AzTrials
AzTrials

Chronic DiseasesNCT07635355

Real-World Data Linkage Research Platform

An observational study of Chronic Diseases and Sub-optimal Health, run by Beijing Friendship Hospital at 1 site in China.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial 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… Read more about this study

Who is it for?
Any ageHealthy volunteers can take part
Conditions the study is for
Chronic Diseases, Sub-optimal Health
How many people
300,000 participants
Where
1 siteChina
Study dates
30 May 2026to 31 Dec 2030
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • Healthy volunteers accepted
  • Child, Adult, Older adult

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.

Who may be able to join

  • 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.

Who cannot join

  • 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).

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Yuanyuan Kong, PhD contact

+86 15810026760 ext. +86 1063139362

kongyy@ccmu.edu.cn

Hao Wang, PhD contact

+86 18301250922 ext. +86 1063139363

hao.wang@mail.ccmu.edu.cn

China1 site

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

    Beijing, Beijing Municipality, 100050

Other trials in China

About this study

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.

Read the 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.

Being tested
This is an observational study. No intervention will be applied.
Keywords
Real-world data, Intelligent data governance, confidential computation, trusted data matrix, chronic diseases
Medical subject headings
Chronic Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

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

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

Study groups in NCT07635355
Study groupRoleWhat they receive
Data-Link CohortThe 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.
Receives: Other: This is an observational study. No intervention will be applied.

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. 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.

    Measured: 2026.5.30 to 2028.12.31

  2. 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.

    Measured: 2026.5.30 to 2028.12.31

Secondary outcomes (3)

  1. Correction Accuracy of Automated Data Governance

    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.

    Measured: 2026.5.30 to 2028.12.31

  2. Data Standardization Rate of Automated Data Governance

    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.

    Measured: 2026.5.30 to 2028.12.31

  3. Cross-institutional Data Usability of Automated Data Governance

    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.

    Measured: 2026.5.30 to 2028.12.31

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted9 Jun 2026
  2. Study start30 May 2026 (estimated)
  3. Primary completion31 Dec 2028 (estimated)
  4. Study completion31 Dec 2030 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Retrospective
Enrolment
300,000 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07635355
Sponsor's protocol ID
Data-Link
Status last verified
May 2026
First submitted
20 May 2026
Last updated
9 Jun 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No

Publications

  • 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.

    PubMed 34964981

  • 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

Questions about this trial

What is this clinical trial studying?

An observational study of Chronic Diseases and Sub-optimal Health, run by Beijing Friendship Hospital at 1 site in China.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 9 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact Yuanyuan Kong, PhD (+86 15810026760 ext. +86 1063139362, kongyy@ccmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Accuracy Rate of Automated Data Governance (time frame: 2026.5.30 to 2028.12.31). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07635355, which also lists 3 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jun 2026.

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