Authors:
Monica Noselli
1
;
Dan Mason
2
;
Mohammed A. Mohammed
3
and
Roy A. Ruddle
1
Affiliations:
1
University of Leeds, United Kingdom
;
2
Bradford Institute for Health Research and Bradford Royal Infirmary, United Kingdom
;
3
University of Bradford, United Kingdom
Keyword(s):
Data Quality, Visualization, Health Data, Longitudinal Data.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Visualization
;
Health Information Systems
;
Sensor Networks
Abstract:
Electronic Health Records (EHRs) are an important asset for clinical research and decision making, but the
utility of EHR data depends on its quality. In health, quality is typically investigated by using statistical
methods to profile data. To complement established methods, we developed a web-based visualisation tool
called MonAT Web Application (MonAT) for profiling the completeness and correctness of EHR. The tool
was evaluated by four researchers using anthropometric data from the Born in Bradford Project (BiB Project),
and this highlighted three advantages. The first was to understand how missingness varied across variables,
and especially to do this for subsets of records. The second was to investigate whether certain variables for
groups of records were sufficiently complete to be used in subsequent analysis. The third was to portray
longitudinally the records for a given person, to improve outlier identification.