Healthy Data: Improving Health Information Quality Using Intelligent Systems

December 6, 2021 updated by: Obinwa Ozonze, University of Portsmouth

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

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.

Study Overview

Status

Not yet recruiting

Conditions

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.

Study Type

Observational

Enrollment (Anticipated)

60000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patient records captured by Portsmouth Hospitals University National Health Service Trust (PHU) between 01/01/2020 and 31/12/2020

Description

Inclusion Criteria:

  • No specific exclusion criteria

Exclusion Criteria:

  • No specific exclusion criteria

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
12 months, between 01/01/2020 and 31/12/2020
Consistency of AI tool
Time Frame: 2 months, through study completion
Consistency of AI tool in generating measures for detecting data quality dimensions
2 months, through study completion
Validity of AI tool detection
Time Frame: 2 months, through study completion
Validity of data quality dimensions identified by the AI tool
2 months, through study completion

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Portsmouth

Collaborators

Portsmouth Hospitals NHS Trust

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

February 1, 2022

Primary Completion (Anticipated)

April 1, 2022

Study Completion (Anticipated)

April 1, 2022

Study Registration Dates

First Submitted

November 21, 2021

First Submitted That Met QC Criteria

November 21, 2021

First Posted (Actual)

December 3, 2021

Study Record Updates

Last Update Posted (Actual)

December 22, 2021

Last Update Submitted That Met QC Criteria

December 6, 2021

Last Verified

December 1, 2021

More Information

Terms related to this study

Other Study ID Numbers

  • UP717295

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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