Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital

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dc.contributor.authorGoel, Kanika
dc.contributor.authorSadeghianasl, Sareh
dc.contributor.authorAndrews, Robert
dc.contributor.authorTer Hofstede, Arthur
dc.contributor.authorWynn, Moe
dc.contributor.authorKapugama Geeganage, Dakshi
dc.contributor.authorLeemans, Sander
dc.contributor.authorMcgree, James
dc.contributor.authorEden, Rebekah
dc.contributor.authorStaib, Andrew
dc.contributor.authorEley, Rob
dc.contributor.authorDonovan, Raelene
dc.date.accessioned2022-12-27T19:07:34Z
dc.date.available2022-12-27T19:07:34Z
dc.date.issued2023-01-03
dc.description.abstractWhilst digital health data provides great benefits for improved and effective patient care and organisational outcomes, the quality of digital health data can sometimes be a significant issue. Healthcare providers are known to spend a significant amount of time on assessing and cleaning data. To address this situation, this paper presents six Digital Health Data Imperfection Patterns that provide insight into data quality issues of digital health data, their root causes, their impact, and how these can be detected. Using the CRISP-DM methodology, we demonstrate the utility and pervasiveness of the patterns at the emergency department of Australia's major tertiary digital hospital. The pattern collection can be used by health providers to identify and prevent key digital health data quality issues contributing to reliable insights for clinical decision making and patient care delivery. The patterns also provide a solid foundation for future research in digital health through its identification of key data quality issues, root causes, detection techniques, and terminology.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.398
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other9e4b80b0-45c9-420f-872c-6de9783a25a9
dc.identifier.urihttps://hdl.handle.net/10125/103029
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectProcess Mining in Healthcare
dc.subjectdata quality
dc.subjectdecision making
dc.subjectdigital health
dc.subjectpatterns
dc.subjectroot causes
dc.titleDigital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital
dc.type.dcmitext
prism.startingpage3235

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