Healthy Data: Improving Health Information Quality Using Intelligent Systems

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

60000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-02-28

Study Completion Date

2022-04-30

Brief Summary

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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

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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

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Data Quality

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

Inclusion Criteria:

* No specific exclusion criteria

Exclusion Criteria:

* No specific exclusion criteria

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Responsible Party

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Obinwa Ozonze

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Central Contacts

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Obinwa Ozonze, MSc

Role: CONTACT

Phone: 07391566946

Email: [email protected]

Adrian Hopgood, PhD

Role: CONTACT

Phone: 02392842946

Email: [email protected]

References

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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.

Reference Type BACKGROUND

PMID: 27713905 (View on PubMed)

Other Identifiers

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UP717295

Identifier Type: -

Identifier Source: org_study_id