The healthcare industry is in the process of moving to a performance-based model of reimbursement after decades of pay-for-service. As part of this move, healthcare organizations and providers are searching for tools that help identify risk before it manifests itself in the form of preventable readmissions or procedures. This is where healthcare and analytics intersect as well as where many healthcare organizations have the potential to choose the wrong healthcare analytics tool.
“One of the biggest challenges is that everybody everywhere now is using the word analytics,” says JaeLynn Williams, Senior VP of Client Operations at 3M. “Everyone is doing big data and healthcare analytics. As an industry, it’s very hard to figure out exactly what you’re evaluating, what you’re buying, what’s real, what’s of value today, what takes incremental investment like developed resources or content experts on top of it.”
According to Williams, the buzz around big data and analytics in healthcare circles is giving the impression that the marketplace has products that can deliver fully on the promise of this emerging technology:
There isn’t today this gorgeous, one-size-fits-all, uber analytics solution that you’re going to buy and it’s going to be the magic eight ball and you’re going to be able to put something in and it’s going to spit out all of your needs on the other side. But I think people are marketing and talking about things in that way. It’s just like any topic: You need to get familiar with it, understand what you’re talking about, and then be able to make wise strategic plans and decisions from there.
So what should a health system or hospital do to avoid investing in the wrong healthcare analytics tools?
First things first, healthcare organizations need to convene a selection committee that includes the CIO and CMIO along with staff already familiar with using similar tools. An organization participating in meaningful use can turn to those working on clinical decision support. “If your organization has a strong decision support, that is a huge place to go because they understand the data. They understand the content; they understand the workflow. Those people should really be brought into the picture,” adds Williams.
Secondly, a health system or hospital must ensure that they have a “big, large, and meaningful” data set that can be accessed efficiently for the purpose of real-time analytics. Coupled with that, privacy and security measures need to be in place to provide conditions of trust, says Williams.
Lastly, healthcare organizations and providers should carefully consider any analytics vendor’s experience in healthcare, domain or content expertise. “Nobody has it across everything, but if you don’t it’s very hard to get and make something that’s applicable and gets to the problems that we’re talking about,” emphasizes Williams.
In the end, the successful selection and adoption of healthcare analytics tools and platforms comes down to being able to show tangible benefits, not just the promise of returns.
“We have to show the financial benefit, and we have to get to where the money is,” Williams explains. “A lot of the data collection or input today is done manually. It takes a lot of people time. Through automation using tools like natural language processing, we have the opportunity to streamline this so you’re removing the labor required for the analytics as well as the avoidable cost of care.”
The use of big data and analytics in healthcare is inevitable. However, similar to other health IT tools and services, healthcare analytics needs time to mature. While certain solutions are currently demonstrating the ability to affect the healthcare cost curve (e.g., readmissions, accountable care), more robust tools need time to emerge to have farther-reaching effects.
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