THE BLOG
09/10/2014 12:08 pm ET Updated Nov 10, 2014

How Do You Determine the Value of a Medicine?

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Chew on this: What does it really mean for a medicine to "work?"

Does it mean the patient survives longer? Does it mean a better quality of life? Does it mean the disease is still there, but doesn't progress? And who's to say which one of these metrics is more valuable than the other?

For Caitlin McQuilling, a senior health economist at WG Consulting, it's part of the job to answer these questions for her clients given the available data. We sat down with McQuilling at Stream Health, an "(un)conference" put on by advertising holding company WPP (disclosure: AOL, the parent company of HuffPost is a partner of Stream and WPP), to learn more about what it means exactly to be a health economist, what some of her biggest challenges are, and how her kind of role will only become more relevant as drugs get more expensive.

HP: What exactly does your role as a health economist entail?

CM: We work on behalf of pharmaceutical companies, but also health systems. Really what we do is we try to help consumers have information, whether that's the pharmaceutical companies themselves, the physicians, or the payers (the Medicare, the Medicaid, the formulary decision-makers). We try to help them understand the value of their products. There's a lot of debate about high-cost drugs. We really see that value is not just about price. Value means different things to different people. My job as a health economist is to get to the bottom of what that value is, and make that transparent and easily communicated to the different stakeholders.

HP: What are some different values you may assess?

CM: Everywhere we look, people are placing value on certain attributes more than others. And so, say for example a new drug coming out may be more expensive, but there may be better adherence because the patient needs to take it one time a day versus 10 times a day, which would improve adherence, then reduce later costs. With any expensive drug, if it's a cure, yes it's expensive up front, but in the long term you may be reducing hospitalizations or liver transplants, or a whole host of things. So those are the broader values that payers look for, especially now that in our health care system we're moving from this fee for service, pay-as-you-go model, to really more of integrated care, incentivizing prevention and quality measures within the Affordable Care Act. Look at the 30-day readmission: If a patient is readmitted within 30 days for certain conditions, the hospital is not going to get paid again. So the hospitals want to keep patients out of the hospital and doctors want to keep their patients well. The incentives are now aligning with a broader definition of value than just price. And I think it's also ensuring that everyone understands that difference, that it's not just about cost containment -- that's not going to solve our health care problems.

One of my favorite lessons in health economics, during my master's, is we studied what they call the "squeeze balloon effect." If you're trying in a hospital system to control costs somewhere, you squeeze the balloon and the costs will go somewhere else. So we need to make sure while we make care more efficient, we're also focusing on quality. We're ready to pay for quality. And of course, it's about finding the right touch points, a lot of what we do is also modeling, but it's around communicating in a transparent way to stakeholders.

HP: How do you paint a broad enough picture for your clients?

CM: I think everyone's aware that all modeling can be manipulated. All numbers, all statistics, you have to create credibility behind the numbers you use. And so that means putting a lot of work in the background to make sure that you're using the best possible data, the best possible analysis, the best possible data sources. When we build a model or analysis, we'll do a scientific, systematic literature review to make sure we're including the entire body of literature. We're considering all studies, not just the one that favors the company.

We try to bring value out of products. so when we're working with our clients, we're looking at, OK, what is the value-add of your product, what is the broader value. We're trying to help our clients look into their data -- is there some subset of your population that's going to be the most cost effective? And it helps target. We work on behalf of our clients to develop this value, but we also can't make stuff up.

HP: How do you go from having the data, to applying that data in a real-world sense?

CM: It's easy to get overwhelmed by data. We get sent reams and reams of paper, huge amounts of data to sift through to find most impactful messages that come out of that data. What we find is that everyone's talking about big data, and the pharmaceutical companies have huge amounts of data. They have their salesforce numbers, they have data coming in from EHRs [electronic health records] and claims, and when you get that much data, it's can almost be overwhelming. How do you use it for decision-making? We find it's 20 percent of that data that you can really find the real data in. We mine for that within the data. We spend time analyzing and reanalyzing to get to that point where you can show the real efficiencies. And we're looking for efficiencies for hospitals, for the government, for the payer, and for the patient. We try to find the sweet spot where pharma wins by having more adherent patients and better outcomes, physicians win by having healthier patients that are more compliant, the patient wins because they're healthier and happier, and the government is happy because it's saving money. Everyone wins. This can be difficult to find in health care.

HP: What are some of the more difficult aspects of your role?

CM: It's those grey areas: How much do we pay for end-of-life care? How do we say no? How do we make these decisions? What health economics tries to do, is we try to give tools for that decision-making. We'll do a cost effectiveness analysis to look at the quality of life, and we try to put a number to that to give you the tools to make a decision. Do I fund a new drug for a cancer treatment that improves progression-free survival by three months, versus do I vaccinate a population for meningitis B? You've got to make these kinds of decisions in the health care sector.

HP: How does the arrival of more insanely expensive drugs impact you?

CM: We have a health care system where we don't have an infinite budget. Care is getting more expensive. We have an increasingly elderly population and more targeted therapies are coming out. more biologics are being developed. So the questions get harder. You don't need to have this kind of advanced analysis for new generic cholesterol products, that's not where we're being brought in. We're brought in when it's tricky, and when that value isn't immediately apparent. And it's really about getting to the base of that value and having it be believable and make sense and work for the health care system. It's not just about marketing, it's about finding value in a product and in the data and in the system.

HP: So once you help a company, or health care system, figure out the value of a medicine, is that the end? Does your work end there?

CM: Randomized controlled trials are developed around a very specific patient population and it's a very controlled environment, not very big, and not representative of the real world, the wild, wild west of health care. Patients are messy; you don't have one typical patient. But say you have a new drug that's just launched -- there is no real world data. The role of modeling is to say, "Here's how it could look in your population." We have to revisit our models after we have enough real-world data. That's where big data comes in, and electronic health records, to revisit that cost effectiveness and see, "Did the model make sense? Did it work? Does the drug live up to its efficacy in the real world?" And that's something we're seeing more and more of, especially in the U.S.While safety and efficacy are always No. 1, then you need real-world studies to show how the drug really works in the wild. Does it really give the savings that it promised to the health system? So we do a lot of analyses after the fact. So it's not just a drug launching, but it's showing value to a health system. At no point is our job done.

This interview has been edited for length and clarity.