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CIOs at some of the nation's largest healthcare organizations say yes, but even the truest big data believers acknowledge that it's better suited to big healthcare systems than to small and midsize hospitals and physician practices. Just over half of healthcare providers either have no plans for big data analytics or they're still evaluating, according to the InformationWeek Healthcare IT Priorities Survey of 363 business technologists. And for the purposes of population health management, some observers say, current applications such as registries and automated messaging and care management tools will serve most healthcare providers as well as big data platforms.
Big data holds particular promise in areas such as personalized care, natural language processing, clinical decision support and medical research. Researchers hope that combining disparate data from clinical, genomic and environmental sources will yield insights into the mechanisms of disease. Progress is being made, including advances in predictive modeling and the genomic classification of cancers. But big data analysis isn't likely to change the practice of medicine on a wide scale anytime soon.
Why Big Data?
Big data entails using scale-up, scale-down computing resources and advanced analytics software to make sense of rapidly growing volumes of data. According to a recent article in the Journal of the American Medical Informatics Association, big data techniques have three characteristics: They manage very large blocks of information (volume); they handle a rapidly increasing flow of information (velocity); and they combine data from disparate sources (variety).
While large institutions and accountable care organizations represent the biggest healthcare market for big data, other healthcare concerns, including pharmaceutical companies and health plans, are interested. For example, Merck is working with the Regenstrief Institute in Indianapolis on ways to apply analytics to clinical data to develop personalized treatments. Blue Cross Blue Shield of Tennessee is deploying Teradata's clinical analytics platform to improve the quality of care provided to its members. And Blue Shield of California is partnering with big data vendor NantHealth to form a "continuous learning center" that, it is hoped, will lead to more personalized care and an ACO infrastructure capable of lowering costs.
The growth of ACOs and the accompanying emphasis on population health management are factors driving interest in big data among healthcare providers. Population health management requires the use of large data sets to track and monitor patients, intervene with high-risk patients, predict which patients will develop serious conditions in the near future, evaluate provider performance and use all those tactics to lower costs.
All of this activity will benefit from the spread of electronic health records, but EHRs from different vendors still aren't interoperable. Health information exchanges, particularly those limited to single enterprises, don't provide access to all of the data that a clinician might need or that an ACO might want to analyze. Claims data can supply much more comprehensive information, but that data isn't easy to integrate with clinical data. As a result, a big data approach is seen as necessary to unite all the disparate data streams that touch on patient care.