AI aims to clean up health data for better research
NCT ID NCT07635355
First seen Jun 25, 2026 · Last updated Jun 27, 2026 · Updated 2 times
Summary
This study will develop an AI system to automatically fix errors and fill gaps in health records from 300,000 people. It also plans to create a secure way for hospitals to share data. The goal is to make health information more useful for research without changing any treatments.
What this could mean
Our plain-language read of the trial. This is informational only, not medical advice or a prediction.
- What this could lead to
- If successful, this could make health data more accurate and easier to share across hospitals, speeding up medical research.
- What could go wrong
- This is an early-stage observational study focused on data tools, not a treatment. Success depends on technology performance and real-world adoption.
This is an AI summary of the original study and may miss details. Read our disclaimer.
Study facts
What this study's own registry entry says, in plain language.
- Participants
-
About 300,000 people
The number the study aims to enrol. It can still change while the study runs.
- Expected to start
-
May 2026
An estimate. Start dates often move.
- Expected to finish
-
Dec 2030
An estimate. End dates often move.
- Lead sponsor
-
Other sponsor
The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.
Who can take part
This study's own entry requirements. Only the study team can say for certain whether you qualify.
Who is studied
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.
- Ages
-
Children (under 18), adults (18 to 64) and older adults (65 and over)
- Sex
-
Anyone
- Healthy volunteers
-
Accepted
You do not need to have the condition being studied to take part.
Show the full entry requirements Hide the full entry requirements
Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.
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).
Get updates
Get notified about this study
Sign up to get updates when this study changes or when new studies for Chronic diseases are added.
Genom att skicka in godkänner du våra Användarvillkor
Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
How to take part
Only the study team decides who joins. These are the ways to reach them.
-
The places running it
1 site. The list below names each one and where it is.
-
The official record
ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.
-
A doctor treating you
A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.
Contacts and locations
Locations
-
Beijing Friendship Hospital, Capital Medical University.No. 95, Yongan Road, Xicheng District, Beijing, 100050, China
Beijing, Beijing Municipality, 100050, China
More trials for these conditions
Other studies related to the condition(s) this trial covers.
- AI-Powered tai chi and baduanjin apps tested for fall prevention in chronic illness
- 1,000 nurses to be tracked for better workplace health
- Fake health news: study reveals how online lies harm chronic patients
- Massive study digs into Diet-Disease link
- New study aims to tackle physical health in mental health wards
- New E-Health system aims to keep tabs on chronic patients remotely