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Organizations and individuals increasingly rely on information systems (IS) for data-driven decision-making (ZareRavasan & Krčál, 2021), making it critical to ensure that the data being used is of sufficient quality. Poor quality data can cause the loss of revenue and even put lives at risk, especially in healthcare. For example, entry errors in a provider system resulted in an inappropriate treatment, with a patient developing seizures and requiring intubation (ECRI Institute, 2015). Duplicated drug orders entered into two separate prescribing systems used by a hospital resulted in nurses administering an excessive amount of insulin to a patient, resulting in death (Rowland, 2014). Furthermore, data not collected systematically in electronic medical records (EMR) or other operational systems can be of poorer quality, limiting its reuse (Kahn et al., 2016).
The poor delivery of evidence-based practice (James, 2013) and the high rate of adverse events (Landrigan et al., 2010) in hospital admissions and general practices motivate service providers to optimize clinical handovers between healthcare settings. This requires extensive efforts to improve the internal quality of data and advance the quality of information exchange programs. A substantial body of literature has identified the issues that may arise when undertaking these efforts, resulting in poor quality of medical or healthcare data (Michel-Verkerke, 2012; Vilic et al., 2016). Research on data quality for healthcare has developed several methods to resolve these issues (Prasser et al., 2018; Zięba, 2014). However, a systematic understanding of the methods is not available in the healthcare IS/informatics literature. It would be useful to link methods to data quality issues for two main reasons.
First, using EMR has created the potential to improve the quality of care and address cost-effectiveness. However, the scope of new risks (e.g., the quality of the data used in healthcare and research settings) for patients is still not fully understood (Rowland, 2014). Data quality issues are more prominent than in earlier healthcare technology initiatives but have not been taken seriously by practice. The widespread use of EMR technology is inevitable, so addressing data quality issues needs to be a priority.
Second, previous literature reviews of data quality for healthcare (Arts et al., 2002; Chen et al., 2014; Johnson et al., 2015; Kahn et al., 2016; Liaw et al., 2013; Thiru et al., 2003; Weiskopf & Weng, 2013) focus on describing and assessing data quality but do not provide a holistic picture that matches the methods to specific issues associated with different dimensions of data quality. As a result, there is a lack of studies to depict an overall understanding of methods used to address data quality issues in healthcare. Such a study could help healthcare and IT professionals ascertain what work has been done to address data quality issues in this field and identify possible gaps for further exploration.
In addition, a challenge arises in that the current data quality literature uses different terms to describe data quality dimensions, thus limiting our understanding of different or similar data quality dimensions discussed (Kahn et al., 2016; Liaw et al., 2013; Wang & Strong, 1996; Weiskopf & Weng, 2013). Furthermore, the lack of consistent use of terms that describe data quality dimensions makes it difficult to explain different or similar issues for a specific data quality dimension and establish relationships between methods and issues associated with different dimensions. Thus, standardizing the terms used in the literature for data quality dimensions, identifying the issues associated with the different dimensions, and understanding the methods used to address these issues could improve our understanding of this phenomenon, as well as the strengths and limitations of current methods, and future research needs.