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Better use of available data can add rigor to analyzing managed-markets contracts, thereby allocating rebates / discounts more effectively
In the current business environment for the biopharma industry, managed-markets relationships have emerged as a key driver of business success. “Managed markets” are those in which product demand is influenced by health plans, insurers, group purchasing organizations (GPOs), pharmacy benefit managers (PBMs) and others. The biggest—and soon to get bigger—is the federal government.
Managed markets organizations generally have formulary plans that define what co-pays, if any, insured patients pay for medications; some have very specific “tiers” that steer patients, and their doctors, toward one type of therapy before another (also known as “step therapy”). Biopharma companies negotiate prices with the managed markets organizations, and the combination of prices and discounts or rebates can, in turn, change the formulary position or usage patterns of medications.
Consider the following typical scenarios:
Each of these scenarios begs the question, “Why is this occurring?” The ability to answer “why” questions, which drives pharmaceutical discovery, is just as valuable on the commercial side of the business. New technologies are now making it possible to apply science to the art of marketing, realizing benefits amounting to millions of dollars. Specifically, companies can understand why contracts with some managed care plans yield such better returns than others—insight that can be used to manage contracts more systematically and profitably.
A data tower of Babel
In the U.S., nearly 90% of all prescriptions are paid for by the government or third-party payers. Meanwhile, pharmaceutical companies spend the equivalent of 15-20% of their gross sales in rebates and discounts to managed markets organizations. For the typical company, this expenditure surpasses all other line items on the profit/loss sheet, including research and development. Yet, manufacturers typically do not devote an equivalent proportion of their attention to managing how these rebate/discount dollars are allocated among the 7,500+ plans in the U.S. In fact, relatively few companies even attempt to study the actual consequences of their contracting decisions or to hold managed markets organizations accountable for living up to their end of a deal. In many respects, “managed markets” go unmanaged.
There are a couple of valid reasons for this situation, alarming as it may sound. First, until recently, frugality has not been a prerequisite for success in the industry. Because pharmaceutical manufacturers can only contract with a few payers, the managed markets function has not had to operate under intense budget pressure. Having already focused on mitigating both financial and regulatory risk as a way of minimizing “revenue leakage” and ensuring regulatory compliance, companies are now turning to other areas of opportunity such as contracting effectiveness, operational efficiency, and improving the customer engagement model.
Second, while most companies do, in fact, have access to the various data sources necessary to monitor contract performance, these data sources are not all integrated. The data reside within disparate environments—some of it in marketing, some in sales operations, some in contracts management, and some in finance. And the third-party datasets are not automatically compatible with the company’s internal datasets, so that they don’t “talk” to one another.
Companies may indeed wonder:
But, finding answers has required waiting for weeks as managed markets analysts pull data and attempt to synthesize the information, pore over spreadsheets, and create reports—too cumbersome a process to become routine as part of a comprehensive and ongoing program to managed business performance. Given these hurdles, it is understandable that if companies monitor contract performance at all, they do so sparingly.
Alternatively, rather than attempting to do this analysis themselves, companies can engage an outside consulting company to perform an ad hoc study. The manufacturer is thus freed from the labor of the undertaking, but the resulting answer still holds for only one point in time, for a limited set of customers and/or products. The next time a question arises, another study would have to be commissioned.
An existing tool, the MedInitiatives (an IMS company) Data Integration Platform (which was originally created for use by payers) can now be used to make short work of the whole process. The first preparatory step is to map all the required data sets to one another, creating a “crosswalk” between them. Through this proprietary methodology, , a company’s own internal data on direct sales, contracts, rebates/discounts, promotional efforts, etc. becomes conversant with relevant third-party data such as prescription volumes at the plan level, plan formularies (and soon benefit designs), plan affiliations, longitudinal patient data, and customer-submitted prescription data.
With all of these previously incompatible data sources harmonized and the appropriate data governance and stewardship models in place, an intuitive, graphical user interface can be used to access the integrated data in order to perform the required business performance analyses (Fig. 1). And the resulting analytics capability can be used again and again for iterative analyses on any and all of the company’s products and customers. Users are not restricted to a point-in-time view, but instead can monitor the performance of contracts routinely and systematically. And the best news? Answers can be had in minutes rather than weeks.
The power of the platform for performing analyses allows companies to progress along the “Analytical Functionality Continuum” illustrated in Fig. 2. They can advance from merely understanding what happened, via a retrospective analysis, to more exploratory analyses into why it happened. The question becomes not just, “What return did we get for our investment in this contract,” but, “Why did we get that return (be it higher or lower than expected)?”
And beyond that, they can achieve the Holy Grail of research: the ability to use predictive modeling methods and tools to forecast what will happen under given scenarios. As the data-integration platform absorbs updated information over time, it is able to establish and refine correlations between multiple factors, thereby “learning” and improving its predictive accuracy. Ultimately, companies can answer such questions such as, “What return can I expect from a contract with these plans/benefits designs, tier structures, etc. under these specific provisos?”
With the ability to easily access and integrate the data that they already have, companies can monitor the performance of a customer contract in the context of formulary positions, benefit designs, and co-pay differentials. Such insight suddenly makes it possible to:
Even if the resulting better business decisions enable companies to cut their rebate budgets by one percent, this would amount to huge savings in absolute terms.
A promising concept
Because the whole practice of performing such analyses with managed markets data is in its infancy, it would be premature to report on an actual success story drawn from life. However, just imagine how the ability to routinely monitor contract performance could lead to a better business decisions and higher ROIs in this hypothetical example…
A major manufacturer in the cholesterol-lowering market, facing an imminent patent expiration, cuts the managed markets rebate budget by $50 million, while stipulating that the change has to be handled so as to not impact sales and market share growth. The managed markets team at once re-assesses where it should be concentrating its more constrained budget in order to maximize its impact. It quickly decides to restrict the number of plans that will be eligible for higher rebate percentages to those plans (benefit designs) that have demonstrated their ability to control formularies and move market share. The team then models returns for the plans that look promising based on their benefit designs (and where available, their past performance) and devises a new contracting strategy accordingly. Moreover, forecasts for accruals and reserve accounts for rebates and chargebacks are prepared with greater accuracy than ever. And sales reps are directed to support the opportunities identified with pull-through messaging. The new contracting strategy optimizes the impact of rebates and actually improves Net Sales by double-digit percentages.
Since pharmaceutical companies must increasingly be selective in how they spend their rebate/discount dollars, they can only benefit from applying more analytical rigor to the process. In recent years, the ability to gather reliable data on market trends has increased, while the gap in time between when events occur and when the data are available has shrunk. The right information, made digestible with a powerful analytical capability, can give companies the conviction to walk away from some contracts and the resolve to pursue others where opportunities have been identified. And in the end, they can invest their limited resources more profitably for superior returns. PC
SIDEBAR: Success Factors
Making multiple sets of information useful for managed markets performance monitoring and improvement is not an easy undertaking (otherwise companies would certainly have done it by now). Creating the necessary analytical capability requires:
ABOUT THE AUTHORS
Joseph Coppola (left) is a Senior Principal in the Managed Markets Services practice of IMS Health and is responsible for its Managed Markets Analytics Services and Solutions. Carlos F. Moreira is Vice President and Global Practice Leader of IMS Health’s Managed Market Services. The group, which includes over 140 resources across the U.S., Europe, and East Asia, specialize in business and technology solutions for commercial and government contracting.