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Title:
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A DIMENSION-ORIENTED TAXONOMY OF DATA QUALITY PROBLEMS IN ELECTRONIC HEALTH RECORDS |
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Author(s):
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Omar Almutiry, Gary Wills, Richard Crowder |
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ISBN:
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978-989-8533-32-6 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Data Quality, Information Quality, Quality Problem, Dirty Data, Data Quality Dimensions Electronic Health Record (EHR). |
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Type:
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Full Paper |
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First Page:
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122 |
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Last Page:
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132 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The provision of high quality data is of considerable importance within the health sector. Healthcare is a domain in which the timely provision of accurate, current and complete patient data is a prime objective and the quality of Electronic Health Record (EHR) data concerns health professionals and researchers for secondary usage. To ensure high quality data in the sector, health-related organizations need to have appropriate methodologies and measurement processes to assess and analyse it, yet little attention has been paid to existing problems (dirty data) in health-related research. In practice, detection of anomalies and cleansing is time-consuming and labour-intensive, which makes it unrealistic for most health-related organizations. This paper proposes a dimension-oriented taxonomy of data quality problems. The mechanism of the data quality assessment relates the business impacts to the dimensions of data quality. |
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