AI-assisted Quality Control Study of Multimodal Data in the Epidemiological Survey of Shanghai Nicheng Cohort Study

Not yet recruiting

• Conditions: Quality Control; Cohort Studies

• Registration Number: NCT06961461
• Lead Sponsor: Shanghai 6th People's Hospital

Brief Summary

This study is based on the Nicheng Cohort study. This study intends to analyze whether AI assistance can effectively improve the efficiency and accuracy of quality control of data collected in large-scale epidemiological surveys based on traditional quality control processes.

Detailed Description

This study randomly divided quality control personnel into an experimental group and a control group. The experimental group adopts AI assisted quality control: the AI system automatically transcribes the recorded text of the questionnaire, extracts keywords, analyzes the consistency of the Q\&A logic, and generates quality control prompts. The quality control personnel will verify the question fragments according to the prompts and determine the qualification of the questionnaire; The control group relies entirely on manual quality control: the quality control personnel listen to the recording word by word, manually record the content, independently identify keyword omissions, logical contradictions, or terminology deviations, and ultimately determine whether the questionnaire is qualified. The core difference lies in the fact that the experimental group uses AI technology to accurately locate risk issues, reducing the burden of manual comprehensive screening, while the control group requires full manual review without targeted support.

Study Timeline

• Status Verified: 2025-04
• Study First Submitted: 2025-04-29
• Study First Submitted QC: 2025-04-29
• Last Update Submitted: 2025-04-29
• Study Start: 2025-05-01
• Study First Posted: 2025-05-08
• Last Update Posted: 2025-05-08
• Primary Completion: 2025-06-15
• Study Completion: 2025-08-15

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 900

Inclusion Criteria

1. Be proficient in using computers;
2. The person responsible for questionnaire quality control needs to have good dialect recognition ability;
3. Have a basic understanding or high acceptance of AI-assisted tools, and be able to adapt to the learning and application of new technologies
4. Be able to participate in the research throughout the process, abide by the research process, receive training, and be willing to complete quality control tasks as required.

Exclusion Criteria

1. The person responsible for questionnaire quality control cannot understand or recognize Shanghai Nanhui dialect proficiently;
2. Unfamiliar with AI-assisted tools and difficult to accept technical operations;
3. Unable to participate in the research, receive training or complete the specified tasks due to other work or academic reasons;

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy	From enrollment to the end of questionnaire quality control at 8 weeks	changes in the efficiency and accuracy of quality control of data collected in large-scale epidemiological surveys with AI-assisted tool

Secondary Outcome Measures

Name	Time	Method
Completeness and consistency	From enrollment to the end of questionnaire quality control at 8 weeks	AI-assisted quality control in questionnaire quality control improves the integrity and consistency of questionnaire data through automated keyword extraction and logical consistency checking.