Clinical trial NCT05144230: Healthy Data: Improving Health Information Quality Using Intelligent Systems
Official title: Collection of Electronic Health Records (EHR) for Validation of Artificial Intelligence Based Tool for Data Quality Assessment
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Data Quality
Lead sponsor: University of Portsmouth
Sponsor class: OTHER
Collaborators: name: Portsmouth Hospitals NHS Trust
First posted: 2021-12-03
Enrollment: 60000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
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

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.

Eligibility criteria: Inclusion Criteria:

* No specific exclusion criteria

Exclusion Criteria:

* No specific exclusion criteria

Primary outcomes:
measure: Data quality dimensions prevalence
timeFrame: 12 months, between 01/01/2020 and 31/12/2020
measure: Consistency of AI tool
timeFrame: 2 months, through study completion
measure: Validity of AI tool detection
timeFrame: 2 months, through study completion
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: 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.