Data QualityNCT05144230
Healthy Data: Improving Health Information Quality Using Intelligent Systems
An observational study of Data Quality, run by University of Portsmouth.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Data Quality
- How many people
- 60,000 participants
- Where
- No sites listed yet
- Study dates
- Feb 2022to Apr 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patient records captured by Portsmouth Hospitals University National Health Service Trust (PHU) between 01/01/2020 and 31/12/2020
Who may be able to join
- No specific exclusion criteria
Who cannot join
- No specific exclusion criteria
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read the 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.
- Conditions
- Data Quality
What is being measured
The primary outcome is the main question the study is designed to answer.
Data quality dimensions prevalence
The number of patient records identified by the AI tool with completeness, conformance and plausibility violations
Measured: 12 months, between 01/01/2020 and 31/12/2020
Consistency of AI tool
Consistency of AI tool in generating measures for detecting data quality dimensions
Measured: 2 months, through study completion
Validity of AI tool detection
Validity of data quality dimensions identified by the AI tool
Measured: 2 months, through study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Dec 2021
- Study startFeb 2022 (estimated)
- Primary completionApr 2022 (estimated)
- Study completionApr 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Data QualityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 60,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Portsmouth
- Sponsor type
- University, hospital or other
- Collaborators
- Portsmouth Hospitals NHS Trust
- Responsible party
- Principal investigator, Obinwa Ozonze, University of Portsmouth
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05144230
- Sponsor's protocol ID
- UP717295
- Status last verified
- Dec 2021
- First submitted
- 21 Nov 2021
- Last updated
- 22 Dec 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, Estiri H, Goerg C, Holve E, Johnson SG, Liaw ST, Hamilton-Lopez M, Meeker D, Ong TC, Ryan P, Shang N, Weiskopf NG, Weng C, Zozus MN, Schilling L. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016 Sep 11;4(1):1244. doi: 10.13063/2327-9214.1244. eCollection 2016.
PubMed 27713905
Study at a glance
From the registry record, last updated by the sponsor on 22 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Collection of Electronic Health Records (EHR) for Validation of Artificial Intelligence Based Tool for Data Quality Assessment
- ClinicalTrials.gov ID
- NCT05144230
- Acronym / sponsor ID
- UP717295
- Conditions
- Data Quality
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 60,000 participants (estimated)
- Lead sponsor
- University of PortsmouthUniversity, hospital or other
- Collaborators
- Portsmouth Hospitals NHS Trust
- Study start
- Feb 2022 (estimated)
- Primary completion
- Apr 2022 (estimated)
- Study completion
- Apr 2022 (estimated)
- First posted
- 3 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patient records captured by Portsmouth Hospitals University National Health Service Trust (PHU) between 01/01/2020 and 31/12/2020
Key inclusion criteria (1 listed)
- No specific exclusion criteria
Key exclusion criteria (1 listed)
- No specific exclusion criteria
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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
Validity of AI tool detection
Time frame: 2 months, through study completion
Validity of data quality dimensions identified by the AI tool
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Dec 2021.
- Background: PubMed 27713905Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, Estiri H, Goerg C, Holve E, Johnson SG, Liaw ST, Hamilton-Lopez M, Meeker D, Ong TC, Ryan P, Shang N, Weiskopf NG, Weng C, Zozus MN, Schilling L. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016 Sep 11;4(1):1244. doi: 10.13063/2327-9214.1244. eCollection 2016.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Data Quality, run by University of Portsmouth.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Dec 2021. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Obinwa Ozonze, MSc (07391566946, obinwa.ozonze@port.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Data quality dimensions prevalence (time frame: 12 months, between 01/01/2020 and 31/12/2020). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05144230, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Dec 2021.