The availability of big data analytical tools for use in cardiovascular practice and research will grow rapidly
Big data analytical applications, such as predictive models for patient risk and resource use, have great potential to improve cardiovascular quality of care and patient outcomes
Big data analytical tools in cardiovascular care are still at a nascent stage of development and evaluation, and evidence showing they improve quality of care and patient outcomes is lacking
Establishing the 'evidence base' for big data applications in relation to cardiovascular quality and outcomes of care is critical; big data analytical tools should be evaluated as health-care delivery interventions
Big data methods are tolerant of poor quality of underlying data; however, big data tools might be more valid and clinically useful in cardiovascular care when based on higher quality data
Substantial attention and resources will be required to integrate big data analytical applications optimally into cardiovascular practice, and to monitor their effect on care and outcomes
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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Journal of Big Data Open Access 06 January 2022
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Krumholz, H. M. Outcomes research: generating evidence for best practice and policies. Circulation 118, 309–318 (2008).
Lampropulos, J. F. et al. Most important outcomes research papers on variation in cardiovascular disease. Circ. Cardiovasc. Qual. Outcomes 6, e9–e16 (2013).
Fisher, E. S. et al. The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Ann. Intern. Med. 138, 273–287 (2003).
Fisher, E. S. et al. The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Ann. Intern. Med. 138, 288–298 (2003).
Committee on the Learning Health Care System in America. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (National Academies Press, 2013).
Raghupathi, W. & Raghupathi, V. Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014).
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. & Escobar, G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. (Millwood) 33, 1123–1131 (2014).
Krumholz, H. M. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff. (Millwood) 33, 1163–1170 (2014).
Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009).
Butler, D. When Google got flu wrong. Nature 494, 155–156 (2013).
Roski, J., Bo-Linn, G. W. & Andrews, T. A. Creating value in health care through big data: opportunities and policy implications. Health Aff. (Millwood) 33, 1115–1122 (2014).
Weber, G. M., Mandi, K. D. & Kohane, I. S. Finding the missing link for big biomedical data. JAMA 311, 2479–2480 (2014).
Sladojevic´, M. et al. Data mining approach for in-hospital treatment outcome in patients with acute coronary syndrome. Med. Pregl. 68, 157–161 (2015).
Lee, J. & Maslove, D. M. Customization of a severity of illness score using local electronic medical record data. J. Intensive Care Med. http://dx.doi.org/10.1177/0885066615585951 (2015).
Panahiazar, M., Taslimitehrani, V., Pereira, N. & Pathak, J. Using EHRs and machine learning for heart failure survival analysis. Stud. Health Technol. Inform. 216, 40–44 (2015).
Escobar, G. J. et al. Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J. Hosp. Med. 7, 388–395 (2012).
Churpek, M. M., Yuen, T. C., Park, S. Y., Gibbons, R. & Edelson, D. P. Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*. Crit. Care Med. 42, 841–848 (2014).
Melillo, P., Orrico, A., Scala, P., Crispino, F. & Pecchia, L. Cloud-based smart health monitoring system for automatic cardiovascular and fall risk assessment in hypertensive patients. J. Med. Syst. 39, 294 (2015).
Murff, H. J. et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 306, 848–855 (2011).
Melillo, P. et al. Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS ONE 10, e0118504 (2015).
Dai, W. et al. Prediction of hospitalization due to heart diseases by supervised learning methods. Int. J. Med. Inform. 84, 189–197 (2015).
Amarasingham, R. et al. Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models. BMC Med. Inform. Decis. Mak. 15, 39 (2015).
Amarasingham, R. et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med. Care 48, 981–988 (2010).
Bayati, M. et al. Data-driven decisions for reducing readmissions for heart failure: general methodology and case study. PLoS ONE 9, e109264 (2014).
Hu, Z. et al. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interact. J. Med. Res. 4, e2 (2015).
Hao, S. et al. Risk prediction of emergency department revisit 30 days post discharge: a prospective study. PLoS ONE 9, e112944 (2014).
Hu, Z. et al. Online prediction of health care utilization in the next six months based on electronic health record information: a cohort and validation study. J. Med. Internet Res. 17, e219 (2015).
Burwell, S. M. Setting value-based payment goals — HHS efforts to improve U.S. health care. N. Engl. J. Med. 372, 897–899 (2015).
Tay, D., Poh, C. L. & Kitney, R. I. A novel neural-inspired learning algorithm with application to clinical risk prediction. J. Biomed. Inform. 54, 305–314 (2015).
Makam, A. N., Nguyen, O. K., Moore, B., Ma, Y. & Amarasingham, R. Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm. BMC Med. Inform. Decis. Mak. 13, 81 (2013).
Yang, H. & Garibaldi, J. M. A hybrid model for automatic identification of risk factors for heart disease. J. Biomed. Inform. 58, S171–S182 (2015).
Jonnagaddala, J. et al. Identification and progression of heart disease risk factors in diabetic patients from longitudinal electronic health records. Biomed Res. Int. 2015, 636371 (2015).
Wang, Y. et al. NLP based congestive heart failure case finding: a prospective analysis on statewide electronic medical records. Int. J. Med. Inform. 84, 1039–1047 (2015).
Vijayakrishnan, R. et al. Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record. J. Card. Fail. 20, 459–464 (2014).
Lillo-Castellano, J. M. et al. Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services. IEEE J. Biomed. Health Inform. 19, 1253–1263 (2015).
Vilar, S., Lorberbaum, T., Hripcsak, G. & Tatonetti, N. P. Improving detection of arrhythmia drug–drug interactions in pharmacovigilance data through the implementation of similarity-based modeling. PLoS ONE 10, e0129974 (2015).
Jiang, G., Liu, H., Solbrig, H. R. & Chute, C. G. Mining severe drug–drug interaction adverse events using Semantic Web technologies: a case study. BioData Min. 8, 12 (2015).
Resnic, F. S. et al. Automated surveillance to detect postprocedure safety signals of approved cardiovascular devices. JAMA 304, 2019–2027 (2010).
Wang, G., Jung, K., Winnenburg, R. & Shah, N. H. A method for systematic discovery of adverse drug events from clinical notes. J. Am. Med. Inform. Assoc. 22, 1196–1204 (2015).
Platt, R. et al. The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction. Pharmacoepidemiol. Drug Saf. 21 (Suppl. 1), 1–8 (2012).
Altman, R. B. & Ashley, E. A. Using 'big data' to dissect clinical heterogeneity. Circulation 131, 232–233 (2015).
Shah, S. J. et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131, 269–279 (2015).
Shivade, C. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J. Am. Med. Inform. Assoc. 21, 221–230 (2014).
Kent, D. M. & Hayward, R. A. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA 298, 1209–1212 (2007).
Murdoch, T. B. & Detsky, A. S. The inevitable application of big data to health care. JAMA 309, 1351–1352 (2013).
Longhurst, C. A., Harrington, R. A. & Shah, N. H. A 'green button' for using aggregate patient data at the point of care. Health Aff. (Millwood) 33, 1229–1235 (2014).
Masoudi, F. A. & Rumsfeld, J. in Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine 10th edn (eds Mann, D. L. et al.) 43–48 (Elsevier Saunders, 2015).
Meystre, S. M. et al. Heart failure medications detection and prescription status classification in clinical narrative documents. Stud. Health Technol. Inform. 216, 609–613 (2015).
Parsons, A., McCullough, C., Wang, J. & Shih, S. Validity of electronic health record-derived quality measurement for performance monitoring. J. Am. Med. Inform. Assoc. 19, 604–609 (2012).
Ayers, J. W., Ribisl, K. M. & Brownstein, J. S. Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. Am. J. Prev. Med. 40, 448–453 (2011).
Coull, B. A. et al. Part 1. Statistical learning methods for the effects of multiple air pollution constituents. Res. Rep. Health Eff. Inst. 183, 5–50 (2015).
Margolis, R. et al. The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data. J. Am. Med. Inform. Assoc. 21, 957–958 (2014).
Denaxas, S. C. et al. Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER). Int. J. Epidemiol. 41, 1625–1638 (2012).
Tu, J. V. et al. The Cardiovascular Health in Ambulatory Care Research Team (CANHEART): using big data to measure and improve cardiovascular health and healthcare services. Circ. Cardiovasc. Qual. Outcomes 8, 204–212 (2015).
Wallace, P. J. et al. Optum Labs: building a novel node in the learning health care system. Health Aff. (Millwood) 33, 1187–1194 (2014).
Curtis, L. H., Brown, J. & Platt, R. Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff. (Millwood) 33, 1178–1186 (2014).
Fleurence, R. L., Beal, A. C., Sheridan, S. E., Johnson, L. B. & Selby, J. V. Patient-powered research networks aim to improve patient care and health research. Health Aff. (Millwood) 33, 1212–1219 (2014).
Thompson, S. G. & Willeit, P. U. K. Biobank comes of age. Lancet 386, 509–510 (2015).
Gottesman, O. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15, 761–771 (2013).
Shah, N. H. et al. Proton pump inhibitor usage and the risk of myocardial infarction in the general population. PLoS ONE 10, e0124653 (2015).
Takada, M., Fujimoto, M., Yamazaki, K., Takamoto, M. & Hosomi, K. Association of statin use with sleep disturbances: data mining of a spontaneous reporting database and a prescription database. Drug Saf. 37, 421–431 (2014).
Klimek, P., Kautzky-Willer, A., Chmiel, A., Schiller-Frühwirth, I. & Thurner, S. Quantification of diabetes comorbidity risks across life using nation-wide big claims data. PLoS Comput. Biol. 11, e1004125 (2015).
Larson, E. B. Building trust in the power of 'big data' research to serve the public good. JAMA 309, 2443–2444 (2013).
Richesson, R. L. et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J. Am. Med. Inform. Assoc. 20, e226–e231 (2013).
Amarasingham, R. et al. Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study. BMJ Qual. Saf. 22, 998–1005 (2013).
Halamka, J. D. Early experiences with big data at an academic medical center. Health Aff. (Millwood) 33, 1132–1138 (2014).
Amarasingham, R., Patzer, R. E., Huesch, M., Nguyen, N. Q. & Xie, B. Implementing electronic health care predictive analytics: considerations and challenges. Health Aff. (Millwood) 33, 1148–1154 (2014).
Narula, J. Are we up to speed?: from big data to rich insights in CV imaging for a hyperconnected world. JACC Cardiovasc. Imaging 6, 1222–1224 (2013).
Gray, E. A. & Thorpe, J. H. Comparative effectiveness research and big data: balancing potential with legal and ethical considerations. J. Comp. Eff. Res. 4, 61–74 (2015).
Neff, G. Why big data won't cure us. Big Data 1, 117–123 (2013).
Wessler, B. S. et al. Clinical prediction models for cardiovascular disease: tufts predictive analytics and comparative effectiveness clinical prediction model database. Circ. Cardiovasc. Qual. Outcomes 8, 368–375 (2015).
Salisbury, A. C. & Spertus, J. A. Realizing the potential of clinical risk prediction models: where are we now and what needs to change to better personalize delivery of care? Circ. Cardiovasc. Qual. Outcomes 8, 332–334 (2015).
Bottle, A., Gaudoin, R., Goudie, R., Jones, S. & Aylin, P. Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study. Health Serv. Deliv. Res. 2, 40 (2014).
Fihn, S. D. et al. Insights from advanced analytics at the Veterans Health Administration. Health Aff. (Millwood) 33, 1203–1211 (2014).
The authors declare no competing financial interests.
About this article
Cite this article
Rumsfeld, J., Joynt, K. & Maddox, T. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 13, 350–359 (2016). https://doi.org/10.1038/nrcardio.2016.42
This article is cited by
Journal of Big Data (2022)