Collection of Electronic Health Records (EHR) for Validation of Artificial Intelligence Based Tool for Data Quality Assessment
试验速览
- 阶段
- 不适用
- 入组人数
- 60,000
- 主要终点
- Validity of AI tool detection
研究概览
简要总结
Electronic Health Record Systems (EHR) play an integral role in healthcare practice, enabling health organisations to collect, access and manage data more consistently. There is also a great deal of interest in using EHR data to improve decision-making and accelerate medical interventions. However, like all information systems, they are prone to data quality problems such as incomplete records, values outside normal ranges and implausible relationships. These problems are expected to become more prevalent as more organisations adopt electronic health record systems, aggregate, share and explore health data. The investigators believe current efforts to improve health data quality can be made more effective if backed by appropriate technology in the form of a readily accessible intelligent tool. Building on this, the investigators developed an Artificial Intelligence (AI) tool for automating data quality assessment of health data. In this study, the investigators evaluate the AI tool using a real-world dataset.
详细描述
The main aim of this study is to assess the reliability and utility of an AI tool in identifying data quality dimensions of interest for secondary use of health data, including completeness, conformance and plausibility. In assessing this tool, this study will retrospectively analyse data captured during routine clinical care and identify records containing listed data quality dimensions. This study will also assess the consistency of the AI tool in generating and executing data quality checks.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •No specific exclusion criteria
排除标准
- •No specific exclusion criteria
结局指标
主要结局
Validity of AI tool detection
时间窗: 2 months, through study completion
Validity of data quality dimensions identified by the AI tool
Data quality dimensions prevalence
时间窗: 12 months, between 01/01/2020 and 31/12/2020
The number of patient records identified by the AI tool with completeness, conformance and plausibility violations
Consistency of AI tool
时间窗: 2 months, through study completion
Consistency of AI tool in generating measures for detecting data quality dimensions
次要结局
未报告次要终点
研究者
Obinwa Ozonze
Principal Investigator
University of Portsmouth
