Optimized Value Analytics – The Next Frontier for Payers and Pharma?
October 9, 2013
By Caitlin Rothermel, MA, MPHc
Introduction
If you’ve been following the health economics industry for the past few years, you’ve heard about value-based pricing agreements between payers and the pharmaceutical industry. With value-based approaches, payment is linked directly to real-world treatment effectiveness. In this way, purchasers’ and manufacturers’ economic incentives are aligned based on clear, mutually defined patient outcomes.
Once or twice a year, I look around to see if anything major has changed in this area – Is the chatter on value-based agreements getting louder? Are value-based approaches becoming more common? My take on the current environment is that stakeholders are talking about value-based approaches more than ever, but their actual, successful implementation is stalling.
In a recent blog on payer value messaging strategies, HealthEconomics.com’s Patti Peeples indicated that a shift is clearly underway “towards more flexible risk-sharing and cost management mechanisms from the payer’s viewpoint.” But she proceeds to argue that substantial misperceptions persist regarding what payers actually want from pharma versus what pharma believes payers want. The ultimate problem? A lack of effective communication between the players.
In a 2011 case-based investigation published in Health Affairs, Peter Neumann and colleagues found very few examples of successful risk-sharing agreements, and noted that U.S. stakeholders continue to focus primarily on payment models not connected to performance assessment or data analytics. They: “The principal lesson thus far seems to be that risk sharing for pharmaceuticals is appealing in theory, but hard in practice.” The primary barriers identified? High implementation costs, lack of a data infrastructure, and challenges with outcomes selection.
To be fair, this issue is multifaceted and doesn’t have easy solutions. But I think a way exists to circumvent at least one key obstacle – the data infrastructure gap. According to Neumann et al: “Risk-sharing agreements require high-quality information systems, databases, and operational expertise.” It takes time to put these capacities in place, but there may be a workaround.
But before going any further, it’s important to understand that the way value is measured is undergoing a sea change.
The Old Ways of Measuring Value Are Changing
It could be said that value (treatment outcomes adjusted for treatment costs) is the newest currency in healthcare. But to be useful, value needs to be defined, measured, predicted, and optimized. Thanks in large part to novel study designs and the availability of advanced analytics, our notion of what aspects of value can be measured is evolving rapidly.
A poster presentation from Dinh and colleagues at the 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) conference outlines some key distinctions between past and future approaches to value strategy development.
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The old way is reactive. It does not consider value until late in the drug development process. This approach: |
The new way is proactive. It applies value prediction in early drug development (for example, by identifying “most valuable” subpopulations using data-based methods). This approach: |
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Getting Around the Information Technology Barrier
A 2012 Deloitte Center for Healthcare Solutions Issue Brief summarizes the information technology aspect of the value analytics gap as follows: “The availability of valid-real-time data for value metric assessments requires widespread electronic exchange of health information among stakeholders and disparate sources…this extensive network needs to be further built as it is not currently widespread.”
In some cases, the pharmaceutical industry and payers are teaming to overcome these data limitations. A collaboration between AstraZeneca and WellPoint, established in 2011, is using electronic medical records, claims information, and patient survey data to evaluate how currently available drugs affect healthcare costs and patient outcomes, particularly in chronic disease.
There are also some electronically enabled tools that are readily available to assess cost-effectiveness. Retrospective database analyses have the virtue of very large samples, substantial data inputs, and access to “real-world” patients. Interactive models simulate future outcomes and costs associated with specific products, and can be designed to reflect specific payers’ resource utilization patterns.
But these approaches have limitations when assessing the clinical and economic impact of new treatments. First, there’s always missing information – claims records don’t necessarily contain the biomarker and lab data needed to assess treatment efficacy, and interactive models are limited in scope. Also, in every case, someone has to take on the work and expense associated with designing and running new studies or building new models. And of course, if a treatment is not already on the market, time needs to pass before you can obtain useful information from retrospective claims data.
I think there is a way to bridge this gap. The data that players need can be augmented by incorporating accurately simulated data, using a tool that’s available today.
A Way To Look Data From Many Sides
When only limited real-world data are available, or the data doesn’t allow for the answering of specific questions, the judicious integration of simulated data can help payers and pharma work together to build a case.
Simulation models can answer the big “what if” questions that are cost-prohibitive or can’t be evaluated with traditional research. For example, what if you had a new dyslipidemia treatment, but had trouble discerning its efficacy against the background noise created by other cholesterol-lowering medications? Models enable you to run unlimited simulations of patient populations with user-defined characteristics.
The Archimedes Model is a large-scale, clinically realistic model of health and healthcare delivery. It contains detailed patient information obtained from public databases, clinical trials, observational studies, and epidemiologic data; this lets users conduct research on the general U.S. population or subpopulations. The Model also integrates with proprietary data, for example, claims or electronic health records containing biomarkers or clinical practice processes.
ARCHeS, the Model’s intuitive, online platform provides a natural venue for collaboration and value strategy development. The results of a recent Quintiles survey show that payers want more involvement during every stage of drug development. With ARCHeS, manufacturers and payers can sit down together (literally or virtually) at any point to “slice and dice” their customized data. ARCHeS can be used in two ways:
- With ARCHeS Population Explorer, users can model the impact of a new treatment on multiple populations (either the U.S. population or defined subpopulations, or users can upload their own patient data).
- With ARCHeS Trial Designer, users can set up virtual clinical trials for new products, compare the results for accuracy, and then set up new virtual trials to gain additional information—all within a few hours.
This is a new approach, and it could make it possible to examine some big economic and clinical questions early in the game:
- What cost savings are likely to be associated with a treatment, and what is the financial hit if any key assumptions are incorrect?
- What downstream effects, both positive and negative, are likely to be associated with a treatment?
When information like this is available up front, it becomes possible for stakeholders to negotiate based on accurate answers to “what if?” statements. For example, at what price point does an intervention become effective? What downstream effects can be expected to accompany a 9 mm Hg drop in blood pressure? What is the cost-benefit relationship of screening select subpopulations for certain cancers?
The next big experiment I’d like to see in value-based pricing involves the Archimedes Model and ARCHeS. With ARCHeS, you can integrate various types of data, hypothesize, add missing information, and receive answers to the questions that will help determine the specific value of an intervention in a particular population. The tools are there, just waiting to be used – and innovation has never been easier or more straightforward.
Caitlin Rothermel, MA, MPHc is a Northwest Washington-based medical and health economics writer. She has paid close attention to the Archimedes Model for years and gets completely geeked-out happy by Bayesian hierarchical modeling. You can learn more about Caitlin by visiting www.MedLitera.com.
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