Table 4. Recommendations for future EHR^a data quality and performance assessments.

Issue	Recommendation
Articlecharacteristics
Paucity of specialty-focused EHR data assessments	Incentivize (eg, through quality improvement initiatives and grants) more EHR data assessments, particularly in psychiatry, emergency medicine, and surgical specialties
Incomplete reporting	Use standardized frameworks for measuring and reporting data quality and performance assessments (eg, Table 1)
Poor replicability	Describe DQAb methods in enough details such that they could be replicated by a research team at a different institution
Limited generalizability	Use already available data quality tools and standards (eg, DQA Guidelines proposed by Weiskopf et al [21]) before developing proprietary methodologies
DQA
Inconsistent methodologies	Analyze completeness, conformance, and plausibility at every DQA (completeness only may be applicable for quick data quality checks)
Data missingness and nonconformity	Use available AI-based data extraction algorithms (eg, Lee et al [22]), and augment data using external and synthetic datasets (eg, Zhang et al [19])
Dataperformanceassessment
Inconsistent methodologies	Augment correctness or accuracy measurement with recency, fairness, stability, and shareability performance metrics
EHR data bias	Automate data fairness assessments by measuring agreement of AI-extracted data against an gold standard dataset (eg, manually extracted data) and preventing drift via condition fuzzying and regularization (eg, Zhang et al [19])
Timeliness of analysis	Calculate dataset robustness prior to detailed data quality and performance analysis (eg, as described by García-de-León-Chocano et al [24])

^aEHR: electronic health record.

^bDQA: data quality assessment.