{"doi":"10.2196/58130","title":"Electronic Health Record Data Quality and Performance Assessments: 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 (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.","journal":"JMIR Medical Informatics","year":2024,"id":440516,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1252480,"name":"Timothy R. Buchanan","orcid":"0009-0002-7815-7967","position":1,"is_corresponding":false},{"id":297257,"name":"Matthew M. Ruppert","orcid":"0000-0001-9757-4454","position":2,"is_corresponding":false},{"id":1252977,"name":"Michelle Liu","orcid":null,"position":3,"is_corresponding":false},{"id":1252481,"name":"Ramin Shekouhi","orcid":"0000-0002-3784-5280","position":4,"is_corresponding":false},{"id":909043,"name":"Ziyuan Guan","orcid":"0009-0009-4824-6927","position":5,"is_corresponding":false},{"id":502517,"name":"Jeremy A. Balch","orcid":"0000-0002-1826-7884","position":6,"is_corresponding":false},{"id":297259,"name":"Tezcan Ozrazgat‐Baslanti","orcid":"0000-0002-1158-9928","position":7,"is_corresponding":false},{"id":849916,"name":"Benjamin Shickel","orcid":"0000-0002-5304-7027","position":8,"is_corresponding":false},{"id":383469,"name":"Tyler J. Loftus","orcid":"0000-0001-5354-443X","position":9,"is_corresponding":false},{"id":108937,"name":"Azra Bihorac","orcid":"0000-0002-5745-2863","position":10,"is_corresponding":false},{"id":323279,"name":"Yordan Penev","orcid":"0000-0001-8520-9417","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T02:01:01.842286Z","pmid":"39504136","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}