“Loss File” Analysis

Analyzing where you’ve lost money on certain drugs but not sure who to ‘point the finger at’?

Looking at claims in a retail pharmacy’s “loss file,” PAC can be used to identify where the retail pharmacy should direct its attention:

Reimbursement Issue – Identify claims where reimbursement
(not including the dispensing fee) was less than PAClow.
PAC low to PAC high
Procurement Issue – Identify claims where the acquisition price from the wholesaler or manufacturer was more than PAChigh.
Use Case:  A large regional retail chain (500+ pharmacies) leverages PAC to see if loss file issues are more likely due payer/PBM reimbursements versus its own procurement issues.

We compare each payment (less the dispensing fee) in the loss file to the PAClow and determine where there is a strong likelihood the payer/PBM has reimbursed at rate lower than what is acceptable.  This is a conservative approach, looking only at the cost of the drug with no profit margin and comparing to the low end of our PAC range.

And we compare each drug acquisition price in the loss file to the PAChigh and determine where there is a strong likelihood the retailer purchased the drug above a price considered acceptable.  Again, this is a conservative approach using the PAChigh and the retailer can be more aggressive.

Results – Based on this retailer’s loss file, 58% of claims were reimbursed below the published PAC which indicates that the payers/PBMs were more aggressive with their reimbursements.  But the majority of these reimbursements did not fall below PAClow.  Based on this conservative parameter (PAClow) for acceptable reimbursement, 15% of claims were reimbursed lower than PAClow.

At the same time, 23% of the claims (based on our conservative parameter of PAChigh) had a procurement price that was higher than acceptable.  In these situations, it was not the reimbursement but rather that procurement price that resulted in these claims being added to the loss file.

Reimbursement < PAClow
Acquisition > PAChigh
15%
23%

 

Balanced SMACs

PAClow and PAChigh

In addition to publishing the PAC, the PAClow and PAChigh are provided to subscribers.  The PAC is an estimation of acquisition cost, but there can be a degree of uncertainty.  PAClow and PAChigh establish a range within which there is a high degree of confidence that the true acquisition cost lies.

For a given pharmacy per-script profit target, this PAC range can translate into a range within which the MAC should lie:
PAC vs MAC Ranges
In order to spot prime opportunities to create a more balanced MAC pricing, we can identify drug groups for which the current MAC is outside the range, i.e. MAClow to MAChigh.

SMAC Analysis

Scenario:  Assuming a pharmacy profit-per-script target of at least $3/script and 10% margin (not including professional services/dispensing fee), the PAC range was used to identify drug groups where an increase or decrease of the SMAC may be appropriate.

SMAC Raise Reduce Unchanged
State 1 609 210 991
State 2 282 1674 776
State 3 591 308 1603
State 4 425 133 668
State 4 361 133 514
State 5 132 546 183
State 6 747 177 925
State 7 212 202 299
State 8 965 506 1714
State 9 292 381 391
State 10 689 527 669
State 11 359 287 409
State 12 169 491 278
State 13 596 210 991
State 14 204 287 436
State 15 169 154 463
State 16 266 637 369
State 17 473 241 812
Overall 7541 7104 12491
We applied this MAClow and MAChighbased analysis to a sample of states where SMACs are available on the state Medicaid web sites.  A count of drug groups with a SMAC falling outside recommended range is summarized in the table with the assumptions above.

We know specific business parameters exist when generating SMAC values and those parameters will vary from state to state. For example, the assumptions made regarding targeted pharmacy profitability can be adjusted and the incorporation of a pharmacy’s utilization can focus on the most relevant drug groups.  Glass Box Analytics is available to provide a pharmacy with additional analytics that are tailored to that pharmacy’s areas of interests.

GER Predictability

Scenario:  A Medicaid manages its generic reimbursement at a generic effective rate (GER, i.e. the average percent discount off the AWP for all generic drugs) of 74%.  

Let’s compare PAC Retail to AWP, each delivering an identical GER of 74% on the current MAC list given the state’s utilization across drug group.

Variability of GER across drug groups

GER Variation Across Drug Groups - AWPAWP
Because AWP is so disconnected from acquisition cost, and hence from the Medicaid’s maximum allowable cost (MAC), the GER varies dramatically across drug groups when based on AWP. (A significant number of drugs even show a negative GER.)

 

 

GER Variation Across Drug Groups - PACPAC Retail
In contrast, looking across drug groups, the PAC Retail based GER is tightly centered around 74%.

The actual GER achieved is highly dependent on the utilization mix in the case of AWP, but much more predictable if based on PAC Retail.

Looking at NDC’s within a drug group
Beyond AWP’s disconnect with true acquisition cost, another issue exists when measuring the performance of a MAC using a GER metric based on AWP. When looking across NDC’s within a drug group, the AWP often varies even though the MAC is fixed at the drug group level.

The resulting GER therefore depends, in part, on which manufacturers a pharmacy purchases from (i.e. which NDCs within a drug group are utilized). This phenomenon, as illustrated below by a couple of examples, adds a further degree of uncertainty for the Medicaid when targeting GER-based performance metrics.

Drug Label  MAC  NDC  AWP GER 
TRETINOIN 0.05% CREAM 1.1860 45802036142 2.09511 43%
TRETINOIN 0.05% CREAM 1.1860 43478024220 2.51100 53%
HYDROCODON-ACETAMINOPHEN 5-500 0.0440 00406035705 0.19686 80%
HYDROCODON-ACETAMINOPHEN 5-500 0.0440 00591034901 0.50650 92%

In fact, the exact same drug group utilization could result in an AWP-based GER of anywhere from 68% to 77%, depending on which NDCs are actually involved.

PAC Retail, on the other hand, exhibits little to no variance across NDCs within a drug group and will deliver a stable PAC Retail-based GER fixed at 74%,.