ReviewGut and liver2024
Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation.
Review in Gut and liver, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Essential Data Elements for Laboratory Test Interoperability: An Expert Consensus-Based Framework.Annals of laboratory medicine · 2026Guideline
- Choosing real-world data for clinical and epidemiological research: methodological lessons from NHIRD and TriNetX-A narrative review.Annals of medicine · 2026Review
- Leveraging electronic health record data to evidence collider stratification bias and inform clinical epidemiology: obesity, diabetes, and rotator cuff tears.American journal of epidemiology · 2026Article
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Inpatient morbidity and structural care burden in oropharyngeal carcinoma in Germany: a nationwide cross-institutional EHR analysis using an AI-enabled data network.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Assessment of Electronic Clinical Monitoring Systems in the Pediatric Intensive Care Unit: Prospective Concordance Study.JMIR formative research · 2026Article
- Validity of ICD-10 codes for identifying gastrointestinal bleeding in anticoagulated patients: an encounter-level diagnostic accuracy study.Journal of thrombosis and thrombolysis · 2026Article
- Primary adherence to medications for the treatment of major depressive disorder: A descriptive study using real-world data.Journal of managed care & specialty pharmacy · 2026Article
- Cervical Cancer Screening and Prevention among Latinas in the United States among a Network of Community Health Centers.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.medRxiv : the preprint server for health sciences · 2026Article
- A High-Accuracy Rule-Based Algorithm for Automated Extraction of Coronary Artery Calcium Scores from Mixed-Language Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Mitigating bias in the analysis and inferences from using longitudinal EHR data in disease outcomes research.BMC medical research methodology · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Rural-Urban Differences in Hypertension Prevalence and Control in a Large Regional Health System Cohort.Journal of clinical medicine · 2026Article
- Toward scalable early cancer detection: evaluating EHR-based predictive models against traditional screening criteria.NPJ precision oncology · 2026Article
- Developing an AI platform for voice-input medical records in Polish: a digital application for optimisation of physicians' workload.Scientific reports · 2026Article
- Enhanced Adverse-Event Detection and Drug-Event Relation Extraction from Clinical Notes.medRxiv : the preprint server for health sciences · 2026Article
- Acceptance of Technological Innovations in Emergency Departments: An Empirical Study Based on an Extended TAM.Healthcare (Basel, Switzerland) · 2026Article
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
8 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
5 authors.
Funding
Abstract
Electronic health records (EHRs) have been increasingly adopted in clinical practices across the United States, providing a primary source of data for clinical research, particularly observational cohort studies. EHRs are a high-yield, low-maintenance source of longitudinal real-world data for large patient populations and provide a wealth of information and clinical contexts that are useful for clinical research and translation into practice. Despite these strengths, it is important to recognize the multiple limitations and challenges related to the use of EHR data in clinical research. Missing data are a major source of error and biases and can affect the representativeness of the cohort of interest, as well as the accuracy of the outcomes and exposures. Here, we aim to provide a critical understanding of the types of data available in EHRs and describe the impact of data heterogeneity, quality, and generalizability, which should be evaluated prior to and during the analysis of EHR data. We also identify challenges pertaining to data quality, including errors and biases, and examine potential sources of such biases and errors. Finally, we discuss approaches to mitigate and remediate these limitations. A proactive approach to addressing these issues can help ensure the integrity and quality of EHR data and the appropriateness of their use in clinical studies.
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.