Snipes and Revenue Cycle Key Performance Indicators – Don’t Take the Bait
[A snipe hunt is a type of practical joke that involves experienced people making fun of credulous newcomers by giving them an impossible or imaginary task.]
With summer now upon us, I think of those lazy summer camp days in Texas as a kid and the ritual of the snipe hunt. In the simplest of terms, a snipe hunt is a hazing tradition where older campers initiate younger campers to “hunt” that illusive small bird, the snipe, shortly after dark on a moonless night with flashlights and burlap bags. The funny thing is that snipes actually exist – just not in central Texas.
What does this have to do with revenue cycle key performance indicators (KPIs)? A lot, these days. We are now in the era of big data, with a virtual endless supply of structured and unstructured data related to the performance of our hospitals and physician practices. Large healthcare organizations have implemented enterprise-wide EHRs and related administrative support systems for patient registration and scheduling, billing, population health, quality reporting, and other specialized functions. These systems come with countless “canned” reports and hundreds of data points. But finding what is important is like finding that illusive snipe. As we all know, data does not always equal actionable information. That’s where business analytics come into play. Healthcare organizations often “bolt-on” companion software applications to their enterprise system so they can find what’s important and actionable in all of that data – in short, making it easier to find a snipe.
(Full disclosure: I’m a data junky. I love business analytics and the focus it can bring to organizations who use it well, but it is really easy to lose sight of the forest for the trees.)
Now, one of the great thing about being a consultant are the great organizations and executives I have the opportunity to work with on a variety of engagements. Over the last couple of months, several colleagues and I have had a chance to work with a great healthcare organization that serves millions of patients across multiple states. The focus of our work was a high-level assessment of professional billing operations for a medical group with a nine-digit annual revenue stream – a big and complex operation. Like most big organizations and medical groups, they have an enterprise-wide EHR with associated administrative support systems, centralized billing with hundreds of employees, a companion business analytics software application, and a passionate and engaged leadership team. Top-level KPIs are good, but they wanted to get better and lower costs – a common 21st century healthcare goal.
As we started our work, it became obvious to us (as the outsiders) that data governance and discipline were needed. Staff members were working really hard preparing weekly dashboards for operating units, supplying data for monthly financial statements and corporate scorecards, and managing the canned system reports. The problem was that too much information was available. Our feedback on business office operations from the clinical executives was good, but a general theme was, “We don’t have time to deal with all of those reports – it’s not actionable.” The corporate scorecard tracked 18 separate KPIs. The weekly divisional dashboard had 10 KPIs. Their impressive analytics software had 27 KPI “views” with a staggering 103,702 discrete filter view options. To help professional workflows, they had set up nearly 10,000 “to do lists” within their billing system. Big data is alive, well, and flourishing. And, who knows, there might be a snipe somewhere in those millions of data points.
The challenge we faced was getting our arms around all of the data to benchmark the client to national norms for professional billing. We chose the KISS principle (a termed coined by the Navy in the early 1960s) and focused on outcomes.
Besides cash (because making payroll is important to most organizations), the most important professional billing KPI is the Days in Accounts Receivable (AR Days) that remain uncollected for a practice. Jim Denny in his 2013 publication and Mark Coronella in his 2014 publication also agree AR Days should be a major focus of medical practices. In addition, several national organizations, such as MGMA, AMGA, and the Faculty Practice Solution Center benchmark AR Days in their survey data. Collectively, these organizations reported AR Days on more than 250 medical groups representing thousands of healthcare providers. AR Days makes benchmarking to other groups more manageable than other revenue cycle metrics such gross collection rates, which can vary widely because differences in where professional charges are set, payer mix, and confidential managed care contract rates.
In the KPI world, AR Days should be viewed as a lagging indicator for a practice’s professional billing process – in short, an outcome to what is going well or not going very well. Other KPIs, such as denial rates, charge entry lag days, or point of service collections should be viewed as leading indicator for professional billing. Each of these leading indicators -along with others – are important to revenue cycle performance, but they all impact AR Days in a positive or negative way.
We suggested to our data-rich client that they become more disciplined and simplified around data governance and focus on actionable, top-of-mind issues that impact AR Days. As they evaluate business initiatives to improve their revenue cycle performance, they should evaluate each of these programmatic endeavors with the simple question, what will it do for my AR Days?
Randy Jones is senior vice president of Culbert Healthcare Solutions.