Big Data in Cardiology

Will big data lead to big improvements in cardiovascular care?A 36-year-old woman presents with dyspnoea and dizziness.The clinician orders blood tests, an echocardiogram, an electrocardiogram, and documents the history in the electronic health record.A computer algorithm finds the patient's prior test results, her genetic profile and demographic characteristics, and links in her wearable biosensor data.The algorithm creates a unique phenotype by processing all of the data sources, compares it with one million other patients, and suggests to the provider that the patient has hypertrophic cardiomyopathy, with an 83% predicted probability of sudden cardiac death in the next 10 years.The information supports an accurate and efficient diagnosis and provides individualized risk to inform shared decision making for a potential implantable cardioverter defibrillator (ICD).This scenario illustrates the promise of big data for cardiovascular clinicians-an automated system combining seemingly disparate data from various sources to provide decision support for diagnosis and treatment, as well as individualized, high-accuracy predictive analytics.How realistic is this future?Big data analytics-and more generally the field of data science-are not new.Other industries have already capitalized on the explosion in data availability, computing power, and analytic methods like machine learning.Yet the 'big data era' in healthcare is just beginning. 1,2Big data analytics support the concept of artificial intelligence and lie at the heart of many new digital health platforms and precision health tools.Ideally, utilization of big data analytic tools in cardiovascular care will translate into better care and outcomes at a lower cost.It is not yet clear, however, to what degree the promise will be fulfilled.In this brief article, we highlight three promising applications for big data in cardiovascular care, followed by 'proof of concept' challenges to be met if the promise of big data is to be realized.