Real-World Data Linkage Research Platform

NCT07635355 | Not Yet Recruiting

• 1 other identifier: Data-Link
• Study Type: observational
• Target: 300,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Chronic Diseases; Sub-optimal Health

Keywords

Real-world data; Intelligent data governance; confidential computation; trusted data matrix; chronic diseases

Outcome Measures

Primary Outcomes (2)

• Accuracy Rate of Automated Data Governance

  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.

  2026.5.30 to 2028.12.31

• Completeness Rate of Automated Data Governance

  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.

  2026.5.30 to 2028.12.31

Secondary Outcomes (3)

• Correction Accuracy of Automated Data Governance

  2026.5.30 to 2028.12.31

• Data Standardization Rate of Automated Data Governance

  2026.5.30 to 2028.12.31

• Cross-institutional Data Usability of Automated Data Governance

  2026.5.30 to 2028.12.31

Study Arms (1)

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.

Other: This is an observational study. No intervention will be applied.

Interventions

This is an observational study. No intervention will be applied. OTHER

This is an observational study. No intervention will be applied.

Data-Link Cohort

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• 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).

Sponsors & Collaborators

• Beijing Friendship Hospital: lead
• Shenzhen Third People's Hospital: collaborator
• First Affiliated Hospital Xi'an Jiaotong University: collaborator

Study Sites (1)

Beijing Friendship Hospital, Capital Medical University.No. 95, Yongan Road, Xicheng District, Beijing, 100050, China

Beijing, Beijing Municipality, 100050, China

Location

Related Publications (3)

• D. Reddy, "Data Engineering Challenges in AI automation," 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Swansea, United Kingdom, 2023, pp. 107-112

  BACKGROUND

• 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.

  PMID: 34964981 BACKGROUND

• 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

  BACKGROUND

MeSH Terms

Conditions

Chronic Disease

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Yuanyuan Kong

  Beijing Friendship Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yuanyuan Kong, PhD

CONTACT

+86 15810026760; kongyy@ccmu.edu.cn

Hao Wang, PhD

CONTACT

+86 18301250922; hao.wang@mail.ccmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 20, 2026
• First Posted: June 9, 2026
• Study Start: May 30, 2026
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2030
• Last Updated: June 9, 2026

Record last verified: 2026-05

Locations

CN (1)