Garth Sundem   00:00

Welcome to this episode of the Medical Affairs Professional Society podcast series: “Elevate”. I’m your host Garth Sundem, Communications Director at MAPS. And today we are speaking with experts from Pfizer and Veeva systems about quantifying unmet medical needs. Joining us are Andres Quintero, Global Medical Affairs Director with Pfizer, Tricia Langlois, Director of Field Medical at Pfizer. And Ilan Behm Practice Manager, Business Consulting Analytics at Veeva. So Andres, we talk about, you know, field medical representatives having conversations and maybe anecdotally, they come across unmet need that would be returned to the organization as an insight. How do we move from anecdote to data and quantify unmet need?

Andres Quintero   01:00

Hi, Garth. So this is a question that’s very near and dear to my heart that I’ll tell you, if I can kind of back up, take a top to bottom approach. If we think about health care, and in general and about medicine in particular, they’re really probabilistic endeavors, right? So I’ll give you an example. In clinical medicine, if you see a patient coming into the emergency room, they’re morbidly obese, they have a history of hypertension, metabolic syndrome, maybe they have some hyperlipidemia, and they’re complaining of chest pain, you’re going to drop a differential diagnosis. And the number one item under differential is myocardial infarction, acute heart attack, right? Okay, if I paint a similar picture of a patient coming in with the same signs and symptoms, but maybe they’re really tall and thin, they’re very pink in the face, they have a history of heavy smoking, you’re going to think of a pneumothorax, or differential may contain some of the similar items, but the probabilities of these items are going to be different, right. So your item number one is going to be different in one scenario than item number one in the other. And in clinical medicine, we’re not necessarily trained explicitly to assign probabilities, but we are nonetheless trained to think probabilistically through that kind of conditional logic or Bayesian logic. And so we apply that to healthcare, everything that we do, that’s worth doing is to mitigate the risk of certain outcomes of interest, whether they’re clinical outcomes, whether they are systems based outcomes, we have limited resources. And in the old school way of doing things, oftentimes, a decision to do something one way or another, has boiled down to kind of heuristics or rules of thumb, decision making in times of imperfect information where there’s an asymmetry of information or information gaps, maybe the information we have is not very good, or complaint, or maybe we have no information. So we kind of have these shortcuts to aid in our decision making. But those we know for behavioral economics are really imperfect guides position making, you’ve read man. Right? Yeah. And so I think that, you know, we can work with that framework, when we try to understand what’s going on in clinical medicine and in healthcare. And that really opens up a nice framework for us to say, okay, in the industry, all of our decisions should ideally be based on some kind of empirically sound, logically defensible sources of information. And I have found that data while they’re not perfect, and certainly when it comes to real world data, there’s a bunch of epidemiological limitations. They are certainly much better than the next best alternative, which is to make decisions in the absence of data. And so when I started working at Pfizer, I started out as a field medical director and the conversations focused around something along the lines of this okay, it’s one field Medical Director, one MSL, who is covering a large region, whether it’s a state, or whether it’s multiple states, and there’s no shortage of healthcare providers for us to engage in, but our time is limited. How do we prioritize which HCPs right? And so, when I started, it was okay, well, you do some Google searches, you talk to your commercial colleagues, you you know, you talk to other field medical directors who have historically managed the reason that you’re taking over. But really, if you’re really just kind of in the hierarchy, or the pyramid of evidence, that’s kind of like the equivalent of level five expert opinion at best, we can do a lot better. And so that was around the time when we were starting to also consider hiring some vendors who were taking a more empirically driven approach to identify key wells with claims data with meta data from Medline and clinical trials.gov records for scientific activity. So as we and we’ve, we have made great progress in that space in the last four or five years, it’s really evolved to keep pushing the envelope and through close partnerships between vendors and in organizations like Pfizer. And so what I have started to see is kind of an opportunity where, you know, there’s we all know, narratives are really important in corporate culture. And in the life sciences industry, one of the big narratives has been, how can we help identify unmet needs? How can we help the patients that are in the greatest need? And so if you think to what I was saying earlier, everything we do in healthcare and medicine is designed to mitigate the risk of certain outcomes. When we think about populations, populations with a given disease are very heterogeneous. Right. So within a given population, there are some patients that are integrate a greater risk for the outcomes that we seek to modify with our products relative to other patients. And this is why in clinical medicine, we have severity classification systems grading systems, where the risk of an outcome is greater among patients who have certain comorbidities, right. And so as we think about how to define unmet need, you’ll probably see some between observer variability and how that’s defined. But in my book, I define unmet need as a big part of a subgroup of patients where you are at the greatest risk for the outcomes of interest that we seek to modify with our products. And those are historically patients that are finding very difficult time with therapeutic options either because they have so many comorbidities that they can’t tolerate side effects, or they’re just not deriving the effectiveness or the efficacy from the product relative to what’s expected from the distribution of patient characteristics in table one of your clinical trials where there may be a selection bias where those patients are generally more healthy. And the results might not be as generalizable to those patients. So this is a very long winded way of saying that we can really start to rely on data to empirically identify these app high risk patients with the greatest unmet need if we define it as being at the greatest risk for these outcomes. And once we do that, we can map out the epidemiology, Epidemiology, or we can map out the epidemiological distribution of burden across the state across the country. Across the United States, defining burden as a function of the prevalence of these comorbidities or risk factors are features that are heavily associated with these outcomes, and also the risks that they introduce for these outcomes. And so all else constant. If I’m a Medical Affairs manager, who has to figure out how to deploy resources, whether those resources are field medical resources, whether its financial resources, I would certainly want to understand what are the geographic regions where the burden is the greatest where the potential for impact when the greatest unmet need exists? So that we can at a population level ensure that the investments and the deployment of resources are going to have the greatest impact on on these lives?

Garth Sundem   08:27

Okay, well, I know the first thing I’m going to do when we get off this recording is I’m going to look up the adjective form of the word epidemiology, and see if it’s epidemiologic, or what in the world that could possibly be. But second, it felt like in the middle of your answer you pivoted from addressing the unmet needs of KOLs to addressing the unmet needs of patients. So it’s one of the first data driven avenues into this ecosystem was to identify KOLs who had unmet needs who maybe weren’t prescriptive, the guidelines. And now, are we going a step further and identifying unmet need and patients?

Andres Quintero   09:08

Absolutely. And you have a good ear, Garth, it really is epidemiologic distribution. And I’ll say that, yeah, so So logically, what we’re doing is once we identify the patients with the greatest unmet need, and we’re literally developing models, regression models, you know, things like Poisson regressions, Cox Proportional Hazards models, you know, Poisson regressions are models where we can distinguish between the patients that has one episode of the outcome of interest, say an emergency room visit or a hospitalization and the patient that has 10 over a given year, and Cox Proportional Hazards modeling which, which is a really nice kind of adjunct to that is to say, among patients who have experienced at least one outcome of interest during the look back period. What is The time that’s most likely to lapse between then and the next event, can we predict the time to the next event, right. But once we’ve done all that, we can identify our enriched pool of patients that are at the greatest risk and link them to the HCPs that are taking care of them. And so is this a much more patient centric way of identifying priority? HCP is like in the past, right, cool. Gotcha. A lot of vendors have looked at claims data and said, Okay, Dr. X, has really, really high claims volumes, much more than Dr. Y. But as it turns out, Dr. X, maybe 95% of his or her patients are not very severe cases, maybe they’re very mild cases. But Dr. Y maybe is taking care of a much higher risk pool of patients, maybe that doctor is at an academic medical center where she or he splits their time between clinical care and academic care. And they have a referral pattern where there’s the sicker patients coming to see them. And so, by risk adjusting these metrics, we’re better able to account not just for clinician throughput, volume, but a risk adjusted throughput volume and placing more emphasis on the HCPs that are taking care of these highly burdened patients. And once we do that, we can then link that to the FMB in their region that they’re covering and incorporate the results of those findings when the FMDs are asked to prioritize their list of HCPs.

Tricia Langlois   11:32

what I’d like to add, Andres, I think what’s novel about this is like what you said earlier, we used to focus on the key Kol, or the docs, and we’re really making this focus on the patients, which really revolutionizes, then how we find the patient’s to make the impact of the change, if you will,

Andres Quintero   11:53

Yes, absolutely.

Garth Sundem   11:54

That’s such a Medical Affairs perspective. You know, it’s not that we are trying to adjust the behaviors of prescribers full stop. It’s that we’re trying to guide the behavior of prescribers to benefit patients. And we’re going to make our metric, not the scripts, we’re going to make our metric where we can identify this unmet need, and intercede to create better patient outcomes. Oh, I’m jumping the gun here, Tricia to take us on the next step of our journey. And that is from now we have identified the unmet need, what what does Medical Affairs do about it? And how do we measure the impact of those actions?

Tricia Langlois   12:43

Right. So that’s something that we’re historically faced with a lot we’re doing work is the work good? How do you measure good work. And I think just to go back to that point that Andres made earlier, we used to just jump on Google find some docs talk with colleagues, well, now we’re finding the patients. And by being able to find the patients tying in Back to the physician, we can jump in and start doing better fsmta deployment, I need to focus my time here, focus my time there. Instead of historically, where I was focusing my time on the physicians, I need to focus it on the patients and by being able to focus it on the patients find out where they are, find those patients with the unmet need, doing education, we can make change, right, and that’s changed is going to be the impact by getting the pill by getting the vaccine to the patient quicker. And this is a novel way that allows us to do that.

Garth Sundem   13:43

Okay, so we identify the unmet need, we address the unmet need, can can we just put a point on how we measure the fact that we’ve addressed it? Are we measuring what changing HCP behaviors or changing sentiment in the landscape or so? So really, what are we measuring to show that we have addressed this unmet need? I mean, anyone jump in?

Andres Quintero   14:16

At the top not to solve or to crack you, Garth, but I think look, this has always been the existential question for Medical Affairs. How do you measure the impact? Right? Yeah, we know that Medical Affairs drives impact. The reason we know that is because nobody would ever very reflected the fact that we know that is it nobody would ever dream of running a pharmaceutical company and not have a Medical Affairs function. It would all fall apart very quickly, right. But I think the problem has always been that we are not financially driven and so it’s very difficult to assign value to measure value, right. I also think part of the channel challenge is that outcomes in healthcare are very multivariable. And we’re, we’re really practicing population health management. I mean, I hate to use that phrase because it took off as a buzzword once upon a time, but it’s really public health, right. And in public health, we have this thing called roses paradox and roses paradox is a phenomenon where if you are taking care of a single patient for hypertension, and you lower their blood pressure on the systolic by five millimeters of mercury, that’s not a big move, it’s not a big change, right? Big whoop. But at a population level, if you lower the average, or even the median blood pressure on a systolic by five millimeters of mercury, that is a huge accomplishment. So there’s there are these types of paradoxes. And when we talk about systems based interventions, and felt Field Medical Director deployment, the field is a systems based intervention, when we talk about FMD impact, we could be having a very large material impact that quantitatively may not look that large. And this is occurring in an environment where there are many other forces that are driving change in the outcomes in one direction or the other. So theoretically, we have a baseline with this analysis, we could do follow up analyses one year, two years out for the outcome of interest, right. So I’ll make it very real. So for migraines, and we’ve done this for asthma, we’re identifying patients who are going to be at the greatest risk or who are at the greatest risk for care that requires encounters in acute care settings or urgent care emergency room hospitalizations. Our goal if you kind of apply what we’ve we’ve been saying our goal is really to focus or prioritize those zip codes where the H patients reside, or they’re getting care. And we know that the zip code where you live in population from a public health standpoint, that does impact your health. And so we would be engaging with key opinion leaders in those areas, and hopefully, the education that we offer, and enhance the quality of care both for disease stay above brand, and maybe even for discussions pertaining to our products, right. But hopefully, the education that we offer can enhance the quality of care that these patients are receiving, right? Imagine, you know, even in an ideal scenario, we publish the results of this analysis, and we go to the HCP. And we share with them these these findings. And we say, you know, we know from the work that you take care of a very large population with unmet needs, that kind of is well represented by the Forest plots, or the cumulative density curves that we generated from these analyses. And so maybe we plant a seed in the mind of that HCP to really keep a close eye out for patients who have high risk. comorbidities are social determinants of health. And maybe it’s happening at a at a level, not just for individual patient care. But ideally, maybe even that as from a systems based perspective, maybe it opens up opportunities for collaborations with healthcare organizations. Maybe this dialogue can open the door to payers with dialogue with payers where we can influence their willingness to cover our products, that we can demonstrate that these products could significantly reduce the risk of outcomes for their beneficiary pool that are higher risk than those of their peers. And so, with all this going on, it’s a dynamic environment. But in the best case scenario, one or two years out, we could do a follow up analysis to say, okay, maybe for these data, we demonstrate that there’s a reduction in ER visits or hospitalizations, or whatever outcomes, we sought to model that we seek to modify through these through these interventions. It is challenging because if we find that the needle moves in one direction or the other, it’ll be difficult to attribute that change to our intervention, right. And if the needle moves in the direction that we would not hope that’s not necessarily to say that we’re not adding value. And so I think that part of the challenge is that there is always a need for a corporate narrative that communicates to key stakeholders the value of Medical Affairs. And this may be one way to kind of potentially support the narrative if it’s done very cautiously, very in a very thoughtful manner, that we have to allow for room that there are gaps of information there are asymmetries of information where info is not perfect enough to attribute outcome or impact to the FMV involvement. So all this is to say that there could be opportunities to innovate around the space and push the needle, but by no means would be easy to to crack that map.

Tricia Langlois   20:00

Well, and I think too, by having better FM ta deployment in those areas of the unmet need, we can make individual impact showing that these patients through these providers or healthcare systems that we’re in, are getting their disease managed earlier and more effectively. So they’re not having Andres, you alluded to frequent physician visits, frequent IDI visits, frequent urgent care visits.

Garth Sundem   20:26

Alright, so even once we quantify, we still require context. And maybe that is a place for for people to innovate. I think we’ve been talking from the clinical side so far. And Ilan, I know you’ve been over there salivating to get in words about the data side of things. So it seems like, okay, we can, we can now identify that needle that needs to move, we can show how the needle has moved, attribution to our activities still remains a little challenging. But what this all hinges on is having the data to to to make some of these analysis. So where do we get the data? And what do we do with it?

Ilan Behm   21:09

Yeah, that’s a great question. So Veeva has access to some really unique data, which can actually help with with some of these engagement trends here. So through our pulse data, we have access to 600 million HCP interactions that are occurring across the globe. Cross over 80% of biopharma is worldwide where we can see field interactions on the medical side on the commercial side. But here we’ll focus on the medical side, with BPS and KOLs, some of the leading health systems across the globe, and really then be able to dive into each of these HCPs patient bases to understand do they have some of these patients with these unmet needs? And then over time, as Andres was mentioning, kind of track, has there been an improvement in what these unmet needs are? Has there been improvement in adherence, earlier diagnosis, patients getting on some of the most innovative and new therapies out there? Overcoming market access barriers, things like that. And so recently, what we’ve done here at Veeva is put together a field Trends report what we call in release that really looking at MSL engagement over the last few years focusing on the US and looking at MSL engagement, specifically with KOLs, before the launch of some of the newest migraine medications out there out there to see can we associate this pre education that MSLs we’re providing to the medical community with treatment adoption post launch, and we did see that there was 1.5 times greater adoption of these newer therapies in areas and health systems where MSLs were active, as compared to health systems where they weren’t active.

Garth Sundem  22:52

Okay, so you see the fact of field medical interaction with an HCP. And you say that HCP who has interacted with field medical personnel has a 1.5 times greater chance of prescribing the the molecule the treatment once it once it once it comes out. So that that is a great case study of identifying this unmet need, we have high risk migraine patients, we want to move the needle, and then we can show the impact of field medical in actually moving that needle. Boy, what I’m looking for is holes in that interpretation where it can be attributed to anything else like you know, Andres, you bring up like if the needle is going in the wrong direction, it’s not always a show of lack of value, maybe the needle was going more slowly in the wrong direction because of what we did. But Elon that that that seems to me like a demonstration of measuring field medical impact. So why do we say that it’s still such a such an existential, existential challenge for field medical, like, didn’t we just do it? Didn’t we just measure the impact? What is this group thing? Didn’t we just solve everything? Everyone on this podcast? Alright, thanks. So we’ll take our bonuses.

Ilan Behm   24:22

And yeah, well, I think, you know, from from our perspective, right, I think this is, you know, first of its kind type of report where we were really able to kind of look at this engagement data. I think this engagement data is unique. I think the unmet needs like Andres and Tricia were mentioning really do vary by TA by disease area, right? So I think we first really need to do a better job of quantifying what those unmet needs are. But then, you know, data like Veeva pulse combined with some of the outcome and healthcare data that we were discussing here earlier, can be used to really kind of support and validate and quantify the impact medical field is having so that there are less questions. All right, and here’s a little less nebulous going forward.

Garth Sundem  25:03

Cool. Well, we are pretty much out of time today. Although we could go two hours on this. Let me just leave us with the idea that we have now answered the existential question in Medical Affairs and we now know how to measure the impact of our activities. Thank you, Andres, Tricia, and Ilan for joining us today. To learn more about how your organization can partner with Veeva visit Veeva.com. MAPS members don’t forget to subscribe. And we hope you enjoyed this episode of the Medical Affairs Professional Society podcast series: “Elevate”.