ACM ByteCast - Holly Urban - Episode 36

Episode Date: April 20, 2023

In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)’s For Your Informatics podcast, hosts Sabrina Hsueh and Sullafa Kadura welcome Holly Urban, a pediatrician and clinical informaticist. After working for several years as a practicing pediatrician, Holly transitioned to working in product management roles for Healthcare IT vendors, including product leadership roles at McKesson and Hearst Health. Most recently, Holly was Chief Medical Informatics Officer (CMIO) at Oracle Cerner before recently starting a new role as VP of Clinical Product Design at CliniComp. Holly describes how she became interested in medical informatics, product design, and management and how that inspired her to serve in an ambassador role between clinical and technical teams. She talks about transitioning from her role as CMIO at Oracle Cerner where she focused on software implementation and deployment to designing a new electronic health record (EHR) system at CliniComp. She stresses the importance of data literacy to analyze the reams of data generated by EHR and the promise of AI and ML in measuring effectiveness of interventions such as medical procedures and medications—as well as the issue of bias with these tools. Lastly, Holly shares valuable advice for professionals who are thinking about switching job roles.

This episode is part of a special collaboration between ACM ByteCast and AMIA For Your Informatics Podcast, a joint podcast series for the Association of Computing Machinery, the world's largest educational and scientific computing society, and the American Medical Informatics Association, the world's largest medical informatics community. In this new series, we talk to women leaders, researchers, practitioners, and innovators who are at the intersection of computing research and practice to apply AI to healthcare and life science. They share their experiences in their interdisciplinary career paths, the lessons learned for health equity, and their own visions for the future

of computing. Hi, hello, and welcome to the ACM AMIA joint podcast series. This joint podcast series aims to explore the interdisciplinary field of medical informatics, where both the practitioners of AI ML solution builders and the stakeholders in the healthcare ecosystem taking interest. I am Dr. Sabrina Shea with the Association of Computing Machinery by CAST Series. And co-hosting with me today is my co-host, Dr. Sulafa Khadura for your informatics podcast series with the American Medical Informatics Association. We have the pleasure of speaking with our series guest, Dr. Holly Urban today. Thank you for joining this podcast.

Today, our special guest is Dr. Holly Urban. Holly is a pediatrician and clinical informaticist. She received her BA from Stanford, her MD from the University of Colorado, and her MBA from Regis University. After working for several years as a practicing pediatrician, Holly transitioned to working in product management roles for healthcare IT vendors, including product leadership roles at McKesson and Hearst Health. Most recently, Holly was CMIO at Oracle Cerner before recently starting a new role as VP of Clinical Product Design at Clinicom. Welcome, Dr. Urban. Thank you for joining us. Hey, Dr. Urban. You had the medical informatics efforts as some leading

companies previously at Oracle Cerner and now Clinicom. Our audience here is from both AMIA and ACM. They are scientists, clinicians, health IT practitioners, and students. Can you share with our audience here a little bit more about yourself and your journey into informatics? Are there some inflection points in your career you want to introduce to them? Sure. Thanks so much, Sabrina. And thanks for the opportunity to chat with you all today. As Salafah mentioned, my clinical background is in pediatrics. I did mostly primary care, but I also took care of my own patients when they were hospitalized for low

acuity conditions. I also was doing some work in quality management. And this was in the mid 2000s when Joint Commission first released its medication reconciliation as a national patient safety goal. At that time, our hospital had its own IT shop, and they went ahead and developed a tool for medication reconciliation. And they developed it without any input from anyone from the clinical setting or any clinicians at all. So they developed the tool and then they tossed it over the fence to the quality management department and said, okay, Holly, why don't you go roll out MedRec to this hospital? And I had to train, I had to monitor compliance on the tool

and our usage of the tool was less than 1%. We were actually doing better on paper than we were with this tool. And I sort of said, why didn't they think about this? Or why didn't they do it this way? Or why, what about making it this way? And I sort of realized that I had a national sort of event to be thinking about how to solve the problems of med rec, which is really important, but in a way that made more sense to a clinician. And I thought that if they'd had a lot more clinical input, that tool would have been much easier to use and would have achieved that goal of patient safety related to medication reconciliation. And, you know, overall, that experience led me to really help me understand how healthcare IT can improve patient outcomes. And I realized what I wanted to do was be on the design side so that I could

build software for clinicians. And that's how I ended up in product management. I've always had a little bit of a technical bent. So it was a bit of a natural segue for me so that I could be that sort of ambassador between the clinical needs and the technical. So having that conversation back and forth where I can represent clinical things to technicians and represent technical things to clinicians. And the other piece that I should let you know is I'm a third generation physician. My grandfather was a GP. My dad is an orthopedic surgeon, retired, and I still don't think he understands what

I do all day. But I love product management. And really, it's all about making sure everything that I do has at the end an ability to make the EHR better for clinicians so that they can take better care of their patients. And that's what gets me up in the morning. And that's what really has driven me throughout my entire career. Oh, that's fantastic. Thank you for sharing your story. Continuing to talk about your journey, how did you get to this role as VP of clinical product design at ClinicComp?

And what do you think the most pressing issues are that you face in this position? Yeah, thank you. In my most recent position in my career at Oracle Cerner, I was mostly focused on software implementation and deployment, which I loved. It was a great opportunity to work with frontline clinicians, you know, sitting with them, working with physicians, understanding their workflows and, and really seeing their pain points with the EHR and where they were struggling and where they needed to have, where they really wanted better tool sets. And I love that work. And I love that daily

interaction with the providers. I miss the product management side. I really miss designing software. And so that's why I changed from that position to taking the role at Clinicomp, because at Clinicomp, we're designing a brand new EHR, which is really unheard of in our industry today. So to be able to get in at the ground floor of designing a brand new EHR, it's just for me, I just felt like it was incredibly exciting work to be able to leverage so much of the work that's gone over the past several years into improving Uterfrase. How can I actually leverage those lessons learned and then practically apply it as I build a new EHR? I think one of the things a lot that you asked me was about pressing

issues that I face. And I think one challenge is that CliniComp doesn't have a lot of brand recognition in the market. The company's been around for over 40 years. They've had a sort of a best of breed clinical documentation system. They have very beautiful flow sheets for ICUs. They have perinatal surveillance and they've done a lot of work with the DOD and VA. And as the DOD and VA have moved to a new vendor, it's, we looked at it, ClinCom looked at it as the opportunity to say, well, maybe what we need to do now is pivot to a full-fledged EHR. So really what we're doing now is like I said, starting from the ground up, building new software to incorporate all the aspects of the EHR.

And, you know, really the thing that we need to do at this point is find an early adopter customer who's going to be willing to partner with us in this journey. High barrier to entry, you know, I think the EHR market is ripe for disruption, but really we've got to have somebody who's willing to partner with us on this exciting journey. And, you know, I'm happy to say that we're going to have something for everybody at the health system. We're going to make sure that we have intuitive UI and intuitive workflows for the clinicians. Our database has been proven. It has no downtime, which is really wonderful. Something for the technology folks. And then for the financial folks, what you have to think about is in product management is we're doing a

fixed pricing model to get away from some of the nickel and diming that you hear about in our industry. So it's been really, really exciting. It's brand new, but already I'm jumping in and designing software to meet the needs of modern clinicians. It's been super, super fun. This sounds like an exciting journey that you're embarking on to design a new EHR from ground up. And this needs to be some field that really need people who are highly interdisciplinary to be in the middle as a bridge to be able to talk to both clinical and technical people on the front in order to know how to design it better, right? So we are also wondering, what is the important part here that from your observation to make a career successful

in this kind of interdisciplinary role between medicine and technology that you are taking in? Have you ever faced any challenges when leading interdisciplinary teams here and if so what's your secret in overcoming them you know one of the things about being a product management is you really do have to be hand in hand with your technical team with the software developments with the clinical architectures with all of the technical resources that collaboration is key you know one thing that I've learned over my career is that clinicians tend to go straight to a solution. They say, okay, guess what? I need this number on this flow sheet or this number on this screen to be read. It needs to flash and it needs

to really jump at you. And so it takes some discipline to really have that informatics mindset where you say, OK, you just told me the solution, but what problem are you solving here? You want this button to be huge and flashing red because you're worried about missing a data element. So you sort of have to have that ability to coax and be patient to talk to your client to say, what problem are you solving? Because what I can tell you is that if I can understand

your problem, and then I go hand in hand with the data architects, with the software developers, they may be able to come up with a solution that neither one of us could have come up with, right? Because they know how to use the technology in a way that maybe I don't even, I'm not aware of, and could blow both of our minds. So I think that making sure that you're very disciplined around the problem, as opposed to going to solutions is something that's critical in product management and critical in software design. And as I mentioned, really the best case scenario is when the clinicians and the technologists are deeply embedded in each other's work. And, you know, sometimes there's a natural push and pull there, right? Things that are obvious to clinicians are not necessarily obvious to your technology partners and vice versa. So I'll give you an example in my career. There's a woman,

I'm going to name her and I'll tell her that she's being called out on this podcast. Bernadette Minton. She is one of the most brilliant people I have ever worked with. She's a technologist, technology background, software development background. And when she and I would work together, she would come at a problem from pretty much 180 degrees different place than I came out of a problem. And we just had this push and pull where we would, you know, sort of with each other influence how we saw that problem. And when we would get to that middle place, I'll tell you that middle place would be a really, really good place. So that partnership, that ability to have that back and forth to understand each other's point of view, which oftentimes are 180 degrees

different, that gets you to a good place. And that partnership and collaboration is really, really key. Yeah, you talked about, you know, a common pain point for physicians kind of jumping to the solution. Are there other common pain points for physicians when it comes to using electronic health records and how do you address those in product design? So, so much of it is around intuitive interfaces, right? So part of the problem is that if you have a design that doesn't sort of natively understand the clinician's workflow, it doesn't translate well to how the clinicians use it. Let me give a concrete example. So I was looking at some mock-ups where for a hospitalist,

there was a list of patients and then this task list. And the task list for each patient was completely opposite of, it wasn't tied natively to each patient. And I said, hold the phone. We're not doing it this way because I'm not going to run through a list of tasks. What I'm going to do is in the hospital setting is I'm going to round and I'm going to look at each patient and then address all the tasks needed for that patient. So to have a task list completely separate from my patient list wasn't useful to me as a clinician because of this notion of rounding. And this is something, again, as a technologist, you don't understand what necessarily rounding in the hospital means. You understand that

the clinician has tasks they need to get done and you understand that they have a list of patients, but making sure that those two things are embedded together, that's the kind of thing that is really critical when you're talking about software design. Does that answer your question, Salafa? Yes, it does. Thank you. That was a good example. Yeah.

And also, I'm very excited to hear about that 180-degree partnership example you gave earlier. Is that how you collaborate with technologies in the design? Are there other examples that you had? What kind of contributions computer scientists, software engineers play in the design and development of EHRs? I would say that kind of discussion is happening on a daily basis. Because again, so at Clinicomp, we have very, very talented architects and software developers who understand the database. Clinicomp has something unique. They have an object-oriented database as opposed to a translation database, which gives them a lot

of flexibility. Now, I don't understand that, but they do. So it's that sort of, they can lead me how to show how having that flexibility in their database can allow them to do things in a different way so that I'm not specifying how they do it because I don't understand the database. I just understand what the workflow needs to be for the clinician, but we have that pull and push, right? Because they're going to push back and say, you know, how do I make sure that it makes sense from a technology standpoint, from the way they have the database set up, as opposed to how it's going to make sense for me as a clinician using the software. So we're having those push and pull discussions basically every

day. And are there discussions about physician wellness and the design at Clinicomp and how, you know, Clinicomp's product can support physician wellness compared to other EHRs. Yeah, it's such an important issue in our industry. It's not just the EHR, COVID-19, staffing issues. I mean, all of those things I think contribute to clinician burnout, but I don't think you can really minimize the impact of the EHR. So again, if you can provide more intuitive user interfaces that reflect the clinician workflow, I think that could really help in terms of the burnout. But in some cases, some legacy cases, that requires a whole new user interface, which,

you know, we're fortunately in the position of being able to do that. And all really making sure that we're focusing on the key work that clinicians need to do, that they need to get the right clinical decision support to help make the right decisions for their patient so that they can efficiently document and so that they can efficiently communicate their patient care decisions and not focus as much on billing or administrative or regulatory that tends to add to a lot of the challenges that clinicians have with EHRs. So one of the things that we're doing is leveraging some of the lessons learned done by, like

the AMIA has a 25 by 5 initiative to reduce EHR related documentation to 25% of the current state. So, you know, they're doing great work talking about what's really important in the note. And so we're able to leverage those lessons learned as we look to build out our own notes. So, you know, they're doing great work talking about what's really important in the note. And so we're able to leverage those lessons learned as we look to build out our own notes. Very similar, EHRA and the HIMSS Physician Committee have done a lot of work on the quote unquote ideal note. So again, looking at what they're, some of the things that they're recognizing, like you don't need to repeat information that's already captured in the record elsewhere and repeat it in the note. So that kind of thing, pretty basic,

but something that is a little bit unique. And the final thing is we're also looking at the safer guides that address EHR safety to make sure that from the get-go we're addressing any of those patient safety related issues that are contained with the EHR. So like I said, you know, there's been great work done out there. And to be able to leverage some of that great work and getting in on the ground floor of building a new EHR is just kind of blows my mind. It's really exciting. It's such a unique opportunity to disrupt the industry. ACM Bycast and AMIA FYI Podcasts are available on Apple Podcasts, Google Podcasts, Spotify,

Stitcher, and other services. If you're enjoying this episode, please subscribe and leave us a review on your favorite platform. That's exciting. But beyond the current company, the current way you are doing, did you see there are any common challenges across the board for the EHR system today? And are there trends behind the scene you want to call out for the audience here to know more in other specific ways that you see that can address those challenges that you also want to go out? One thing I think we can think about is all the data that's generated by EHR. So there's a huge amount of data out there. You know, how do you best challenge and analyze that data? And I think a lot about data literacy. I mean, first, you have to have access to the data, of course. Second is you have

to have a place to store it, dump it so you can analyze it. Third, you have to make sure you can normalize the data to make sure like for like is, you know, similar clinical concepts or representing similar clinical concepts. And that's where you get to your data literacy, where how do you analyze the data? How do you interpret it? How do you present it? Even more importantly is how do you have that data visualization so that others can easily interpret an action based on the data that you've analyzed? And I think the core of data-driven decisions is having access to high quality data that presents in a way that's actionable. And that's what I mean by data literacy. And I think that's something that's common, you know, broadly across the industry. You know, all EHRs have this, have that ability to generate data, but having the

data literacy, especially if you're using disparate systems, which many, many systems are, how do you make sure that it's all together in a way that's actionable? And it's really important to have clinical input in that way so that you can help interpret that data and recognize what's the signal and the noise, because it's not always obvious to a technician or to a technologist what data is or isn't relevant, and you really need clinicians to help guide. So I'll just give an example from a company I worked at. And I had an engineer approach me with a data visualization about how to, a new way to visualize growth chart data. I'm a pediatrician, so it's a good person to talk to, right? In his mind, it was just data, right? Head circumference, height, weight against, plotted against the patient age. So we thought, oh, I'll

just do a new innovative way to display this data. And, you know, this engineer had no way to know that the CDC growth chart is a clinically validated tool that every pediatrician in America is trained on. And that as a pediatrician, I recognize potential disease states based on, you know, based on errant or anomalous growth chart patterns on the growth chart. And so, you know, this was one instance where we didn't need a better mousetrap to visualize the data. But so you can see how having the clinicians involved to do that, to help represent where the data visualization is going to make a difference is really important. Did that answer your question?

Definitely answer my question. But I'm also wondering with all this high quality data collected right now, we have a chance to use them differently or for the secondary use purposes. And there has been a lot of potential that has been hypothesized that AI ML can play a role here. I'm wondering what you would take here. Is it really such a potential here or it's still a hype? I think the potential is huge. One of the things that I think a lot about is how you evaluate medical interventions, broadly medications, procedures, et cetera. Anything that we as providers and physicians try to do to improve patient care, how do you evaluate the effectiveness of the interventions we do and tie that to a clinical outcome? So, you know, how do I know concretely that the medication that I give or the procedure that I do is really going to make a difference from outcomes in a

standardized way. Because if you think about things like quality measures, for years, it's been focused on process measures, right? So it's around, did you prescribe VTE prophylaxis as opposed to measuring an outcome? Like did a VTE happen, right? Did a thrombotic event happen? And I know there's a lot of pushback on outcome measures because of challenges. Oh, there's things happening outside the provider's control. Is there the right attribution to the correct provider? But when I think about how AI or machine learning could work here, if you apply it to vast data sets, you can use kind of, you know, it's that notion of real world evidence. So you can understand, you know, tying to an outcome really were the factors and what kind of risk adjustments need to be due protects related to social determinants of health or other factors that are going to impact these outcomes that we're looking for in ways that we're not understanding currently. When we're just, you know, using our, you know, randomized clinical trials as the gold standard, they just isolate one clinical variable. How can we use real world evidence where it's real practitioners providing care and then tying what they're doing and seeing if that really does get the outcome? AI ML, I think, could really help us do that in ways that we don't even understand today. And I think we're only scratching the surface.

So as an example, there were studies done that show, this is my favorite example, at lumbar spinal stenosis. So after two years, they've done studies, they show there's no clear benefits to surgery over a non-surgical treatment. And as a patient, I sure want to know that, you know, like if I'm going to have no difference between physical therapy and surgery after two years, heck, I'm going to go with the physical therapy. So can you imagine if we could use AI to look at all of these interventions we do as clinicians,

medications, procedures, and then truly compare and understand how they impact patient outcomes in a real world setting, you know, done by providers, not study investigators. I just think the potential to how we provide healthcare is enormous. Absolutely. I agree. And, you know, with this amount of promise with AI, are there, you know, what are the reasons you think that adoption has been so low? I know, you know, you kind of mentioned, you know, figuring out the attributes, having clinicians involved. Are there other challenges that you've observed? I agree that adoption is low. I think one thing we also need to understand that AI is a powerful

tool, but it's not the end, right? It's not improving patient care. It's just a tool to help you improve patient care. So I think we need to all remember that. And you also need just, you know, there's basic kind of organizational issues you need to think about because you need the access to the right kinds of data. And then you need the right infrastructure framework to support these innovative techniques. And so the EHRs, for example, have the data, but is it available to AI developers? Or if you have AI developers, then do you have the right infrastructure and the right access to the right data? So there's just some sort of basic infrastructure that probably needs to be addressed. And, you know, the other probably even bigger issue I think is related to the, is to bias. You know, there's been

so much discussion about bias, and I think that's really one of the biggest factors limiting the use of AI in healthcare. And, you know, the example I always think of is the Framington Heart Study, which was developed not using AI, but, you know, we've learned now that the Framington cardiovascular risk score works very well for white patients, but it doesn't work very well for black patients. And, you know, that leads to potential undertreatment of Black patients, which could lead to worse outcomes for that vulnerable population, which is totally unacceptable. And when you think about how that was designed, you can see how AI-generated models could also be biased, leading again to undertreatmentnerable groups, which again is definitely not acceptable. So I think a fundamental issue is making sure that you have the right data in your data sets to make sure that you're able to represent any issues related to health equity. So we do need to make sure we're doing better as an industry and how we standardize and normalize how we collect

data for health equity. So, you know, ICD-Z codes, I think are a good start, but I think across the board, there's a huge amount of variability in how we collect, how we assess health issues related to health equity and to social determinants of health. And I think that's true across different organizations, not just EHR vendors. I think there's a lot of variability between EHR vendors, but also between different organizations that are using the same EHR vendors, they're collecting that data in a different way. So I think that we probably, you know, leaders in the field really need to be thinking about how we standardize that data collection and normalize it so that we have better data sets to utilize. Other things I think

about in terms of when you are creating AI models, how you can combat bias. I think you have to have diverse teams. Confirmation bias is something we should all be well aware of where you're just going to look at the evidence that supports your preconceived notions. But if you have diverse teams, that helps with that confirmation bias issue. Again, making sure that any AI models you're developing include data around social determinants of health, race and gender, health equity issues. And then again, making sure you have engaged clinicians at the table to ensure that those trial questions are clinically relevant and that you're able to evaluate any study results with a conscious mind toward whether

or not there may be biases involved. Yeah, that reminds me that last year in AMIA, we had this keynote speaker talking about disability. But one thing she noted is that disability wasn't even captured well in the data today. So when it comes to the measurement and to be able to combat bias against disability, they can't even have a fair start to begin with, right? Because there is just no data to help them. Yeah.

It's foundational. We're not going to be able to get anywhere in improving issues related to health inequity and to bias without having the right data that reflects some of that. So even stuff like zip code data, which may suggest, you know, if certain zip codes have, you know, food deserts, that's probably in some cases going to have a material impact on a disease state or a clinical outcome. And we don't know that. We can make assumptions, we can make guesses, but without having the data to test against,

it's all, you know, you're making assumptions. You're not really proving out in a real world evidence kind of framework. So here, that seems that we have that biggest community here from both AC and AMIA in the audience, like one Stanford, more computer scientists and practitioners, the other more stand for medical informatics. Are there any things that you would quote out that we can help with these data challenges or any upper level HR challenge you have seen

in your career that you feel that intersection of these two communities can start looking into more to help address? When I think about my career, some of the most, by far the most exciting opportunities have involved the use of enabling technologies to improve patient care. And so, you know, if clinicians and scientists, technologists can partner in how they evaluate and assess enabling technologies and how they can be leveraged in healthcare. That's the happy path. I think that provides the best outcome. Because I, as a clinician, I can make hypotheses how maybe a new technology like AI, I think AI is a good example, you know, how AI can be applied. But

I really need computer scientists to really help me understand, you know, show me can be applied. But I really need computer scientists to really help me understand, you know, show me the way, right? They're the ones who are going to be able to say what the technology can or can't do. And so that's why it really comes back to that partnership, because you need both that technical understanding and the clinical context in order to truly understand how enabling technologies like AI can be leveraged in healthcare. So, you know, the partnership in my mind is absolutely critical. And, you know, I think when I think about the two organizations that are represented on this podcast, you know, AMIA can certainly help identify some of the broader issues, the context, you know, how does health equity

impact patient outcomes, and then partnering with ACM on equitable applications of technology, right? So ensuring safer algorithms, that kind of thing. So it's definitely all about that partnership. And talking about that partnership, do you have, and you shared with us your journey from being a primary care physician and rounding on your own patients, and then now, you then now transitioning to industry. Do you have any advice that you could give mid-career professionals that are tuning in from AMIA and ACM thinking about exploring different career paths or transitioning? I had the opportunity at AMIA last fall to do one of those Ignite style sessions, which I titled tongue in cheek,

going to the dark side about moving to a vendor role. I was expecting that, you know, it'd be me and, you know, probably two or three people sitting at a table having an informal conversation and about 35 people showed up. So it was a lot of fun and it just kind of demonstrated to me that there's a fair amount of interest in, or people are at least wanting to explore roles in our informatics committee, looking at vendor potential. So I'll repeat some of the things I said in that talk, but I do think volunteering or getting involved in informatic initiatives at your organization goes a long way, you know, helping with go-lives, being a super user, doing EHR optimization projects, especially, you know,

that helps you do a couple things. It helps you understand, you know, is this the kind of work that I really like, that I really enjoy? And it also demonstrates to a potential employer that you've got some hands-on experience, even if you don't have formal training, but that you've got that sort of informatics mindset and aptitude and gotten involved in a way that you could leverage in then a vendor role. I think there's a lot of great training resources out there. For example, AMIA 10x10 program is a great introduction to medical informatics and that AMIA 10x10 session can serve as the first class sort of full master's if people want to go that way, but it's a good starter to just get introduced to informatics

in general. The only other thing that I'd say if you're serious about looking at vendor roles, networking is key. I mean, network, network, network. It does a couple things. So one, it helps you understand that there's a lot of variety of roles available on the vendor side, and they're really different in terms of what your day-to-day job responsibilities would be. So an example, if you work at a startup where you're wearing a lot of different hats, that can be really fun. And you're doing a lot of different things. You're called and you have a lot of input clinically in a lot of different areas. Sort of like my position now, we're not a startup, but it sort of feels like a startup and how we're all wearing a lot of different hats. But there's also a lot

of risk, right? So you don't know that your startup is going to be successful. So you have to have some tolerance for risk if you want to go that route. The other route is, do you work for an established vendor that's been in the system a long time? A lot less risk, but potentially more organizational resistance to change. So you sort of have to, as you network and talk to people, you can get a sense of what they're doing day to day and that balance between the clinical input and the risk. The other opportunity, the other advantage of networking is you may find actual opportunities where people are looking to hire clinicians and hire informatics. So I've now in my career worked at four healthcare IT companies and every single job I've gotten has been

networking. So knowing a person at the company who made the initial introductions and then helping me get the interview. So I've gotten zero opportunities through cold applying. It's all been through my network. So, and this is going to be sound crazy. My first job at McKesson, I had applied. I had not gotten an interview. I was sitting at my son's baseball game. If you have kids who play baseball, you know, there's a lot of downtime.

You get to know the other parents pretty well. One of the other parents' sister worked at McKesson. And so she sent my resume to the person who ended up hiring me. So you'd be surprised where network opportunities happen. My position at Clinicomp, I was at a former colleague's retirement party and bumped into a woman I'd worked with previously. And she said, hey, I'm working at Clinicomp and we're building a new EHR and we're looking for a VP of product.

You should come apply. I was like, wow, this was at a retirement party. So you just never know what the universe is going to bring you in terms of opportunities. That's why I say you have to network, network, network. It's really key. And the other question I get, especially from early stage folks, is like, how do I do this? And there are a lot of opportunities. So, you know, this audience is AMIA.

So go to AMIA. Sign up for the networking sessions. You know, talk to everyone. I mean, that's one of the great things about AMIA. It's all like-minded people who like to geek out about informatics stuff. So just having those conversations is a good way to meet people and understand what their work is. And there's a lot of online communities, too. If you can't, you know, maybe attend the conference, you can join Women in AMIA or many of the other professional associations with AMIA.

You could join your local chapter of HIMSS. You know, there's a lot of really strong local chapters of HIMSS where you have an opportunity to network. So I just I cannot emphasize that enough. It's really all about the network and just talking to people and you'd be surprised what may come just based out of that. Yeah. Can't agree more. Opportunities are for those who are prepared. That's right. Put yourself out there in order to be discovered, right? So networking and all this career advice you gave on the spot for people to really put themselves out there to be fun that's right yeah it's great advice thank you before we close

are there any parting words you feel that you would like to share with our audience here? Now you know them better through our conversations. Are there any advice or any other parting words you feel you would like to share? Sabrina and Salaf, I just thank you so much for the opportunity to participate and have this conversation. I'm very passionate about especially some of the career development because I felt like when I first started my career, I really didn't have the opportunity to have someone who could mentor me or give advice. So I'm always happy to do that sort of mentorship and help young, especially people early in their careers as they look to grow in informatics. So thank you for the opportunity to talk about that. And, you know, this whole conversation really represents one of the things I do love about AMIA, you know, learning about all

the amazing things that scientists are doing across the country. It's very inspiring. And it's just also seeking those like-minded people that, you know, sit down and you're having conversation at lunch and then all of a sudden you're geeking out over some new technology that you know you couldn't probably have this conversation with anybody else but you know it's those like-minded people that are in the same boat as you and just loving the informatics world so again I just say thank you so much for this opportunity I've really enjoyed it and I guess that's all I'll say today well thank you so much for joining us it was such a pleasure talking thank you and I believe that this year there will be even more people coming to the Ignite sessions at the EMEA Symposium now.

It's for more of the different kind of career paths you're talking about here. And of course, for the ACM people, there are more conferences where we are partnering with medical informatics, like in KDD, in many other health informatics-related conferences in ACM, there are also opportunities that we can talk more about those issues we care here. Yes, so thank you again, Dr. Evan. Let's go to Holly now. Yeah. After this conversation, we are all going together thank you so much

thank you for listening to today's episode ACM Bycast is a production of the Association for Computing Machineries Practitioners Board and AMIA's For Your Informatics is a production of Women in AMIA

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