Real-World Data Linkage Research Platform

June 3, 2026 updated by: Kong Yuanyuan, Beijing Friendship Hospital

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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:

  1. 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.
  2. 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.

Study Type

Observational

Enrollment (Estimated)

300000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Yuanyuan Kong, PhD
  • Phone Number: +86 1063139362 +86 15810026760
  • Email: kongyy@ccmu.edu.cn

Study Contact Backup

Study Locations

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

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

  • Participants will be eligible for inclusion if they meet all of the following criteria:

    1. Availability of any health-related data generated from routine clinical care, health examinations, or disease surveillance systems, regardless of disease type or health status.
    2. 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.
    3. 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:

    1. Records lacking minimal essential information required to distinguish individual records or support basic analysis (e.g., completely missing identifiers or time information).
    2. Records confirmed to be invalid, including system-generated test data, corrupted entries, or records that do not represent real clinical or health-related events.
    3. Exact duplicate records that cannot be resolved through standard data processing (only one record will be retained when duplicates are identifiable).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
This is an observational study. No intervention will be applied.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy Rate of Automated Data Governance
Time Frame: 2026.5.30 to 2028.12.31
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
Time Frame: 2026.5.30 to 2028.12.31
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correction Accuracy of Automated Data Governance
Time Frame: 2026.5.30 to 2028.12.31
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.
2026.5.30 to 2028.12.31
Data Standardization Rate of Automated Data Governance
Time Frame: 2026.5.30 to 2028.12.31
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.
2026.5.30 to 2028.12.31
Cross-institutional Data Usability of Automated Data Governance
Time Frame: 2026.5.30 to 2028.12.31
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.
2026.5.30 to 2028.12.31

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Beijing Friendship Hospital

Collaborators

First Affiliated Hospital Xi'an Jiaotong University
Shenzhen Third People's Hospital

Investigators

  • Principal Investigator: Yuanyuan Kong, Beijing Friendship Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 30, 2026

Primary Completion (Estimated)

December 31, 2028

Study Completion (Estimated)

December 31, 2030

Study Registration Dates

First Submitted

May 20, 2026

First Submitted That Met QC Criteria

June 3, 2026

First Posted (Actual)

June 9, 2026

Study Record Updates

Last Update Posted (Actual)

June 9, 2026

Last Update Submitted That Met QC Criteria

June 3, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Data-Link

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Chronic Diseases

Clinical Trials on This is an observational study. No intervention will be applied.

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Saudi Arabia

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe