ArticleBMC medical informatics and decision making2014
Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research.
Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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
90 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Using classification and regression trees (CART) to investigate unknown ethnicity records in NHS emergency care data.BMJ open · 2026Article
- Linking "Big" Geospatial and Health Data: Implications for Research in Environmental Epidemiology.Environmental health perspectives · 2026Article
- Pre-diagnostic biomarkers and risk of stress-related disorders: a cohort study based on electronic health records.BMC medicine · 2026Article
- Imputation Free Deep Survival Prediction with Conditional Variational Autoencoders.Journal of healthcare informatics research · 2026Article
- Use of Clinical Notes to Assess Neuropsychiatric Events After Montelukast Initiation.JAMA network open · 2026Article
- A multifaceted approach to advancing data quality and fitness standards in multi-institutional networks.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Assessment of the integrity of real-time electronic health record data used in clinical research.PloS one · 2026Article
- Retrospective cohort study applying membership and utilisation criteria to electronic health records for diabetes incidence estimation in an integrated healthcare delivery system in Southern California.BMJ public health · 2026Article
- Article
- National centre for healthy ageing data platform: Developing a core set of research data from hospital electronic health record systems: A modified Delphi approach.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Natural language processing improves reliable identification of COVID-19 compared to diagnostic codes alone.American journal of epidemiology · 2025Article
- Misleading Results in Posttraumatic Stress Disorder Predictive Models Using Electronic Health Record Data: Algorithm Validation Study.Journal of medical Internet research · 2025Article
- Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables.BMC medical informatics and decision making · 2025Article
- Data Resource Profile: Paediatric Regional Healthcare Information Platform in Halland, Sweden.International journal of epidemiology · 2025Article
- Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for "Complete Data": Observational Clinical Data Analysis.JMIR medical informatics · 2025Article
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Bias recognition and mitigation strategies in artificial intelligence healthcare applications.NPJ digital medicine · 2025Review
- 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
- The role and limitations of electronic medical records versus patient interviews for determining symptoms of, underlying comorbidities of, and medication use by patients with COVID-19.American journal of epidemiology · 2024Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundTo demonstrate that subject selection based on sufficient laboratory results and medication orders in electronic health records can be biased towards sick patients.
methodsUsing electronic health record data from 10,000 patients who received anesthetic services at a major metropolitan tertiary care academic medical center, an affiliated hospital for women and children, and an affiliated urban primary care hospital, the correlation between patient health status and counts of days with laboratory results or medication orders, as indicated by the American Society of Anesthesiologists Physical Status Classification (ASA Class), was assessed with a Negative Binomial Regression model.
resultsHigher ASA Class was associated with more points of data: compared to ASA Class 1 patients, ASA Class 4 patients had 5.05 times the number of days with laboratory results and 6.85 times the number of days with medication orders, controlling for age, sex, emergency status, admission type, primary diagnosis, and procedure.
conclusionsImposing data sufficiency requirements for subject selection allows researchers to minimize missing data when reusing electronic health records for research, but introduces a bias towards the selection of sicker patients. We demonstrated the relationship between patient health and quantity of data, which may result in a systematic bias towards the selection of sicker patients for research studies and limit the external validity of research conducted using electronic health record data. Additionally, we discovered other variables (i.e., admission status, age, emergency classification, procedure, and diagnosis) that independently affect data sufficiency.
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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.