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We sit down with Maria Iliakova, a bariatric and general surgeon, to explore the exciting ways AI tools like ChatGPT are reshaping clinical practice. Maria shares her experiences using AI to enhance hospital staffing, analyze patient data, and predict patient flow in real time. We discuss how AI can help address health care staffing shortages, improve the efficiency of call schedules, and provide more personalized care.
Maria Iliakova is a bariatric and general surgeon.
She discusses the KevinMD article, “Can AI truly improve hospital staffing?”
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Transcript
Kevin Pho: Hi, and welcome to the show. Subscribe at KevinMD.com/podcast. Today, we welcome back Maria Iliakova. She’s a bariatric and general surgeon. Today’s KevinMD article is “Can AI Truly Improve Hospital Staffing?” Maria, welcome back to the show.
Maria Iliakova: Thank you so much, Kevin. It’s really nice to be with you.
Kevin Pho: So we were talking offline, and I know you’ve been on the show before, and you’re a bariatric and general surgeon, but now your career has taken a little bit of an AI tangent. So tell us what that is.
Maria Iliakova: Yeah, I think a lot of people’s careers, honestly, have been taking a little bit of an AI tangent. It’s definitely the buzzword that’s around everywhere right now. It’s big in investing right now, and a lot of companies are getting started around AI. So, funny enough, I actually worked on AI projects a long time ago when I was doing a master’s in bioinformatics. I did some protein structure calculations and modeling—not the cool kind, but the kind that you do with Python ages ago, like more than a decade ago. And it’s great to see these kinds of tools. We’ve taken a quantum leap, honestly, since that time. So what we’re able to do with AI tools now is just leagues beyond what we were able to do just 5, 10 years ago.
There are actually some interesting factors driving that. One is that processing power has changed radically since that time. So, imagine what cell phones used to look like even 10 years ago or what computers used to look like 10 years ago. Now, you can essentially carry an entire library of songs or even videos, which isn’t something we could even imagine 10 years ago. So the processing power and computing power have changed radically. And then also, concepts like big data, for instance, have matured a lot in the past 10 years. We have databases of everything from patient information to molecules and how they interact, to even the data that Google, for instance, has been collecting on all of us and how we use our devices and technology. Though they do it for advertising purposes, it can obviously be deployed for other purposes, too.
So yes, my career has, yet again, taken a bit of a detour into AI.
Kevin Pho: All right. So we’re going to talk about one of those AI-related articles. It’s titled “Can AI Truly Improve Hospital Staffing?” Tell us why you wrote this article in the first place. And then, for those who didn’t get a chance to read it, tell us about the article itself.
Maria Iliakova: Sure, I’d be surprised if many people actually read it, but in short, I think in health care, we know of AI, we’ve heard of AI, but we don’t necessarily think we’re using a lot of AI. And we don’t really know how it’s going to be applied to what we do or how it’s going to change the work that we do. So this article is addressing that, but specifically asking the question of, you know, what about one really, really important question and topic, which is hospital staffing?
And hospital staffing is something that you’re probably wondering why on earth a surgeon is talking about hospital staffing. Well, let me tell you—it is the bane of our existence when we don’t have staff in the OR, for instance, because it delays our cases, delays our patients, and sometimes even means we can’t do the surgery. Over 90 percent of hospitals at this point are actually using locum physician staffing. That’s not just temporary nursing staffing; that’s temporary doctor staffing. And that’s 90 percent, not 1990, which I think is pretty shocking. I didn’t know that until I started looking into it. And I’ve had the privilege of working as a locum surgeon in hospitals in Iowa, Florida, and a couple of other states. I’ve really started to understand that this is a crisis, especially for our rural and community hospitals, but it also reaches into cities and academic institutions as well.
So it really crossed my mind—what if we could use some AI tools to make hospital staffing a little bit better? We can’t just create new doctors out of nowhere. Creating more residency positions is literally an act of Congress, so you can’t just materialize people. But what if we could use the people we have more effectively? And that’s exactly where tools like AI can help us: using the resources we have in ways that maybe people haven’t thought of yet or aren’t as good at doing.
This is combined with my experience at the hospital where I was working in Iowa City, Mercy Iowa City, which went bankrupt last year and became part of the larger university institution basically next door. And one of the biggest factors driving that bankruptcy was staffing and the inability for this small independent community hospital, which had been standing for almost 150 years, to actually staff physicians.
There are a lot of different opportunities here, but I figured it would be cool to address this question in the article. I specifically talk about something I was learning in one of my MBA classes. On top of all this, I also started a full-time tech MBA at NYU in May this year. We actually do coding. We use Python, which is a programming language, in something called Google Colab, which is a notebook you can use to deploy code. I was blown away—like I told you, Kevin, I have done programming and things in the past, but the ability to have such a powerful tool and to not just start programming but have AI built into the notebook was amazing. It’s called a Jupyter notebook, created by Google Colab, and it was literally writing code for me as I was commenting on it. The insights, data understanding, and images you can generate with it blew me away because it’s not just giving you random answers—you’re actually generating the code yourself. So you understand exactly how the results you’re getting are being produced. I really wanted to share my experience because, as a doctor, I haven’t been coding for the past 10 years or so, but even someone like me could generate this in minutes using powerful tools involving AI.
Kevin Pho: So tell us about some of these AI tools. How can they address some of the hospital staffing issues you brought up earlier?
Maria Iliakova: Yeah, absolutely. I think we’ve all really felt the crunch, even if you’re in a place that happens to be well-staffed. We all saw people leaving the workforce due to COVID, and some have not reentered. Some people who did come back are more cautious about signing up for full-time work or going to certain locations where there may be instability. So it’s a big issue.
When you start asking questions like, “How can we fix these problems?” we can’t just generate more doctors. What AI is really, really good at are things like sorting, recommendations, predictions, and analyzing lots of data to uncover trends and insights. One of the biggest hurdles I encountered as a locum physician was actually credentialing. You wouldn’t necessarily think of it—it’s not a sexy topic. Nobody’s up at night thinking about how cool credentialing is—maybe except me at this point—but it’s a huge hurdle. It can take months. I think every physician has encountered credentialing issues, where something didn’t come through on time, or they were left waiting for weeks or months to start a job, especially if you’re transferring states because we have state licensing for doctors in the U.S.
The last time I went through credentialing for an assignment, it took 37 emails, 7.5 hours of work for five different people. And that was with a motivated hospital staff, motivated surgeon, and a process moving as fast as it could. It still took that long. I think every doctor listening can relate to how time-consuming credentialing can be, especially when you’re excited to get started in a new role.
From the hospital’s side, it’s a huge issue too. They’re losing the ability to care for patients, losing money, and losing credibility. If you don’t have a doctor for a program—say, a GI doctor for an endoscopy lab—you don’t have nurses or techs either. If the doctor isn’t working, the whole team isn’t working. It affects both clinical and inpatient settings, so it’s a problem with ripple effects.
So I started a company initially called Search, but we rebranded to DocDotGo. We take credentialing documents from doctors, have them upload everything in a document dump, and we deploy AI on the backend to analyze those documents and automatically fill out digital forms to create a profile, a CV, and something that can be shared with employers, hospitals, insurance companies, electronic health record companies, and others. It helps doctors track and update credentials, manage renewals, and more. It’s shocking that something like this doesn’t already exist. The best thing I’ve seen out there is FCVS, but it’s not comprehensive, doesn’t track everything, and isn’t the easiest to use.
We deploy AI to automate much of the manual work that otherwise requires dozens of emails and form-filling. AI just makes it faster and more accurate.
Kevin Pho: Give us a sense of that piecemeal approach that physicians currently use for credentialing. You mentioned dozens of emails and various documents. How many parties are involved, and what’s the scope of this complexity?
Maria Iliakova: It’s a great question. The challenging part is that, even though we do this repeatedly, it’s the same documents every time that every hospital is requesting.
Maria Iliakova: So, it’s really a process ready to be standardized and made consistent across different hospitals. What you’re looking for includes things like your degrees, diplomas, certificates, credentials for your residency program and fellowship, state licenses, your DEA number, your CDS, ACLS, BLS, all those certifications, your CV, your medical malpractice coverage, and even immunization records. And Kevin, I was born in Russia, so half of my immunization records from childhood are in Russian. You can imagine how difficult that is for hospital administrators here to read and use as part of my credentials.
So, it’s a challenging process. Some of these documents are handwritten; some don’t exist online. You may have a document that’s been scanned and re-scanned so many times that it barely looks legible anymore. It’s ready to be something that doesn’t require hours or days of hunting down and emailing these documents back and forth. And many of these documents are highly sensitive, like your DEA number or immunization records, yet we’re emailing them back and forth with administrators. On average, this process takes three to six months and costs over $1,000 to collect and verify these documents each time you go through it. Most credentials, like your state license, BLS, or ACLS certifications, require renewal every two to three years. So, we’re spending a significant amount of time and money to keep all these credentials current.
I don’t know a single doctor who hasn’t had a pretty negative experience with credentialing.
Kevin Pho: After using this AI approach, how much time does it shave off the typical credentialing process?
Maria Iliakova: Once the doctor uploads their records, we help locate them if needed, and in some cases, we can even integrate with other databases like NPI to automatically pull records without the doctor needing to upload them. This changes the timeline from months to minutes or hours. And that’s a game-changer, not only for doctors, who find it much more convenient, but also for hospitals. Now, hospitals can hire and verify credentials at a speed that was never possible before.
The administrators I’ve worked with during credentialing as a locum are saints. They handle each document, step-by-step, making sure everything on the checklist is complete. But this is the kind of process that’s ripe for automation. Most of these administrators have other responsibilities beyond credentialing. Often, they’re wearing multiple hats, sometimes even being the CEO, CMO, or CFO of the hospital in addition to helping with credentialing.
Automating this process frees up time for these administrators to focus on more impactful tasks. And it’s incredibly beneficial for doctors as well, reducing the stress and time spent hunting down documents and dealing with back-and-forth emails. So, it’s a personal goal of mine to make this process as efficient as possible.
Kevin Pho: Just to clarify, this AI approach can turn that months-long credentialing process into something that now takes just minutes or hours?
Maria Iliakova: Yes, minutes to hours, depending on the readability of the documents. We’re also testing it with hospitals, so their onboarding documents can be digitized in the same way. Imagine your onboarding documents being auto-completed with the doctor’s existing records—no more emails or manual form-filling. It all becomes a digital process that happens instantly once the doctor and hospital are matched in our system.
Think about it—this same technology could even be applied to other fields. Any kind of application process, like applying for school or even my own MBA program, could benefit from a tool like this, where you just click a button, and your entire CV is uploaded without having to fill out forms manually. This is what AI and digital processes are made for: to eliminate busy work and what we call “scut work.” Nobody wants to spend time on that.
Kevin Pho: In your article, you touched upon other ways AI could help improve hospital staffing nationwide. Could you expand on some of those other potential applications?
Maria Iliakova: Absolutely. One exciting application of AI is in predicting staffing needs. For example, AI can analyze data to predict when a hospital will need more staff or fewer staff, which can help with dynamic scheduling. It can forecast things like patient volume, surgical cases, peak times, and specific staffing requirements. For instance, do we need two surgeons on a certain day or just one? Should we schedule more nurses in the ICU because of a flu outbreak?
AI can even take location data into account, which is especially useful if a hospital has multiple locations or frequently transfers patients. It can predict transfer volume, allowing hospitals to better allocate resources. In health care, doctors and nurses generally prefer to stay busy if they’re on duty, and AI helps match supply and demand so that staff aren’t sitting idle or overworked.
Another great use is in dynamic staffing. AI can help manage backup staff more effectively, or “flex” people between units based on immediate needs. It’s especially beneficial for support staff, like nurses, techs, and medical assistants, to be deployed where they’re needed most. There’s so much potential here to address staffing in a way that matches our current needs and keeps our hospitals running efficiently.
A huge opportunity we’re seeing is also in telehealth staffing. Tele-stroke programs, for instance, have improved stroke care by providing quick access to neurologists. Ten years ago, less than 20 percent of the country had access to a neurologist within 30 minutes to diagnose a stroke. Now, over 70 percent of the country does because tele-stroke has become the standard of care in many places.
To make telehealth more accessible, we can use AI to facilitate what’s called “proxy credentialing.” If a neurologist is credentialed at NYU, for example, and NYU partners with a rural hospital, the neurologist’s credentials at NYU can serve as a proxy, allowing them to provide virtual care without going through a separate credentialing process for each hospital. This has dramatically reduced costs, improved access to real-time care, and ultimately saved lives.
As robotic consoles and procedural telemedicine evolve, we’ll likely see AI used even in the procedural space, an area that’s just starting to open up. The applications truly are endless.
Kevin Pho: We’re talking to Maria Iliakova. She’s a bariatric and general surgeon. Today’s KevinMD article is “Can AI Truly Improve Hospital Staffing?” Maria, as always, let’s end with some of your take-home messages to the KevinMD audience.
Maria Iliakova: Absolutely. I’d say my first message is that, while there’s a lot of opportunity with AI, it’s essential to remember it’s just a tool. How you deploy it matters a lot. You wouldn’t use a scalpel to solve a problem unrelated to surgery, like treating diabetes. In the same way, if we use AI for something it’s not made for, or without proper consideration, it can cause harm instead of helping. Engaging people who know AI and are aware of social, ethical, and privacy issues is crucial before implementing it systematically in health care.
My second takeaway is that AI can significantly enhance what we already do. AI won’t replace doctors anytime soon, and that’s a good thing. Physicians bring something unique—an ability to validate results and take responsibility for them. No tech tool can replace a doctor’s responsibility and judgment in patient care.
And lastly, AI isn’t here to take your job as a doctor, but it can help you do your job better. Imagine if you knew what to expect in terms of patient volume, if you had the staff you needed when you needed them, and if you could start working at a new job immediately instead of waiting months for credentials. AI can do a lot of the “scut work,” allowing doctors to focus on patient care. With tools like this, we can make the best use of the doctors we have to meet the needs of hospitals and patients nationwide.
Kevin Pho: Maria, thank you so much for sharing your perspective and insights. Thanks again for coming back on the show.
Maria Iliakova: Thank you so much, Kevin. It was such a joy to be with you.
