Advanced Revenue Cycle Analytics: An Overview

Physician practices are stuck in an unfair game.  Government and commercial payers have larger budgets, larger staffs and they make the rules. Practices can automate and improve revenue cycle outcomes.  Practice leaders need visibility into revenue cycle processes and opportunities to automate and improve revenue cycle outcomes.

Michael Lewis is one of my favorite writers.  He is a top shelf storyteller with a gift for unraveling complex subjects.  His 2003 best seller Moneyball examines how the underdog small market

Oakland A’s competed with teams like the New York Yankees on a tight budget.

Billy Beane, the A’s General Manager, was playing an unfair game.  He competed against teams with triple the money to spend on players.  By thinking differently and leveraging analytics he found undervalued players that could compete and beat the most expensive teams in Major League Baseball.

There are many parallels between Moneyball and today’s revenue cycle challenges.  Physician practices are stuck in an unfair game.  Government and commercial payers have larger budgets, larger staffs and have the advantage of making the rules (which constantly change).  How can practices keep up?  How can practices win?  We must think differently about how we play the revenue cycle game.

Traditional revenue cycle metrics measure outcomes, not processes.

The top three revenue cycle metrics tracked by physician practices are:

  1. Denial Rate
  2. AR Days on Hand
  3. % or AR > 120 Days

These are great metrics, however they all measure outcomes of the revenue cycle.  Outcomes metrics can tell you how well the revenue cycle is operating and if everything is going smoothly that may be enough for your practice.  Administrators and revenue cycle managers need more advanced revenue cycle analytics to improve revenue cycle outcomes.  We need to be able to answer questions about where are we spending our revenue cycle resources?  What types of coding changes are we making most frequently?


Revenue Cycle Processes are a “Black Box”

Many practices lack visibility into their revenue cycle processes.  We know billing and coding staff work hard.

They ensure that correct coding and paid claims.  We don’t know exactly what they do.

Before we can automate the revenue cycle process or improve revenue cycle outcomes, we need visibility to understand what billers and coders are doing, how productive they are and where there are opportunities to improve.


Top 5 Advanced Revenue Cycle Metrics

  1. Encounter Change Rate – What percentage of encounters are corrected or changed by revenue cycle staff?
  2. Encounters per Hour – How many encounters per hour can each revenue cycle employee process?
  3. Coding Change Frequency – What coding changes are made most frequently?
  4. Revenue Cycle Resources by Physician – How much revenue cycle time do we expect to spend for each physician? How much revenue cycle time do we actually spend for each physician?
  5. Risk Adjusted Coding – HCC Scores and Comorbidity Percentage – How sick are my patients? Does my coding accurately reflect patient acuity?



Revenue Cycle teams stand in the gap between competing requirements of physicians and payers.  Most physicians are quick to point out that they did not go to medical school to be coders.  They chose the profession of medicine to help people, not to satisfy the coding requirements of government payers and commercial insurance companies.  On the other side payers have very strict and ever changing coding requirements necessary to prevent fraud and ensure that claims are paid accurately and fairly.

In order to protect physician productivity and ensure that claims are paid completely and correctly, most practices use a manual post encounter coding process.  This means that coders, billers and or revenue cycle staff review encounter data after the clinical team has completed the note in their EHR.

Do you know what percentage of encounters billers and coders typically have to change?  This metric significantly impacts revenue cycle staffing levels, physician to coder ratios, charge lag and practice profitability.

The average practice makes encounter changes on 52% of all encounters.  These changes are usually a combination of coding corrections and billing changes.  Coding corrections may be linking CPT codes to medically necessary diagnosis codes, adding correct modifiers, or correctly billing global periods.  Billing changes to comply with payer specific preferences include splitting bi-lateral procedures to two lines, using G codes when appropriate or correctly billing New vs Established Patient E&M codes.

All of these encounter changes are necessary evils in the fee for service world.  If practices want claims paid completely and correctly, the first time these demands must be met.  Don’t turn physicians into coders.  Billers, coders and revenue cycle staff ensure that all of these requirements are met.

Most practices expected billing staff to decrease as a result of their EHR, but are unable to unlock these time savings.  Some practices report an increase in billing staff as a result of their EHR adoption.  One practice called me reporting that coders were correcting nearly every encounter created from their EHR.  The physicians did not want to take the extra step required in the EHR to correctly associate each encounter line item with a specific active problem. Rather than adding staff, they decided to automate the revenue cycle without changing the clinical workflows.

The first step to improving revenue cycle productivity and outcomes is knowing where your practice currently stands on these advanced metrics.  Encounter change rate is a particularly powerful metric when compared with average encounters per hour per revenue cycle employee.  The next step is to drill down and identify which encounter changes are occurring most frequently and how to automate those changes in order to improve revenue cycle outcomes and productivity.



One of the most critical advanced revenue cycle metrics measures revenue cycle productivity.  Many revenue cycle directors operate in the dark, because they do not have a good benchmark for how productive their staff should be.  If everyone on your team processes 20 encounters per hour, and your superstar can process 30 it is easy to assume that 30 may be the maximum anyone can do in your practice.

How many encounters per hour can the average biller process?

Encounters per hour is always impacted by the Encounter Change Rate metric.  Practices with higher post-encounter coding burdens will always be slower than practices that receive high quality encounter data from clinical staff.  Billing managers and revenue cycle directors gain valuable insight into staff efficiency by combining Encounter Change Rate and Encounters Per Hour.

The highest time savings returns for practices on currently on the low end of this metric.  Improving from 25 to 100 encounters per hour, saves the average 10 provider practice up to 12 hours per day or 1.5 FTE!  The same improvement moving from 100 to 175 encounters per hour saves an additional 2.25 hours.  First time revenue cycle automation creates the biggest return in staff time savings.

It is important to consider the Encounters Per Hour metric in the context of coder workflow.  Coders who have to touch a higher percentage of encounters to make billing or coding changes will not be able to process as many encounters per hour.  It is always helpful to use Encounters per Hour and Encounter Change Rate together.  There are several external factors that could impact Encounter Per Hour that are outside the control of the individual coder.  Encounter workload distribution can also impact encounters per hour.  If certain revenue cycle staff process encounters for a difficult payer, a physician who codes worse than his peers or the majority of the surgeries that employee will not be able to process as many encounters per hour as their colleague who reviews mostly office visits.

The first step to improving revenue cycle productivity and outcomes is knowing where your practice currently stands on these advanced metrics.  Encounters Per Hour is a particularly powerful metric when compared with Encounter Change Rate.  The next step is to drill down and identify which revenue cycle team members are most productive.  Then decide how to leverage those best practices across the team to improve the performance of the entire department.



Once a practice measures Encounters Per Hour and Encounter Change Rate, the next step is to drill down and understand the details underneath those Advanced Revenue Cycle Metrics.  These details are critical for improving revenue cycle outcomes, but are very difficult to grasp without the underlying data.

This is a frustrating challenge for many revenue cycle managers.  The staff produces excellent denial rates, but practice leadership does not want to add headcount as the practice continues to add physicians.  Their billers and coders work hard and always have full plates, but they do not have the data to see what types of work is taking up the majority of their time.  Billing managers can ask their teams for information on what types of coding changes they make most frequently, but this can be very difficult to articulate and nail down.  It feels like trying to nail jello to the wall!

Another advanced revenue cycle metric, Coding change frequency quantifies which types of coding changes are made most frequently and then drills down to look at the specific circumstances that necessitate these changes.  Do you know what types of coding changes your billing and coding teams make most frequently?

Let me share one example of a revenue cycle manager using advanced analytics to improve revenue cycle outcomes. A multi-specialty practice uses EHR to generate encounter data, which is reviewed in AccelaSMART before posting to their practice management system.  This practice uses dummy codes to track appointments canceled appointments, rescheduled appointments and no-shows.  The EHR generates the dummy code, but does not send a diagnosis code.  The PM system requires the dummy CPT code to have a linked diagnosis in order to post correctly.  To her horror, the manager discovered that her staff were manually correcting this problem over 800 time per month.  Her limited coding resources were spending their time adding a dummy diagnosis to a dummy CPT code.

Changing coder behavior can be an even bigger challenge.  When the manager asked her coding staff about these encounters, they replied that it did not take them very long to add the diagnosis code.  These were $0 encounters and every second spent on these encounters was a waste of time and resources.  Thankfully the manager had access to the data and was able to identify the high frequency changes and put in an automated process to ensure that the change happened every time without wasting valuable coder time.




Many coding and billing managers have great intuition.  They know off the top of their head which physicians require the most coding help.  Proving this with data can be a bit more difficult.  Most physicians are competitive and love data.  If you want to improve revenue cycle outcomes and revenue cycle productivity you need advanced metrics to uncover potential areas for improvement and data to support your recommendations to practice leadership.

A standard metric that most practices use Physician to Coder ratio.  While this is a helpful metric, it over simplifies the work required to keep the practice financially healthy.  This metric assumes that all physicians consume an equal amount of revenue cycle staff time and that is rarely true.  Based on encounter volume, specialty and encounter change rate there are some physicians that consume a larger amount of revenue cycle resources.

Do you know much revenue cycle time each physician requires?  Do you know how much revenue cycle time you expect each physician to require?

When revenue cycle managers quantify the difference between expected and actual revenue cycle time per physician they typically have one of two responses.  Either “I knew it!” or “I am so surprised!”.  The ability to validate the gut intuition and quantify where revenue cycle resources is an incredibly valuable first step to improving revenue cycle productivity and outcomes.

Combining advanced metrics allows managers to drill down to identify opportunities to improve and automate the revenue cycle process.