About the talk
This video is part of the R/Medicine 2020 Virtual Conference.
Hello everybody. So the next presentation will be by Tesla Darris talkin about teaching clinicians data analytics with r. Thanks Beth and I just wanted to thank everyone. This is been an awesome morning and it's really been cool to see all of the communities of practice that everyone's kind of established and Cassie. Just want to just shout out that like, I think that worked your talk was amazing. So today I'm going to be talking about our experience teaching data analytics with r.
Okay, so like we've been teaching this data analytics course for about 6 years. So this is just kind of what I'm going to cover it today. So we'll do kind of a brief introduction to the course introduction, talking about the course and talking about the outcomes. So, who am I? So I'm an assistant professor at Oregon Health & Science University, I consider myself. Well, I'm a buy one if informatician, but I am also consider myself a professional collaborator. I just love working with other people, enabling them to kind of go farther with analysis
and are. Now, I'm just a quick plug. So I'm in rstudio. Certified instructor said these are kind of too kind of resources that are kind of freely available for everyone. So I did the course that and all of them videos materials available is call Brady Farrar. And then there is an interactive version. Learn learning the tidyverse lessons called are boot camp. I just want, I'd not bragging, but I do like this review that a clinician told me. I am that. I am a very patient man. So, really the point of the course is, how do you
deliver actionable analytics in healthcare? So, we want to deliver this kind of experience to our students. So, you know, part of it is, you know, the part of the course, and this is what I really do want to emphasize is that data science is kind of only one part of it is the other, and that's kind of what I focus on at OHSU on during the course, but Kaiser Permanente inside and Brian sikora's side. Also talks a lot about the organizational aspects and strategy. And in the end, we force our students to
actually apply both of these by having a final presentation. Then I'll talk more about what that final presentation looks like in the second. So I want to just talk about clinicians as Learners and this is no offense to clinicians. This is kind of been built up over a long time of interacting with clinicians in this course. So I just saying like mariza clinician who wants to understand how analytics can be delivered in her healthcare organization. What her special and thinking about what her special needs. She has very little time. She
likes to learn on her own and she has a hard time asking for help. And at the same time as hard on herself, So, how do we meet these needs? So in terms of no time, we've tried to structure the assignments to gradually increase in difficulty and we think very much like in terms of kind of just in time instructions. So what do you need to do to accomplish? You need to know to accomplish a task. Terms of everything we really tried has worked on this kind of self learning model
and this kind of started kind of evolving in the beginning. You know, first we kind of started having the students work in our mark down. But you know, as kind of things progressed and we learned more about kind of the r and r Studio ecosystem. We started kind of putting more and more kind of aspects, kind of support this. So we started using our Studio Projects as a way to kind of package assignments. And now finally, I'm in the last two years we've been working with rstudio cloud and which is basically kind of this online
platform. You can basically point of students who attend, they have like a full instance of our, with all of the assignments and everything in the, in the workspace. So, this has been a really kind of helpful tools. We're going to continue to use it. Also, so like, you know, like when when they have a hard time asking for help, you know, we do, you do things like, you know, cast mention, kind of having a Friday, we do do that. We make, we make the students team up, my try to be available as much as possible. Like in terms
of slack for quick question, I mean office hours available and now I've kind of been working like with making making myself available via schedule, appointments, so just kind of making making giving support to the students when they really need it. Don't let's talk a little bit about what like, the overlying like problem. With we are trying to solve in the course. So the overall problem is we're trying to predict 30-day readmissions and and this is within a simulated
Hospital patient award. And we do this by implementing a metric called lace sew lace is short for length of stay Acuity of admission of any comorbidities. Patient has and the number of er, admissions. So this is, it's nice because there's kind of a very focused task will have to pull the data out of our simulated data warehouse. But again it's not just about implementing the metric, they have to communicate how effective it is in the patient population.
Still talking a little bit about the data. So the data is a simulated data warehouse. So these are some of the tables that are in there. So you can see you like, you know, there's a patient table, there's a hospital and counter table. So these are all of the Encounters of patients have within the hospital. And then also there's a diagnosis yet and we know that this is very simplified, but we've kind of kind of phone this down to kind of get to the essentials and terms of learning. And it's also. So it's a structure. Does a 4-month
extract of patients Miss based on real clinical data? I so, you know, I've been trying to figure out exactly how to incorporate Brian's piece on here and this is Elder. This is just one slide. I just want to emphasize that this is like Brian, and that is kind of an amazing job at kind of talking about talking about kind of the organizational challenges of bringing analytics to like a healthcare organization. I mean, there's all sorts of real real, real hard hard-earned lessons that he
gives the students in terms of. How do you kind of get your project visibility? He had you get sponsorship and how do you ask for things? So these are all very kind of very very kind of Social and organizational Parts but you know we filled it like this is like an essential part of teaching data analytics, not just about teaching our So, I just wanted to give you the kind of idea of what the assignments look like. So like and I like we said like the assignments are very kind of the kind of bread you want
terms of things restart in terms with like doing exploratory data analysis. So this is a great visualization tool. By Nick Cherney are open side and it's called a visit that and it what it is is it's like kind of the quick kind of dashboard. Look at the data. So you can understand weather variables. What weather there's kind of missing valuables and what kinds of variables and values are are in the data. Okay. Well, why did my slide. sorry, I'm having issues with I'm having issues with. Just. Okay, so let's talk about. So this is
kind of introductory sequel assignment. And again, our our clinicians are not necessarily like, you know, completely Converse you are and SQL. So like really kind of having these kinds of great graduated kind of assignments. So this first assignment is about just kind of selecting columns in data and looking at the actual data and the tables. an amazingly enough like, you know, as like the students kind of go on and they learn They they start being able to do lots more kind of sophisticated queries. So here they're
calculating, kind of what are the number of emergency room visits from the clinical data warehouse and you can see this is not simple sqlcode so they are starting to really think about ways of summarizing and aggregating the data. So we we feel that this is like a really important thing, like, we could have started started out with, like, you know, having all of these different score is calculated, but I mean, it's I think it is important for them to have ownership of the whole process. So, once they have this kind of scores calculated, they can
build predictive models, we teach them basically logistic regression and we talked about lots of Concepts, such as RSD and kind of, in this part of this again, like thinking about how to work kind of the organizational part. Like, how do you work in organizational values into kind of deciding? How I predict, how a predictive model is applied, I'm in, you know, I mix I was excited about the tree trains treated or talked on because I always love to see good teaching tools on. This is kind of the party outfit. But, you
know, they can build. They can bill. Basically, do machine learning with decision trees and we have and I'm excited. I was so excited for the new models Workshop because like I'm definitely going to be working in degrading more tidymodels into the into the into the lessons. Okay. So I've been kind of stepping around this final presentation, but like the thought is that they present they want, they're doing a presentation to an executive team. So this kind of lace calculating
the slice score is like a pilot project in part of it. So this is like, I just love working with Brian because, you know, he really kind of breaks it down. What is kind of an effective presentation? And it's, it's all kind of building towards having a caller asked to action. So, like, you know, we want you to invest more in this, in our program. Are we want to implement something? So, you know, he's very good at talking about things like. So what is the impact of not changing? So if we just kind of have the status quo or what what's going to happen and then you know really
we spend a lot of time talking about like how to prevent results and this is been harder and less than we've had to do. It's just basically show our model outputs and that has not been successful. So, we spend a lot of time thinking about data, presentation and data storytelling. So answer it, let's just want to talk about in terms of outcomes like she knows some of the presentations and I think this is one of the best outcomes to students come out with different ways of looking at the data. So, I don't know if Kevin is online, but so just shout out to Kevin and Mina, so this
is their way of kind of presenting the data. And you can see that they, they've really kind of thought through like, you know, in terms of highlighting, you know, different kind of aspects of data. the neatest thing it really is that, you know, The students think of different ways of presenting the data. I mean, here are real rose in Alfonso decided to talk about kind of gender bias in the data. I'm so Shadows to appear at low. I know she's on here just so this was kind of how they kind of visualize the date and kind of talked through it.
Dance later, Laura Hickerson. So they decided to kind of show, you know, the distribution of scores across and Mike highlight the highest risk group. So like, you know, that was kind of the focus of their talk was like kind of targeting these kinds of high-risk individuals, I'm and this is Justine Dan and Chelle. This is your presentation and they're really thinking about kind of in terms of like, you know, us EMS penalties. And of course you know implementing this has a cost but, you know, thinking about what the balance is.
I'm into this is this what is? I'm not saying all of any of these presentations are better than the others, but I thought this was really effective. So Megan wait, Sean. And Colleen thought about, you know, what are the cost Savings in terms of implementing the project. So I think, you know, this is, you know, it's one of these things. I feel like the students have been really empowered to think about the data and they get very creative and they think about kind of the data and in different ways and it's always fun to see that. so, I want to just talk about,
I'll just kind of wrap things up talking about some of the student testimonies about the course, So a Christian Stevens, she said basically the course is made her a much more patient and effective collaborator. So I think that's been one of the real strengths about the course is like the kind of that comradery the students get with working with each other. Another theme students, you know, the students really like kind of the diverse set of Learners that come into the course of the other set of Learners that I haven't talked about our kind of our bioinformatics
students. We make them take this course, but it's been a very useful bridge to kind of getting them to talk to each other. And I think there's been a lot of useful conversations as come out I'm so embarrassed. Again but so you know it's she really feels like the course is, you know, it's Soup To Nuts. I mean, it really kind of covers a lot and it's just very useful to anyone who's interested in working with data and a healthcare setting. I'm and finally. So Frank Lugano. So he's an MD clinician and
he's just really talking about that, this like he really thought that the course was helpful. And like, you know, more information program should have like a course like this for clinicians and other clinical informatics students. I'm not to toot my own horn, but we did win an award. The word itself is not that important, but the fact is that we had multiple students nominate us, so that was really amazing on the students, really loved like, you know, our availability like, you know, kind of the way, the stretch up projects for planned. And then, you know, they
really felt like it really resulted in essential research skills, not in, not only bioinformatics computer science, but generally across biology and medicine. So to me, that's one of the best outcomes I could have ever hoped for So just do it. I'm wrapping things up. So the course combines like practical and organizational skills and through that kind of final project. The students are forced to combine them and the other side is like you know it's really kind of short with you is real life lessons. I didn't talk too much about this but
Brian's team talks about like you know, that a Kaiser Permanente like implementation of lace and there's a lot of talk about kind of evaluating it for a sect of this. And so again like applying the knowledge is really important and I feel like it kind of cement everything that's come before it So just want to thank both. So, it's just you and Kaiser teams like this, has been, like, kind of a huge kind of undertaking. It's like, you know, not only the interdepartmental, but its interior like, you know, interested to Chenal. So, it's just been kind of Testament to
everybody's really hard work and making this this go do the rides. Are here, like I hopefully people have seen the little link as we've gone on. Here is my information, this is the GitHub repository. So if you're interested in looking at the our notebooks and everything, like I'm happy, you can take a look at it. I'm I need to clean it up a little bit. I didn't have enough time before this class of this talk. And I just, you know, I just want to end up at this is our class from last year so I'm happy to take any questions.
Is that talking about the Buddy? Coding paired, programming and, is there a virtual platform that facilitates paired screen sharing? So like we, you know, I know that art are Studio. It had implemented some sort of way to do, kind of like, having to multiple people work on code. At the same time, we haven't looked into that. So I like what we really just kind of talked about pair programming strategy, sit work for people like for example like you know, one person is kind of the coder and the other person is kind of the driver and so kind of talking about these kinds of strategies of working
together. I will save like moving this course. Completely online has been a real challenge because the course itself has been it. It's been like some On online learning and then there is this in person week. And I feel like having that in person week like, literally, you know, four and a half days of students kind of being at us and where the instructors are accessible and they can ask questions really great. As a professional collaborator, educator Mentor, how do you guard against Giving Tree style burnout?
Yeah, this is a good question and I don't really have a good answer for it. I have been working with more kind of studying professional boundaries so there's a great book remember her name but it's called on F your boundaries and it's really about thinking about you know what is kinda Twitter? What are the things you want to do versus? What are things? People thinks asking of you, And I want to have you spend any time thinking about teaching get with a gooey for truly distributed analyses.
Yes. So I've been having conversations with lots of people about how the best teacher get and we're still working on that. You know, it's it's I feel like you know the the course is really packed right now so is trying to at least kind of show them what it's for that's kind of the level. We can kind of cover it. Okay, I think that we need to wrap things up and we'll head to the next session everyone. This was super fun.
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