Big data analytics to improve cardiovascular care: promise and challenges

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.

The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association

“…13 As the LHS data infrastructure continues to grow and mature, predictive analytic tools will be fundamental in transforming these data into actionable insights. 113 In addition, as mentioned above, as new patient and environmental data sources become available, predictive analytic techniques will be integral in understanding how the aggregated information can best be harnessed to determine the best approach to maximizing both individual and population health. 114 Finally, with the integration of predictive analytic tools into EHRs, insights can be generated at the point of clinical care, allowing minute-by-minute assessments and changes in clinical management to optimize care.…”

Data Science in Healthcare

“…1 Data science in health care has seen recent and rapid progress along 3 paths: (1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices 2 ; (2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing 3 ; and (3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data. 4 As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation? Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible?…”

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study (Preprint)

“…In contrast, machine learning analytic methods can be used to build flexible, customized and automated predictive models using information available in electronic medical records (EMR). [11] The promise of extracting predictive insights in real-time from complex and voluminous EMR data has fueled a lot of excitement around the application of machine learning-based predictive methods in healthcare, where even a marginal increase in performance could translate to meaningful gains in efficiency and quality.…”