Knowing When to Pay Physicians

Before compensating a physician for taking call or serving in a leadership position, it’s important to ask if it’s reasonable to pay in the first place. Though more and more hospitals are compensating doctors for these types of services, it’s still not a given that coverage will be compensated. It’s common for hospital executives to scratch their heads when approached by a physician asking for additional compensation, especially since this information is not widely available.

MD Ranger addresses this challenge by analyzing contracts not in our database. Though at first it might seem counterintuitive, the idea is straightforward: because we collect data on our hospital partners’ physician contracts holistically, we can determine if there are services the hospital provides that do not have a contract in place (or have a contract that doesn’t compensate the physician).  From this, we calculate what percentage of our subscribers pay for a given service in the first place.

Here are the five most common positions that hospitals report paying physicians to perform:

  1. General Surgery
  2. Orthopedic Surgery
  3. Neurology
  4. Neurosurgery
  5. Otolaryngology

Curious about what percentage of our subscribers pay for these above services? Email me at This email address is being protected from spambots. You need JavaScript enabled to view it. and I’ll send you more info.

allison

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Comparing Apples to Apples While Using Market Data

Market data can be a fast, reliable, and cost effective way for hospitals and health systems to benchmark their contract rates.  

It is important to assess the comparability of market data to the job description of the benchmarks and your facility’s position.  A broad dataset of specialties, directorships, and administrative positions allows better documentation of market rates.  Leadership positions, such as chief of staff or EHR/IT implementation, often do not vary by specialty.  Using a rate for a specialty-specific directorship might set a rate that is not fair market value.  MD Ranger reports dozens of medical directorships and coverage positions as well as leadership positions and ad hoc positions such as committees, teaching, and quality initiatives–giving its subscribers the opportunity to make accurate comparisons.

julia 110

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Outliers: Useless or Critical?

“I don’t see the logic of rejecting data just because they seem incredible.”  –Fred Hoyle

An outlier is an incredible data point, one that is much greater or much smaller than other data points. Outliers can be influential on benchmarks in a small data set, but often they have no effect.  In the case of compensation data, particularly physician contract data, there are two types of outliers:  those that do not compensate for a service (zero value) and those who represent either a very low or very high dollar value.  MD Ranger addresses the “zero” data points by reporting the percent of subscribers who pay for a service.  This statistic helps facilities determine if they should be paying for a service in the first place.

In a large data set like MD Ranger’s, an outlier will have little or no effect on the quantiles.  For example, in a dataset with 50 data points where no provider represents more than 25% of the contracts (to learn more about how we calculate benchmarks, see “Our Approach to Calculating Benchmarks and Market Data”), each data point holds only a 2% weight in the percentile calculation.  If the rest of the data falls fairly close together, then the outlier has a small effect on the quantiles.  The opposite is also true: in a small data set, each data point has a large effect on the percentile calculations and an outlier could greatly affect the percentiles.

julia 110

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Distribution of Rates in Market Data

Market data can be a fast, reliable, and cost effective way for hospitals and health systems to benchmark their contract rates.  

The distribution of payment rates can be revealing, depending on the attributes of the sample size and specific data set.  When looking at a distribution of market rates, observe the variance in the data.  If the distribution is fairly concentrated and there’s not much difference between rates, this could suggest that rates are very consistent across markets and facility types. However, even if quantile ranges are narrow, it doesn’t mean that there aren’t significant and meaningful outliers.  In a robust data set the outliers don’t impact the range significantly; however, in a large dataset the facilities over the 90th percentile sometimes represent special circumstances that may be valid reasons for higher pay.  Examples might include highly specialized or nationally recognized leaders or physicians with scope of services and hours that are significantly different.  If there is a much larger distribution of rates, it could mean many things.  If the sample size isn’t large and the rates are widely distributed, it could indicate poor quality data or disparate situations.  If the sample size is satisfactory or even large, variation could indicate many things, from a less-competitive market to variations in the types of organizations included in the data.

julia 110

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