MGMA DataDive is an excellent resource to help you make the decisions needed to improve your operations. The following tips will help you understand and interpret the data within MGMA DataDive Cost and Revenue.
Money coming into the practice: above the median
The median is the midpoint of the data submitted, laid out from lowest to highest.
Using the values of $150k, $200k and $400k, the mean would be $250k ($150k + $200k + $400k/3 = $250k) and the median would be $200k.
Metrics such as A/R aging buckets and payer mix provide a complete picture. Benchmarking against the days in A/R aging buckets, we would expect the sum of the 0-30 days, 31-60 days, 61-90 days, 91-120 and 120+ days to equal 100%. Using the mean, we are likely to get to 100%. The median values aren't likely to equal 100% and may be substantially above or under 100%.
Centralized services found in hospital systems (billing and coding, laboratory and imaging, call center, etc.) will result in the hospital-owned practices reporting lower full-time-equivalent (FTE) staffing than physician-owned practices. The centralized staff aren’t employed at the practice level and are not captured as FTEs in the survey.
Split-fee billing occurs when a service is moved out of the practice. A hospital-owned practice may require a patient to visit an imaging center for an X-ray. The professional component is billed by the practice and the technical component is billed by the hospital, resulting in the revenue being split between the practice and the hospital. The result is a much lower revenue stream into the hospital-owned practice.
For example, if the total provider per FTE physician was 1.47 at the median and the total support staff per FTE physician was 5.85, we would not state for every 1.47 providers a practice should employ 5.85 support staff. As the cut is still based on a 1.0 FTE physician, we would only state for every 1.0 FTE physician a practice would employ 5.85 support staff.
Glossary:
MGMA always reports the data back from lowest to highest values.
How does this affect the interpretation of the data? Depending on the metric, it will change the desired benchmarking ranking. Here’s a simple rule of thumb to determine the best methodology for benchmarking your data:Money coming into the practice: above the median
- Fee for service
- Other medical revenue
- Total medical revenue
- Support staff cost
- General operating cost
- Total operating cost
- A/R Aging Buckets: A practice should aspire to be above the median in the 0-30 days bucket, but below the median in the 120+ days bucket. A higher percentage in 0-30 days is indicative of a practice having success collecting its revenue in a timely manner. Inversely, a higher percentage in the 120+ days is indicative of a practice letting its revenue slip through the cracks.
- Days (or months) in A/R: A practice needs to be below the median in the number of days (months) it takes to collect on the practice’s charges.
Benchmark totals are not likely to add up
There are several reasons the benchmark totals aren’t likely to add up, with the participation counts being the most obvious. Trying to sum the detailed data under the totals and subtotals is in essence taking the details from practices A-D and trying to add them up to practice E's total.Mean versus median
The mean is the average of all the values submitted by participants divided by the number of participants.The median is the midpoint of the data submitted, laid out from lowest to highest.
Using the values of $150k, $200k and $400k, the mean would be $250k ($150k + $200k + $400k/3 = $250k) and the median would be $200k.
Metrics such as A/R aging buckets and payer mix provide a complete picture. Benchmarking against the days in A/R aging buckets, we would expect the sum of the 0-30 days, 31-60 days, 61-90 days, 91-120 and 120+ days to equal 100%. Using the mean, we are likely to get to 100%. The median values aren't likely to equal 100% and may be substantially above or under 100%.
Hospital-owned practices versus physician-owned practices
Due to centralized services found in hospital systems and split-fee billing, it is recommended the organization ownership filter be applied to separate the hospital-owned and physician-owned practice benchmarks.Centralized services found in hospital systems (billing and coding, laboratory and imaging, call center, etc.) will result in the hospital-owned practices reporting lower full-time-equivalent (FTE) staffing than physician-owned practices. The centralized staff aren’t employed at the practice level and are not captured as FTEs in the survey.
Split-fee billing occurs when a service is moved out of the practice. A hospital-owned practice may require a patient to visit an imaging center for an X-ray. The professional component is billed by the practice and the technical component is billed by the hospital, resulting in the revenue being split between the practice and the hospital. The result is a much lower revenue stream into the hospital-owned practice.
Data are always based on the ‘cut’ selected
MGMA DataDive Cost and Revenue allows the user to cut the data from a variety of selections, including:- Per FTE Physician
- As a % of total medical revenue
- Per work RVU (wRVU) or per 10,000 wRVUs.
For example, if the total provider per FTE physician was 1.47 at the median and the total support staff per FTE physician was 5.85, we would not state for every 1.47 providers a practice should employ 5.85 support staff. As the cut is still based on a 1.0 FTE physician, we would only state for every 1.0 FTE physician a practice would employ 5.85 support staff.
Glossary:
- Adjusted fee-for-service (FFS) collection percentage: Net collections percentage
- Net income, excluding financial support: Profit/loss; investment; subsidy
- Primary care: Combination of family medicine, internal medicine, pediatrics, etc.
- Provider: A combination of physicians and nonphysician providers
- Total medical revenue: Net charges
- Total operating cost: Overhead or the expenses a practice pays to operate.
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