Quest Analytics Group

Transcription

1 Quest Analytics Group EMPLOYER STRATEGY BRIEF COST MODEL BASED DECISION SUPPORT April 1, 2010

2 Abstract As in both medical and pharamacy benefit sourcing, there is an inherent risk that purchasers/consultants can be easily confused regarding the economic levers which drive dental claim costs, and as a result, supplier sourcing determinations are made based upon inappropriate criteria. The most frequent mistake made is a sourcing decision based upon disclosed (and understood) plan administration costs (i.e. the monthly administration fee per employee per month). Typically, plan administration costs equate to 5 10% of the total cost of the benefit, with claims costs representing the vast majority of the financial expenditure. For the purshaser/consultant to be empowered to make (dental, medical or Rx) sourcing decisions based upon identifying the low cost producer, the following factors must be understood: Service mix (which often links to clinical interventions by the plan administrator) Network scope and network utilization rates Core network discount yields specific to defined georgraphies Network discount yields linked to wrap-around networks designed to produce disounts (and service enhancements) for beneficiaries accessing dental/medical services outside the scope of the Plan Administrator s core provider network. While many Plan Administrators in the dental, medical and Rx arena have invested resources to provide purchasers/consultants with anlaytics on the afore-mentioned claim cost factors, their efforts are fundamentally undermined by the self-reported nature of the analytics, combined with the fact that the financial guarantees (relating to the claim cost analytics) are typically in the range of 2-5% on the dollar. The author has spent ten years developing proprietary solutions designed to address the dilema as outlined, with two analytics tools (i.e. ClaimsQuest and RxQuest) in the marketplace today. Users of ClaimsQuest and RxQuest include the largest health insurers in the U.S., the largest pharmacy benefit manager in the U.S., a multitude of Fortune 100 companies, and a large number of national and regional brokerage houses.

3 Cost Model Based Financial Analysis - Background The evolution of the author s work in terms of providing the purchasing/consulting community with a claims-based decision support process (studying the effect of bidder specific claims cost) began in the Summer of Specifically, the Blue Cross Consortium (i.e. Consortium Health Plans) sought a process whereby they could document the strength of their hospital and physician network discounts, to include the value of their wrap-around PAR network. The Blues faced several problems which were severely limiting their sales success in the National Account (multi-state employer) arena: Realtively high administration fees given the nature of their franchise system Lack of purchaser/consultant understanding on the economics of market share, network scope and network discounts Competitors who claimed equal to/higher than discount rates as the Blues generate, with no rigor or substantiation of any kind. A fundamental lack of understanding in terms of weighted average discount yields (i.e. aggregated in and out of network services) versus in-network discount yields. Selling barriers linked to a d above, compounded by the fact that BCBS underwriters would not make dollar for dollar financial guarantees linked to the premise that their combination of network scope and deal term strength would produce lower claim costs. As a byproduct of the conversation with the Blues the author highlighted the potential for the use of an actuarial cost model (specific to every MSA in the U.S.) to highlight the relationship between billed and allowed charges. The premise here is that the procedure/code level allowed charge metrics specific to an MSA (or any three digit zip code therein) addresses several pieces of the claim cost puzzle for purchasers/ consultants: 1. It captures the effect of in/out of network utilization 2. It captures the effect of clinical interventions 3. It captures the effect of discount rates and/or payment based upon a usual and cutomary index

4 Bear in mind that the cost model (medical) uses a set of code mapping algorithms producing geographic and code specific data sets (see the exhibit)

5 Cost Model Application to the Blues Selling Environment With the premise of the cost model established, a beta test was conducted via Georgia Pacific (GP). We received twelve months of paid claims data from GP s medical plan administrator and developed a baseline cost model for roughly 20 specific employement locations (this process allowed us to see discount rates in the aggregate, by location, and by service category). We subsequently received twelve months of BCBS paid claims data from zip code matched services areas (to the point of service where the GP associated received medical services). The data from BCBS allowed us to observe their relative discount yields (versus those of the incumbent administrator) from the same zip code and billing code as evidenced in the GP historical data. This combination of data allowed us to re-price the historical GP claim data using BCBS discount rates. The process evidenced a significant financial value proposition for BCBS (versus the incumbent). ClaimsQuest Given the results on Georgia Pacific, we collected twelve months of paid claims data from virtually every BCBS plan in the U.S., with the intent of building a data repository of Blue discounts that would allow the Georgia Pacific exercise to be repeated. Furthermore, the Blues trademarked the process as ClaimsQuest. Subsequently, the author spent roughly two years traveling throughout the U.S. (with BCBS sales associates) to present the ClaimsQuest concepts to Fortune 100 companies and benefit consultants. Nine years downstream from the initial strategy brief, 175 ClaimsQuest studies were funded by the Blues in CY The Blues will readily acknowledge that ClaimsQuest revolutionized their success rate in the national account marketplace.

6 RxQuest With the success of ClaimsQuest, the author developed a corollary product for the pharmaceutical benefit management marketplace that was designed as a two dimensional look at claim costs both discount rates and drug mix (whereas ClaimsQuest was fundamentally one dimensional on discounts). The launch of RxQuest occurred in the summer of Downstream, 88 RxQuest studies have been conducted with companies as large as Fortune 50, but also includes work completed for several Health Maintenance Organizations. Contrary to the ClaimsQuest project (where an analytical process was built, but distribution was fundamentally left to the Blues), a national distribution network of insurance brokers was developed to distribute the RxQuest product. Consistent with ClaimsQuest, the RxQuest analytics revolve around the intake and manipulation of raw claims data, consistent with the cost model approach. Pricing Term PBM A PBM B Admin PEPM ($) $0.00 $0.00 Rebate PEPM ($) $6.19 $7.93 Discounts Retail/Generic (%) 61.50% 62.00% Retail/Brand (%) 6.50% 16.00% Mail/Generic (%) 64.50% 65.00% Mail/Brand (%) 23.00% 23.50% Dispensing Fees Retail/Generic ($) $1.55 $1.60 Retail/Brand ($) $1.40 $1.60 Mail/Generic ($) $0.00 $0.00 Mail/Brand ($) $0.00 $0.00 PBM B enjoyed a nearly $2.00 rebate advantage, higher discounts, and nearly identical dispensing fees. If drug mix were not considered, PBM B would have been a natural winner. However, for the client s utilization profile, PBM A was found to have superior drug mix capabilities. Their protocols substituted more generic for brand drugs and more multi-source brands for single-source brands. As a result, a greater percentage of PBM A s pharmacy claims received a generic discount as opposed to a brand discount dramatically lowering average script cost. In fact, the observed 7.5% differential in generic fill rates (GFRs) implied a cost savings for PBM A of $700,000 over PBM B within the first contract year.

7 Dental Cost Model Would Mirror the Outputs of the Medical Model

8 DentalQuest Information Requirements & Process Steps Having previously established the power of detailed claims data and the actuarial cost model, the preceeding chart is a proxy for a dental cost model which would facilate a DentalQuest decision support analysis. At the Client s request, and based upon receipt of twelve month s of the Client s historical claims data, we would develop an employent location specific cost model for all locations with greater than 500 employees. In addition, we would develop an aggregate cost model which rolls up the overall performance of the incumbent dental plan administrator (for both in and out of network services rendered). Secondly, we would coduct a request for proposal exercise whereby prospective dental plan administrators would be requested to provide book of business claims data (specific to the zip code ranges where the Client s employees reside). Upon receipt of the bidder claims data, we would build a competing cost model which can be benchmarked against the incumbent cost models (for purposes of modeling claim experditures. This exercise would accomplish the following: 1. Development of a fact based analysis on the performance of the incumbent. 2. Elimination of bidder requests that the Client release proprietary claims data of the incumbent dental plan administrator. 3. Elimination of bidders self-reporting financial information that is not guaranteed in any meaningful way. 4. Ensures that the Client is in compliance with its fiduciary responsibility to conserve Plan Assets. According to the Department of Labor, monies deducted from employee wages become Plan Assets at 90 days post-deduction.

9 While rigourus financial guarantees will continue to be difficult to obtain, DentalQuest will eliminate the inherent conflict of interest whrereby bidders are allowed to re-price claims with no third party oversight, while at the same time offering an administration contract that provides no guarantees on the financial information submitted. As with both ClaimsQuest and RxQuest, the winning bidder will be charged for the cost of the DentalQuest analysis. About Us Michael Sammons is the Founder & Chief Executive of Quest Analytics Group. Sammons is an economist and former employee benefits practice leader at Milliman, Inc. Quest Analytics Group is an Atlanta based consultancy comprised of actuaries, physicians, CPA s, lawyers, Wharton School graduates, pharmacists, economists, and supply chain management experts. For more information, you can visit us online at References are available upon request.