“When you’re thinking about doing a research study, define and design. So you define what you wanna know, then you design how you’re gonna find that out. That’s everything. Everything goes back to those two words.”

If you’re one of those people who think that scientific research is just for academic scientists or the lab rats working hard at their lab bench benches with their pipettes, today’s episode is for you. I am joined by Neil Krohn, founder and principal of Advanced Healthcare Analytics, to share his expertise in doing research that really matters, and how research isn’t just for academic scientists. Stick around till the very end of this episode and learn how your organization, even a small nonprofit, could benefit from designing your own research projects.


Neil’s LinkedIn – https://www.linkedin.com/in/neil-krohn

Click to read the auto-generated transcript of today’s episode

Alexandra: Thank you so much for joining me today. I am here with Neil Krone and I am going to have him introduce himself.

Neil: Yeah, thanks Alexandra for, inviting me. I’m really happy to be here today. so. I am, trained as a clinical psychologist and, um, where I was first introduced to data really was in preparation for my graduate, studies in, clinical psychology. I joined some research teams before I went. I joined a team in, neuropsychology.

in, child clinical services, in anxiety disorders, clinic that we worked in and also in visual perception. Every single one of those teams emphasized data collection and data analysis. And, to be honest, that was really the first time that I came across it in any kind of systematic way. and then, that also kind of influenced me to choose a clinical psychology graduate program that emphasized research.

So clinical psychologists, as you may know. We can do clinical work, in the office, we can do teaching or we can do research. so I chose a program that gravitated toward research. So all during grad school, I got, a really good foundation and research design and, data analysis. and then I wanna went on to have a career, about 20 years in, behavioral health research and program evaluation.

So data has been a huge part of my entire adult life really.

Alexandra: And that’s why I’m really excited. To have you here today because we are going to talk about your decades of experience in the research space and what all of us can learn about doing research projects to help us answer questions and to start with, I wanted to just dive into a little bit, what do we even mean when we talk about research projects or doing a research study?

Like is that, do you have to be a clinical psychologist, a PhD in academic to be able to do those?

Neil: No, you don’t. fortunately, because there, there’s not enough of us to go around, but really what you really need to have is a willingness to learn how to structure a study. So basically when we say we wanna have a research study, we have an area that we want to investigate, and, we wanna find out something about that area.

It could be, how we provide services. It could be,a certain kind of phenomenon that we’re looking into. but we wanna go about it in a systematic way. So we actually think about what it is we wanna know, and then we design,the way to, undertake the study and then collect data.

And then at the end we roll up our sleeves, see what we have. with the various, data that we’ve collected and we analyze it and then we write up our results. So that’s a re research study in a nutshell.

Alexandra: Now like you, I’ve been enmeshed in research, especially in that more academic space, doing it as in my PhD and afterward. For those of us who don’t have quite that depth of experience, why is it so important sometimes to create a structured study rather than just sort of poke around and see what you find?

Like what’s the advantage to really, as you said, asking and answering a question in a systematic way?

Neil: Yeah, well sometimes it’s kind of fun to just poke around and see what you get,in the world of, Scientific study and really if you want to be able to say conclusively that, something that you’re working on worked or didn’t work, it’s, better to go about it in a systematic manner.

And,I like to ask certain kinds of, Questions of my, client or research partner you might say, as an evaluator. I like to ask certain things to make sure that we’re both on the same page before we even get started.

Alexandra: bring up two really good points. One is that. If we want to be able to say something with a high degree of confidence following certain rules about how we go about asking questions of data can help make the findings what we call more rigorous, right? That they stand up to scrutiny. We can justify the choices we made.

We can explain why we did things a certain way and not another, and that’s why our findings, can be interpreted a bit more confidently that they support. The effectiveness of a program or whatever the question was. But then the second thing I like that you brought up of like making sure everyone’s on the same page to start with.

We oftentimes don’t have that many resources to waste. So while it’s fun poking around, we may need to make sure that we get the answers we need the first time we try something, because we don’t have time or money or people power to go back and do it. Lots of different ways to see which one gets us the right answer.

So being more intentional from the beginning can help us apply our resources more effectively.

Neil: Yeah, you bring up a very good point and, I would be remiss if I, didn’t admit that I have actually designed some research studies myself where I went off and did not consult with people, who were actually gonna be helping me. and we were not on the same page. So, uh,I learned from my mistakes.

you know, let’s get in a huddle and let’s talk about this before we get started. That’s the best way to do it. That’s not the only way to do it, but it’s the best way.

Alexandra: and to that end, we sort of have, five areas that you’ve mentioned to me before about what you really want to make sure you have solidified those systematic steps of. How are you actually going to go about creating and answering questions that will be as effective as possible? So I was wondering if you could walk us through those five general areas that you wanna make sure you’ve got real clear across the whole group of stakeholders before you actually start carrying out your research.

Neil: Yeah, absolutely. So I’ll just go over these, very briefly. We could talk,half an hour about each one of them. And actually whole books have been written, virtually about each one of these five things. So, but we’re gonna go over ’em very briefly. first is, what is the overarching goal or mission or purpose of your organization?

What does your organization exist for? Our. more narrowly, if you’re doing a project which is a part of your organization’s, mission, what is the goal of that project? So we need to talk about those kind of things before we really go any further at all. The second thing is how does the study that’s being proposed or the project further, the organization’s goals?

So let’s hope that,you’ve got a clear idea of what your organization is all about, and I think most people do. but then. There might be a tendency to get a little bit, off base or a little bit, kind of stretching, what they’re doing. And if you have any doubt, that is the person who’s wanting this study done, that the study does.

further those organization’s goals and maybe you can, rethink it and,work on something that’s a little more congruent with those goals. the third thing is what precisely do you, as, A person who’s doing this research, hope to conclude when the study ends. So you want to think about what are your goals, but after it’s all said and done, what do you want to be able to say? that’s a really important thing. it’s not something we think about, but it’s like, okay, here’s what I learned from doing this. another element is, Okay, you’ve got a study designed. you know what you wanna look for, but where do you intend to look for your answers? In other words, where is the study population located and how do you define it? So, again, very important to what you were saying earlier about having a rigorous study, because if you don’t design that study population, you’re gonna run into two problems. One is that, you’re flounder around, like, what, who are we trying to reach here? And then also it’s like, well, okay, so we collected all this data, but what does it say about.

Who, you know, we need to know who that population is and where to find it, how to define it. And finally, After it’s all said and done, how will you actually know if your study objectives have been met? So you have, this is where it comes in, where you have to have these clear objectives, and then you’re gonna go in and you’re gonna do some, data analysis.

And then you’re going to scratch your head a little bit and say, okay, looks like we got what we were looking for. this is a great thing, or maybe not. We didn’t quite get what we wanted, in which case it’s, let’s do another stay later on and we’ll talk about that too.

Alexandra: Absolutely. I joke that in this podcast as well as just in science in general, that sometimes ending up with new questions is just as valuable as actually getting some answers, and science is that way. So you may finish the study and go, well, hang on now, I have a whole new set of questions I didn’t have before.

We’re gonna have to ask some new questions.

Neil: And also there’s the, most people are familiar with what a hypothesis is. So you go into a study with a reasonable idea of what you expect to find, and sometimes that hypothesis is supported. It looks like your data is wow. Right. Right in line with that. Yay. Yay me. Yes. but then other times it doesn’t work out that way.

That’s very important too. That can be very helpful because now you’ve shut off this area that, is not gonna be very productive for what you want to accomplish.

Alexandra: So let’s dive into these steps, cuz I think these five areas are so critical in order to get the work right. And I’d like to point out that all of these happen before you’re even gonna set up like the technical structure of your study. We haven’t even gotten into study design. All of this happens really to help make sure you’re.

Asking the most effective question that you can ask of your data or of your research project, which I think that so many of these areas get skipped too readily and dive straight into, well, let’s get into the data. Let’s start collecting data, whatever it might be. So the fact that you say like, what is the overarching goal of your organization or project like, can you talk a little bit more about how that ties to.

The research projects that you might pick, or how research projects can even be connected to an organizational goal.

Neil: Yeah. so you have this goal and then So you have a mission, you have a purpose, and you can just go along and say like, oh yeah, we’re doing what we said we were gonna do, and the board of director’s fine with it and so on. But really, you might want to, give some concrete examples of how your.

helping to support the mission. And so this is where you actually then start rolling up your sleeves and say, what kind of outreach do we wanna do? So we want to define a program, define some kind of outreach, and precisely what it is we’re going to do. And we can give some concrete examples of that later on, that I think that might, Help us understand it a little bit better.

Who exactly are you trying to reach? Define your population very clearly. it needs to be a population that is part of your organization’s,purview, right? and then, you need to have, clear, well-defined, achievable, measurable goals. So those are things that are the real foundation stones for your research study.

Alexandra: Right, and so the idea is, the reason we start with getting clear about the organizational mission, or if you’re doing a smaller breakout, like your program mission or your project mission is because the way you. You answer the next four questions is going to be driven by the answer to that first one Cuz you, you brought up the point that the population you picked to study, there might be many populations you could study the particular question you’re bringing up, but you wanna make sure that you’re defining that study population in a way that relates directly to your organizational or programmatic.

Mission or goal. And so by getting that clear from the very beginning, it’s gonna eliminate some of the many possible answers to the subsequent questions so that you’re already honing in on the ones that are gonna have the greatest impact and get you at the end something that really contributes to your organization or to your program.

Neil: Right. Absolutely.

Alexandra: So could we talk a little bit more about how a research study even could help advance an organizational goal or an, or a programmatic mission? do you have, and maybe an example of a re particular research study that was able to really contribute to an overarching organizational goal.

Neil: Well, sure. so, many ways, that can, this can be done and, depending on the size of your organization, you could have all these different, arms if you will, that are all looking at slightly different things. But, I’m thinking like, so again, as I said, in the beginning, my background is in, mental health and, so I thought about this.

Program that we did. so our mission was to essentially, support. And treat and prevent mental health issues in the community. So that’s the overarching goal. So what’s one thing that we could do that would help us out with that? Well, we know that certain, certain individuals with.

serious mental health conditions have a problem with their medication adherence. And, so we designed a program for outreaching to, individuals who had bipolar disorder now, bipolar disorder. Is one of the most, serious mental health, conditions. There is seriously, it has the highest per capita suicide rate, frequent hospitalizations, visits to the emergency room, very disruptive to a person’s life.

The good news is that we actually have a number of medications that can. Control this, these wild, swings from depression to mania and to keep people on an even keel. So what we decided to do was to create a re, a program that, monitored how frequently people were. Filling their, medications.

And we have that from, pharmacy data. And then we would send them reminder calls if they were five days late doing it. And then of course, the way that this, supports us is that if it’s successful, then we’re going to keep them on an even keel. We’re gonna keep them out of the hospital, we’re gonna keep them.

Or help them, live, stable, productive lives.

Alexandra: I love that example because it walks through really clearly. One you identified, here’s our overarching mission. We want to help treat people who have serious mental illness, and we want to help prevent some of the negative consequences that come from living with mental health illness. and then you said, all right, of that whole huge mission, we’re gonna hone in on a particular.

Problem area that we are currently struggling with. We know that, and you’ve sort of actually gave examples of some of the other ones you honed in on our particular research population are, clients who are suffering from bipolar disorder and you identified a problem, lack of medication adherence.

And a potential solution that goes back to your hypothesis, right? We have a possible solution, but the question we need to answer that conclusion that we might need to find is does sending a reminder phone call when they haven’t filled their prescription, actually increase adherence? And so then you can find out from that study if it does increase adherence, then you have a new intervention that you can do to help accomplish that overarching mission.

Neil: Bingo.

That’s it.

Alexandra: And this is why people laugh when I get so excited about science and data, because they’re like, why is this something you get so into? And I said, because this is what it can do, right? It can help you answer with greater certainty because you’re using more rigor in your exploration of these questions, whether you could do something that’s gonna drive better outcomes for the people you’re trying to serve.

Neil: Absolutely. And we can talk about later if we have time, about how. We can take the results of one study and apply them to future studies, and this is a perfect example of that.

Alexandra: So the next step that you talk about is getting clear about what you hope to actually conclude. At the end of the study, and I love that you brought this one up because I think this may be one of the easiest ones to miss because you’ll start with, well, we have this question, right? We wanna know about outreach calls, but when you’re not really specific about the kind of conclusion and the level of the conclusion you wanna make, It’s really easy to mess up some part of your study design and not get exactly what you need.

And I was thinking about, when I was helping a patient safety team understand whether this new readmission program had helped reduce readmissions to the hospital. And they had done the project and collected the data prior to me connecting with them. And they came to me and they said, we really wanna know which of the seven steps now of this program was the most effective.

And I looked at it and I looked at the data they collected, and they didn’t have any data that referenced the breakout of those steps, right? We had overall readmission rate and we knew, you know, who had participated in the program and who hadn’t. But we hadn’t collected data along the way of, you know, who got different steps where some steps may not have been implemented.

And so because they hadn’t gotten clear that the conclusion they wanted wasn’t just. Is this program effective, but which of the seven steps was most effective? We weren’t able to actually help them make the conclusions that they wanted when they got to the end of the study. And so I think this is such an important question to ask.

Neil: right. So I forgot what the question was after all that, but, uh,

Alexandra: I’m not sure I actually asked you a question. I was just, I love that as a point of view that you really have to pay attention to and explicitly address. Right. Not just what’s our question, but what are the answers that we’re hoping to be able to actually make? Right. Do we, I was thinking as well, like the difference between being able to say a causal, right.

Does this thing cause that. Are we just seeing an association, because of course those would require different kinds of study and different kinds of data. So I was wondering if you had anything else to add about, getting clear of what you’re hoping to conclude or if you’ve noticed any pitfalls when people try to answer that question.

Neil: Well, right. I mean, and the example that you gave is,it’s very clear that they wanted to know right from the start or maybe even after they had done it. I don’t know, but probably. Way from the start, which one of these we can do these seven things, which one is gonna be most effective? But they didn’t set up the, the project in a way that they could actually tease that out.

And so that is why I. Just, cannot emphasize enough, that one of the first things you need to do is huddle with the people that want to do the study. get as many people that are involved are going to be involved in both the, execution and the analysis involved in the room at the same time or on Zoom, whatever.

And. Say, what are we trying to do and how are we gonna know if we’re there? And when you have all these people,in one place, I firmly believe that, the, some is. Greater than the parts. And so you can work out those things and you can say like, okay, after we do step one, we’re gonna take this kind of measurement after step two, this kind, and so on.

So just that whole idea of collaboration, and through the collaboration, understanding precisely what it is you wanna do. What’s the exact nature of the intervention or the program, then the exact nature of the metrics, the things that you want to measure, How would they look?

and I might add too that, it’s not, a bad idea to measure the same thing more than one way. It keeps you from having all your eggs in one basket. so, what is it you wanna study? Who do you wanna study it with? how are you going to measure success? And then, you need to make sure also that you, do it long enough so that you have enough people in your study so that, the results are meaningful.

I’m sorry, we’re kind of going all over the place here.

Alexandra: But this is also the point that these questions, while we laid them out as like five distinct questions, they all interplay with each other. And so you’re not gonna just answer one and leave it alone and move on to the next one. The discussions that you have about answering each of them are gonna, Inform and feed each other, right?

Because to some extent they cascade into each other that each information that you pull from the first question feeds into those lower ones. And you may have to go back and revise it a little bit because you may realize that, oh, you can’t reach a certain population, so do you need to revise the objectives of the organization or of the project or the research, study or things like that.

And I think that you brought up another interesting point here that. When you’re bringing everybody together to make sure that you’re on the same page, it’s not just the people who are going to be directly involved in executing the study. You want the people who might use the findings from the study to be in on this from the beginning.

Because if you finish it, like you said, that we’ve all done this as researchers, that you think you know what people wanna hear and it turns out that actually you didn’t get it and that you missed something that they needed at the end, but also that you’re involved with, you know, if there’s gonna be a different group doing the analysis.

If you’re defining the study, but someone else is gonna do the analysis or a different group’s gonna support you in that, are you making sure that all the pieces are in place in the data collection you know, to be able to support the analysis that’s gonna happen down the road? Because that’s another breakdown that I definitely see happen sometimes.

Neil: Yeah, absolutely. Alexander, great point. so I’d like to have in the same room, the research designer, Namely me or somebody I’m working with. And then, clinicians often in mental health, clinicians are the ones who actually have contact with the people in the community. The individuals that are, needing help are being given an assessment.

Then, it helps also to have members from the community there. So if you’re trying to find the population, they might be able to help you, save some time and stay out of dead ends. and also just to kind of get their buy-in to this,program that you’re introducing.

The other thing is, the administration. of your organization. they are fine tuned into the mission or of the organization, A and b, guess what? They have the money, they control the resources to a great extent, for what you want to do. And then finally, the people who are, Collecting the data and analyzing the data.

if you say like, let’s just ask ’em this one, question. did you feel better after, after we talked to you? okay. not so helpful. you probably need a few more questions, with, a little more,breadth in the answers that they give. So having all those different, experts in the room at one time is gonna be very helpful, really essential.

Alexandra: I think that makes so much sense, and for those of us who aren’t in the medical field instead of. Clinicians, it’ll be your front frontline workers, the people who are actually doing the work that you’re trying to help evaluate or improve or understand better. And all of us are in that way. Any of us who are doing any kind of intervention are gonna have the people who are actually carrying out that intervention.

I love this list. I think that this is so important. Every one of those people should be at the table, whether it’s the same person with multiple hats or whoever needs to be there to have those discussions about what you’re trying to answer. And I love that you included the community members in that idea that our next question is about the population study or population definition.

Who is the population? That you want to study or include in this, and you identified that community members can both help you think about who that population should be, but they also may be able to forge the connections to that

population. And I was wondering if you could talk a little bit more about how one even goes about thinking who the population study, you know, the study population should be.

I think there’s a tendency to wanna be like, it’s everybody. Or to just throw it out there. Right. Just throw out your study and see who ends up in it. So could you talk a little bit more about why it’s important to define your study population and then how you go about getting them to participate?

Neil: Yeah, absolutely. so, generally speaking,the impetus for creating a study is that something is not, the results that you’re seeing are not what you want them to be. and I mean, that’s not the only reason, but it’s one of the big reasons. So things are not working. So let’s see if we can find out, what’s not going right, what is going right, and create a program to do that.

So, for example, in community mental health, there are lots of ways to define populations. one of the. Areas that, gets targeted a lot are what we call underserved populations. So these are people that don’t live in a big city. They do not have a community mental health center right down the street.

they are in the rural areas. I’m here in Colorado. Colorado is a very big state, of which about, 80 to 90% is rural. And, so there’s gonna be some, people that have trouble getting access to help if they do run into a problem. So, people in, rural areas are a key element of underserved.

also, individuals with lower incomes, maybe they live in a city, but they just don’t. Feel like they have the resources to even go to the community mental health center, which are, very, reasonable and sometimes, subsidized by Medicaid. But,they. Don’t feel like they can go.

Maybe there’s even a stigma that they have, in their particular group that, oh, you know, you need to have a stiff upper lip and, um, solve your own problems. So those are all kinds of different populations that you could look at. so you can take a broad approach, which in the case of Colorado is.

we have, centers all throughout the state and we do,in the different regions. We have different regions set up for that. And, so we could actually have a study in each one of those areas, and then we could compare them to one another. We could, just put them together and say, okay, now we have looked at this particular issue of serving, of reaching underserved individuals throughout the state.

the good thing about doing a study like that is that it has high generalizability. That is It can, the results can be applied to essentially all of Colorado. The downside is it’s very resource intensive, right? so we might not be able to do that. Then we can have a more narrow focus, which is okay, well let’s just focus on, the northeast corner.

We know historically that this. People are just not getting the services they need here. We’re gonna focus on that. The good thing about that is that it’s doable and, you have a constrained area that, you can focus on and, everybody can sort of converge on the area and,and. Reach out to the community.

and then the good part of that is you have increased reliability of the results because they’re all in one place and they’re sort of, they have certain things in common in that area. The downside is it’s not generalizable. So I don’t know if I answered your question, but.

Alexandra: Oh fabulously. And I was taking a whole bunch of notes about it because I think that there’s a lot of critical components for people to consider cuz there isn’t just one right way to define your study population. It’s going to be influenced by the answers to your first three questions we went through, right?

What your goal is. What you’re being, you’re hoping to be able to conclude because if you need to be able to conclude something about the entire state of Colorado or about your entire client base, right? You’re gonna have to design a different study population to answer that question verse. If you’re saying no, we want to make sure that we can really help this group, this particular group.

Especially with when you’re talking about underserved populations or populations that may not be as readily represented in a general study. So like yes. Something that covers all of Colorado. May generally talk about what Colorado can be, but if there’s a subgroup that is different, right. Maybe a group like, I know there’s particular cultural and racial and ethnic groups that have a high distrust of the sort of mainstream medical environment for a lot of good reasons.

And so they may get left out of a general study that isn’t aimed. Deliberately at addressing issues with that particular group and trying to solve problems related with a particular study population. And so you also mentioned the idea of resource intensity intensiveness, right? That. It requires a lot of resources to reach a really big population, and you need to have a certain volume in any group that you wanna understand.

And so if you don’t have unlimited resources, being specific about the group you’re gonna target can help you get more bang for your buck, really, that if you target a narrower group, you’ll get better results for that group with the resources that you have at hand. So I think that was a very helpful orientation for thinking about really how do we want to define the group or the population, or the categories of people that, that we want this research to be relevant for.

Neil: Absolutely.

Alexandra: Alright, so we get to our last question then of how will we then know we’ve threw these four steps really gotten clear about. What we’re trying to achieve, how we’re going to do it in what areas, with what groups. Can you talk a little bit more about how we actually know that we’ve succeeded when we finish it?

And why should we talk about that before we even try anything

Neil: Well, it’s, it’s the same reason that people use maps.

Alexandra: I.

Neil: you need to, know where you’re going in order to get there. I mean, of course, you might wander around and have. Be lucky, but,it’s better if you have a roadmap. and so we talked about this idea of creating. you get all the people in the room, you say, here’s what we wanna study, here’s how we wanna study.

It seems like these things, these kind of metrics, these kind of measurements are going to be helpful, to determine whether it was successful or not. We collected those things. now we got the same group of people all back in the room. or maybe it’s just,the research people and the data people and we’re rolling up our sleeves and we’re saying, did we get where we wanted to go?

Okay. So we do some number crunching and, so what I said earlier, this is where I think it’s important. You create, easily measurable things. You don’t want there to be any vagueness about them. You want them to be very clear what it is you’re measuring. If one person takes a measurement in this office on one side of town, that’s another person on ano in another office.

You want them to get the same results. If they were. Talking to the same person. so we look at this, careful analysis of our goals and the data that we’ve collected. And so let’s just say in this case of, reaching out to underserved populations, we had like maybe three different ways that we were gonna look at that.

We’re gonna look at, the percent of individuals that were served. that is who actually showed up at a community mental health Maybe. Maybe our program was education. It was like telling people, here’s this thing and it doesn’t cost anything, and please come if you have this. Okay. So what we would hope is that we started out with.

10% of this target population. and then now after our program, 20% are showing up. Okay. So, so now that’s our, one of our metrics. Another thing that we could do is if we’re into. Maybe looking at prevention. So not everybody needs to go, but maybe we can do a screening, we can do a depression screening, we can do, an anxiety screening, we can do a learning disorder screening.

how many of those people were assessed, versus, historically what was done. and then something that might be. A little bit more complex, but you could say what percentage, of the population actually improved in their symptomology. So now this is a little more complex because now you have to give, you have to give them,a psychological assessment of some kind, let’s say like a assessment of.

Levels of depression before they enter treatment and then after they leave treatment, so those are maybe three different metrics that you’re hoping to. say, yes, we did it all. But usually what happens is you were successful in one of the three or two of the three, or maybe, oh, you had some, incomplete data and it needed to be, not counted for the study.

And so unfortunately we weren’t able to measure that correctly. But in the end, then you call everybody together and you say, these are our results. and then you get into the, the statistical, side of things.

Alexandra: Well, and you talk about for two categories of success. the first is saying, let’s define from the beginning. How will evaluate success of the program or the intervention that we’re trying to judge or whatever it might be. And you wanna define that from the beginning cuz it will influence what kind of data you find.

And it also means that everyone’s in agreement when you get to the end that, like you said, if we reach this percentage of engagement, we’ll view the program of successful versus if we don’t, we’ll view it as needing to be ad adjusted or we need to change something about the program or we need to pivot and do something different.

And putting those in place before you see the numbers. Make sure that everyone agrees and there’s not that argument about, well, okay, actually that percentage is good enough. No, it’s not. We want hire, right? Like you’ve set that from the beginning, but then there’s sort of a second category of success, which goes back to like what conclusions you wanna be able to make, where it doesn’t actually matter what percentage of engagement you got or what percentage of your score of treating depression.

The study itself will be a success when you can answer. A certain set of questions at a certain level that everyone agrees is rigorous enough or that you agree with the methodologies like you’ve felt like, yes, we’ve successfully answered the question. Even if it turns out that the program wasn’t successful in the metrics that you set up to say, are we happy the outcomes or not?

Neil: So you’re saying, let me see if I understand. You’re saying that you could then say, We implemented the program successfully. We didn’t get the results that we hoped to get, and those are two different things.

Alexandra: Yeah, exactly. And when we’re talking about a research study, there’s sort of those two halves of it, which is you’re using the research study. To say, is our program successful? Like, is this intervention a successful intervention? But you also wanna make sure that your work in your research study is successful, that you’ve done the things that you wanted to achieve, that you were able to draw the conclusions you wanted to do, or it’s not because you missed a study, you know, particular part of the population that it turned out you wanted to reach.

Or in my example with the patient safety group, like, oh, you weren’t actually able to answer a question that you wanted to like, That was a less successful research study than we would’ve hoped.

Neil: Right. And so, I think you and I have talked about, and I think you mentioned earlier, that science is, iterative. It’s not one and done. So one of the great things about doing research studies is not only what you find out about your own. Study your own population, your own intervention, but then you create the ability to then do further studies based on the good results that you got, or sometimes based on the not so good results that you got.

So you move on. It’s not one and done. It goes on and on and on. This is the way science works.

Alexandra: I like to think of science as a spiral. It’s not just cyclic cuz you’re not coming back to where you started. You’re coming back having advanced your knowledge, but you’re coming back to ask questions again in a new way about that topic. And you keep kind of spiraling up as you ask more questions and learn more.

And end up with new questions and ask those questions and continue your way up.

Neil: Exactly right.

Alexandra: Excellent. Well, thank you so much for your time today. I think you’ve given us some incredibly clear ways of thinking about how we can use research studies to help us advance the missions of our organization to do a better job in reaching our clients, in putting in effective programs.

So really appreciate your time today. If people wanted to follow up with you or learn more about you, where could they go?

Neil: Well, I can give you that, contact information. I have several ways I can be contacted. but,can I just put in one last plug? It really, it’s very catchy

when you’re, when you’re thinking about doing a research study, define and design. So you define what you wanna know, then you design how you’re gonna find that out. That’s everything. Everything goes back to those two words.

Alexandra: It is perfect, and I am very sure that will, uh, be the quote that goes on our social media image for this episode, cuz that is perfect. So thank you. Thank you so much again for today. I

really appreciate your time.

That was Dr. Neil Kron helping us understand how we can use research to help us learn and improve. So he went through five major questions. We need to be able to ask ourselves and answer before we start any kind of research project. Now, even though we laid these questions out a little bit linearly, as we talked about, you answer these questions and return to them as.

Different answers may refine how you answered a previous question or may inform what happens later on. But the first question was, what is your overarching goal, mission, or purpose of your organization, or specifically the team or project that you’re working on? How could a study or project advance that goal?

What do you want to be able to conclude when the study is finished? Where do you intend to look for answers? What study population are you going to look at? What group do you need to understand in order to be able to conclude the things you want to conclude? Advance your mission the way that you want to advance it.

And finally, how will you know if you met the objectives of your study or not? And this could be both. How do you know if your program is working, but also how do you know if the research project itself was successful? So be sure to check out the show notes if you want to learn more about Dr. Kron. So you can get the show notes at heart, soul on the Heart, soul, and Data episode list. Thank you so much for joining me today. I am grateful to you for all the incredible work that you do, and I hope you find something that you are grateful for today too.

Thank you again.

Neil Krohn

Neil Krohn is the founder and principal of Advanced Healthcare Analytics, which provides both quantitative and qualitative analytic services. Neil earned his Ph.D. in Clinical Psychology from the University of Tulsa and completed his post-doctoral fellowship in Administration and Evaluation Psychology from the University of Colorado Denver. For more than 20 years Neil has worked in healthcare analytics in both the public and corporate domains. He is particularly interested in helping organizations design and implement research studies that assist them in defining their goals and furthering their missions. He also taught Statistics and Research Methods to psychology graduate students for many years and served as the research design member on doctoral papers and dissertations. Currently, Neil is launching an initiative to have experts in science and technology share their knowledge in small-group discussions in the community. Neil lives with his wife in Denver, Colorado. He’s an avid cyclist, riding about 150 miles per week on Denver’s amazing trail system. Connect with Neil at https://www.linkedin.com/in/neil-krohn

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