Oklahoma State Medicaid Adopts Drug Price Benchmark, PAC
TAMPA, Florida, October 1, 2013 /PRNewswire/ --
OK Medicaid selects Predictive Acquisition Cost (PAC) to improve efficiencies in managing pharmacy reimbursement and reduce drug spend
Elsevier, the leading provider of scientific, technical and medical information products and services, announced today that Oklahoma State Medicaid, managed by Oklahoma Health Care Authority - SoonerCare, has selected Predictive Acquisition Cost (PAC) to better manage its State Maximum Allowable Cost (SMAC) drug price list to improve administration efficiencies and reduce overall drug spend.
PAC, developed by Glass Box Analytics and exclusively published by Elsevier's Gold Standard, is a new drug price type that more closely tracks true acquisition cost by leveraging proven concepts from other industries and applying the power of predictive analytics to drug pricing. Independent tests show that PAC comes closer to true acquisition cost than alternatives.
SoonerCare (Oklahoma Medicaid) is a health coverage program jointly funded by the federal and state government. The Oklahoma Health Care Authority (OHCA) is the state agency that administers and determines financial eligibility for the program. SoonerCare currently serves more than 790,000 members under the fee for service pharmacy benefit.
In search of a more efficient method for managing pharmacy reimbursement, the agency found that PAC is cost effective and allows for better analysis of drug cost and reimbursement amounts.
"PAC provides an accurate reflection of the true acquisition cost for all drugs and helps our agency know where to set SMAC prices that reflect the true drug cost. The goal of the SMAC program is to set reimbursement that is fair to both the state and to the parties we reimburse," said SoonerCare Pharmacy Director Nancy Nesser. "It gives us accurate information that is easily accessible and requires fewer resources. Reimbursement should reflect the best estimate of acquisition cost, and we feel with PAC that we can achieve this."
State Medicaid programs can save significantly by using PAC to evaluate their state MAC lists. PAC displays a price range for each specific drug. The state MAC should lie within that range. PAC identifies cases where reimbursement is too high or too low, which allows Medicaid programs to ensure that their pharmacies are being reimbursed at market-based rates. SoonerCare will also be utilizing PAC to track the true costs of specialty drugs.
"All state Medicaid programs are seeking ways to improve pharmacy reimbursement management and Oklahoma was already doing a great job," said Kay Morgan, Vice President, Drug Products & Industry Standards, Elsevier. "In PAC, they recognized an innovation that would help them reduce prescription drug costs further, while also improving operational efficiencies."
For more information about Elsevier's Gold Standard, visit http://www.goldstandard.com.
About Elsevier
Elsevier is a world-leading provider of scientific, technical and medical information products and services. The company works in partnership with the global science and health communities to publish more than 2,000 journals, including The Lancet and Cell, and close to 20,000 book titles, including major reference works from Mosby and Saunders. Elsevier's online solutions include ScienceDirect, Scopus, SciVal, Reaxys, ClinicalKey and Mosby's Suite, which enhance the productivity of science and health professionals, helping research and health care institutions deliver better outcomes more cost-effectively.
A global business headquartered in Amsterdam, Elsevier employs 7,000 people worldwide. The company is part of Reed Elsevier Group plc, a world leading provider of professional information solutions. The group employs more than 30,000 people, including more than 15,000 in North America. Reed Elsevier Group plc is owned equally by two parent companies, Reed Elsevier PLC and Reed Elsevier NV.
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Media contact
Christopher Capot
Director, Corporate Relations, Elsevier
+1-212-633-3164
c.capot@elsevier.com
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