In this episode, we speak about one of the most important work relationships – the one with the physician. As a statistician, we work with them on all kinds of things on the job. They also might become great friends.
In the episode, we will cover the following topics:
- Topics to increase the understanding of where the physician is coming from such as
- How was he successful in his previous job – e.g. emergency room setting with command and follow setting to solve urgent crisis
- Hospitals and physician’s offices are built with the physician at the center as he is the bottleneck
- Has he even been a KOL before joining the company – that’ll make it hard for him to switch to a new culture
- Mindset and tactics to create a successful partnership such as
- Have 1:1s in which they you can answer their perceived “stupid” questions
- be a partner and not a service provider
- Present data together with the physician and show him, how you both can benefit
- How to become a more valuable partner to the physician by e.g.
- Provide draft conclusions based on analyses
- Learn his language as you encourage him to learn yours
- Have a physician as a mentor to learn how to best work with them
- Tips on how to give the physician insights into your job and that it is not a push-the-button exercise
Featured courses
Click on the button to see our Teachble Inc. cources.
Transcript
How to work with a physician within Pharma to become a valuable partner
00:00
Welcome to The Effective
00:10
Statistician with Alexander Schacht and Benjamin Piske. The weekly podcast for statisticians in the health sector designed to improve your leadership skills, widen your business acumen and enhance your efficiency. In today’s episode number one, we’ll talk about how to work with a physician and pharma to become a valuable partner.
00:34
This podcast is sponsored by PSI, a global member organization dedicated to leading and promoting best practices and industry initiatives for statisticians. Learn more about upcoming events at PSIweb.com
00:59
This is Benjamin Pisgis speaking from the podcast, The Effective Stylistician. Today we’re talking about how to work with a physician within pharma and I’m here with my co-talker Alexander. Hi Alexander. Hi Benjamin, nice to speak again. Alexander, I think as I said, we are today discussing how to work with a physician and I think…
01:28
This is something where you have a lot of experience with, a lot more than I do actually. So therefore, maybe you can quickly introduce what this is or where this is coming from, the topic in itself, and maybe describe a little bit about how you worked in the past with a physician, your first experience, your experience in general, and a little bit about
01:55
you know, how this is daily on a daily basis going. And maybe we can then start discussing and how this is different to my experience. Yeah. Okay. With, you know, interactions with physicians, I think is one of the key aspects of a statistician where we as statisticians can have a big influence and impact on the work and see the relationship with the physicians.
02:26
that decisions that work within the pharma industry or specifically within the sponsors, very, very key. And maybe today I really want to focus on speaking about the physician as a colleague, not the physician as a customer. So of course we have the physicians prescribe our therapies.
02:50
We work with them as so-called key opinion leaders or thought leaders. We write papers together with them and there’s lots of other engagement with external physicians, so to say, so external to the pharma companies. But today, I really want to focus on how we can work effectively with physicians within the pharma companies.
03:20
And they, of course, have lots of different responsibilities. We write together with them papers, we run the studies. We prepare all kind of negotiations with payers or with other regulators. So lots of the interactions is between statisticians and physicians. It’s probably the first people that…
03:49
work on the study as well as the last people that work on the study for example. Together with the statistician. Yeah, obviously together with the statistician. So that’s why I think it’s really such a key relationship for us as biostatisticians. But isn’t a physician like a little bit of a statistician anyway? I remember that in the medic, you know, at university, they usually have…
04:19
part of their lessons is about statistics. Isn’t this of great help for all the relations that we as a statistician have with them? Yes, that is of help. However, there’s a lot of variation in terms of stats knowledge between the different physicians. There’s lots of variation about medical knowledge among the statisticians. That’s true.
04:46
You speak to a very, very interesting point. I think one of the key things to have a successful relationship, a successful working relationship, is that you try to speak each other’s language. And as the physicians learn to speak stats, it’s great that you learn to speak medic. And that was one of the key…
05:14
things that I learned over time is the more I learn their terms, their way of thinking, the easier it becomes. And of course, you also have the possibility to teach them about stats language. What is p-value? I don’t know how often I have explained that concept, but yeah, I’m probably not alone in that.
05:43
you know, like a shared responsibility then that on the one side they, you know, we are asking them to learn what we are doing and on the other hand, we expect or they should expect us to learn what they are doing? Yeah, I think that is, that for me is an ideal scenario. However, I think when I see across the industry.
06:11
that ideal scenario is very often not reached. And I think that is for a variety of reasons. The first is, I think,
06:25
When physicians come into the industry, and maybe they were already very well established within their medical society, so maybe they were head of a department or head of a clinic, and then they joined the industry.
06:47
They actually have a very, very hard time at the beginning to work in this very, very different culture. So imagine you’re in physician and you work day in, day out in an emergency room setting where every minute or every second counts and what you do has a direct impact. And so…
07:17
everything that you say and tell really needs to be followed. So it’s really kind of in crisis situation and very quick decisions need to be made and then there’s no room for discussion or lots of additional explanations if you come from that setting and then you come into a study team where people push back.
07:46
and people want to know the background and people want to argue with you, it might feel very, very different or it probably will feel very, very different. And lots of physicians, I think, at the beginning struggle with this cultural change. So I think…
08:10
Lots of physicians come with into the industry and have this natural thing of thinking, okay, I’m the lead, I tell what it’s done and then you as a statistician, you go and implement. And
08:32
So that is I think very often something that’s quite naturally coming from them because that’s how they have done their job in the past. And also isn’t it, it seems that people or physicians coming from hospitals might then also think rather on the short term, isn’t it? Then if they have a problem, if they have a patient.
09:00
if they have a problem to solve, they need to solve it as soon as possible. I mean, in order to find surgery or whatever the requirement is. However, if they work in pharma, they are about to plan a strategy for a product or for a study on a long term, so years in advance. So they have to plan more than just the next two weeks or how long it takes to get a patient well and up.
09:29
up and running, but it might take years to get a product on the market. There are some other aspects of the whole thinking that might be difficult. We might need to anticipate. Because it is the same for statisticians, the statistician is always, on the one side, is asked to solve problems. We have data, we have a problem, we need to get a result.
09:59
work with a medic on the planning for a study, it is more than this. It’s really like a partnership with the medic in understanding each other’s thinking and each other’s strategies in order to get the results in a two years, three years time. Exactly. As I said, sometimes the physicians
10:28
struggle to come into this kind of partnership mode, but likewise, I see many statisticians struggle to get into this partnership mode, just maybe more from the opposite side. So, you know, if I think as a function, as a statisticians overall, we have a tendency to be rather introverted.
10:57
And if we then kind of, if you’re more an introverted person and then you need to work with maybe a very, very extroverted physician, that maybe has a little bit of a belief kind of, you know, he knows it all. And well, it’s for he has treated these patients.
11:27
you are developing a new therapy for.
11:32
then that might not lead to this partnership. So it might lead to a situation where the situation is just on the receiving end or feels he’s just on the receiving end and just implements what he’s told to implement. And I think that’s not how you can have an impact as a statistician. And
12:01
What I want to see that decision doing is going into these kind of discussions and meetings with a mindset of playing on the same level.
12:19
I think that is very, very, very important.
12:26
Both in this teamwork, you need to have all the different pieces. You need to have a very good statistician and you need to have a very good physician on the other hand. Of course, there’s lots of other functions that we will not speak about today, but I think if you work on the same level, then you can really drive the project forward in completely different ways.
12:58
that I think is important is here to really think about this as a partnership. I remember one incident earlier in my career where I went with a couple of other statisticians to lunch and there was also this statistician that was coming fresh out of university and he was…
13:23
hearing all the other kind of statisticians complaining about their medical counterparts. In a discussion with him, he mentioned, well, yeah, I’ve learned a couple of things in the first weeks here in the pharma industry. One of the things is the physicians are the enemies. I said, wow, okay. No, I think. Only because lots of people are complaining about it.
13:53
positions as counterparts doesn’t mean that they are the enemies. But I still, you know, that struck me really that, you know, even because there is this misunderstanding that can even be perceived in that negative ways. Well, no, they are definitely not our enemies.
14:20
I mean, as you said, they are the partners. And even though it might be difficult to get along with them for several reasons, I agree that the success in any of the tasks that we have as a statistician is only there if we do work with a physician together. And well, I mean, I think sometimes, as I said, I think it might be sometimes that the
14:49
of the physician. The career steps of the physician may be part of the reasons why they’re acting as if they were the boss. Sometimes I experienced that they were actually the budget holders. In a way, they were the boss because they could make decisions that others couldn’t, especially not the statistician. Nevertheless, I think in my
15:17
From my experience, it is always that the success or the smoothest studies that we were running were if the medic was involving the statistician before making any decision. Well, not any decision, but any relevant decision, for example, regarding protocol or changes to the protocol. I think the partnership…
15:46
Partnering with the statistician from the medic side was one, and vice versa obviously, was one of the key experiences which led to successful and smooth working on studies in the past. Completely agree. I think if there’s a really good relationship, you can…
16:11
create something that is much bigger than if people work in isolation. Just as an example,
16:20
When the data of the study are presented, it’s sometimes really helpful if that is done by the physician and the statistician together. So I have very good experience with that, that you can then speak to the, let’s say, medical specialties quite well because you have the medic there. But then…
16:48
If it comes to the core data, then hand over to the statistician and explain where are the strengths and where are the limitations of the data. Because that is really where the statistician usually can speak much better to the synthetic. And you have a much better presentation with two presenters that can throw the ball over to the other side.
17:18
backwards and forwards and have a much more lively presentation than if you try to train the physician to speak about a stats methodology that he’s not really familiar with. And what would you say is then a good strategy for convincing the medic to, for example, not do a decision or to not do this? I mean, there are some quite often reasons.
17:46
from a statistician, let’s say the data quality, the data, just the extent of the data that is available that would lead the statistician to say, well, I wouldn’t recommend to run the same in a specific subgroup, for example, because. But the medic would say, well, I mean, but this subgroup is the one that the audience will be interested in.
18:15
So we need to present something. So what is your strategy then to convince or to discuss the point or the issue with the medic?
18:29
I think there’s two different strategies here. The first strategy that I’ve seen is that people say really push for it in terms of that they say, well, I’m the decision-maker of the study so I have the ownership and this is my play field so I will present that.
18:57
That’s one way of doing it. And sometimes it works. What I found works actually better is that you try to sell it more like of helpful for the physician as well. So for example, on the stats side, to give him a couple of challenge questions.
19:27
If you see that he is uncomfortable with that or that he gets awake about the answers, then just, well, what about if I present that and you make the introduction and the conclusion and these kind of difficult parts, the heavy parts, I do present that. And then you’re relieved from the pain to answer these kind of nagging questions.
19:57
that you’re not fully prepared for. So I think coming from this being helpful is usually a better position. Of course, it gets you a little bit kind of in an inferior position, but over time, people will really acknowledge that. And I think that…
20:25
brings you in a better position to come into this, to present together, for example. But sometimes it also helps to be a little bit pushy. So I’ve seen that, for example, with first and senior authorship and publications where very often there’s this tendency that by default the physician or the physicians take these positions.
20:54
And sometimes it’s really necessary to put a stake into the grounds and say, okay, the majority for this study or for this paper was actually done by the statistician and therefore that statistician should be first or last author on that.
21:25
than for example, the statisticians working in the CRO business. Yeah, I guess so. Just one more thought on working together with the medic and making decisions together. What I’ve also done in the past is to kind of provide the medic with data to support his ideas. But not…
21:54
provide everything that he’s asking for. So kind of finding a compromise. For example, I mean, physicians love p-values. I mean, a p-value is a thing that they, you know, they have a p-value. So they have a, let’s say, like a proof, so some kind of significance. And so they are happy with it. But sometimes the data doesn’t allow to provide p-values.
22:21
And I’ve had many discussions about p-values, providing p-values, not to provide p-values. And I think this is one of the things, for example, where you can find a good compromise saying, okay, this is the data, we provide the data, we show what it is, we can explain it, but we don’t give a statistical significance with the p-value saying, you know, and now everyone, you know, kind of preventing the physicians to prove to the world something which is not…
22:50
reliable from a statistician’s point of view. Yeah. So, P-value is actually a very, very good point. So, I think that is also where in this relationship, you need to push back. Sometimes, you really kind of need to stand the ground and don’t just do what you’re told. So, as you speak about P-values, you
23:19
One thing is, for example, P values for baseline tables for randomized studies. And of course, the physician will come with some kind of paper where there’s for each baseline covariate like age and gender and pretreatment and what have you, there’s a P value. And they will say, well, yes, the other colleagues has done this as well.
23:48
we need to have these p-values in our paper as well. No, you don’t. But then really explain it and explain him why it makes no sense and why it may be even perceived as being
24:17
unscientific, I think that is the key thing. So I completely agree, from time to time, you actually need to push back. The other thing that you said I think is also important, to not just provide the tables and then think that job done, but provide the tables together with a good interpretation. But of course for that, you need to really understand what the physician…
24:43
needs and you really need to translate it into his language. So I think for that, initial listening for what is really the problem is really key. I think this is what you mentioned before with trying to learn the language of a physician and understanding where he’s coming from. And so probably to
25:09
For delivering any results or any interpretation of the results to the physician, I think it’s a good idea to always spend a lot of time with the physician to understand what he’s looking for. Just because there’s a sentence in the SAP, let’s say, or somewhere in an email asking you to provide this and this, it doesn’t mean that you understand what the medic is asking you to do.
25:36
This is sometimes completely two different languages, two different meaning. So really about going back to a point, you know, which is key in many, many things is really communication. So really get down with that person, talk to them, understand, maybe repeat whatever he’s saying in your language. So to understand that this is the same, this has the same meaning as what he’s asking for.
26:06
provide to the physician what the statistician should do, the data, the outputs, the results and the interpretation for the medic. Yeah, completely agree. It’s really about early checking, a common understanding. And sometimes, you know,
26:25
Physicians may only be able to see it if you present them with a table, even with maybe fake numbers in it at the beginning, or made up numbers, just to get him a feeling for how that will look like. Because the vast majority of physicians I have worked with have really big trouble to read the SAP.
26:52
Primarily because the SAP is not a document that is targeted towards physicians. It’s more targeted towards other statisticians or to programmers. If you just provide them with a 50 or 500 word document of specifications, he will not really understand it.
27:21
understand it and that he is used to will help a lot to clarify that there’s a common understanding of what needs to be done.
27:33
And if you provide the data to like a dummy data or shells to the physician, explain it to him. Exercise explaining the results to the physician because I mean whatever you present in this output I mean if the medic doesn’t understand who else should understand it. I mean these guys are clever, these guys are smart. So if you provide an explanation or an output to them.
28:00
without explanation or with incorrect explanation and he doesn’t understand, nobody else will understand the outputs except other statisticians. But I mean other statisticians are not writing the clinical study report, other statisticians are not writing the publications or presenting the results. These are the physicians. So they need to understand what the statistician is trying to put into a table. Completely agree. I kind of think of it as the statistician explaining it to the study physician.
28:30
is kind of the first step of getting the results into the medical community. So if you think about it, from that, it goes into the publication. It goes to key opinion leaders, and then it trickles down into medical society. So if you fail to explain what these data actually mean in the first step, well, this whole cascade cannot work. So that is really, really important.
29:00
idea of better understanding physicians is actually to have a physician as a mentor. Because that helps you to get a completely different view on the industry and also to get a better understanding of their way of thinking as well as a better understanding of their language.
29:30
And I think if you’re working together with a medic every day over months or years, I think this is a really good idea. Yep. Completely agree. Okay. I think we are coming here to the end of our half an hour podcast already. So thanks a lot for listening and talk to you next time. Talk to you next time.
29:59
We thank PSI for sponsoring this show. Thanks for listening. Please visit thee to find the show notes and learn more about our podcast to boost your career as a statistician in the health sector. If you enjoyed the show, please tell your colleagues about it.
Join The Effective Statistician LinkedIn group
This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
