Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

Abstract

This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

“…EHR influences the decisions made by physicians [12][13][14][15]; thus more research in this area is greatly needed. With the emergence of smartphones the healthcare sector has ventured in mobile health applications for patients.…”

Transtheoretical model of health behavioral change: A systematic review

“…The authors used a data extraction template [Table 1] based on previous studies of SR team leader (AR). [9] The template kept the extracted information consistent: study types, subject area, results, goals, methods, outputs, society, dimension of data quality, and critical thinking about research. Three consensus meetings were arranged among AR, MH, and each of the coauthors to discuss and achieve final consensus and synthesis of the findings.…”

Evaluating digital libraries: a systematised review

“…All studies were analysed by the authors with a fixed tool (Table 1). This tool has been created and used by Rahimi, Liaw, Ray, Taggart, and Yu (2014) in other systematic reviews and in this study.…”