ArticleStudies in health technology and informatics2019
Detecting Systemic Data Quality Issues in Electronic Health Records.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Process for Quality Management of Electronic Medical Records-Based Data: Case Study Using Real Colorectal Cancer Data.JMIR medical informatics · 2025Article
- Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Digital biobanks are underutilized in dermatology and create opportunities to reduce the burden of skin disease.The British journal of dermatology · 2024Article
- Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives.American journal of obstetrics & gynecology MFM · 2023Article
- Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Real-world data for 21Journal of clinical and translational science · 2023Article
- Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives.medRxiv : the preprint server for health sciences · 2022Article
- Data needs and models for the opioid epidemic.Molecular psychiatry · 2022Review
- Columbia Open Health Data for COVID-19 Research: Database Analysis.Journal of medical Internet research · 2021Article
- Simulation-derived best practices for clustering clinical data.Journal of biomedical informatics · 2021Article
- Comparative effectiveness of medical concept embedding for feature engineering in phenotyping.JAMIA open · 2021Article
- Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The Relationship Between the Survey of Well-being of Young Children and Speech-language Delay Diagnosis.Journal of developmental and behavioral pediatrics : JDBPArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Secondary analysis of electronic health records for clinical research faces significant challenges due to known data quality issues in health data observationally collected for clinical care and the data biases caused by standard healthcare processes. In this manuscript, we contribute methodology for data quality assessment by plotting domain-level (conditions (diagnoses), drugs, and procedures) aggregate statistics and concept-level temporal frequencies (i.e., annual prevalence rates of clinical concepts). We detect common temporal patterns in concept frequencies by normalizing and clustering annual concept frequencies using K-means clustering. We apply these methods to the Columbia University Irving Medical Center Observational Medical Outcomes Partnership database. The resulting domain-aggregate and cluster plots show a variety of patterns. We review the patterns found in the condition domain and investigate the processes that shape them. We find that these patterns suggest data quality issues influenced by system-wide factors that affect individual concept frequencies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.