Most physician payment rates fall within a reasonable market range. But in some cases, a payment rate may be well beyond the norm. These unusual payment rates, which can sometimes impact benchmark calculations, are outliers. Understanding how outliers affect market data and what to do if your contract falls outside market ranges is an important aspect of a physician contracting compliance program.
Evaluating Data Quality
The distribution of payment rates can reveal market trends or anomalies. If the distribution between benchmark quantiles is fairly concentrated, this could suggest that rates are consistent across markets and facility types. Even if quantile ranges are narrow, it doesn’t necessarily mean that there aren’t meaningful outliers within the sample; however these outliers are not affecting the benchmark values. Good examples are medical directorship hourly rates. If the majority of hourly rates of a cardiology medical directorship are $150 per hour, but there are several facilities that pay $250 per hour, the facilities that are paying more may be paying fair market value for the duties being performed, but they do not affect the benchmark values because they are accounting for the values above the 90th percentile.
If there is a wide distribution of rates, it could indicate several things. If the sample size isn’t large and the rates are widely distributed, it could indicate inadequate data. It could also represent contracts that are not comparable, either because scope of services varies or hospital characteristics are too different (e.g. rural versus trauma or academic medical center). If the sample size is adequate, variation could indicate real differences between individual circumstances or types of organizations.
Even within a large sample size, contracts over the 90th percentile sometimes represent special circumstances that justify higher pay rates. Examples might include highly specialized or nationally recognized physicians, a broader scope of service, and/or higher hours due to program start-ups etc. Most FMV experts consider payment rates under the 75th percentile to be reasonable, assuming adequate justification of the position and time spent. However, payment rates over that level are not by definition inappropriate, they just require particular documentation of the reasons why a higher payment is justified.
An outlier is a data point that is either much greater or smaller relative to the sample. Outliers can be influential in a small data set, but in a robust sample they rarely have an effect. For compensation data, particularly physician contract data, there are two types of outliers: those that do not compensate for a service (zero value) and those that 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 instead of including zeros in the quantile calculations. This statistic can help facilities determine commercial reasonableness of a service. We advise first determining if payment is necessary, and if it is, then the benchmarks reflect the range of market payment rates. We also validate outliers with our subscribers to ensure there were no input errors with the survey process.
In large data sets 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, each data point holds only a 2% weight in the percentile calculation. 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. To learn more about how we calculate benchmarks, see “Our Approach to Calculating Benchmarks and Market Data”. In small samples, the addition of a single data point at the high or low end of the market ranges can have a major impact on the benchmark ranges.
Here’s an example. If five different providers independently negotiate a rate of $150 per hour, then we would report all four percentile values as $150. If the data consisted of four values of $150 and one of $500, then we would report $150 for the 25th, 50th, and 75th percentiles, and $325 for the 90th percentile (325 is midway between 150 at 0.80 cumulative weight and 500 at 1.0). Here’s a graph showing the effect:
Negotiating an “Atypical” Rate
Most physician payments are straightforward per diem or hourly rates. However, many organizations have a few contracts that exceed the comfort zone set by the compliance policy. Whether the complexity arises from market conditions, such as limited supply or burdensome call, the scope of services of the position in question, or the credentials and experience of a program director, your organization is responsible for finding an appropriate and fair payment rate.
After evaluating the sample size and variation of available market data, as well as the specific requirements of the contract, you may be able to determine if payment rates can or should be documented with market data. It could be the case that using another valuation method or engaging an expert who can objectively document the FMV for the particular situation is required.