ReviewThe American journal of cardiology2023
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record.
Review in The American journal of cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Perceptions of HIV-Related Comorbidities and Usability of a Virtual Environment for Cardiovascular Disease Prevention Education in Sexual Minority Men With HIV: Formative Phases of a Pilot Randomized Controlled Trial.Journal of medical Internet research · 2024Trial
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Bridging the Gap: Multidisciplinary Decision Making to Address Systemic Barriers in Cardiovascular Care.Current cardiology reports · 2026Review
- Untreated Atrial Fibrillation: Public Health Threat or Data Blind Spot?Journal of general internal medicine · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.Stroke · 2026Review
- A real-world evaluation of longitudinal healthcare expenses in a health system registry of type-2 diabetes mellitus and cardiovascular disease enabled by the 21st century cures act.American journal of preventive cardiology · 2026Article
- Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- An Integrated Predictive Impact-Enhanced Process Mining Framework for Strategic Oncology Workflow Optimization: Case Study in Iran.Bioengineering (Basel, Switzerland) · 2025Article
- Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review.Healthcare informatics research · 2025Article
- Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models.European heart journal. Digital health · 2025Article
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.JAMA cardiology · 2025Article
- The emerging role of AI in transforming cardiovascular care.Future cardiology · 2025Article
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.European heart journal · 2025Article
- Artificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms.medRxiv : the preprint server for health sciences · 2024Article
- Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European heart journal · 2024Review
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
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
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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.