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Real-World Data Linkage Research Platform

3. juni 2026 opdateret af: 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.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

300000

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

  • Navn: Yuanyuan Kong, PhD
  • Telefonnummer: +86 1063139362 +86 15810026760
  • E-mail: kongyy@ccmu.edu.cn

Undersøgelse Kontakt Backup

Studiesteder

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

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Barn
  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ja

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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).

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
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.
Andet: This is an observational study. No intervention will be applied.
This is an observational study. No intervention will be applied.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Accuracy Rate of Automated Data Governance
Tidsramme: 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
Tidsramme: 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

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Correction Accuracy of Automated Data Governance
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Beijing Friendship Hospital

Samarbejdspartnere

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

Efterforskere

  • Ledende efterforsker: Yuanyuan Kong, Beijing Friendship Hospital

Publikationer og nyttige links

Den person, der er ansvarlig for at indtaste oplysninger om undersøgelsen, leverer frivilligt disse publikationer. Disse kan handle om alt relateret til undersøgelsen.

Generelle publikationer

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

30. maj 2026

Primær færdiggørelse (Anslået)

31. december 2028

Studieafslutning (Anslået)

31. december 2030

Datoer for studieregistrering

Først indsendt

20. maj 2026

Først indsendt, der opfyldte QC-kriterier

3. juni 2026

Først opslået (Faktiske)

9. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

9. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

3. juni 2026

Sidst verificeret

1. maj 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • Data-Link

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