ArticleEGEMS (Washington, DC)2016
Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.
Article in EGEMS (Washington, DC), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data Quality-Driven Improvement in Health Care: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
- A cohort study of engagement in telehealth psychotherapy versus in-person services.Psychotherapy research : journal of the Society for Psychotherapy Research · 2025Article
- A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.Journal of public health dentistry · 2024Article
- Research data warehouse: using electronic health records to conduct population-based observational studies.JAMIA open · 2023Article
- BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.The annals of applied statistics · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Data report on three datasets: Mortality patterns between agricultural and non-agricultural ward areas.Frontiers in genetics · 2022Article
- An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge.BMC medical informatics and decision making · 2021Article
- Application of openEHR archetypes to automate data quality rules for electronic health records: a case study.BMC medical informatics and decision making · 2021Article
- Aligning EHR Data for Pediatric Leukemia With Standard Protocol Therapy.JCO clinical cancer informatics · 2021Article
- Data cleaning process for HIV-indicator data extracted from DHIS2 national reporting system: a case study of Kenya.BMC medical informatics and decision making · 2020Article
- Assessment of Data Usage of Cancer e-Interventions (ADUCI) Framework for Health App Use of Cancer Patients and Their Caregivers: Framework Development Study.JMIR cancer · 2020Article
- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.Neurospine · 2019Article
- Yield and bias in defining a cohort study baseline from electronic health record data.Journal of biomedical informatics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectivesWe examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework.
backgroundThe use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies.
methodsTwo emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided.
conclusionsSPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012)1 was developed. The data set cleaned using Kahn's framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.
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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.