George discusses research that compares acquisition cost reimbursement benchmarks—National Average Drug Acquisition Cost (NADAC) and Predictive Acquisition Cost (PAC)—to such usual list price benchmarks as Average Wholesale Price (AWP). He provides examples showing that acquisition cost methods can reduce payer costs while also improving pharmacy profits. Anyone interested in more effective pricing metrics should take a look.
Visit the Centers for Medicare & Medicaid Services to learn about NADAC. Download Elsevier’s most recent case study and news article to learn how Predictive Acquisition Cost (PAC) aids retail pharmacies and payers.
Please contact George Kitchens (email@example.com) with any questions about the article.
Impact of Drug Price Benchmarks for Payers and Pharmacy Networks
By George Kitchens, RPh, President, Artia Solutions
The NCPDP Special Committee on AWP issued recommended criteria for a new drug price benchmark and other organizations, such as AMPAA and NASMD, have offered opinions on which price type should be used. A number of state Medicaid agencies have followed the example of Alabama and are surveying their pharmacies to determine the average acquisition cost (AAC) and CMS has embarked on a national survey of drug stores to calculate and publish a National Average Drug Acquisition Cost (NADAC). Predictive Acquisition Cost (PAC), from Glass Box Analytics, uses a predictive analytics model to determine an estimation of acquisition cost for all drugs.
So, we have options. And generally we have agreement that the closer a price benchmark is to the provider’s true acquisition cost, the better. But what does this mean in practical terms? If a payer uses an acquisition-based benchmark, such as PAC or NADAC, rather than AWP or WAC, what is the impact on its drug spend and how are its provider pharmacies affected?
A comprehensive study including the most commonly used drug price types across a variety of payers and providers would be required to fully answer these questions, of course. But analysis of a few select examples indicates that the choice of one price benchmark over another can have a significant impact and should not be taken lightly.
In the development and delivery of PAC, Glass Box Analytics has conducted research and analysis for government and commercial payers, PBM’s, wholesalers, and retail pharmacies in an effort to establish transparent, fair and balanced ways to address drug pricing challenges between stakeholders. Without divulging identities of the participants or any proprietary information, some highlights from their findings are:
- Using published State Maximum Allowable Cost (SMAC) values from 16 state Medicaid websites and comparing those SMAC values to PAC reveals that, on average, 28% of SMAC reimbursement values are too high, while 26% are actually too low. Optimally, SMAC price lists ensure that pharmacy networks are not reimbursed below the drug’s actual cost, while at the same time ensuring the state Medicaid is not over reimbursing for the drug.
- Analysis of 580 SMACs for a state Medicaid identified 67 drugs for which reimbursement would be too high and 104 for which reimbursement would be too low. The annualized cost increase from raising reimbursement rates that are below the pharmacy network’s acquisition cost total approximately $584,000. The annualized cost savings from reducing reimbursement rates to a level of 50% profit-per-script over acquisition cost total $2,024,000. The Medicaid is able to ensure the SMAC list is balanced and fair to the pharmacy network, which results in approximately $1,440,000 in savings to the state Medicaid.
- Similar analysis using a larger sample size of 722 drugs for another state Medicaid revealed that the Medicaid is significantly over reimbursing for 86 drugs and significantly under reimbursing for 81 drugs. Adjusting the SMACs that are too low increases the spending for those drugs by approximately $1,131,000 annually. Reducing the SMACs that are too high decreases the drug spend by approximately $3,406,000. Again, the state Medicaid achieves a more fair and balanced SMAC list, saving a total of approximately $2,275,000 annually.
- A health plan chose to take ownership of price setting activities from its PBM and identified approximately 10-20 percent in savings while at the same time better balancing the MAC. The health plan returned all of these savings back to the network via a high fill fee and through pay-for-performance programs.
- A comparison of PAC versus NADAC for one state Medicaid across 37,541 NDC’s indicated that using PAC to set appropriate MACs would save an additional $4,000,000 per year. However, when accounting for all NDC’s, including those for which no NADAC value is available, PAC would enable the state Medicaid to realize an additional $40,000,000 in savings. This is because a PAC value is available for an additional 10,625 NDCs, including brand, specialty, and physician-administered drugs. With such significant savings, this state Medicaid would provide a fill fee to its pharmacy network of nearly $10 per script.
- For a regional retail chain with more than 400 pharmacy locations maintaining a “loss file,” which identifies claims reimbursed below acquisition cost, analysis using PAC indicated that for 21 percent of the claims the payer did not reimburse the pharmacy chain sufficiently and for 22 percent of the claims, the retail chain purchased the drug at a higher than optimal price in the market.
- For another state Medicaid that utilizes a lesser-of logic involving (AMP)-based Federal Upper Limit (FUL), PAC analysis determined that for approximately one third of drugs, the pharmacy would be reimbursed a rate less than what it paid to acquire the drug. An analysis of the overall SMAC for this state identified sufficient opportunities for price reduction to eliminate the use of the FUL in lesser-of logic and thereby move to eliminate situations where the pharmacy is under-water when filling a script.
- Working with pharmacy chains to calculate a more stable Generic Effective Rate (GER) based on PAC, as opposed to AWP, resulting data was used to demonstrate under-reimbursement when requesting payment adjustments and to make tough decisions about whether claims will be filled for specific cases.
Download Elsevier’s most recent case study and news article to learn more about how Predictive Acquisition Cost (PAC).