ArticleJournal of the American Medical Informatics Association : JAMIA2017
Biases introduced by filtering electronic health records for patients with "complete data".
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2026Review
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data.JAMIA open · 2026Article
- Respiratory Biologics Utilization, Prescription Predictors, and Outcomes in Patients With Asthma and Comorbid Overweight or Obesity.The journal of allergy and clinical immunology. In practice · 2026Observational
- Develop and Validate A Fair Machine Learning Model to Indentify Patients with High Care-Continuity in Electronic Health Records Data.medRxiv : the preprint server for health sciences · 2025Article
- Documentation of social determinants of health for patients with type 2 diabetes in Epic Cosmos.JAMIA open · 2025Article
- Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer.JCO clinical cancer informatics · 2025Article
- Evaluation of the impact of defining observable time in real-world data on outcome incidence.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Predicting Early Outcomes of Prostatic Artery Embolization UsingDiagnostics (Basel, Switzerland) · 2025Article
- Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for "Complete Data": Observational Clinical Data Analysis.JMIR medical informatics · 2025Article
- Article
- Collaborative Filtering for the Imputation of Patient Reported Outcomes.Database and expert systems applications : 35th International Conference, DEXA 2024, Naples, Italy, August 26-28, 2024, Proceedings. Part I. Database and Expert Systems Applications Conference (35th : 2024 : Naples, Italy) · 2024Article
- Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).Medical care · 2023Article
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Second-Line Pharmaceutical Treatments for Patients with Type 2 Diabetes.JAMA network open · 2023Article
- The relationship between hyperglycaemia on admission and patient outcome is modified by hyperlactatemia and diabetic status: a retrospective analysis of the eICU collaborative research database.Scientific reports · 2023Article
- Diagnostic rate estimation from Medicare records: Dependence on claim numbers and latent clinical features.Journal of biomedical informatics · 2023Article
- Deep imputation of missing values in time series health data: A review with benchmarking.Journal of biomedical informatics · 2023Review
2 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
11 authors at 9 institutions in 1 country.
Funding
Abstract
objectiveOne promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases. MATERIALS AND
methodsWe began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members.
resultsEHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete. DISCUSSION AND
conclusionAs investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.
Indexed as
Identifiers
What OpenQuestion holds
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.