Enterprise Analytics: Moving on Up
What is “enterprise” analytics? That question must be answered by providers and payers shifting to enterprise strategies, says Joe Van De Graaff, research director at KLAS. The core of “enterprise” analytics is to create a technological and management infrastructure to get an enterprisewide, accurate understanding of performance and develop a single source of truth. What it DOESN’T mean, at this juncture, is a single enterprise vendor.
Van De Graaff says that KLAS research indicates 80 percent of providers are trying to launch enterprise analytics programs, but few are trying to use one vendor or product as their foundation. “It’s unrealistic for one vendor to do it all, though many organizations might want that model,” Van De Graaf says. At this stage, pioneers are starting to shape a strategy by identifying the best analytics software for four purposes:
* EHR-focused: Tools that can extract data from electronic records
* Area-focused: Software with very specific functions like case management or clinical benchmarking
* Emerging-focused: Applications for population health care a prime example
* Enterprise-focused: A tool that standardizes the reporting and fits in with data warehouse infrastructure
The reason for the rush to enterprise analytics is straightforward: it’s all about risk, says Graham Hughes, M.D. chief medical officer at the SAS Center for Health Analytics and Insights. “The future of health care is risk-based contracts, and provider C-level leaders simply don’t know much about their risks at this point. They know that by digitizing they have created financial and process improvement opportunities, but they can’t really identify them accurately, which is a big disadvantage when they’re at the negotiating table with payers.”
The move to enterprise analytics could more accurately be described as a doubling down on information management and data governance, Hughes says.
"Right now most providers are still doing retrospective analysis, and unless they get their data tamed they can’t move into a predictive model where they can do simulations and forecasting. That’s the core of risk analysis and enables organizations to understand knowledge management life cycles and how analytics is a corporate asset.”
An enterprise analytics effort typically starts with a master data management strategy, built on a revised enterprise data warehousing plan. At Carolinas Healthcare System, those initiatives led to a organizational restructure that carved out four groups working on enterprise analytics:
* A Data Extraction group drawing information from the electronic records system for reporting.
* An Informatics shop focused on enterprise data warehousing and provisioning data for automated reporting and dashboards.
* A Client Services group focusing on project management and departmental needs.
* An Analytics group comprising biostatisticians, epidemiologists, economists and health researchers that performs the hard core analytics.
A key to any enterprise effort is that core analytics group; but finding people with those skill sets is a huge challenge because of the demand for trained analytics by multiple industries.
Joe Kimura, M.D., medical director of analytics at Atrius Health, says he has been at a disadvantage in Boston because of larger, deep-pocketed organizations targeting the same workers he needs. But he has had success nonetheless because intense data governance and management has yielded clean data and standard tool sets
"Our tools are pretty laid out and we sell the fact that it's easier to break into our organization and do more interesting analytics because analysts won't get tripped up by the intricacies of health care data. They can pull clean data instead of spending the bulk of their time trying to clean it up, which I think a lot of analysts at other organizations are being forced to do."
Hughes at SAS says that over the past year, his audience at provider organizations has changed dramatically. “When we were asked to discuss analytics, we typically spoke directly to the biostatisticians or researcher scientists, but that changed suddenly and dramatically.
"Now, we’re being asked to present to the C-suite at operations meetings. My impression is the C-level executives have come around to the idea that enterprise analytics is a matter of survival."
Equally dramatic during the shift to enterprise analytics is taking what has long been viewed as an information technology function out of I.T. John Walton, solutions manager at CTG Health Solutions, says keeping enterprise analytics within the I.T. realm is dangerous. “That's a recipe for disaster, there's really no other way to say it," Walton says.
But Kimura at Atrius Health, says that keeping analytics in I.T. creates degrees of separation from the business issues.
"Here's the thing: the I.T. department is usually not the first department to hear about clinical initiatives around diabetes or other quality efforts, but that's exactly where analytics has to be focused. We have the analytics and business requirements up-front, which connects it much more tightly with the business."
When struggling with the decision on where to put analytics in the corporate structure, leaders should ask themselves a simple question: When bad data occurs, who pays the costs?
The answer is ... not the information technology department. The observation should give organizations a clue to what direction to take, says Peter Aiken, associate professor at Virginia Commonwealth University's School of Business Information Systems Department and the owner of Data Blueprint, a data management and I.T. consulting firm.
"It's the business that pays the price of bad data, so the emphasis should be on getting analytics as close to the business as possible," Aiken says. "While any analytics effort needs those database experts to work closely with them, this isn't an I.T. project."
C-level executives know that an enterprise analytics effort can help monetize efficiencies and use a wider pool of data than EHRs to give them critical operational insights. But enterprise initiatives will fail if organizations rush pell-mell into it.
“You don’t want to boil the ocean when you’re moving into analytics,” says Hughes from SAS. “Governance comes first, and then it becomes clearer what use cases make sense for an enterprise play.”
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