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New Revenue Cycle Metrics You Should Know

...but probably don't (yet!)

Introduction

In today's healthcare landscape, it is more important than ever to utilize your current employees well. With the workforce shortage caused by the Great Resignation, increasing practice consolidation, and constant turnover, most medical groups are struggling to find and retain employees.
Whether you are facing shortages in clinical staff, admin staff, or management, getting the highest quality performance and productivity with your current resources is the key to solving this problem. By driving productivity in your current revenue cycle team, you can leverage their expertise to fill in the gaps. Today’s best practices have discovered that they MUST find efficiencies within their current workforce to be successful.
Traditional revenue cycle metrics focused on quality and accuracy but failed to measure productivity, leading to higher operational expenses. Leveraging productivity metrics in addition to traditional metrics can increase productivity and profitability. Practices who improve these metrics can save up to $22k per provider annually.
In this book we will cover:
Note that for the purposes of this e-book, "coder" or "coding team" refers to rev cycle team members who review and/or correct coded encounter data downstream of providers, EHR, and/or CAC.

MD Perfect Rate and
Automation Rate

MD Perfect Rate

Changes driven by government regulations like Meaningful Use, ICD-10 and MIPS generate a lot of confusion around the coding review and charge entry process. Physicians spend more time than ever doing administrative work and documentation in the EHR. At the same time, the coding team spends as much if not more time correcting encounters downstream. Doctors think they are coding perfectly, while coders insist that they clean up a lot behind the doctors.
Without the data, there is no way to know:
who is right? who is wrong? and how to fix the problem.
Without the data, there is no way to know: who is right? who is wrong? and how to fix the problem. Our goal is to improve efficiencies for the coding team so that they are not cleaning up behind the doctors on every encounter without placing any additional burden on doctors.
This is where the MD Perfect Rate can provide great insight. This metric helps you understand how accurately your doctors code encounters. Analysis of the MD Perfect Rate also reveals the patterns and trends of what is being corrected so that automation can be deployed to drive higher productivity for the coding team.
Here’s the bottom line. Most coding teams spend too much time checking every single encounter, and most physicians are much less accurate in coding their encounters than they realize. This leads to miscommunication and inefficiencies for the entire practice. The national average for the MD Perfect Rate is 44.6% of encounters. Quantifying MD Perfect is the first step towards investing time only on the encounters that are not yet perfect. However, False Alarms, Unnecessary Touches and low Automation Rates are major barriers.

MD Perfect and Automation Rate

The flip side of MD Perfect Rate is the percentage of encounters that require billing or coding changes before the claim is created. On average, 55.4% of encounters created in the EHR or CAC require some level of effort to perfect those encounters. These are the encounters where we want to invest our coder time. How do we best maximize the return on that investment?
White Plume helps coding teams reach peak performance by further quantifying their Automation vs. Coder Change Rate. The more we can automate the better. However, we have to be careful. Coding is part art and part science. Knowing what can be automated and what needs to be carefully reviewed by a coder is an important point of differentiation. More coding teams make the mistake of automating too little rather than automating too much. We use detailed machine generated analytics combined with expert analysis to discover the best opportunities for automation for your practice.
Of course, not all changes made behind the EHR or CAC can be automated, and there are certain scenarios where human review is required or preferred. The average Automation Rate for top performing teams is 20.8%, which means the Coder Change Rate is 34.6%. For practices that have maximized efficiency in their revenue cycle, the coding team only has to review the 34.6% of encounters that require human intervention.

False Alarm and
Unprompted Change Rate

Every organization has multiple layers of edits or rules designed to catch and prevent billing and coding problems. Executives and physicians often assume that their staff only touch encounters that require their expert attention. However, coding staff are still spending time hunting for the mistakes that they believe their technology is not finding. Most practices use systems that do a good job with the first 90%, but do not have the flexibility, data or expert attention to solve the last 10%. This is the underlying problem that creates a lack of trust in the technology. Coding staff hunt for errors to find and fix mistakes that slipped through the cracks to prevent denials or lost revenue.
False Alarms and Unprompted Changes are the two central problems that undermine this process and create unnecessary work and accuracy gaps. The False Alarm Rate and the Unprompted Change Rate help us to measure and understand these problems.
The False Alarm rate measures the percentage of encounters where an alert fires, but the coding team does not make a change to the encounter.
Unprompted Changes rate measures the percentage of encounters where coding staff is making changes without an alert by the system.

False Alarm Rate

The effects of false alarms from an EHR on clinical staff are well documented. The high volume of alarms and repetitive alerts create cognitive overload and desensitization, making work less productive and reducing the intended efficacy of the system.1 The exact same problem lurks beneath the surface of the rev cycle process.

Average Revenu Cycle Team False Alarm Rate

The average Revenue Cycle team has a False Alarm Percentage of 15.6%. These encounters have an active alert, but no change was made by the coder. Armed with high quality data, the team can explore deeper questions about these encounters. Which alerts are most likely to be False Alarms? Can we identify any patterns by payer and code combinations? How do we actively work to reduce the False Alarm Rate?
White Plume leads practices through these questions and helps analyze behavior across coders to see if team members are responding differently in similar situations. This level of analysis can point out if the root problem is driven by the alert or by the user and helps White Plume generate highly individualized recommended solutions. Our top performing teams have a False Alarm rate of less than 6%.
While False Alarm Percentage helps identify and prevent unnecessary review of encounters that are already perfect, Unprompted Change Rate helps coders further eliminate the review of more encounters than necessary.
Top performing practices have a False Alarm Rate equal to or lower than 6%.

Unprompted Change Rate

Unprompted Change Rate helps eliminate the "last mile problem" that causes coders to review more encounters than necessary to find and fix all of the problems behind the doctor, EHR and CAC.
The best coders, billers and perfecters are like detail-oriented detectives that look for clues to solve the case. They love to find and fix problems and are not satisfied until they have caught every single one. This strength can also be a weakness that creates productivity gaps. The fear of missing something coupled with the lack of detailed data on advanced revenue cycle metrics creates an environment in which encounters are unnecessarily touched and reviewed with zero change to the encounter. A good coder rightfully asks, “How can we know if the encounter review is necessary until after it has gone through this expert review?” This is a great question that is reinforced every time the coder finds a mistake that slipped through the cracks.
The average coding team has an Unprompted Change Rate of 8.1%, which doesn’t sound that bad. After all, just missing 8% gets you an A on a high school test. However, an error on over 8% of encounters is unacceptable in the Rev Cycle of a high-volume group. To further complicate matters, coders are reviewing a huge volume of encounters to find and fix the last 8%. A team must be able to see how many encounters are being touched to find and fix that last 8%.
By measuring and tracking the Unprompted Change Rate, White Plume helps practices see what types of changes coders are capturing behind their tools. Quantifying the Unprompted Change Rate by provider and by coder, and then categorizing the data into specific types of changes allows teams visibility into what is slipping through the cracks and how to best fill those cracks going forward. White Plume helps groups apply the data to solve this "last mile problem" and radically improve their productivity.

Revenue Leakage Prevented

When most people think about accuracy in the Rev Cycle, the top metric to come to mind is denial rate. Another key Rev Cycle accuracy metric to add is Revenue Leakage Prevented (RLP). If you miss an earned charge and leave it off the claim, the payers will never tell you about this. These are the types of silent mistakes that leave revenue on the table.
One of the surprising things we learned working with top performing Rev Cycle teams is that productivity and accuracy go together. Most people (wrongly) assume that there is a tradeoff between accuracy and productivity.
Rev Cycle teams who maximize their productivity (see EPH below) through increasing their Automation Rate, minimizing False Alarms, and eliminating Unprompted Changes also outperform their peers in Revenue Leakage Prevented. Our teams are working faster AND more accurately.
RLP — The average dollar amount that the coder catches behind the doctor, capturing revenue that would have otherwise been missed and given back to payors.
The average Rev Cycle team finds $0.15 per encounter of RLP, while practices at or above 105 EPH capture $2.16 per encounter of RLP. When coders remove visual clutter and focus their attention, they are finally able to better identify and systematically capture RLP, rather than just fixing doctors' mistakes.
For a high-volume practice, RLP can be more than $8,000 per provider per year.
The difference between an average team and a top performing Rev Cycle team is $1.99 per encounter. Changes are usually made on roughly 5.6% of encounters, and these changes add up to big revenue opportunities over time. For a high-volume practice, RLP can be more than $8,000 per provider per year. Being able to quantify RLP and report on this data by provider and by coder is transformative for the way many top performing groups think about their coding staff.

E&M Change Rate

The most glamorous type of coding change that happens behind the doctor, EHR or CAC is the E&M level of service change. After all, this is a highly visible issue that earns its fair share of the spotlight.
Many coders are quick to point out the extensive training and review of clinical documentation required to accurately code level of service. Many physicians can get into patterns where they routinely choose the same level of service, or where they choose the level of service by sense of feel. Accurate E&M coding is extremely important as are reviewing frequency curves and clinical documentation improvement projects.
However, a clear look at E&M data suggests this is not the most practical use of your coding team's time and skills.
White Plume reviewed E&M change data across 16 million encounters. This data gives us two helpful insights:
  • Coders only made an E&M change on 2.7% of encounters.
  • Coders are more likely to change between New, Established and Post-op visit type than they are to change the level of service.
If you cannot quantify the frequency of E&M changes by type, by coder and by provider, you may be inefficiently allocating your resources, in the right areas that have the biggest impacts on the accuracy and productivity of your revenue cycle.

Average percent of encounters where
coders make an E&M change

Increased Encounters Per Hour (EPH) and White Glove Ninjas

Advanced Revenue Cycle Metrics provide visibility and transparency to make your process more scalable and durable. Ultimately, the best way to measure productivity for your coding staff is Encounters per Hour (EPH). This throughput metric measures the number of encounters a single coder can manage within an hour.
Each of the metrics discussed in this e-book are all vital pieces of the puzzle toward helping your coding team increase their EPH. As practices begin utilize these advanced metrics, coding staff are performing at levels 5 times more efficiently than the average practice. At White Plume, we have appropriately named them White Glove Ninjas!
The national average is 21 EPH; however, White Glove Ninjas perform at a minimum of 105 EPH. White Glove Ninjas are incredible assets to their team. Not only can they handle huge encounter volumes, but they also do a better job of Revenue Leakage Prevented per encounter. To find out more about measuring this metric in your practice, download a copy of our e-book, What the Heck is EPH?!
White Glove Ninjas make their teams more scalable and more durable. For fast growing organizations who expect to increase encounter volume through organic growth or acquisitions, White Glove Ninjas can handle the growth without additional hiring needs. Other practices struggling with turnover due to the Great Resignation use White Glove Ninjas to improve productivity and eliminate the need to backfill a position.
National Average EPH — 21
White Glove Ninja EPH — 105

Conclusion

Organizations with good visibility into Advanced Revenue Cycle metrics have actionable data that allows them to grow their practice or weather the storm of constant turnover without any risks to cash flow. Practice leaders who measure and improve these five metrics are ahead of the curve, and they turn their practices into top- performing practices with the ability to scale and grow.
If you want help measuring these Rev Cycle metrics in your own practice, White Plume can help. Through the White Glove service, we monitor these metrics for you, create a tailor-made plan for improvement, and help practices improve their productivity by 5x within 6 months or less.
If you'd like to learn more about the baseline for these metrics in your practice, contact us today for a free consultation.
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  • Contact us at whiteplume.com/contact

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About White Plume and White Glove Practice

We work with large practices who want to measure and improve EPH. For practices who are a good fit, we can guarantee a minimum EPH improvement within the first 6 months.

Schedule a White Glove Appointment

  • Email us with questions This email address is being protected from spambots. You need JavaScript enabled to view it.

  • Contact us at whiteplume.com/contact

  • Call us at 877.633.7226 x134