Editor’s Note: Healthcare Informatics has compiled together a five-story Special Report section on data analytics for its July/August print issue. This story, and one that can be read here about a fast analytics team at the University of Michigan Health System, are part of that special section.
More and more, across healthcare organizations nationwide, data and analytics tools are being seen as a means to improve efficiency and quality. Yet, according to one survey from KPMG LLP, a New York City-based audit, tax and advisory firm, only a small fraction of those in the industry are using these capabilities to their fullest potential.
The March 2015 survey of nearly 300 respondents who identified themselves as being employed by providers, payers, or life sciences companies, found that only 10 percent are using advanced tools for data collection with analytics and predictive capabilities. Twenty-one percent indicated that they are still only “planning their journey.” Of the other respondents, 16 percent said they are using data in strategic decision making, while 28 percent are relying on data warehouses to track key performance indicators.
Indeed, as the provider community continues to prepare for the shift to value-based care and being at risk for various patient populations, it’s as clear as ever that sophisticated analytics tools will be a necessity going forward—even if adoption levels are still currently low. Speaking to the survey’s results, Bharat Rao, Ph.D., KPMG LLP’s national leader for healthcare and life sciences data analytics, says that many organizations are not where they need to be in leveraging this technology, and that providers need to employ new approaches to examining healthcare data to uncover patterns about cost and quality.
Dr. Rao, who has more than 60 patents tied into the realm of healthcare informatics and analytics, personally looks at analytics on a “full stage,” that moves from descriptive (what happened and why it happened) to predictive (what will happen), and then to prescriptive (what I should do about it). “I will say that we have gotten pretty good at descriptive analytics,” Rao attests. “There are tools out there that do a good job of painting a picture of the near past. It’s no longer acceptable to take 45 days to get quality measure reports back out,” he says, offering an example of improvement in that area.
Bharat Rao, Ph.D.
However, Rao points to both the huge gap and potential for predictive analytics, and he notes that prescriptive analytics is something that doesn’t happen in healthcare today. “Readmissions tools do a reasonable job, but there is a big gap there. One thing that has changed over the last decade is the recognition by provider organizations that analytics is not a nice shiny toy, but something that has become increasingly important for them to survive,” Rao says.
Diving Into Uncharted Waters
It was a few years ago when David Seo, M.D., current University of Miami (UM) Health System associate vice president, information technology for clinical applications, and chief medical informatics officer (CMIO) of the Miller School of Medicine, and other health IT leaders at the health system began to truly understand the evolution of where healthcare was going. “Patient-centered medical homes and ACOs [accountable care organizations] were the trends under the main idea of managing risk,” Dr. Seo says. “I was getting multiple calls and visits from vendors offering analytics solutions, one after the other, and what became clear was they were not offering a true full suite of what a health system needs to manage risk. Our own EHR [electronic health record] vendor talked to us, but even what they could provide was limited.”
Seo says that UM Health System was looking for company that had a long track record of understanding data analytics and security, so it ended up choosing Lockheed Martin, a Bethesda, Md.-based global security and aerospace company with involvement in healthcare analytics. “We knew were headed towards a clinically integrated network and other things of that nature,” Seo says, noting the need to establish a data environment, implement big data analytics and predictive modeling tools, and start to stratify patient data and conduct risk assessments.
David Seo, M.D.
Seo agrees with Rao in that predictive analytics in healthcare “is still very much in its infancy no matter who you talk to.” Indeed, aside from the basics such as readmissions, true predictive analytics has not come to fruition, he notes. To this end, University of Miami Health System started out with a diabetes risk model, and clinician leaders have shown that the model can fit within providers’ workflows, Seo says. He adds that the risk model can be ordered through the organization’s order entry system, or it can have a patient ask to run that risk model themselves in test environments. “The risk model returns a score, so you understand your risk of developing diabetes over the next five years, for example. And now we are engaged with our clinical staff to [look at] things such as what is the threshold we would set to apply an intervention, for instance,” Seo says.
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