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Real-World Data Linkage Research Platform

3 giugno 2026 aggiornato da: 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.

Panoramica dello studio

Stato

Non ancora reclutamento

Condizioni

Intervento / Trattamento

Descrizione dettagliata

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.

Tipo di studio

Osservativo

Iscrizione (Stimato)

300000

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Contatto studio

  • Nome: Yuanyuan Kong, PhD
  • Numero di telefono: +86 1063139362 +86 15810026760
  • Email: kongyy@ccmu.edu.cn

Backup dei contatti dello studio

Luoghi di studio

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

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

  • Bambino
  • Adulto
  • Adulto più anziano

Accetta volontari sani

Sì

Metodo di campionamento

Campione non probabilistico

Popolazione di studio

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.

Descrizione

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).

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Coorti e interventi

Gruppo / Coorte
Intervento / Trattamento
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.
Altro: This is an observational study. No intervention will be applied.
This is an observational study. No intervention will be applied.

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Accuracy Rate of Automated Data Governance
Lasso di tempo: 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
Lasso di tempo: 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

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Correction Accuracy of Automated Data Governance
Lasso di tempo: 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
Lasso di tempo: 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
Lasso di tempo: 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

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

Beijing Friendship Hospital

Collaboratori

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

Investigatori

  • Investigatore principale: Yuanyuan Kong, Beijing Friendship Hospital

Pubblicazioni e link utili

La persona responsabile dell'inserimento delle informazioni sullo studio fornisce volontariamente queste pubblicazioni. Questi possono riguardare qualsiasi cosa relativa allo studio.

Pubblicazioni generali

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Stimato)

30 maggio 2026

Completamento primario (Stimato)

31 dicembre 2028

Completamento dello studio (Stimato)

31 dicembre 2030

Date di iscrizione allo studio

Primo inviato

20 maggio 2026

Primo inviato che soddisfa i criteri di controllo qualità

3 giugno 2026

Primo Inserito (Effettivo)

9 giugno 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

9 giugno 2026

Ultimo aggiornamento inviato che soddisfa i criteri QC

3 giugno 2026

Ultimo verificato

1 maggio 2026

Maggiori informazioni

Termini relativi a questo studio

Parole chiave

Termini MeSH pertinenti aggiuntivi

Altri numeri di identificazione dello studio

  • Data-Link

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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