclusion, with minimal restrictions to maximize data coverage and representativeness. Both retrospective and prospective data will be incorporated and linked at the individual level using standardized protocols within a secure data governance and privacy protection framework.
Descripción
Inclusion Criteria:
Participants will be eligible for inclusion if they meet all of the following criteria:
- Availability of any health-related data generated from routine clinical care, health examinations, or disease surveillance systems, regardless of disease type or health status.
- Presence of at least one type of usable data, including but not limited to diagnostic information (structured or unstructured), laboratory results, imaging data, or basic demographic information.
- Records contain sufficient information (appropriately anonymized) to allow data organization and, where feasible, linkage at the individual level across time points or data sources.
Exclusion Criteria:
Participants or records meeting any of the following criteria will be excluded:
- Records lacking minimal essential information required to distinguish individual records or support basic analysis (e.g., completely missing identifiers or time information).
- Records confirmed to be invalid, including system-generated test data, corrupted entries, or records that do not represent real clinical or health-related events.
- Exact duplicate records that cannot be resolved through standard data processing (only one record will be retained when duplicates are identifiable).
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
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Data-Link Cohort
The study cohort is derived from a multicenter, population-based real-world data platform that integrates longitudinal data from electronic medical records, disease registries, and routine health examinations across multiple institutions.
The platform is designed to support broad, disease-agnostic research and enable dynamic evaluation of health status, disease risk, and outcomes in real-world settings.
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This is an observational study.
No intervention will be applied.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Accuracy Rate of Automated Data Governance
Periodo de tiempo: 2026.5.30 to 2028.12.31
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Using a manually curated gold-standard dataset, the effectiveness of the intelligent agent in improving data accuracy will be evaluated by measuring the proportion of data values that correctly match the gold-standard reference after automated data governance.
The accuracy rate will be calculated as the percentage of correctly recorded or corrected data elements among all evaluated data elements.
Values range from 0% to 100%, with higher values indicating better data accuracy.
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2026.5.30 to 2028.12.31
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Completeness Rate of Automated Data Governance
Periodo de tiempo: 2026.5.30 to 2028.12.31
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Using a manually curated gold-standard dataset, the effectiveness of the intelligent agent in improving data completeness will be evaluated by measuring the proportion of required data fields that are complete after automated data governance.
The completeness rate will be calculated as the percentage of non-missing required data elements among all required data elements.
Values range from 0% to 100%, with higher values indicating better data completeness.
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2026.5.30 to 2028.12.31
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Correction Accuracy of Automated Data Governance
Periodo de tiempo: 2026.5.30 to 2028.12.31
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Using a manually curated gold-standard dataset, the effectiveness of the intelligent agent in resolving identified data quality issues will be evaluated by measuring correction accuracy.
Correction accuracy will be calculated as the percentage of identified data quality issues (e.g., missing values, format inconsistencies, and logical conflicts) that are correctly resolved after automated data governance, compared with the gold-standard reference dataset.
Values range from 0% to 100%, with higher values indicating better correction performance.
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2026.5.30 to 2028.12.31
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Data Standardization Rate of Automated Data Governance
Periodo de tiempo: 2026.5.30 to 2028.12.31
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Using a manually curated gold-standard dataset, the effectiveness of the intelligent agent in standardizing data will be evaluated by measuring the proportion of data elements that conform to predefined data standards, terminologies, and formatting rules after automated data governance.
The data standardization rate will be calculated as the percentage of evaluated data elements that meet standardized data specifications among all assessed data elements.
Values range from 0% to 100%, with higher values indicating better data standardization.
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2026.5.30 to 2028.12.31
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Cross-institutional Data Usability of Automated Data Governance
Periodo de tiempo: 2026.5.30 to 2028.12.31
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Using datasets derived from participating institutions, the effectiveness of the intelligent agent in improving cross-institutional data usability will be evaluated by measuring the proportion of governed datasets that can be successfully integrated, interpreted, and used across different institutions according to predefined interoperability and usability criteria after automated data governance.
Cross-institutional data usability will be calculated as the percentage of datasets meeting prespecified usability criteria among all evaluated datasets.
Values range from 0% to 100%, with higher values indicating better cross-institutional usability.
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2026.5.30 to 2028.12.31
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Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Yuanyuan Kong, Beijing Friendship Hospital
Publicaciones y enlaces útiles
Publicaciones Generales
- D. Reddy, "Data Engineering Challenges in AI automation," 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Swansea, United Kingdom, 2023, pp. 107-112
- Penberthy LT, Rivera DR, Lund JL, Bruno MA, Meyer AM. An overview of real-world data sources for oncology and considerations for research. CA Cancer J Clin. 2022 May;72(3):287-300. doi: 10.3322/caac.21714. Epub 2021 Dec 29.
- Kam K.H. Ng, Chun-Hsien Chen, C.K.M. Lee, Jianxin (Roger) Jiao, Zhi-Xin Yang; A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives; Advanced Engineering Informatics; 2021 January; Volume 47; 101246
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Estimado)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- Data-Link
Información sobre medicamentos y dispositivos, documentos del estudio
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