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

Обзор

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.

Подробное описание

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.

Вмешательства

• Другое AI-assisted quality control tool
  In this study, quality control personnel will be randomly divided into an experimental group and a control group, and will use artificial intelligence-assisted quality control and manual quality control to conduct quality control on the data collected from the epidemiological survey. Experimental group: Quality control personnel will use the AI system to perform quality control on the questionnaire recordings. Control group: The questionnaire content was checked by manually listening back to t

Первичные конечные точки

• Accuracy [Срок оценки: From enrollment to the end of questionnaire quality control at 8 weeks]

Вторичные конечные точки (1)

• Completeness and consistency [Срок оценки: From enrollment to the end of questionnaire quality control at 8 weeks]

Критерии участия

Критерии включения

• Be proficient in using computers;
• The person responsible for questionnaire quality control needs to have good dialect recognition ability;
• Have a basic understanding or high acceptance of AI-assisted tools, and be able to adapt to the learning and application of new technologies
• 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.

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Другое

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Идентификаторы

NCT: NCT06961461 · 2025-04

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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