There are three primary data sets used throughout the Marketscape solution. Each one has an associated patient count metric. This article describes the differences between each data set from which we produce the counts.
The three different data sets are:
- Medicare FFS (Fee for service) - based on 100% Original Medicare FFS claims - used to calculate Annual Patient Count (FFS)
- MA-Year - based on 100% Medicare Advantage encounter data from CMS - the year listed represents the end of the one-year reporting period for the metric - used to calculate Annual Patient Count (MA-2018)
- MA/Commercial 65+ - based on commercial claims obtained from clearinghouses at an approximate 70% national sample - used to calculate Annual Patient Count (MA/Commercial 65+)
This image shows the three patient count metrics derived from the data sets as found on the Explore page:
The Big Picture
We sure wish that we were able to provide metrics calculated from 100% of medical care provided to patients aged 65+. At this point, Trella Health has access to about 90%. The chart below shows the overall picture of the breakdown of claims used by Trella Health. Each of the three datasets described below can be found in the image.
Medicare Fee for Service
The majority of metrics included in Marketscape are calculated from claims submitted to CMS as part of the Medicare FFS program. Often called Original Medicare, Trella Health runs analytics on all claims that are reflected in the latest Medicare data available to create the metrics in Marketscape. This represents both Part A and Part B claims, over 2 billion submitted each year.
For Patient counts, we provide unduplicated counts of patients from a one-year reporting period ending in most recent reported quarter.
Medicare Advantage is an alternative to Original Medicare offered by private companies approved by Medicare. Like our metrics for Medicare FFS, Trella Health uses 100% of Medicare Advantage encounters submitted to CMS to calculate the metrics in Marketscape. The major, and significant, difference between FFS and MA is that the availability of MA data lags behind the Medicare FFS data, which is why we identify the year in the header. The reporting period is the calendar year identified.
Because of the difference between the reporting periods for MA and other patient counts, the MA metrics should be used as an independent insight into the provider. Difference in reporting periods should be considered when comparing metrics across data sources.
Medicare Advantage / Commercial 65+
This patient count comes from claims clearinghouses that transmit MA and commercial claims. The claim counts are derived from the subset of commercial claims for patients that are aged 65+ (inclusive of Medicare Advantage payers).
Some important details:
- The data comes from the same reporting period as Medicare FFS. That is, the one-year period ending with the most recent reported quarter. See Data Release Timeline to determine the approximate date of the most recent release.
- The total claims coverage for this dataset is approximately 75% of all non-FFS patients in the United States over the age of 65. If you look at the image below, you will see that 25% of the 65+ patient population are not available in our solution, and therefore will not be included in this metric. See What about that 25% below. Data from payers/providers that utilize clearinghouses not covered by our upstream contracts are not included in our solution.
- Any patient covered by MA/Commercial carriers aged 65 and over during the reporting period will only be counted once.
If you click on the Medicare Advantage tab on any Analyze page, you will be able to see the table below which has a breakdown of the MA/Commercial 65+ metrics.
The "Payer Name" included in this table is what the clearinghouse provides. Due to the nature of claims transactions, there may be some inconsistences in the listed names.
What about that 25%
Because Marketscape contains 75% national coverage, there may be variations at the geographic level and you should be cautious when drawing conclusions using the data. It is advised to look at higher level trends, rather than the details of each number. Think “directionally accurate” versus precise.
What you can do:
In the table above, you can compare between payers in different rows. This works because the metrics are calculated from the same population. In light of the size of this population, the missing 25% should impact all payers similarly.
What you can't do:
Because of the missing data, the counts from that data are partial. You should not try to extrapolate the number in any way to get a "what if we had 100%" metric.