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 a pleasure to be talking with you today.
I'm especially excited because I have a special guest today, Dr. Teresa Coles.
Teresa, welcome to the show.
Thank you.
I'm excited to be here.
So, Teresa, how would you introduce yourself to the audience, especially with regard to
your role as an academic faculty member?
Sure.
So, I am an assistant professor, and I am very interested in measuring health.
How can we better measure health, particularly quality of life?
So, Teresa, I first met you, I feel like about a year and a half ago, and at the time we
met because I was interested in health measurements, specifically patient-reported outcome measures,
and I would like you to just talk about how different our perspectives were in terms of
what I was thinking of as a clinician with regard to patient-reported outcome measures
and how you think about it as the expert.
Oh, okay.
Well, let's see what I can remember.
So, I think one of the first things we discussed at the very beginning was what outcomes do
we think might be important to patients?
And there's so many different quality of life outcomes that might be of interest to patients,
but actually we don't really know.
So, I think that was one of our very first conversations is which outcomes should we
focus on and why?
We could go about measuring all types.
We can measure like 90 different types of quality of life outcomes, but that would be
a lot to have patients to do.
So, we try to focus in on what are the most important ones for them.
Sure.
So, one of the things that I thought about was using something like the SF-36, for example,
in my population.
And you talked about the need to have validity evidence to be able to use it in my specific
population.
And I was thinking, validity, what?
So, I want you to speak about that.
Speak about the fact that there are all these, I mean, they're validated or they have validity
evidence, but they may not have it in the disease in which I'm interested in.
How should clinicians be thinking about these tools as they're trying to use them in their
research?
Yeah.
Thank you for bringing that up.
So, here's the situation.
There are a lot of questionnaires, patient reported outcome measures, clinical outcomes
assessments out there.
And just because they exist or even if they've been used in various populations successfully,
that doesn't mean that they have enough validity evidence for every single use case.
So, for example, we might have a patient reported outcome measure or questionnaire that is working
really well for folks who are 65 and older with some sort of physical functioning issues.
Okay.
We may not be able to take that same exact questionnaire and apply it to adolescents.
And the reason for that is because these individuals will have different types of, let's say, physical
functioning impairments or issues.
And if we're asking questions that are not relevant to that patient, then we end up with
biases and we will not be actually measuring the right things.
And then we miss an opportunity to intervene and help folks with their quality of life.
That's a really great point.
Now I recognize it because we've worked together and you've taught me a lot.
But what I do see is a lot of people like, for example, the SF-36, maybe one of the more
widely used ones, why would it be a problem to just take it and apply it to your population,
especially because it's just so widely used?
Yeah.
So there's nothing wrong with SF-36.
I like the SF-36.
The strength of the SF-36 is also its limitation.
So the SF-36 is very broad.
It's used to look at general quality of life.
That's great if you want to detect general quality of life.
If you want to look at specific quality of life impacts based on a particular treatment
or particular condition, something like the SF-36 may not be able to detect those changes
or the very specific issues that patients experience.
So one example we use a lot in the class that Kristi Ziegler and I teach is fatigue, for
example.
Somebody might say that they're fatigued.
Okay.
Well, there's a thousand different measures we can use to evaluate fatigue.
But are we looking at physical fatigue?
Are we looking at emotional fatigue, mental fatigue?
Unless we actually know what those experiences are, we can't measure them effectively.
So SF-36 is great at measuring those broad experiences, but it also is so broad it may
not be able to detect the specific experiences we're looking for.
So both its kind of strength and its weakness.
Okay.
Okay, Teresa, it sounds pretty complicated.
So why should a clinician researcher think about working with an expert like you if they're
trying to use patient reported outcome measures in their projects?
So there are some best practices and how do I say this?
You're going to have to cut this out.
Why would you want to work with somebody like myself?
Because you're awesome.
Well, one of the reasons is because folks like myself who are trained in health measurement
know the methodologies, which include both qualitative and quantitative psychometric
methodologies to ensure we're measuring what it is that we actually want to measure.
If we do not approach measurement with this type of rigor, we risk bias in how we measure.
So we may have great sample sizes and we're measuring something, but we don't actually
really know what that is.
We also risk the potential of missing information.
So we may not be able to detect changes based on treatment or changes based on a condition
that we need to intervene on.
Thank you.
Thank you for highlighting what's important, why it's important.
I mean, because honestly, before we started working together, I really just thought, you
know, you take a measure and there's no reason why you couldn't use it.
But really there's a whole science behind it.
You are clearly a measurement expert and you've been doing this for a long time.
And so one of the things you highlight is just the need for collaboration.
So I'm really excited to talk about collaboration.
So I want to say, Teresa, that it's been an amazing opportunity for me to work with you
as a fellow researcher, as we've submitted some projects.
And one of the things I thought it was really just important to highlight is why should
clinicians and PhD trained researchers work together?
Oh my gosh.
The reasons are endless.
So I think I'll start with like a personal example.
For somebody like that, myself that's trained in methodology specifically, we're great at
coming up with methods, but we are not great at knowing what the clinical problems are.
So where are the opportunities to actually use those methods and make a difference?
So we often have what we call hammers, right?
So like we might want to use the same hammers of methodologies across many studies.
That's fine.
But really at the end of the day, that's not the most important thing.
The most important thing is that we're pushing science forward clinically and we're causing
improvement in quality of life and survival for patients.
So in order for us to like meet the needs of that issue, we have to collaborate with
clinicians who know what the issues are.
Not only that, but the clinicians kind of throughout a project, like for example, when
I collaborate with clinicians on a project, they keep it centered on the clinical issues
and the clinical relevance of what it is that we're doing.
So what are some patient experiences?
What are clinical experiences?
The clinicians also have direct contact with patients.
So how might we recruit patients?
They also have knowledge of what do the results actually mean, right?
So we, you know, like from a methodological standpoint, we have a certain perspective
on what these results might mean and we can provide percentages and tell you about IRT
loadings.
Like we can tell you all sorts of things, but at the end of the day, what does it mean?
How do we interpret those results?
And I think that's extremely important to work with clinicians on.
Absolutely.
You know, one thing you said reminded me of something that's come up before is the difference
between developing a measure with developing validity evidence for a measure for clinical
use versus for like a clinical trial.
Can you just speak to that difference?
Sure.
So there's a lot of common methodologies we would use for measures that we might use in
clinical care versus clinical trials.
It's all about the use case.
So in clinical trials, typically we would use, let's say, a patient reported outcome
measure to look at some sort of quality of life outcome over time and answer the question
whether the treatment is making a difference, improving or worsening whatever that outcome
is, or maybe not doing either of those.
Maybe that outcome is just staple.
That's like the primary use case in clinical trials and that could be a primary endpoint,
secondary endpoint, exploratory endpoint.
Another use case in clinical trials is screening.
You can use a patient reported outcome measure to screen individuals for participation in
the trial, so like inclusion and exclusion criteria.
How we set up the PRO measure and provide the validity evidence for those two different
use cases requires two different types of methodologies.
So that's just even within clinical trials.
And then we look at clinical care, there's lots of different ways we can use patient
reported outcome measures.
One is, similar to clinical trials, we can track patients' outcomes over time.
One additional thing we might do in clinical care is not only continually track those outcomes
over time, but identify specific instances for specific patients when they've either
decreased or increased on an outcome in an unacceptable way, where we know some sort
of intervention needs to take place.
That is a really important clinical care application and requires particular methods.
Same thing, so like most of us are familiar with something like the PHQ-9, which is a
depression screener.
That is something that we use in clinical care and the methodologies behind something
like the PHQ-9 are unique to screeners.
So do you want me to talk about the different methodologies or shall I stop there?
Please, I'm learning a lot and I do feel it's important because it's nuanced, so I think
it's helpful to know.
Sure.
So I wish I had my slides so I could show everybody.
So for clinical trials, anytime we're looking at outcomes over time, we are interested in
lots of different types of evidence.
That evidence is based on what is the use case.
So if we're looking at something over time, we first need to ensure that we're measuring
what we think we're measuring.
That's a type of validity.
We need to look at how we might score that measure and what would the scores actually
mean.
That gets into methodologies for understanding what we call the dimensionality of that measure.
So how many scores might there be coming out of that measure?
For example, the SF-36 has multiple different types of scores for different dimensions of
quality of life.
Another thing we need to look at if we're looking at scores over time is reliability.
So can we be sure that if somebody is, let's say, rated a 33 on day one and they have no
change in their quality of life by day seven, are they still going to be a 33 or somewhere
close to that?
That is ensuring that type of reliability is called test-retest reliability.
Another important aspect if we're measuring quality of life over time is being able to
detect change.
So just like with a thermometer, if I get a fever, that thermometer we hope is going
to detect that fever, right?
If it doesn't, we have problems.
Same thing with quality of life measures.
If we see an improvement or a worsening in quality of life, we use methodologies to be
able to ensure that we can detect that change.
Finally, all of this kind of filters into the final issue, which is how do we interpret
those scores?
So at the end of the day, all the methodology that we're using is all based in supporting
the interpretation of those scores from the measure.
We cannot interpret or we can't do a great job of interpreting scores if we don't have
evidence to prove that they're reliable or we don't have evidence to show that we're
able to detect change over time or if we don't have evidence to show what is the dimensionality
of our measure, what is it that we're actually measuring?
So that's just one example in clinical trials.
Those are typical things that we'll look for.
That's really excellent.
Thank you for summarizing that.
That was really, really well summarized and clearly you've been doing this a long time.
So for a clinician like me who says, okay, I want to work with a health measurement expert
like you, what kinds of questions should I be asking and what kinds of things would you
want to know to help decide if this collaboration makes sense?
Oh, great question.
So I think the key piece about working with folks who are health measurements focused
is one, learning what their interests are.
Some of us, for example, specialize in different types of populations.
So I typically work in studies that focus on adults.
Some of my colleagues focus on pediatric patients.
Those are slightly different methodologies that we might use to address these different
populations and how we might ask questions about quality of life for a three-year-old
versus an 18-year-old versus a 45-year-old.
Another piece is some of us have a lot of experience in certain conditions or diseases.
So for example, at UNC, there are a number of people who are really focused in cancer
and have lots of expertise on measuring quality of life specifically in cancer populations.
Some of us are more generalists.
So it's like, what is our background and where are we coming from and then how can we contribute
to the study?
So another piece is I would say in most studies where we're doing prospective data collection,
there's some sort of measurement component.
And a collaboration with a measurement colleague may be really small where that person is just
kind of like providing advice.
And they may not even actually be under study.
They just may be behind the scenes giving you some ideas.
And then all the way up to something like a co-PI, which is what you and I have done
before.
So as a co-PI, those would be very measurement specific studies where we are trying to optimize
measurement of some sort of quality of life outcome.
I also think another question that clinicians might want to bring to the table with potential
health measurement colleagues is what level of involvement do they like to have?
So that helps to kind of like focus in on I think the size of the study that you can
pull off and what types of studies you can move forward.
And a third thing that I would recommend is if that health measurement researcher is not
the right person, then who else might be because there's so many connections among all of us
and we have a good sense, like a telephone book in our heads of who's an expert in X,
Y, or Z, and then we can connect to you with the right health measurement person.
That's good.
Thank you for sharing that.
I wonder from your perspective in working with clinicians, what are some concerns that
you have?
I think at the get-go, sometimes clinicians don't have a sense for kind of like the best
practices for health measurement.
So unfortunately, sometimes a lot of our fundamental work to get a study up and running measurement-wise
takes a lot of time.
So there's like formative qualitative work or formative literature reviews that need
to happen and that just sucks out a lot of time where clinicians feel like they could
just move forward really quickly and just throw some patient-reported outcome measures
or COAs into a trial or in clinical care.
People do that all the time.
I think, and sometimes it's fine because that measure has already has validity evidence.
But a lot of times, well, I'm not going to say a lot of times, sometimes it's not okay.
So I don't even remember what the question was anymore.
Just concerns that you have about working with clinicians.
Well, I love working with clinicians.
I absolutely love working with them because they keep me focused on what are the actual
issues.
I can go down my circles and caves of measurement ideas and math and qualitative inquiry, but
at the end of the day, is that really going to make a difference?
So I feel like the clinicians help keep me centered on what are the actual clinical issues
that are going to move us forward in terms of outcomes of research.
So how are we going to improve patient's quality of life?
I like that.
So it's almost like what you're really speaking to is the fact that you're a partner in the
work.
And earlier you started talking about the speed to move forward because there's a sense
that starting the project and administering the outcomes, administering the PRO measures
is the most important thing.
But really there is a need for clinicians to understand that there's a lot of foundational
work that may not feel like active work, but is really important so that when they're moving
forward with the big clinical trial, it's valid.
And that the questions that they're asking, the answers they're getting are actually answers
to the questions they want.
They want.
Yeah, absolutely.
But formative work, it's like underpinning rigor that will pay off the dividends when
you want to use those data over and over again or continue to expand your project or your
research program.
Okay, so it's worth doing it well, especially the first time you're trying to get evidence
for a measure.
I love it.
Okay, so Teresa, one of the things that I think we come up against is that this kind
of work when it comes to PRO measures is not necessarily, I think, as well-funded as perhaps
if we came in just proposing a clinical trial doing some other things.
So I'm curious to know what kind of funding opportunities may exist a clinician researcher
should be thinking about for this kind of work.
Great question.
So here's the deal.
Here's what I have learned slowly over time, primarily in schools of public health or population
health or schools of medicine, what we find is that there's an emphasis on getting funded
through NIH, and that is fantastic.
The deal with NIH is that they're typically looking for studies that improve patient outcomes.
So that could include quality of life outcomes, right?
A lot of times, methodological work is not actually improving the outcome.
It's helping us better assess whatever that quality of life outcome is.
So for studies like what I do, NIH, sometimes we can get funded by NIH depending upon what
is the focus of that RFA.
But it's often not our biggest funder.
Other funders include a myriad of options, and I feel like there's more that I haven't
even discovered yet, but one is FDA.
So FDA is very interested in methodological rigor for the use in clinical trials so that
we're evaluating patient reported outcomes that are meaningful to the patient and with
rigor.
So FDA has what they call the broad agency announcement that comes out in the fall of
every year, and that is a big honking proposal.
So there's two stages to that.
There is a 10-pager, sort of like a LOI, like a letter of intent, and then there's a 50-pager
if you get invited to do the full thing.
And that's just volume one because there's also volume two.
However, the VA's, and I've had two of them, are a really great way to focus in on methodological
issues.
And FDA loves it because they want to support rigor in clinical trials for outcomes measurement.
They're very supportive of that.
Another opportunity with FDA for some institutions is the FDA CERC.
So some institutions, including Duke, UNC, NC State, were in a CERC, and there's other
CERCs with like, I think Yale has one, Mayo has one.
These are opportunities to work directly with FDA and address kind of like smaller research
opportunities.
Maybe I would say between 250,000 to 750,000-ish in size, but they are more frequently coming
through and really great opportunities.
Oh, you'll have to tell us.
What is CERC?
What does that stand for?
Centers of Excellence in Regulatory Science and Innovation, CERCs.
Okay.
So that's University of Maryland, Johns Hopkins, Yale, Mayo, University of California, San
Francisco, and Stanford.
So if any of your listeners are in those institutions, and Duke, and UNC, and NC State, then you're
covered by a CERC.
Yeah, so there's a few of us.
Okay.
So FDA is a potential funder and thinking about these CERC opportunities, if your institution
is connected to that, what are their opportunities?
Another opportunity is PCORI.
So PCORI focuses on comparative effectiveness research.
So if you are doing a study that might be comparing different interventions and looking
at those outcomes, that might be a good fit for you.
We've looked at the engagement awards together.
Actually one of the things that I did not mention before that's really important to
methodologies for health measurement is inclusion of patients and other stakeholders in what
you're doing.
So a lot of times we have stakeholder panels to kind of guide our measurement approaches,
and PCORI is really a great funder for those types of opportunities.
Other funders include industry.
So I'm actually working with David Leverins from Duke.
He's funded by Pfizer and supporting him on a project where we're developing a super short
questionnaire for use in clinical care.
Sometimes there's AHRQ.
Typically those are very health services related.
So if you have a health services project, let's say you are going to improve resource utilization
using your patient reported outcome measure AHRQ might be a great fit.
That's cool.
Thanks, Teresa.
So NIH, we all know, but also looking at FDA, the PCORI and then industry as well and AHRQ.
Awesome.
Oh my goodness.
It's so funny.
When you started out, you were like, oh, there's lots of options.
I'm like, what?
There are lots of options, but it's good.
It's good to know because you're right.
I think as clinician researchers, we're really focused on NIH and really thinking about which
funder really aligns with what you're looking at.
And it sounds like clinicians and researchers should think about these other potential opportunities.
That is great.
Okay.
So I'm wondering what question should I ask you as someone who's a measurement expert
and I'm a clinician researcher, what question should I be asking you that I might not think
to ask you?
Oh, let's see.
Are you thinking about for like grant submission, study design, what might be happening in clinical
care?
I think all of the above because what I'm hearing you say is that your work is relevant,
not just for research, but even in clinical care as well.
And so maybe we should ask the question two ways.
Maybe the first question is as a clinician, what should I be thinking about and what questions
should I ask my health measurement person who's at my institution that may be relevant
to me in the clinical space?
Oh yeah.
So in the clinical space, one really great kind of low-hanging fruit is to work with
health measurement experts to look at questionnaires you might already be administering in clinical
care.
Are they actually up to the task of doing whatever it is that you want them to do?
Right?
The health measurement expert will give you the support to be able to look at the evidence
as there for those measures and also how you can use those scores to kind of support decision
making within clinical care.
Another opportunity or question that you could work with health measurement folks on is what
questionnaires might I want to administer in clinical care?
And then I can guarantee you that the health measurement person is going to ask you, well,
how would you want to use the scores?
Okay?
So there are a lot of really great opportunities to incorporate screeners in clinical care
to identify patients that really need help at that time.
So if there is, for example, a symptom that you need to know more about, maybe it's pain,
maybe it's itch, we can either implement or identify questionnaires that might help you
be able to do that quickly.
And then a third question that's a little bit more broader is, you know, tell us about
your research program and then where might health measurements kind of support and help
the rigor of what it is that you're trying to do?
I love it.
You know, the first one of the thoughts that came to my mind is, you know, our patients
are sitting in the waiting room on an awful long time.
And so could there be a measure that's actually relevant and valid for that particular question
that the clinician has that they could fill out in the waiting room that actually contributes
to the information the clinician needs to help further their care?
Yeah, that is literally the best time to get patients to ask questions.
We've seen it anecdotally and we've also like evidence about that that's published.
Great.
Okay.
How about how about research?
What questions?
I mean, I think we touched on a lot of them earlier, but what question is on asked that
we should we should definitely make sure people are considering?
Well, I'll tell you what's asked that I would like for people to potentially consider modifying.
So people, people might come to us and say, what questionnaire should I include in my
research program?
And it's that's not really the question to be asking.
The question is, how can I measure this thing that I want to measure?
Because it's not so much about it is about the questionnaire, but more importantly, it's
about what it is that you're trying to measure.
So one thing that I would like to communicate is that it's like I don't have a Rolodex of
questionnaires on my desk where I can say, oh, yeah, you know, here's the 50 measures
on fatigue and let's choose one of them.
There's a lot of work that goes into choosing the most appropriate measure for any given
circumstance.
A lot of that is just background literature review, reading what evidence is there, what
work has already been done, and then what we also what we know about our population
of interest and then how that matches to what the questionnaire is actually evaluating.
That is so, so awesome.
And to be honest, I started laughing because I was like, yeah, that's what I thought.
I thought you just you know, all the questionnaires that are out there.
I do.
I think that's a common I think this is a common misperception, you know, misperception, because
I think some people think that we're kind of like librarians of instruments and we're
not.
Thank you.
Thank you for clarifying that you're not.
OK, so what I should do is ask the question is be clear about what I want to measure.
OK, let's use fatigue as an example.
OK, if I say I want to measure fatigue, is that fatigue?
Is that enough?
So here's some of the questions that I will ask you.
If you tell me you want to measure fatigue, I will ask you, well, what is it that you'd
need to know about fatigue?
What is the purpose of measuring?
Right.
Is this a clinical care application?
Is this clinical trials, clinical research?
How often do you want to measure fatigue?
How do you want to use those scores to make decisions?
I think that's like that final question is like zing.
That's what really gets us the answer.
So that helps us really define and zone in to be able to get you the measure that is
going to be most useful.
Sure, sure.
So I'm hearing that.
So you want to, you know, you want to understand the concept you want to measure.
So for example, fatigue, but you also want to be clear about why you want to measure
it, what you're going to do once you find the fatigue and how you're going to respond
to that.
Yeah, absolutely.
OK, all right.
This is very nuanced.
OK, everybody who's listening, if you are trying to use a patient report at outcome
measure in your study, you got to get with an expert.
And they all have this amazing network.
So if you find one, they can find you the right person.
OK, I think there's one more thing I want to ask, because I feel like this is something
that I learned from you too, is so when I think about patient report outcome measures,
I just think of them as just, oh, it's patient reported outcome measures, and they're so
important.
But really, there's a bigger context of clinical outcome assessments under which patient reported
outcome measures are a subset.
So can you just speak to the broader perspective of clinical outcome assessment and how maybe
patient reported outcome measures may not be what we want to use?
Yeah, absolutely.
So I think patient reported outcome measures are typically the most often discussed type
of clinical outcome assessment, but there are so many more.
So actually, on FDA's website, there's a really nice set of definitions for each of these
assessments, but I'll give you an overview.
So we think about clinical outcomes assessments, these are ways that we can measure patient
outcomes.
OK, so there's four primary ways we can do it.
And for some quality of life outcomes, we can use all four of these ways.
So let me go through each one of those, and then we'll talk about it more.
So one that we've already discussed is patient reported outcome measures, or PROMs.
Another one is observer reported outcome measures.
So that is where we are OBSRO.
So that is where an observer tells us about a patient's signs.
So what can they observe about how a patient is functioning, for example?
Another type of clinical outcome assessments that I'm sure a lot of the clinicians here
are used to are ClinRose.
So clinician outcomes assessment.
So that's a clinician reported measure, and there are a number of those in clinical care.
So for those in cancer, like the ECOG, for example, is a clinician reported outcome measure.
Finally, there's performance measures, or PRFOs.
So those are opportunities to essentially test patients on their functioning.
So like the six minute walk test, or stand up and go, those are examples of PRFOs.
All four of those types of clinical outcomes assessments can be used to evaluate patient
health status, and they can be used as primary, secondary, exploratory outcome measures in
clinical trials, or even obviously for use in clinical care as well.
We use these all in clinical care all the time.
The choice of which one of those you use depends upon what it is that you're measuring and
why you're measuring it.
How are you going to use those scores?
That's really good.
Okay.
All right.
So we're at the end of the show, and the last question on my mind, which to some extent
you've answered, but so if there's a clinician researcher out there who's now like, whoa,
I had no idea, where should they start first as they investigate using patient reported
outcome measures or other clinical outcome assessments in their projects?
Wow.
So I think the first thing that I would recommend for that clinician to do is get clear on what
are the outcomes that you think would be important to measure and why.
The next thing that I would do is contact somebody who works in health outcomes.
Sometimes that's fellow clinicians.
There's a lot of fantastic clinician researchers out there who are doing work specifically
in clinical outcomes assessments.
Sometimes it's a clinician.
Sometimes it's PhD trained researcher like myself.
At Duke, for example, we have the Center for Health Measurement.
All of us that are in the center are interested in measuring health and trying to improve
the accuracy of how we do it.
So you can look for somebody who has a background in psychometrics, for example, somebody with
health measurement or questionnaire design background.
The ideal situation is that person either has both qualitative experience and psychometric
experience or has one or the other and then knows somebody else who does the other one.
And just have informal conversations with these folks.
When I first came to Duke, one of my collaborators that I'm now working with now, the whole way
we started collaborating with each other was just random conversations.
That's how we started.
We were thinking, oh, there's an important aspect of hearing health care that we're not
tapping into in clinical care.
Let's measure that.
Let's Sherri Smith.
And then it started this whole series of ideas that we've had for how to improve measurements
in hearing health care.
That is so awesome.
Teresa, thank you so much.
That's been gold.
I've learned new things and I feel like you've been teaching me a lot already.
So that was really awesome.
Thank you for being on the show.
It's my pleasure.
Thank you for having me.
All right, everyone.
You've heard Teresa.
There is a lot to think about when it comes to measuring health.
And as you're designing your clinical studies, you want to make sure that what you're measuring
with whatever outcome measure you're using is actually what you want to measure.
And there is no better person to talk to about it than actually a health measurement expert,
the health measurement expert.
So definitely reach out to someone at your institution and look for ways to collaborate
because collaborations with these experts is awesome.
Okay.
It has been a pleasure to talk with you today.
I look forward to talking with you again the next time.
Bye-bye.
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
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