Measuring Staff Performance - Part I
December 9, 2015

"Measurement is the first step that leads to control and eventually to improvement. If you can't measure something, you  can't understand it. If you can't understand it, you can't control it. If you can't control it, you can't improve it."                                                                             - H. James Harrington

                   

I work for a billing company.  That’s RCM (revenue cycle management) for those of you who have been in the business side of healthcare for less than five years.  Every Monday I attend a “Team Lead Meeting” where we review the status of our accounts and discuss challenges and opportunities for becoming better at serving our clients and the patients in their care.

Leadership is getting people to want to do something.  Management is the process of getting there.  Measurement is the tool that tells you how you’re doing.  Without measurement, it is impossible to know if your team is effective in their jobs.  Back in the day when making money in healthcare was much simpler and more lucrative, many docs were content to use their checkbook balance as the only measure that mattered.  Lots of money in the account meant everybody must be doing their job.

Right.

In our world we measure two things:  Speed and accuracy.  Well, that’s not really true.  Actually, we measure lots of things, all of which boil down to speed and accuracy.  Speed without accuracy equals unclean claims and harmfully affects cash flow.  Accuracy without speed is better, but is expensive and ultimately cost-prohibitive.  Ideally, we want lots of speed and lots of accuracy.  Knowing that people do what’s inspected, not expected, there are several metrics that can help us progress toward the best possible results.  By category, we look at the following...

  • Clean Claim Rates
  • Denials
  • Claim Velocity
  • Payment Velocity
  • Follow-up Success

Each of these areas includes several metrics for evaluating success.  As with all metrics, trends analysis is important, so compare results from user to user, and month to month.  In Part II, we'll break them down in detail.