Electronic health records in IS research: Quality issues, essential thresholds and remedial actions

Abstract The increase in adoption of Electronic Health Record (EHR) systems by healthcare organizations has led to the proliferation of the use of EHR as a secondary data source in both IS and supporting fields. It is imperative that EHR data is exploited appropriately, which would lead to high quality results and enhanced reproducibility. However, the quality of the EHR data being used can vary significantly and can have repercussions for research outcomes. In this paper, we first discuss four major data quality issues present in EHR data. These issues are: (a) non-standard coding schemes, (b) missing data, (c) inconsistencies and (d) aggregation and augmentation of EHR data. Then, we discuss quality thresholds that need to be met in order to avoid the negative impacts of quality issues. Lastly, we discuss some remedial actions that researchers can take to enhance the quality of EHR data to meet the quality thresholds. The discussed issues, thresholds and remedial actions can also apply to a much wider set of data sources when used as secondary data in research.

• Publication year

  2019

• Venue

  Decision Support Systems

• Publication date

  2019-11-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1016/J.DSS.2019.113137
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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