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    Timothy Smith, CPA, ABV





















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    Many healthcare systems pay their physicians based on data from physician compensation surveys or on valuations prepared using such data. Use of survey data alone in setting physician compensation levels, however, can lead to practice losses. Health systems that fail to consider the full range of economic factors are at risk for being in the red.

    Survey data are designed to provide information in statistical format (also known as descriptive statistics) about survey respondents. As such, surveys are highly useful for benchmarking, providing a range of data for comparison relative to the survey population.

    The problem arises in the ways survey data are used in a physician compensation setting for health system practices. There are two broad approaches for using survey data in today’s marketplace.

    In the simplest approach, a specific percentile for compensation is selected as the appropriate level to pay a physician. Typically, the median or 75th percentile is selected for total compensation or the compensation per wRVU ratio. In practice, these rates may be blanketly used for all physicians with no consideration of the physician’s practice. Such blanket use of percentiles is usually supported by claims about market conditions, regulatory guidance or appeals to common practice.

    The second approach attempts to match compensation with a physician’s production, typically based on wRVUs. A common form of matching involves paying a physician at the total compensation percentile that corresponds with the physician’s benchmark level of wRVU production. For example, a physician whose benchmark is at the 65th percentile for wRVUs is paid at the 65th percentile for total compensation.

    Another form matches the compensation per wRVU ratio to the benchmark level of wRVUs — under this variation, the physician whose wRVUs benchmark at the 75th percentile is paid at the 75th percentile compensation per wRVU ratio. Yet another form uses the median compensation ratio for the quartile of production level for the physician as the blanket rate for paying a physician.

    This second approach is based on the belief that production is the only critical driver of compensation levels in the marketplace. No other economic factors affect market compensation for physicians. So, if you can track or benchmark a physician’s wRVU production, you can predict the market compensation for that physician.

    This view, however, does not square with the reality of the data. Figure 1 provides a scatter-plot diagram from MGMA’s pay-to-production plotter report from the 2016 MGMA DataDive Provider Compensation for noninvasive cardiology.

    The diagram maps the physicians who submitted wRVU and total compensation data. Each dot represents what a physician made in total compensation (x-axis on the left) based on the level of wRVUs he or she produced (y-axis at the bottom).

    The diagram also plots the three commonly used methods for establishing compensation based on wRVU production:

    • The compensation match black line plots the compensation level based on matching percentiles of total compensation with percentiles of wRVU production.
    • The ratio match green line plots the compensation level based on matching percentiles for the compensation to wRVU ratio with percentiles for wRVU production.
    • The quartile tool blue line plots the compensation level based on using the median compensation to wRVU ratio for the quartile of wRVU production level.

    As the diagram indicates, all these methods plot a line through a widely arrayed dataset in which most physicians plot well above or below each line. No line encompasses or reflects the majority of the actual datapoints.

    What does this mean? At any given level of wRVU production, physicians earn a wide range of total compensation. Production based solely on wRVUs is not a definitive predictor of what a physician will earn. In other words, these commonly used methods do not correlate with the data.

    Not only are these methods out of sync with the data, they also ignore other factors that can impact the economics of a physician practice, including:

    1. Payer rates in the local market
    2. Level of ancillary net earnings
    3. Use of nonphysician providers in the practice
    4. Compensation from various nonclinical services, such as call coverage, medical directorship, clinical co-management and research
    5. Compensation from alternative payment models, quality bonuses and pay-for-performance plans
    6. Cost structure, cost efficiency and cost allocation
    7. Group practice compensation models that distribute group

    Applying these survey-based compensation methods and ignoring other factors affecting the economics of a physician practice is a formula for losing money. A practice cannot simply pay its physicians in the abstract using only these methods and not run the risk of practice losses.

    Physician-owned practices have to live within their means. Thus, we do not observe the loss pattern as we do in health system practices. This difference in outcomes is also reflected in survey data usage by physician-owned practices. They may benchmark their compensation levels, but they cannot afford to turn benchmarking into the basis of their compensation planning.

    The opposite is frequently true in health system practices: benchmarking sets compensation levels, while overall economics are set aside. But, as many health systems have found, their practices are always in the red.

    How much can you afford?

    As reimbursement pressures continue to mount, health systems need to look at every aspect of their operations and ask critical questions. These reviews frequently lead to the physician enterprise, where health systems lose money on their employed physicians.

    Today, many health systems are starting to ask whether they can afford to underwrite their physician enterprise on an ongoing basis. Can the health system sustain current losses into the future? At what point can a hospital system no longer afford to subsidize physician employment on a large scale?

    Many industry participants respond by saying practice losses are inevitable. They’re simply a cost of doing business. Some participants will also point out that many hospital departments lose money, and physician practices should be viewed no differently.

    Others will say that physician practices are “loss leaders” or that health systems ultimately make up the difference elsewhere in the overall enterprise. “At the end of the day, it’s all one pot of money,” they say.

    It should be apparent that offsetting practice losses by inpatient and outpatient referral profits from employed physicians is a bad idea. Crunching the numbers for individual physicians or groups is a key allegation in certain high-profile and costly whistleblower cases. Losses should not be justified on this basis. A health system, therefore, needs to think about its physician enterprise apart from referrals.

    Indeed, talk of “loss leaders” and “making it up elsewhere” may lend themselves to accusations of talking in “code” or using euphemisms for payments for referrals. From an enforcement risk perspective, health systems are better off avoiding such thinking when evaluating their practice losses.

    Yet, the common view that “everyone loses money on their physicians” leads many industry players to view practice losses with a sense of inevitability and even futility. A financial dilemma emerges, however, when the health system contemplates losses from its physician enterprise relative to flat or even shrinking revenues and profitability across other service lines.

    In response to this dilemma, some industry players downplay the issue and suggest that losing money on physician practices is a problem that will go away on its own. The physician practice as an operating entity may soon look very different. The move to value and the rise of alternative payment models may push many physicians to become employees of health systems. Reimbursement will essentially be bundled or capitated and will be paid at the health system level. In the interim, practice losses will continue to exist as long as physician practices do likewise.

    The difficulty with many of these popular responses to practice losses is that they usually cause inaction, avoidance and rationalization. Health systems console themselves with popular industry talking points about practice losses and maintain business as usual. In the meantime, they continue to lose money from their physician practices.

    This problem is particularly acute when the status quo is justified based on the idea that physician practices will soon cease to exist. Yet, it’s a slow-moving train to value. How many millions can a health system afford to lose while waiting for this train to arrive at the station?

    This view also involves significant risk because no one knows what the long-term future of U.S. healthcare will look like. In the meantime, health systems must tackle practice losses head-on and address the issue, rather than avoid it or hope it simply goes away.

    The first step is to identify the specific reasons why individual practices lose money. You cannot address the problem if you don’t know the sources of the problem.

    Dig deeper

    MGMA DataDive is the premier data benchmarking tool in healthcare. Access the industry’s largest benchmarking datasets in topics such as compensation, operations and cost and revenue. For more information on how MGMA DataDive can help your organization, contact or call 877.275.6462, ext. 1801.

    MGMA Insider podcast

    Timothy Smith, CPA, ABV, principal, TS Healthcare Consulting LLC, considers himself a skeptic of myths in healthcare, especially when it comes to physician compensation. He recently joined MGMA senior editor Daniel Williams, MBA, MSEM, on the MGMA Insider podcast to talk about his time working in physician services and physician development during the 1990s, amid a frenzy of transactions around primary care practices — and how it prompted him to “drill into the numbers” and study what was actually happening to confirm his healthy skepticism. “We have a data problem in healthcare,” Smith says. “And the problem isn’t the data we have — it’s the way we use it and really abuse it.” Hear the full conversation:

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