ReviewNature reviews. Cardiology2016
Big data analytics to improve cardiovascular care: promise and challenges.
Review in Nature reviews. Cardiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers, 5 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
136 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- What have we learned in minimally invasive colorectal surgery from NSQIP and NIS large databases? A systematic review.International journal of colorectal disease · 2018Pooled it
- Outcomes after bariatric surgery according to large databases: a systematic review.Langenbeck's archives of surgery · 2017Pooled it
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Big data: from signal to action.Proceedings (Baylor University. Medical Center) · 2026Article
- Wearable Devices in Cardiovascular Care: A Narrative Review of the Transition Toward Predictive, Preventive, Personalized, and Participatory Medicine.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Review
- AI-Enabled Customer Relationship Management Platforms for Patient Services in Health Care, Early Lessons From Governance, and Program-Level Outcomes.JMIR medical informatics · 2026Article
- A strategic framework for data science integration in undergraduate medical education and clinical training.Journal of education and health promotion · 2026Review
- Review
- Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Bridging the gaps in interventional cardiology disparities-socioeconomic, geographic, and political inequalities.European heart journal open · 2025Review
- Differential effects of demographics and risk factors on the nonlinear orthotropic mechanical properties of human femoropopliteal arteries.Biomechanics and modeling in mechanobiology · 2025Article
- Establishing organizational AI governance in healthcare: a case study in Canada.NPJ digital medicine · 2025Article
- Clinical research and trials in echocardiography: rationale, requirements and future opportunities.Echo research and practice · 2025Review
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives.Biosensors · 2025Review
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
76 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.
Indexed as
Identifiers
27009423What 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.