The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review
OO Madandola, RI Bjarnadottir, Y Yao… - Journal of the …, 2024 - academic.oup.com
Journal of the American Medical Informatics Association, 2024•academic.oup.com
Abstract Objectives Electronic health records (EHRs) user interfaces (UI) designed for data
entry can potentially impact the quality of patient information captured in the EHRs. This
review identified and synthesized the literature evidence about the relationship of UI
features in EHRs on data quality (DQ). Materials and methods We performed an integrative
review of research studies by conducting a structured search in 5 databases completed on
October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature …
entry can potentially impact the quality of patient information captured in the EHRs. This
review identified and synthesized the literature evidence about the relationship of UI
features in EHRs on data quality (DQ). Materials and methods We performed an integrative
review of research studies by conducting a structured search in 5 databases completed on
October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature …
Objectives
Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).
Materials and methods
We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl’s methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998).
Results
Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce.
Discussion
The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.
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