How should I pick between multiple rates?

Let's say you are a now a pro at searching providers after reading our guide and are now inside the data and want to figure out what 1 provider bills for 1 code but you see multiple rates — what is the right way to interpret the data and 'pick' the right rate?

For example, let's say you want to figure out what Alma bills for 90837 inside Cigna — you may end up with a 3 row dataset that looks like this:

In grey, you have already filtered code down to 90837 and entity_name to Alma and you see two rates:

$109, $74. To pick between these,

Step 1: Filter down the data as much as you can

First, you want to confirm you cannot filter the data down any further by using another dimension that may change the rate a provider bills, including:

1. EIN — as discussed in our provider search guide, EIN indicates a unique contracting entity; a given provider, like Alma, may have multiple contracting entities if they negotiate different fee schedules for each of their groups —
   1. In this case, EIN — 871927518 corresponds with "CA" based on 'npi_region' and EIN — 841856765 corresponds with "NY" but given the large npi-list (e.g., close to ~7K NPIs) 841856765 actually likely indicates their non-CA, national contract for 90837
2. Modifier-list — for example, if you are looking at radiology codes, a '26' modifier would indicate the professional component and a 'TC' modifier would indicate the technical component
3. Billing-class — for example, if you are looking for facility fees, you would want to filter out 'professional' and look for 'institutional'
4. Place-of-service list — for example, if you are looking for telehealth, you would want to filter for place of service contains '02' or '10'
5. Negotiation_type
6. Arrangement

Given modifier-list, billing-class, place-of-service list, etc. is the same across the three rows we filtered on, the only way to filter down the data further is to only look at Alma's national EIN — 841856765:

Step 2: Evaluate the npi_list_length

Once you have confirmed the data cannot be filtered further, you then want to look at the npi_list_length field (in blue above) to see if the rates correspond to different npi-groups.

In this case, we see two groups — a group of 1 NPI, and a group of 6745 NPIs.

Step 3: Pick the rate associated with the largest npi-group

Typically, a rate a provider bills is the one associated with the largest number of NPIs. In this case, that would be the list of 6745 NPIs for Alma, and their "rate" for 90837 would be $109:

Some caveats:

What if for the exact same npi-list you have filtered down the data as much as possible and still see different rates?

In this case, we either advise you to pick the highest rate given a provider would likely seek to maximize their insurance reimbursement, or show a range of potential reimbursements depending on the various billing scenarios the payer is attempting to account for.

For example, in the sample below — we are looking at the same exact row of data for every field except rate, where we see a $170.14 and $190.98. Given 90837 is a code for 1 hour therapy, this could indicate the payer is attempting to communicate they reimburse the $170.14 rate for Masters level clinicians (e.g., social workers, counselors) and the $190.98 rate for PhDs (e.g., psychologists).

Just like with anything involving data, the answers often come with nuance, interpretation, and additional context!