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Development of a Methodological Approach for Data Quality Ontology in Diabetes Management Publisher



Rahimi A1, 2, 3, 4 ; Parameswaran N3, 5 ; Ray PK3, 6 ; Taggart J4, 7 ; Yu H7 ; Liaw ST1, 3, 4, 7
Authors
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Authors Affiliations
  1. 1. UNSW, School of Public Health and Community Medicine, Sydney, Australia
  2. 2. Isfahan University of Medical Sciences, Health Information Technology Research Centre, Iran
  3. 3. UNSW Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia
  4. 4. SWSLHD General Practice Unit, Fairfield, Sydney, Australia
  5. 5. UNSW, School of Computer Science and Engineering, Sydney, Australia
  6. 6. UNSW, Australian School of Business, Sydney, Australia
  7. 7. UNSW, Centre for Primary Health Care and Equity, Sydney, Australia

Source: International Journal of E-Health and Medical Communications Published:2014


Abstract

The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3) Semantic modeling; (4) Knowledge representation; and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains. Copyright © 2014, IGI Global.
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