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Clinical Trials/NCT05144230
NCT05144230
Unknown
Not Applicable

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

University of Portsmouth0 sites60,000 target enrollmentFebruary 2022
ConditionsData Quality

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Data Quality
Sponsor
University of Portsmouth
Enrollment
60000
Primary Endpoint
Validity of AI tool detection
Last Updated
4 years ago

Overview

Brief Summary

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.

Detailed Description

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.

Registry
clinicaltrials.gov
Start Date
February 2022
End Date
April 2022
Last Updated
4 years ago
Study Type
Observational
Sex
All

Investigators

Responsible Party
Principal Investigator
Principal Investigator

Obinwa Ozonze

Principal Investigator

University of Portsmouth

Eligibility Criteria

Inclusion Criteria

  • No specific exclusion criteria

Exclusion Criteria

  • No specific exclusion criteria

Outcomes

Primary Outcomes

Validity of AI tool detection

Time Frame: 2 months, through study completion

Validity of data quality dimensions identified by the AI tool

Data quality dimensions prevalence

Time Frame: 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

Time Frame: 2 months, through study completion

Consistency of AI tool in generating measures for detecting data quality dimensions

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