With EHRs now largely ubiquitous, healthcare organizations are rapidly amassing data and are ready to realize the payoff with predictive analytics. In fact, 86 percent of healthcare business decision makers plan to be using predictive analytics in the next 12 months, compared to just 34 percent who said the same in 2016, according to a study conducted by Intel and Forrester.
"We've invested hundreds of millions, in some cases billions, of dollars into these digital systems, and job one was getting them implemented," Andy Bartley, senior solutions architect at Intel Corp., said at the Becker's Hospital Review 3rd Annual Health IT + Revenue Cycle Conference in Chicago. "But now with the dust starting to settle on that, the question becomes what do we do with the data we've created? How do we extract more ROI from that asset that we've invested in?"
Using data to drive operational efficiencies in clinical systems is step one, according to Mr. Bartley. However, he believes predictive analytics can be used for much more — it can help hospitals and healthcare organizations operate smarter, control costs better, improve patient access and enhance outcomes.
Many organizations that are already using data for simple trend analysis, to identify outliers and their causes and for other small projects, struggle to make the jump to predictive analytics. Mr. Bartley identified four common areas healthcare organizations should focus on to make the jump into predictive analytics.
1. Scalable infrastructure. The availability of data in healthcare is fairly new — major EHR adoption has happened only over the last six years, Mr. Bartley notes. Because of this, a lot of innovation is happening in the predictive analytics space and the pace of change is not likely to slow any time soon. Mr. Bartley advises hospitals to look for flexible, extensible analytics platforms that are able to support new types of frameworks and technologies as the industry evolves.
2. Executive sponsorship. However, choosing the right infrastructure is not the most difficult part of adopting predictive analytics. "In many ways, the technology problem is the easiest part to solve," Mr. Bartley said. "The organization and political challenges end up being the major road blocks to adoption." Executive sponsorship is what Mr. Bartley calls the "make-or-break factor" for long-term success with predictive analytics. Many organizations often begin projects in the IT department, but work to gain buy-in from the chief medical information officer to ensure balance between the clinical and technical views.
3. Change management. About one in four healthcare organizations struggle with clinical workflow integration, according to the Intel-Forrester study. Mr. Bartley believes this challenge boils down to two main issues: engagement and adaptability, or changing workflows in real-time. The best way to optimize change management is through communication, according to Mr. Bartley. This means ensuring there are strong feedback loops between clinical and data science teams. It also means managing expectations within the organization as a whole so staff doesn't expect too much too early and lose motivation.
4. Use case selection. The best strategy to use with predictive analytics is to "balance quick wins and big bets," according to Mr. Bartley. This means finding quick 10-week projects with real results to demonstrate the utility of the technology, while simultaneously building momentum and working on longer-term projects that truly define the importance of the predictive analytics team in the organization. The key with use case selection is choosing cases where the hospital has sufficient data and to pick cases with few external factors that could limit the overall impact of the project.
"We expect that [predictive analytics] is going to start to become table stakes in the future," Mr. Bartley said. "There's a ways to go before we get there, but the industry is definitely moving in that direction."
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