Observational studyBMJ (Clinical research ed.)2018
Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.
Observational study in BMJ (Clinical research ed.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 209 papers, 6 of them syntheses 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
209 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.International journal of computer assisted radiology and surgery · 2023Pooled it
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Are interventions in reproductive medicine assessed for plausible and clinically relevant effects? A systematic review of power and precision in trials and meta-analyses.Human reproduction (Oxford, England) · 2019Pooled it
- Trial
- Clinical trajectories and genetic architecture across the neurological-psychiatric boundary.medRxiv : the preprint server for health sciences · 2026Article
- AI Cautionary Guide: Pitfalls and Strategies for the Use of Machine Learning in Medical Research.Clinical pharmacology and therapeutics · 2026Review
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Representational Veracity in Data Science Health Research: Targets, Proxies, Labels, and Descriptors.Journal of medical Internet research · 2026Article
- Methodological considerations in interpreting the effect of topical vancomycin in posterior spinal fusion.Spine deformity · 2026Article
- Association of cardiovascular-kidney-metabolic syndrome with blinding eye diseases: insights from a UK Biobank cohort analysis.Eye (London, England) · 2026Article
- Machine Learning on the Pediatric Intensive Care (PIC) Database: PIC-Powered Prediction.Bioengineering (Basel, Switzerland) · 2026Review
- Otolaryngology Involvement in Emergency Care for Dizziness During the 2024 Korean Resident Physician Workforce Disruption and Subsequent Return: A Single-Center Retrospective Study.Healthcare (Basel, Switzerland) · 2026Article
- Decentralized rare disease studies in Germany: first results and hurdles of secondary use of patient data.BMC medical informatics and decision making · 2026Article
- Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data.JAMIA open · 2026Article
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Predicting Adverse ICU Outcomes from Admission-Time Frailty and Nocturnal Sleep Fragmentation Using Explainable AI.Journal of clinical medicine · 2026Article
- Are Traditional Registries Becoming Obsolete in the Modern Digital Health Ecosystem?Journal of medical Internet research · 2026Article
149 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
objectiveTo evaluate on a large scale, across 272 common types of laboratory tests, the impact of healthcare processes on the predictive value of electronic health record (EHR) data.
designRetrospective observational study.
settingTwo large hospitals in Boston, Massachusetts, with inpatient, emergency, and ambulatory care.
participantsAll 669 452 patients treated at the two hospitals over one year between 2005 and 2006.
main outcome measuresThe relative predictive accuracy of each laboratory test for three year survival, using the time of the day, day of the week, and ordering frequency of the test, compared to the value of the test result.
resultsThe presence of a laboratory test order, regardless of any other information about the test result, has a significant association (P<0.001) with the odds of survival in 233 of 272 (86%) tests. Data about the timing of when laboratory tests were ordered were more accurate than the test results in predicting survival in 118 of 174 tests (68%).
conclusionsHealthcare processes must be addressed and accounted for in analysis of observational health data. Without careful consideration to context, EHR data are unsuitable for many research questions. However, if explicitly modeled, the same processes that make EHR data complex can be leveraged to gain insight into patients' state of health.
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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.