Top 5 industry challenges of 2016
Challenge #2: Turning data into action
While the shift to value-based payment is challenging, utilizing technology can help ensure success in this type of reimbursement model. For example, payers might be able to use data analytics technology to identify at-risk members so that providers can intervene before serious problems occur. Or, seamless data exchange between electronic health records (EHRs) might help primary-care providers stay up-to-date on patient health issues, such as recent emergency room visits.
Many organizations, however, are struggling to realize technology's full potential, according to the survey. When asked, "What is your most pressing information technology problem" the answer that received the highest percentage of responses was, "turning data into action."
This does not surprise Anita Nair-Hartman, vice president of strategy and business operations for payer business at Truven Health Analytics. "Lots of organizations or companies can take information and put it in sort of a data repository, but asking the right questions of that data and pulling out the right information so that you can use it to help determine your business decisions--that is really where it becomes difficult," she says.
One reason it is so challenging is that it requires a specific skill set, says Nair-Hartman. "We have noticed that it is becoming more difficult for health plans to find the analytically oriented and business oriented individuals who can bridge that gap to help answer these questions," she says. " ... A computer can't do that—there's no easy button, it truly does require some expertise in understanding not the business, not just the data, but also the analytics, and putting those three pieces together."
Scott says another major barrier to creating actionable data is lack of seamless information exchange between payers and providers and within health systems. While health plans have a large amount of administrative data at their disposal, such as members' basic demographic information, diagnosis codes, and procedure codes, they lack easy access to clinical data, he says. If they had it, and it could be easily combined with administrative data and shared with providers, providers would be able to better identify and address patients' health needs in real time, he says. "Certainly the data in a health plan data warehouse is not nearly as actionable as it would be if it were combined with more of the clinical data that you get from a provider electronic health record."
Larry Yuhasz, national practice leader, population health, at Truven Health Analytics, agrees that lack of information exchange within individual healthcare systems is a key aspect of the problem. "There's still a high number of siloed pieces of data across the enterprise, so if you look at a typical health system, you'll find that they have multiple [EHRs], they have different registries for different types of disease groups, they'll have cost and revenue cycle management systems in different silos ... " he says.
So what can health plans do to improve data exchange and application? Here are some recommendations:
Move toward a single data repository. Most plans have many data repositories that are accessed in different ways by different departments, says Nair-Hartman. "I think that they are starting to, and they need to continue this investment of having a single source of health information to help drive their business," she says.
Improve plan-provider collaboration. "Only by working together can we consolidate and normalize and utilize the various sources and types of data that are required to really have actionable information," says Scott.
Make sure payment aligns. "The financial integration will support clinical integration," says Scott. "... If the money is suggesting that plans and various types of providers need to link arms in a more collaborative way to focus on the information required to make the new value-based payment system work for all the stakeholders—that's big."
Have the right governance. Managed care organizations must understand each data source they have, who's responsible for each data source, and what activities they are responsible for within each data source, says Yuhasz. "It's hard work to sit around and identify [governance] and to build it into the organization," he says. "But those organizations that treat it as best practices do very, very well analytically. It's kind of like the roll up your sleeves part of making sure that you get actionable data coming out of your systems."
For more tips, visit bit.ly/analytics-action.