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Can Cloud Big Data Analytics Fix Healthcares Insight Problem?

Can Cloud Big Data Analytics Fix Healthcares Insight Problem?

Having your head in the clouds is more a complement than a criticism these days as more and more technology services shift off site and into the hands of third-party providers.

For the healthcare industry, the cloud seems a natural fit.  From EHRs to data storage to software as a service (SaaS) capabilities, cloud-based products offer lower costs, greater capacity for scalability, dedicated service and support, and near-continuous uptime.

But providers are no longer satisfied with merely having terabytes of virtual storage for their clinical and administrative information available to them.  They want to be able to access and analyze it in a speedy, secure, and sophisticated manner.  Can big data analytics become a viable cloud service offering, too?

Cloud EHR vendors like athenahealth and Practice Fusion, both of which have seen success in the general marketplace, may say that the question has already been answered.  Both companies offer real-time analytics capabilities that draw on the huge pools of user data held in centralized repositories, ready for slicing and dicing into actionable insights.

Health information exchange organizations like Maine’s HealthInfoNet are also operating as cloud-based analytics hubs, collecting data from their member organizations and distributing information that can be used for population health management and clinical decision support.

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For providers looking inward, however, the options aren’t quite as clear cut.  Many organizations with server-based EHR systems or in-house data warehouses suffer from a condition known as being “information rich but knowledge poor,” write researchers from the University of Ontario Institute of Technology in a study published in the Journal of Medical Internet Research.

“Over the past few decades, our society has transitioned to a state where bottlenecks have shifted from a lack of data to limitations in extracting meaningful knowledge from an abundance of data and subsequently using that knowledge to drive decisions,” the authors write.

Huge volumes of clinical data added to EHRs at every moment cannot be quickly and thoroughly translated into concrete, timely clinical decision support (CDS) information due to the limited computing resources of most healthcare organizations.

Cloud-based analytics-as-a-service tools may be able to alleviate those pressure points and provide real-time CDS capabilities that will improve the quality of patient care by “combining the on-demand aspects of cloud computing with the democratization of information enabled by big data analytics.”

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The researchers examined the impact of a cloud-based big data analytics framework in the NICU of a children’s hospital, one of the most challenging and data-heavy environments in healthcare.  The sheer volume of available data is staggering.

“Heart rate, respiration rate, and blood oxygen are displayed each second resulting in 86,400 readings each day,” the authors write. “A premature newborn infant’s heart beats more than 7000 times an hour, which is approximately 170,000 times a day.”

“A newborn infant’s neurological function could also be monitored resulting in multiple waveforms each generating tens of millions of data points per patient per day. Drug and nutrition infusion data from smart infusion pumps can be more than 60 different fields provided every 10 seconds. Given that these infants can have more than 10 infusions concurrently, infusion can generate more than 1 GB of drug infusion data from a single patient per day.”

Yet very few healthcare organizations have the dedicated resources and tools that can generate meaningful reporting from these enormous data sets.  Analytics-as-a-service providers do, and providers are increasingly becoming aware of it.

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