Welcome to the Clinician Researcher podcast, where academic clinicians learn the skills
to build their own research program, whether or not they have a mentor.
As clinicians, we spend a decade or more as trainees learning to take care of patients.
When we finally start our careers, we want to build research programs, but then we find
that our years of clinical training did not adequately prepare us to lead our research
program.
Through no fault of our own, we struggle to find mentors, and when we can't, we quit.
However, clinicians hold the keys to the greatest research breakthroughs.
For this reason, the Clinician Researcher podcast exists to give academic clinicians
the tools to build their own research program, whether or not they have a mentor.
Now introducing your host, Toyosi Onwuemene.
Welcome to the Clinician Researcher podcast.
I'm your host Toyosi Onwuemene, and it is such a privilege to be here.
I am super excited about today's episode because I have a really extra special guest.
It's Dr. Eman Metwaly.
And Iman, I want to thank you.
Welcome to the show.
Hi, Teoci.
Actually, I want to thank you too.
Thank you so much for the invitation, and I'm really excited.
Thank you for being here.
So Iman, the audience is excited to get to know you.
I want you to introduce yourself to the audience, especially from the perspective of yourself
as a clinician and a researcher.
Yeah.
So currently, I am a second year post-doctoral center in the epidemiology department of the
School of Public Health at the University of North Carolina, Chappell Hand.
I used actually to be a pulmonary and critical care physician.
I practiced for nine years in Egypt as a pulmonary and critical care.
So during my residency and then my PhD program after I finished my residency, and then maybe
one year after, I practiced pulmonary medicine.
Then when I immigrated here to the U.S., I studied biomedical health informatics, and
then I joined UNC as a research fellow.
And this is my current position nowadays.
Wow.
Iman, thank you for sharing.
So you have a unique story where you actually finished your clinical training, and you practiced
as a clinician, and then you made the transition to research.
So I would love for you to share with our audience, what were the big ahas for you,
where you had already been the expert clinically, and now you are kind of starting training
as a researcher?
What were the big surprises for you?
Well, thank you.
This is a great question, and I really love this question.
When did it start?
When did the spark start?
So training started at the bedside.
So I was a resident, maybe towards the end of my third year as a pulmonary and critical
care resident.
And the system there in Egypt is a little bit different with me back up.
So in Egypt, we do six years for medical school, and then one year as an internship, and then
we apply for our residency.
And the residency does not start with internal medicine, it just starts with the specialty.
So I went straight from the internship to pulmonary and critical care specialty.
And as I said, I was interacting with patients with lung cancer and chronic obstructive pulmonary
disease.
And treaties got my attention into how the patients, for example, with lung cancer, would
be staged based on radiology, CT, and other investigations into stage one.
And maybe after following up this patient, you would find different trajectory for them.
Like some patients with stage one would do very well, and some patients with stage one
would do very unwell.
And some would have the same for survival.
It's not only like their quality of life, but also for survival.
So this heterogeneity in the patient's outcome, despite they have the same classification
that we classified them initially on clinical side, made me think about the heterogeneity
of disease and the link between how we classify and diagnose patient and put them into boxes
based on our classification, like lung cancer based on the stage and the histology, COPD
based on the pulmonary function severity.
And how this box that we put the patient in, this are in their outcome when we follow up
them during their clinical course and during our clinical care for them.
So I started in, during residency in Egypt, we have to prepare like a master degree.
So I prepared my proposal was about like description of the clinical profile, histology, radiology
for patients with lung cancer.
That is attending our university hospital.
So the hospital that I worked in was a tertiary level hospital that served a big area in Egypt.
It's not only in Alexandria city, but also in the area, like the small towns around Alexandria
city.
So I had maybe around more than 300 patients for my study, 112 of them had lung cancer
and the rest were controls, patients who did not have lung cancer.
And I started to look up into these patients at all levels from how they are diagnosed
initially and how their diagnosis was confirmed.
And later on their trajectory towards treatment, who got surgical treatment and who got like
chemotherapy or radiotherapy.
And I got fascinated by, as I said, how much different their course would end up despite
they had been classified initially into the same category, based on clinical side.
Then after finishing my master degree, I, in the same year, it was around 2011 maybe,
I attached it to the BHD program.
And my initial proposal was actually to continue into understanding more about heterogeneity
of lung cancer.
I was looking into doing some, you know, molecular signature type of study to understand how
the genotyping of these patients might explain the heterogeneity in their clinical outcome
later on.
But the techniques that is necessary for doing the genotyping and the molecular data analysis
was not feasible to me at the institution I worked in.
So I had to switch gears.
So I'm glad because I said, okay, so maybe lung cancer was very complex and, you know,
the neoplasm stuff and the progression of the disease is very aggressive.
So maybe that's why there was heterogeneous outcome in the patient.
Let's switch to another maybe chronic, more benign disease, and that's what COVD.
And then after, just not to make it like a long story, after I started the clinical profile
again, radiology, and yeah, at that time during my PhD, I had to do something more complicated.
So I also did endoscopic visualization of their airways and found that the heterogeneity
is so severe.
The more you go into detail, the more you examine the patient, not only clinically and
maybe physiologically and based on the lab, but the more you go inside the patient by
the endoscope, for example, in my case, and looking into how their endobronchial erythema
is visualized and process their samples from the bronchial wall, the more you discover
the shortage of how we classify them based on the clinical side.
So I got fascinated by that we should improve classification of our diseases.
And these are common diseases, COVD and lung cancer.
They are very common everywhere in the world, not only in developed or developing countries.
So that was like the thing that's pushing me that we have some, I might have something
that I can contribute to improve how the patient can be classified.
Because our classification determine our management of the disease.
So if we classify them more precisely, we would be able to treat them more precisely
and accordingly we will improve their outcome at the end.
So yeah, that's a lot.
Wow.
No, thank you for sharing.
One of the things I have to say is, first of all, I love the passion with which you
speak about the work.
I mean, it's just so awesome.
And another thing I see is that, so you're talking about lung cancer classification or
COPD classification or classification of lung diseases.
And these are diseases that have been classified.
But as a clinician, you could see that there's a gap.
And so the classification doesn't always help you tell how they're going to do.
And so you're seeing opportunity for a new or a more refined classification.
And so we already have solutions, but you're looking for finer, more specific solutions
so that we can classify patients according to how they actually do because it's going
to affect outcomes.
I love the excitement that you talk about with which you talk about it.
It's so awesome.
I want you to speak to what are the advantages you've had as a clinician who's gone into
kind of like a very solitary research focus.
And what are the disadvantages of being a clinician in this space?
Yeah, thank you for asking this question.
So I feel that one of the biggest advantage I had as a clinician is that I was at the
bedside of the patient.
I felt the patient, how the patient is confused at the beginning, their suffering to know
their diagnosis.
Are they like, and their prognosis.
How much are they going?
How much time I have doctors, especially when it is a new blast, getting in touch with their
caregivers, knowing about the burden of caring for a patient with lung cancer or whatever.
And at that time during my residency, I was around 2008 and there was in the institution
I worked at and there was no much, if I would say like targeted treatment or maybe advanced
treatment for lung cancer.
So even if the patient was diagnosed at an early stage, it was like kind of maybe an
early notice of death for their caregivers.
And some would like even hide the diagnosis from their patient.
So feeling all of that, I think was understand like the force that is moving me and pushing
me forward to like what I am doing, the research I'm doing now will make a difference, not
only in the life of the patient, but also in the life of the people who are surrounding
the patient, who are taking care of the patient.
So this was one of the biggest advantage I feel that I had and that is living with me
now even after I left the clinical practice side.
Another point is that the mentorship, I really was like, it was my pleasure actually to work
under the supervision of great mentors who despite many of the like maybe like there
was in, I was working in a university hospital, tertiary level hospital, but not all the techniques
that we need to conduct the research was there.
And actually one of my like mentorship team like connected me with some of the mentors
or some of his colleagues and friends in Europe.
And we started to talk about maybe some of the stuff that we can do together, but again,
some of the regulations, especially regarding like transferring some of the samples to be
processed overseas was like kind of not allowed maybe from this point.
And it stopped the research from going farther, but at least just having access to other like
the experts in this field through my mentors was a great advantage.
And it was like, there is no limit.
You can reach there or maybe after you finish your PhD here, maybe you can travel overseas
to continue your line of research.
So I was fortunate to have these mentors as role models and as like someone who always
encourages someone who listen despite all, you know, I was doing my PhD in palm tree
medicine at the same time I was practicing and just having someone who supervise you
who is flexible, who is understanding was a big thing to me.
And then some of this advantage is that not everything was bright and good.
Like as I had some good mentors, I also had some other voices who would tell me because
who would tell me like, why you are aiming too big in your research?
Why you are like, like part of my PhD actually research was done in collaboration with Harvard
University.
So I was in Egypt in doing my research and I was sending them some of the image radiological
images over the email and they were developing a new software for quantitative image analysis.
And their software was not yet on, you know, ready to be used, but they really helped with
me and we were like experimenting together.
How can we use my relatively primitive CT images to get into how the COPD can be classified
in a quantitative manner?
So while I was doing that, I heard other voices actually from around me in the institution
I work in and why I'm going too far?
Why are you doing this?
Maybe this will be your last maybe research you are doing in this area.
So just finish, you know, finish and do a good job, but you don't have to be very sophisticated
to this extent.
But I didn't listen to that.
Like I know that if I wanted to do something, I want to, like, I don't know what will come
next.
I didn't know that I'm going to immigrate to the US.
I didn't know anything about that.
But I was fascinated when I go, when I finish my like here for my patient and at when I
at the end of the day at night, I would go into my laptop and I greet other people research
in this area and I would say there are a lot to do.
There are lots and rules.
There are a lot that I can collaborate on.
I can bring to the, you know, to the table.
And that's really what fascinated me about research is that no matter your background
is, no matter your country of origin is, no matter your, how you look is, it doesn't matter
as long as you are going to have a good idea and you will have the good people to collaborate
with it will work.
So yeah, so that's how I tried to use my advantage to overcome my disadvantage.
And I think that I did something good.
Yeah, thank you so much.
That was, that was really, really insightful.
I hear you talk about just the relationships with mentors that really helped you move forward
and allowed you to find the expertise you needed to move your research forward.
But then also people who were naysayers who say, well, you're moving too fast.
You're trying to do too much.
You're being too sophisticated.
Slow down or pull back.
And really you had a sense that this was so important.
And that's why even though people told you don't do it, you still moved forward, which
is so awesome.
And I just want to encourage our listeners because this is something that comes up all
the time where people will hold you back or people will say you're trying too hard or,
you know, slow down or try a different perspective.
I think what I'm hearing, and you didn't say it explicitly, Iman, was just that your heart
and your gut, like your sense of the importance of your work really does matter.
And even when you don't find support, it's important to continue to push forward.
Yes, exactly.
Thank you for liking it, for using it in maybe a much better way than mine.
It is this.
Yeah, it is this.
The message is that when you believe in the cause, even when you believe that the cause
is bigger than me, it is not me that I wanted to do it.
It's just that I wanted to be part in order to push this, you know, push this cause to
a better place.
Push our patient care to a better place.
And I see some light.
There are some light here and not a lot of people are paying attention to this area.
So let's highlight it.
I don't know how I'm going to highlight it, but I will keep pushing and see how it goes
from there.
So absolutely.
And that's where great breakthroughs come.
So one thing you talked about that I really want us to talk about is collaboration.
So now you're a full-time researcher, sometimes looking to collaborate with clinicians.
Tell me about how clinician scientists should think about their collaborations and especially
with regard to the clinician perspective.
Yeah.
So let me talk about when I started as a clinician who collaborated during my residency and my
PhD study.
Like I did research in lung cancer and in COPD.
And at that time I was collaborating with other people who maybe are not practicing
with patients like pathology professors and those from the community medicine or public
health school in Egypt.
So really as a clinician, I had the idea that I wanted to look into this question, but I
didn't know that I should go maybe might, I should might have gone earlier in my research
question phase to maybe an epidemiology expert just to see how I should design them.
I study how many patients are required, sample size calculation, all of these details.
So being a clinician who wanted to do something like at population level, I should have gone
earlier to someone who's expert just to give me guidelines about how many should I enroll,
what are the settings of enrollment.
These are important.
So collaborating and engaging and talking about your research question while you are
a clinician with a large, larger group of people, people who are outside your departments,
people who are not practicing medicine.
Even what I learned later on is that just talk it with the day people, people in your
family, see how they see that research question and its impact on whatever their friends or
whoever had the disease and they know of.
So listening to many perspectives would add a lot of depth to your research question.
So this is a thing when I came here to, and be patient.
So as a clinician, we have also like narrow time maybe to spend in research and we had
in our mind, like some things are common sense.
Like the collaborator in front of me should have, you know, understand it from my first
time I have said it.
But the actual or the reality is not that.
Like what is common sense to you from your perspective is not necessarily the same from
the other person perspective.
So maybe talking about your research in different ways, visualize your research question, visualize
why it is needed in different ways and multiple ways.
And now it is very easy because we have a lot of visualization tools and we have a lot
of evidence that is available, you know, online from previous research.
All of this like advertise for your research question.
And if you don't know how to talk about it, eloquently talk with the people from your,
you know, field who can talk about it and, you know, steal their words.
If this is like after under their permission.
The whole idea is try to highlight the need for your research question as much possible
as you can.
Use all the resources around you and be patient.
So this is I think when I was a clinician.
And now when I moved to the U.N. and I am included, like I'm enrolled only in research
sites, what I see, like what I would love for my clinical collaborators to do is that
maybe attend more often the research group meetings, like listen to how they receive
your research question.
How they there are many decisions that are needed to be taken regarding the study design,
regarding the number of patients needed, regarding the study settings that will not be solved
unless they had this clinical understanding of how the magnitude of the problem at the
clinical sites.
So really talking with the people back and forth and being patient sometimes like the
person who abstracted data from electronic cancer record or, you know, manipulate the
data to create the variables we need.
They need a lot of back and forth just to create the correct variable to make sure that
after the data analysis is done, that you are, that the data analysis is answering your
question, not answering another question.
And be patient because sometimes after all of that work, there might be a small error
in the coding that you have to maybe repeat the data analyst has to repeat the data analysis
again in order to answer your research question.
So this is another point that I learned really during doing my research and I am learning
now at UNC is that always question your results.
So just don't be very happy about breakthrough or very large association you found.
Always question the validity of your results and try to do as you know, sensitivity analysis
as much as you can in order to make sure that what I am exporting to the scientific world
is correct.
Do the best of my not.
So this kind of checking, check, checking my results and, and making sure that what
I am saying is credible, I think is very beneficial for myself first as, as a researcher, you
know, early stage researcher trying to build a name for myself in the research world and
also for the research, scientific research environment in general.
We wanted to have like good data.
We want to have to build the trust in the research community.
So again, it needs patience, a lot of involvement, talking a lot to each other at the collaboration
table.
So yeah, that's, that's really awesome.
Thank you, Iman.
What I hear you saying is that, so clinicians have a lot of knowledge that they don't remember
that nobody else has.
Yeah.
Yes.
And the importance of not being, not being afraid to over communicate, like continue
to just because you shared something once doesn't mean you can't share it again or doesn't
mean that they got it the first time.
And so taking responsibility for communicating and also checking that the communication was
delivered or understood.
Correctly.
Yeah.
Yes.
Because they might seem that they got it, but they do not get your point, your find
point.
Sure.
Yeah.
And then I'm also hearing that it's important for the clinician to stay involved and to
help question the data when it comes out, where it's like, okay, why does this say this?
Does this make sense?
Even if it's an exciting finding to actually be willing to question the data so that you
can make sure that what you have is high quality before you release it to the scientific community.
Yes, exactly.
Yeah.
Yes.
I love that.
I love your comment about staying involved because clinicians can be very busy.
And so I want you to just, actually you gave great recommendations.
You said attend the meetings.
Yeah.
So that they can help answer questions at that point, which is awesome.
I'm wondering if there are any other practical steps that clinicians can use to stay involved
in the research?
I think also having basic information about how the data is generated.
For example, I'm not sure.
For me myself, I'll talk about my journey.
When I first started just looking at the research, I started here as a fellow, I started just
looking at the data output.
This is how, for example, lung cancer is coded based on histology.
This is how the patient's socio-demographic is created.
But I did not know what happened behind the scenes.
Why socio-dermography?
For example, why the race is classified this way?
Why the socioeconomic status was classified this way?
What was behind the score that I used in my classification?
And what are the implications of describing the socioeconomic status using this score
versus another score?
I was not getting into details of that.
And I was just trying to understand the table, the Excel sheet in front of me, and maybe
talk with the analyst to why not to look into the situation between that and that until
I started to learn about how the data was generated, first of all.
And the difference between using this score versus that score and how this affects the
outcome of the analysis at the end.
So just maybe not all the clinicians will have the chance to do a master's program in
clinical research.
But just having a basic or maybe listening how the data was created, was generated, is
very helpful for them because it will affect, again, how their research question can be
answered.
The second thing is also to have maybe basic also knowledge about the available data analysis
tool that are there.
Like for example, I want to make sure that this research question can be asked using
electronic health records because it is really different than using maybe another data set
like claims data.
So taking some time to understand which data source would be best for my research question
is very important because I learned that the hard way.
I formulated research questions for a grant proposal and I submitted my grant.
I proposed to use actually both data, electronic health record and claims data.
And at the end, when I received the feedback, of course, it wasn't accepted, but the feedback
was one of them.
This is a little bit theoretical because this kind, maybe the first question cannot be answered
using this type of data set.
And again, this kind of information will not be known until you talk with the people.
It will not be known using the ABAP Med and searching for similar study that try to answer
your research question because that's what I did.
But just talking with the people who used both different types of data sets will help
a lot to choose and to save you time and effort and emotions after the grant got accepted
or whatever or rejected.
So yeah, it's again talking with the people.
I appreciate your comments and thank you for that.
So what I'm hearing is that don't try to do it by yourself.
Connect with the people who know and ask questions and don't just accept things at face value.
I appreciate you saying that.
And I can see that it's born out of your experience.
You've done that before and then you've learned after the fact.
And so what you're doing is giving people shortcuts, which is awesome.
Yeah, yeah, yeah, exactly.
Exactly, this is the case.
And also my first point was that try to learn how the data was generated.
Yes, this is very important because before I learned that, I was just like, you know,
there was something missing in my understanding what happened.
And sometimes I miss parts of the conversation because people who are not clinician are very
knowledgeable about the scores and the measurements and how they can operationalize, like how
to get a concept and make it consumable during data analysis.
This is called operationalization of a term or of a concept.
So this operationalization kind of thing, I think the clinician need to understand it
because this is the transition between the idea in their heads and how it can be analyzed
on the ground.
I love that.
You know what it makes me think about?
It's like as clinicians, when we're getting data from patients, we're first of all getting
stories and we're converting stories into data.
When epidemiologists and biostatisticians are looking at the data, all they have is
data without story.
And so it's a different way of looking at it and they don't have the element of story
to interpret things.
And so as you say, when they're trying to operationalize race or social demographic
information, or even diagnosis, they have to look at all these codes to come up with
the story.
And so being able to see how that story is being created from the data helps you, the
clinician, because you can say, oh no, that story is not plausible.
This is what makes more sense.
And so I like that idea.
Just make sure you understand how that data is being generated.
Don't just take it at face value.
Yes, exactly.
Thank you so much.
I love the description of a story and you have to listen to for different sides of the
story.
Yeah, I love it.
Yeah.
Thank you.
That's awesome.
Well, thank you.
So, okay, we're coming up on the end of our podcast episode and I want you to just share
any insights with people who are thinking, this is too hard.
I can't do it.
I'm just a clinician.
I can't do research.
What advice do you have for them and what things should they consider as they're moving
forward in their decision?
You are needed.
I have to say to that to the clinician.
You are really needed.
No research can advance without the input from the people at the front line.
And being a front line like heroes as clinician, really you have the responsibility, besides
your responsibility, taking care of the patients.
If you really have the passion for the research, because doing any work, doing any job can
be tedious if you don't have the passion for it.
So if you really have this question that you couldn't answer during your conversation
with the patient and you know that the answer for this question will come from researching
it more, you have the passion, but maybe you did not dig deeper into it.
So just talk.
And the need for clinician in the research, you might not need to devote big time.
Just conversation with someone with the researcher.
You don't know how much, like for example, me myself, I practiced for nine years and
then I shifted into research.
But sometimes I feel that because I practice in different setting than the US, just talking
with clinical research or the clinician, really you can say that, illuminate me into
how I can better tweak my or refine my question in order to fit and benefit the patient at
the bedside.
So clinician can really contribute to the research that is going on at different level,
at different time, you know, requirements.
And like I would say just to start with a small time devoted and then how it goes, maybe
you will get into it and you'd love to continue and contribute more.
But don't make like the time and business like a big barrier for contributing to the
research because I am sure that every clinician at some point, whatever experience they had,
they had unanswered questions with their patients and they are responsible.
That's how I see it.
We are responsible to answer this question somehow.
And how this how can vary from few minutes talking with a researcher to maybe a few hours
every week and so on.
So yeah.
That's really awesome.
Iman, thank you so much.
I love what you said.
Just having passion is important and having unanswered questions.
So for any clinician who has unanswered questions, you're already able to contribute because
your unanswered question is the source of much research that could lead to an answer
to the question, which is so awesome.
And you started, you said clinicians are needed.
We absolutely are.
And I just want to invite as many people who are listening to just recognize that if you
don't ask the question, nobody else may answer it.
But you're asking the question, can precipitate others answering that you're asking the question
may precipitate others answering that question.
So definitely recognize that you are needed.
Iman, you have been just such a wonderful, wonderful guest.
I appreciate your time, your insights, and it's just been a pleasure having you on the
show.
Thank you for being here.
Thank you so much, Toyosia.
The same here really, I felt so happy and so glad.
And thank you.
Thank you for your exciting questions.
It was an honor.
Thank you.
Thank you, Iman.
All right, everybody.
You've heard Dr. Metwally.
If you're a clinician, you're absolutely needed in this research enterprise and definitely
connect with your collaborators and really be connected as the research questions are
being answered.
All right, I will, I am excited to see you on the next episode.
Thanks for joining us today.
And I'll talk to you again the next time.
Thanks for listening to this episode of the Clinician Researcher podcast, where academic
clinicians learn the skills to build their own research program, whether or not they
have a mentor.
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