Painting by numbers: the way to go to make the right business decisions
James M. Berklan
The reasons to pursue more robust data-crunching activities keep growing. Especially for long-term care providers, who could have a leg up on other providers and payers.
Often described as an albatross necessary for payment, the Minimum Data Set could prove to be nursing homes' ticket to analytics success, which in turn could cure a variety of ills. The MDS, after all, is a unified data set to which acute care providers don't have an equivalent.
The trick is to advance from “drowning in data,” as some describe it, to deriving helpful information from it. That, however, would still be just a middle station to a higher plane: knowledge. If the information developed from long-term care data can be turned into useful knowledge, then providers, would have built a new source of power. Sounds simple, or perhaps even obvious, but many companies have taken just the first step, or even the first two, without ever reaching the powerful “knowledge” platform.
Opportunities for improvement clearly exist. This from no less an expert than Thomas H. Davenport, the co-author of “Competing on Analytics: The New Science of Winning” and “Big Data at Work,” both from the Harvard Business Review Press.
Admittedly not a long-term care wonk, Davenport nonetheless is a goldmine of information and perspective for senior care operators. “Opportunities exist,” he told a gathering of long-term care providers last week in Boston. Why? Because only about one-half of 1% of all data gathered (throughout various industries) is ever analyzed. In fact, 40% of business decisions are based on “gut” instinct, he noted.
On the face of it, that figure seems vastly overblown. However, when you get the President's Distinguished Professor in Management and Information Technology at Babson College, a former instructor at the Harvard Business School, a past University of Chicago teacher and a former Dartmouth educator telling you something, you can pretty much take it to the bank. Even if all of the above personas are the same person — Davenport.
He explained last week that statistical predictions outperform gut-based predictions, but added that expert decision-making and predictions don't really have that good of a track record, though they're improved with analytics. (To fill the analysis, intuition is the right choice only when either time is short or you've done something often enough already to know a quick, quality response.)
Since the word “analytics” is tossed around quite a bit, it's a good idea to define what's being referred to. Davenport calls it “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” A subset of “business intelligence,” analytics can be input for human decisions or they may drive fully automated decisions. Good analytical capabilities also require good information management capabilities to “integrate, extract, transform, and access business transaction data,” he notes.
Heard anyone stress the importance of good information management capabilities in long-term care lately? Exactly.
Where are you in the analytics spectrum? It can tell you a lot about your ability to compete. There are three types, Davenport says.
First, there's “Descriptive Analytics,” which formerly was called “reporting.” This is your report card, which drills down a bit but is retrospective and doesn't interpret. Then comes “Predictive Analytics,” which needs optimization, brings randomized testing into play and deals with statistical models and forecasts/predictions. A lot of business and providers find themselves here.
The goal should be “Prescriptive Analytics,” Davenport emphasizes. What's the best that can happen moving forward? It also entertains leaders who ask, “What if we try this?” and delves deeper into causes and effects.
The effect on your bottom line can be profound. When you use analytics, you're more likely to find the best customers and the best prices you can charge; minimize risks; and allocate costs more accurately. When you compete on analytics, you become more aggressive.
Think of the base of a pyramid. At the bottom, in a big section are the businesses that could be called “Analytically Impaired.” They like their gut decisions. The next tier is a bit shorter and features those who use localized analytics, followed by a tier of companies with “analytical aspirations” but aren't quite successful with them yet. Then, “Analytical Companies” are the next tier nearer the peak — “They're good at it but they're not passionate,” Davenport says. At the very last level are the “Analytical Competitors.” It's a place all business should strive to be, he says.
Healthcare providers generally rank low for several reasons, Davenport explained. The reasons include an overall lack of good electronic medical record data, an absence of full data standards, and the physical autonomy flexed by many providers. There's also a lack of communication and collaboration between payers and providers, he added with a broad brush. An inability to hire enough mission-driven skilled analysts is another hurdle.
The upside of all of this? “Healthcare is probably accelerating faster than others,” Davenport earnestly maintains.
I've written before that data is the new “currency” in long-term care, and this is where Davenport's analyses mesh nicely. “Data is a prerequisite for everything,” he told the Boston long-term care crowd. The data must be clean, common, integrated and accessible, and it must measure something new and important. These are mileposts that trip up many would-be data-gatherers.
Davenport endorsed a DELTA process, which stands for Data, Enterprise, Leadership, Targets and Analysts. Steven Littlehale, the clinical vice president at analytics firm PointRight, astutely noted that long-term care already has its own type of DELTA-type framework. A provision of the Affordable Care Act that's not fully put in play by the federal government yet, most of us know the systematic way of approaching quality and data as QAPI, or Quality Assurance Performance Improvement programs.
Time and resources are tight in long-term care, but even such taxed professions can make a good go of it, Davenport believes. To create a fruitful analytical culture, strive to deal with facts and data, he advises. “Test and learn” when there aren't enough facts already. Pushback is OK, as long as it's investigated. And remember that there is still room for intuition-based experiences and decisions.
But you have to have a process. Be a “student of error” and always examine what's gone right and what's gone wrong in post-event meetings. It is not a short process, Davenport cautions, but it is a journey worth starting today if you want to optimize your chances for success.
James M. Berklan is McKnight's Editor. Follow him @LTCEditorsDesk.