Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 6, 2024
Date Accepted: Jun 8, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Electronic Health Record Data Quality and Performance Assessments: A Scoping Review
ABSTRACT
Background:
Electronic Health Records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality and performance assessment.
Objective:
This review aims to streamline the current best practices on EHR Data Quality and Performance assessments as a replicable standard for researchers in the field.
Methods:
PubMed was systematically searched for original research articles assessing EHR data quality and/or performance from inception until May 7, 2023.
Results:
Our search yielded 26 original research articles. Most articles suffered from one or more significant limitations, including incomplete or inconsistent reporting (30%), poor replicability (25%), and lacking generalizability of results (25%). Completeness (81%), Conformance (69%), and Plausibility (62%) were the most cited indicators of Data Quality, while Correctness/Accuracy (54%) was most cited for Data Performance, with context-specific supplementation by Recency (27%), Fairness (23%), Stability (15%), and Shareability (8%) assessments. Artificial Intelligence (AI)-based techniques including natural language data extraction, data imputation, and fairness algorithms were demonstrated to play a rising role in improving both dataset quality and performance.
Conclusions:
This review highlights the need for incentivizing data quality and performance assessments and their standardization. The results suggest utility of the adoption of AI-based techniques for enhancing data quality and performance to unlock the full potential of EHRs to improve medical research and practice.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.