ReviewNature medicine2024
Harnessing EHR data for health research.
Review in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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
53 citing papers in PubMed.
- Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition.Learning health systems · 2026Article
- Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.Nature medicine · 2026Article
- Observational
- Intelligent Medication Recommendation via Dynamic Prescription Modeling and Molecular Substructure Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Data Resource Profile: The General Practice Database-Amsterdam, North Holland, Almere (GPD-ANHA).International journal of epidemiology · 2026Article
- Electronic Health Record Burden Among Gastroenterology Providers in a Large Tri-state Tertiary Referral Center.Digestive diseases and sciences · 2026Article
- Distinct Patterns of Mobility Recovery After Stroke Using Routine Clinical Data.medRxiv : the preprint server for health sciences · 2026Article
- Early Prediction of Parkinson's Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning.medRxiv : the preprint server for health sciences · 2026Article
- Leveraging Real-World Data for Clinical Decision Making in Geriatric Oncology.Drugs & aging · 2026Review
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Mitigating bias in the analysis and inferences from using longitudinal EHR data in disease outcomes research.BMC medical research methodology · 2026Article
- A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases.medRxiv : the preprint server for health sciences · 2026Article
- SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records.NPJ digital medicine · 2026Article
- Transdiagnostic Symptoms in Electronic Health Records of Sick-Listed Employees.Journal of occupational rehabilitation · 2026Article
- Data Resource Profile: Cheeloo Lifespan Electronic-health reseArch Data-library (Cheeloo LEAD).International journal of epidemiology · 2026Article
- It Is Time to Move Beyond Task-Specific Critical Care Prediction Models and Prioritize a Foundation Model Built on Continuous Physiological Data.Critical care medicine · 2026Article
- Epidemiology of Aspergillosis Diagnoses in US Adults Using a National EHR Database, 2013-2023.Open forum infectious diseases · 2026Article
- CardioEHR: A longitudinal electronic health record dataset of cardiovascular patients from central China.Scientific data · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- The Centralized Pain Score: A Method to Identify Nociplastic Pain in Electronic Health Records.Pain management nursing : official journal of the American Society of Pain Management Nurses · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities-while addressing limitations and potential pitfalls to avoid.
Indexed as
Identifiers
38965433What 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.