TY  - JOUR
AU  - Penev, Yordan P
AU  - Buchanan, Timothy R
AU  - Ruppert, Matthew M
AU  - Liu, Michelle
AU  - Shekouhi, Ramin
AU  - Guan, Ziyuan
AU  - Balch, Jeremy
AU  - Ozrazgat-Baslanti, Tezcan
AU  - Shickel, Benjamin
AU  - Loftus, Tyler J
AU  - Bihorac, Azra
PY  - 2024
DA  - 2024/11/6
TI  - Electronic Health Record Data Quality and Performance Assessments: Scoping Review
JO  - JMIR Med Inform
SP  - e58130
VL  - 12
KW  - electronic health record
KW  - EHR
KW  - record
KW  - data quality
KW  - data performance
KW  - clinical informatics
KW  - performance
KW  - data science
KW  - synthesis
KW  - review methods
KW  - review methodology
KW  - search
KW  - scoping
AB  - 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 (DQ) and performance assessment. Objective: This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field. Methods: PubMed was systematically searched for original research articles assessing EHR DQ and performance from inception until May 7, 2023. Results: Our search yielded 26 original research articles. Most articles had 1 or more significant limitations, including incomplete or inconsistent reporting (n=6, 30%), poor replicability (n=5, 25%), and limited generalizability of results (n=5, 25%). Completeness (n=21, 81%), conformance (n=18, 69%), and plausibility (n=16, 62%) were the most cited indicators of DQ, while correctness or accuracy (n=14, 54%) was most cited for data performance, with context-specific supplementation by recency (n=7, 27%), fairness (n=6, 23%), stability (n=4, 15%), and shareability (n=2, 8%) assessments. Artificial intelligence–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 DQ and performance assessments and their standardization. The results suggest the usefulness of artificial intelligence–based techniques for enhancing DQ and performance to unlock the full potential of EHRs to improve medical research and practice. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e58130
UR  - https://doi.org/10.2196/58130
DO  - 10.2196/58130
ID  - info:doi/10.2196/58130
ER  -