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临床试验/NCT05144230
NCT05144230Unknown不适用

Collection of Electronic Health Records (EHR) for Validation of Artificial Intelligence Based Tool for Data Quality Assessment

University of Portsmouth0 个研究点目标入组 60,000 人开始时间: 2022年2月最近更新:
适应症

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Obinwa Ozonze

Principal Investigator

University of Portsmouth

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