Hello, I’m Dr. Jeff Kingsley and welcome to another edition of Riding in Cars With Researchers. Today we’re going to talk about fair market value. We’re going to talk about what it is and why you should care. Specifically, I’m going to try and convince you that it’s flawed in how we’re assessing what fair market value is in clinical research and why that matters, why it hurts.
Fair Market Value – Different in Healthcare?
First, let’s explain fair market value in the rest of the world versus fair market value in healthcare and research. Fair market value is whatever someone’s willing to pay for a company: in the technology space, in private equity, in venture capital. For an example, Facebook can decide to pay $1 billion for some startup company that doesn’t even have revenue or has never been profitable, and the rest of the world goes “well, then that’s what somebody was willing to pay.” So that’s what ‘rest of the world’ fair market value is. Voila, that business is now worth $1 billion. When Facebook pays one billion, the value of that company becomes one billion. And now the next investors expect the value to be above one billion, and it continues on and on and on.
Now that is not the case in healthcare. Quite literally, it’s illegal to pay anything other than ‘real’ fair market value. Why? The route of that legal precedent is that one should not be able to buy a physician’s conscience. If you can pay a physician so much money that they don’t want to risk losing their job with you, then in theory you could buy their ethics, you could buy their decision making. That’s fundamentally why we have these rules in healthcare that say you can’t pay anything other than fair market value, and that’s sensible. I have no issue with that. That’s why it exists.
So, what’s the definition of fair market value? How do we define fair market value? Fair market value is the amount that a knowledgeable, willing, unpressured buyer would pay to a knowledgeable, willing, unpressured seller in the marketplace. And those terms are important: knowledgeable, willing, and unpressured. That’s how we determined fair market value.
Determining Fair Market Value in Healthcare
Now, how do we determine it in the clinical research space? Well, largely the industry uses two leading databases and they aggregate the data on all of the investigators across the U.S. And what they have agreed upon in previous contract and budget negotiations. It’s similar to how we get fair market value on real estate: what are the most recent transactions that have taken place in your neighborhood with homes of a similar size? It’s called comps, comparables, comparable data, and so the industry is collecting data, aggregating data, on what all other investigators have agreed to in terms of the value of the services rendered.
The other place that this fair market value data comes from is existing healthcare data and Medicare data on what physicians are paid for services in standard of care healthcare. Now, let’s talk about the errors that are inherent in how doing this in clinical research. First would be the errors around these aggregations of data in contract and budget. What are the two things that are contributing to big errors? You’ve probably heard me talk before about Tufts University Center for the Study of Drug Development, Center Watch, etc. They “research research” and they publish regularly on how we’re doing and what we have discussed. 51% of all physicians who get involved in research fail in their first research trial and never go back. Those are called the ‘one and done’s’. They did one trial and never did a second. And we ask them, why did you never go back? And what they say is they could not be profitable.
They didn’t realize how much work was involved. They didn’t realize the regulatory burden. They couldn’t choose the correct research trials. Well, what does all that mean? It means they were unknowledgeable. Now remember, the definition of fair market value is the value that a buyer would pay a seller who is knowledgeable, willing, and unpressured. The one and done’s are willing and unpressured, but they weren’t knowledgeable. They didn’t know the actual costs. But, the one and done’s, their data is in those database aggregates that is determining fair market value. The people that didn’t know what they were doing? Their data has the exact same weight in those databases as the people who did a really outstanding job and truly knew their costs and negotiated aggressively. They are weighted equally, which means that the people that didn’t know what they were doing, their data shouldn’t be in there in the first place. Their data is holding down the people who did know what they were doing, the people who were knowledgeable.
The EQTCS of Fair Market Value
Those very same sources publish on how well we do on research, specifically on enrollment and what do we find? Again, 11% of all research sites fail to enroll a single patient and about 40% of sites underperformed. There’s another 51% of sites underperforming and or enrolling. No patience whatsoever. That’s just the enrollment metric and you’ve heard me talk about EQTCS – enrollment, quality, timelines, customer service – sites are underperforming on all of these metrics, but what do we find out? We find out that despite underperforming, all of those contract and budget metrics are treated equally in the databases. The fair market value databases.
Let’s make an example: your toilet is clogged and you’re going to hire a plumber. Let’s say you knew that this plumber almost always under performs. This plumber tends not to actually get rid of the clog and the plumber is not good at customer service: doesn’t adhere to timelines, takes weeks and weeks to actually arrive to unblock your toilet. EQTCS on plumbing. Would you pay the same for that plumber as opposed to a plumber who would show up promptly, had high quality customer service? There are different fair market values. You’d pay differently for those two plumbers, but in the research world, they are considered the same. They are weighted the same. Our databases aren’t complex enough, aren’t sophisticated enough, to weigh those two contract and budget negotiations differently. The other point that I made is that there’s a lot of fair market value determination that derives from healthcare data, from Medicare data, in the delivery of standard of care in healthcare. Why is that flawed? There’s a dramatic difference in the complexity of the work that a physician is doing in standard of care healthcare versus research.
I explain to doctors all the time: you are a cardiologist and receive a call that you have a patient in the emergency room. Their CHF (congestive heart failure) has just dramatically gotten worse and now they’re in the ER. You can walk into the ER and do a history and physical. What are her current medications? Did she take her medications today? Has there been any deviation in her diet? What does her urine output look like? Okay, now let’s write some orders. Let’s give her some IV Lasix. We’re going to do this, going to do that. We’re going to put her up in a bed. We’re going to make her better rapidly and get her back out of the hospital and back into her own home. You can do it in your sleep. You’ve done it so much. It’s very rote.
It’s relatively low complexity because it’s what you’ve done ever since your fellowship. Now, very same patient hits the ER in a research trial. Same cardiologist, same patient. You walk into that patient’s room – entirely different amount of complexity: “Okay. The patient is in a research trial, let me get the investigator brochure. Let me relook at the medication that this patient is on. Is there any indication that this medication could possibly have contributed to the congestive heart failure? What things can I write for in light of the fact that this patient is currently in a research protocol? Have there been any other adverse events in the research protocol?” Entirely different level of complexity because the patients is in a research protocol: same physician, same patient, different complexity.
There is no reason that the physician’s compensation while working in a research trial should have any build upon that same physician’s compensation while performing standard of care healthcare. They are entirely different. There should be a substantial multiple against normal standard of care rates or there should be an entirely new calculation that determines fair market value for those services rendered in a research trial. Why does it matter? It matters because the sites that are trying to be excellent on EQT, ECS, enrollment, quality timelines and customer service, and the sites that are trying to invest heavily in infrastructure, their budgets are being held down by the people that didn’t know what they were doing. The fact that some of these values are derived from standard of care values, that means that sites that are trying to invest to perform exceptionally well are being hurt by the current way we calculate what fair market value is. That’s why it’s a big issue. It’s not just that these sites are being paid less.
The Ripple Effect
It’s that there is a ripple effect in that, by being paid less, sites invest less in producing excellent service to the industry and to the world. It’s a vicious cycle and we’ll talk about vicious cycles in a bit. So here’s my call to action: educate yourselves on what fair market value is and how it’s calculated in the research space. It has big flaws in it, and the reason you should care about these big flaws is because the ripple effect is that sites are handicapped, sites are handcuffed. It’s harder for sites to actually invest in performing better because there’s a flaw in how they’re paid. That’s why it matters. As always, thank you for riding along with me. Fair market value. It’s flawed and it hurts and it’s preventing and handicapping sites from being able to improve. It’s not good for our industry. So my call to action? Pay attention to fair market value. Let’s fix this paradigm. It doesn’t make any sense the way it’s happening today.
Thanks for riding along!
Send me topics you want me to talk about at jeff.kingsley@centricityresearch.com!
Subscribe to our blog HERE