Advanced Revenue Cycle Analytics: An Overview

Correctly configured EHRs and appropriate user behavior, can solve many revenue cycle problems.  However, we often see systems that create more revenue cycle headaches than they solve.  Here are the top three revenue cycle problems caused by EHR:

#1 – EHR installed, but physicians are still using paper superbills.

The most common EHR revenue cycle problem is that physicians often reject point and click EHR templates and continue to use a paper superbill.  One unintended consequence of the Meaningful Use program was the standardization of point and click templates across most EHR systems.  Many physician practices find themselves continuing to use paper superbills due to physician productivity concerns.  Physician frustration with lifestyle, productivity and financial impacts of EHR have been well documented.  The paper superbill may be faster for the physician, but the paper superbill causes revenue cycle problems (link to blog post below) of its own.

#2 – Increase in Charge Lag

Physicians who use their EHR to generate encounter billing data often see charge lag days increase, due to physician workload.  The increase in charge lag is another EHR revenue cycle problem.  Many physicians believe documenting within the EHR at the point of care is intrusive to the physician patient relationship.  In an effort to preserve the art of medicine, documentation must be completed at lunch, at night or days after the encounter.  Revenue cycle teams who used to receive encounter data the same day are now waiting days (or weeks) for encounter data from their EHR, and in some cases spending time chasing down physicians with incomplete encounters.

#3 – Charge Entry Time Reallocated to Coding Review Time

For the fortunate practice whose physicians are able to capture charge data through their EHR in a timely manner, there is still a final EHR revenue cycle challenge to overcome.  Automated charge entry was the ROI goal for many EHR implementations.  However, practices end up spending as much time reviewing coding data and making corrections behind the physician in the first place.  The average coder can review and correct 20-30 encounters per hour, with AccelaSMART the average user can correct 107 encounters per hour.