The Proposed 2019 E&M Overhaul: A Preliminary Financial Impact Analysis

Significant – and wildly varying – changes to reimbursement loom ahead.

You don’t have to be a coder to realize what the financial impact of proposed changes from the Centers for Medicare & Medicaid Services (CMS) will be, or to see that there will be winners and losers. Using 2016 data (since data from last year hasn’t yet been released), in the aggregate, winners would have received nearly a billion dollars, while losers would be impacted to the tune of $722 million in losses.

So let’s take a deep dive into the data, remembering too that this analysis is of a preliminary nature, and that I will present subsequent updates as new data becomes available.

To begin, I read with interest the recent RACmonitor article regarding the proposed 2019 evaluation and management (E&M) overhaul. Admittedly, unlike Shannon Deconda, the author of that article, I am not a coder. But you don’t have to be to realize what the financial impact of these changes might be, since CMS already went to the trouble of creating an estimated impact (both in relative value units/RVUs and dollars) for each new and established office E&M procedure code. If you look at the July 19 article, you will see the table that was published, but to make things easier, I am republishing it again below – except this time, with 2016 data. The reason for this is due to the fact that the final 2017 data set has not yet been released – but again, when it is, I will update my projections. In general, however, I don’t expect that it will change much, as there was not a significant change in RVU values from 2016 to 2017.

Table 1: Financial Impact for Office Visits based on 2016 data

COHEN1

The first column represents the E&M code, and I have limited the analysis to office visits. The second column shows the 2016 work and practice expense RVUs. The third column shows the approximate RVU value that has been estimated by CMS, and the last column (impact) reports the approximate difference in payment calculated by multiplying the Delta times the 2016 conversion factor of 35.8043.  

To calculate the financial impact, I took utilization data from the 2016 P/SPS Master File, which contains 100 percent of all Medicare Part B fee-for-service claims. I kept only the office visits codes (99201-99205 and 99211-99215) and used the allowed frequency as opposed to the billed frequency. This, then, represents only those encounters that were actually paid by Medicare. Let’s look at an example for orthopedic surgery and internal medicine. Orthopedic surgery first:

In 2016, Medicare paid 11,939 times for the lowest level of new office visits (99201). If you look at the table above, you can see that there was no impact for that code, so the net effect is zero. Next, we look at E&M code 99202, which was allowed 168,095 times. Multiply this by the impact for that code ($56.21) and you get a total effect of $9,449,082. This means that orthopedists would have been paid a total of $9.5 million more for 99202 under this proposed change. OK, let’s continue. Here is what all 10 codes look like:

Table 2: Financial Impact for Office Visits based on 2016 data for Orthopedic Surgeons 

COHEN2

So, had this model been in place in 2016, orthopedists would have realized a net positive financial impact of $135 million. Obviously, this may not be true for every such physician, as it is based on current distribution of E&M levels. Remember, CMS made it pretty clear that those physicians billing the lower-level codes would see an increase while those billing at the upper levels would see a decrease.

Let’s do this one more time, except this time for internal medicine physicians:

Table 3: Financial Impact for Office Visits based on 2016 data for Internal Medicine physicians

COHEN3

As you can see above, for internal medicine physicians, the tables are reversed, so to speak. Because the distribution for them is a bit shifted right of center, they stand to lose nearly $120 million under this model; at least they would have, if this had been implemented in 2016.

Overall, for 2016, the net impact would have been somewhere in the neighborhood of an additional $250 million paid out to physicians. My estimate for the2019 impact is almost double that, and I just don’t see CMS offering an additional half a billion dollars in payments to physicians without taking payments from somewhere else. After all, they are still bound by the plus-or-minus $20 million rule under the budget neutrality act. Like everything else in life, there aren’t any free lunches!

So, who are the biggest winners and losers? Here you go:

Illustration 1: Top 10 biggest winners

COHEN4

Illustration 2: Top 10 biggest losers

COHEN5

Unfortunately, what I haven’t seen is any talk or analyses of the impact this will have on physicians tied into work RVU-based compensation programs. In fact, having created models for that over the past 20 years, it is my opinion that this presents the biggest nightmare of all: renegotiating productivity-based compensation agreements using numbers (work RVUs) that will be nothing more than speculation for until at least 2021, when we will have enough benchmark data to even know whether those payment models work. How can you test the model when CMS is always at least two calendar years behind on its data release? Think about how hard it has been to convince physicians to trust the Resource-Based Relative Value Scale (RBRVS) database when it comes to their compensation. Now, think about how much harder it is going to be proposing an untested and unverified new model that, like we have seen above, will have positive impacts for some and very negative impacts for others.

And that’s the world according to Frank.

 

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Frank Cohen, MPA

Frank D. Cohen is Senior Director of Analytics and Business Intelligence at VMG Health, LLC, and is Chief Statistician for Advanced Healthcare Analytics. He has served as a testifying expert witness in more than 300 healthcare compliance litigation matters spanning nearly five decades in computational statistics and predictive analytics.

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