Unleash Your Healthcare Data Archives to Empower AI Innovation

John Lynn: Hey, everyone. I’m John Lynn, the founder and chief editor of HealthCare IT Today. We’re excited to bring you another in our series of interviews with top leaders in health IT. And our guest today is Jim Jacobs, President and CEO at MediQuant. Welcome, Jim. Jim Jacobs: Thank you. Appreciate you having me. John Lynn: Excited to have you back. We’ve talked with you before. You always have great insights and perspectives. But for those that don’t know you, tell us a little bit about yourself in MediQuant. Jim Jacobs: Thank you. I’ve been in healthcare IT for quite a while. MediQuant’s been around since 1999 and we’ve been pioneering the space of active archive. John Lynn: Wow. Yeah. It’s amazing how long you’ve had to do that. Right? And that’s a lot of experience in the space. Tell me, what is MediQuant’s philosophy and approach to implementing AI? It’s all the talk here at the conference, but what’s your approach to it? Jim Jacobs: I think you could almost rename the conference the AI almost. There’s so much of it out here. You know, MediQuant has a reputation in healthcare IT as a trusted advisor. So, we have to think about AI through that lens. There’s a lot of discussion about AI and a lot of different approaches to it. And so, what we’ve done is take a strategic view of AI and view it in pillars. Okay, so we have pillars around how to deliver better products, faster projects, and better results for our customers. That’s one way. We look at our own productivity improvements internally. We’re looking at use cases for our customers because they need help with productivity. They need help getting to the punch line faster. We have patient financial services people who already have way too much work to do. How do we help them get to the right work at the right time? And then we’re looking at data use cases, actual places where we can drive insights from the data we have from the various systems. That’s to help through decision support, decision making, and help the hospitals with their own data do more because we view that data as an asset. And the more insights we can bring from that data, the better off we are. John Lynn: Tell me more about the factors helping to shape MediQuant’s use of AI and even other technologies, like machine learning technologies, to solve what you exactly said, which is making them more efficient or making you more efficient to serve them better. Jim Jacobs: Sure. One of the fun parts you just mentioned is AI is quite a nebulous word. Is it, machine learning, large language models? Right now, we are lumping it together with many different things because we believe there’s a multipronged approach. It depends on the use cases we’re talking about. For us, it’s making sure that we’re partnered with institutions that view data as an asset. Do they value the data? Because we want it in a discrete format. Our Simon Sinek “Why” is to liberate data for secondary use. That’s what we’ve always done as a business. So, by bringing the data from disparate sources, pulling into a data lake, making it accessible through DataArk®, then we can figure out where are the best places to apply technology, but you have got to make sure the data is clean. You have got to make sure the data is right. We’ve got to make sure it’s all pulled together. Getting data ready for the use of AI is a big job. The promise of AI is reliant on good data that’s accessible in discrete format. You can make great presentations about AI, but you must feed it somehow in the first place. John Lynn: I think it’s interesting to watch the evolution of your space because I would almost describe it in many ways as a “nice to have before.” Sure, there were software licenses, there were security issues, there were other things that were driving it. But what you just described is changing it from this, “Oh, yeah. We must deal with this old software and this data somehow,” to now saying, “No, can we be proactive with our data and unleash the power of it.” Is that kind of how you’re seeing the mindset change from your customers? Jim Jacobs: We are. Especially if they view the data as an asset. If they understand that data has value, and especially if you marry the data from disparate systems, especially now you combine it with whatever the new EHR is going forward or marry older patient accounting data with brand new. You could trend data; you can build scorecards. It’s an amazing number of transactions that you can start putting together, but you have to start with the data. If you only look at the live system, it might be a year old or six months old. You may be missing a huge part of the picture as some of the large language models or some of the original machine learning models needed vast amounts of data. If you just fed them a few quarters worth of data, it really wasn’t enough for them to learn. John Lynn: Interesting. And what are you hearing from your customers about this proliferation of AI in the market right now? What are they telling you about it? Help? Jim Jacobs: They see AI everywhere. It has so many definitions. That’s why, as a trusted adviser, we have a recommendation to an approach to looking at AI. Because you need to be careful. You’ve got to be cautious. I even say to be skeptical, right? There’s a lot of promise. Where is the AI engine located? Where is your data going to go? Is it in just any cloud? How do you know? Is there a bad actor potentially that could get access to the cloud? None of us needs a data breach. So, there’s a huge checklist to make sure is there. The other thing we advise our customers to look at is starting with the use case. Is there a return on investment? Are you going to get any results any time soon? Or is this some futuristic promise? That’s why most of the use cases we’ve started with are very measurable, defined results. Start to walk, then run, which is a very classic approach to that, but you also must think about adoption because they’re end users. They must make use of the tools. They must think about adoption. So, starting with some big flash pie in the sky means you must be careful. The other thing that we see is our customers are getting pulled into too many AI meetings. And so, how do they filter? How do they really begin to discern where do they spend their time? And again, it starts with what are the use cases? What are the end users that are going to be affected by the opportunities? John Lynn: Yeah. I mean, your first point is really interesting because we’re in a best-of-breed AI situation right now, and there’s not an all-in-one AI platform as we discover what AI can do. But what that means is all the data is going to these best-of-breed systems and spreading the risk. You know, it’s fascinating how that’s going to evolve. Are there some things that companies are getting wrong about AI that, you know, you see, and you’re like, that seems like that’s going to cause trouble later? Jim Jacobs: We’ve seen a few examples where AI was used liberally. So, the engines underneath weren’t true AI. So be careful there. It may still be good technology but do the proper diligence to see what’s there. I think there’s also some very large promises of productivity gains that may not be there. Or vendors talk about their ROI being based on staff reductions or other types of things and I think most hospital systems best benefit come from staff augmentation to help the existing staff, not to cut staff. They need to be able to get to more with the staff they have. Cutting staff is not a great way to meet the needs of the hospital system. They’re already short-staffed in so many ways. So, the final piece that we see is just technology for technology’s sake, and it’s cool stuff, but you can’t get to an ROI. You don’t get to what the value is. In hospitals, someday, at some point, they have to make decisions based on a financial result of some kind. They are measured ultimately by financials, and they’ve got to have something that really points to, you know, how do we really measure the benefit to the organization? John Lynn: I mean, that comment kind of begs the question, “is AI really a distraction for these healthcare organizations, or is it a solution to the problems they have?” How do you kind of view that? Jim Jacobs: Depending on which AI, it’s both. It can be both, because the hospitals see so many opportunities for AI. And the promise of AI is phenomenal. But what’s real? What’s in the ground? What’s measurable? There are already work lists, rules engines, and types of technologies that solve certain problems. Patient admissions, queuing up patients, demographics, and propensity to pay aren’t new use cases. So, if you’re going to look at AI, is that enough of an incremental add to justify an addition of change, an addition of technology? You know, how do you know what you’re going to get? So again, not AI for AI sake. What is the actual benefit for the institution? We see an uptick in our hospital executives asking harder questions, more financially based ROI type questions, and then also making sure that end user constituents are involved in the decision-making. Not just buying AI and plopping it down. If you’re going to deploy this in the patient financial services department, have that group at the table. Have them as part of the decision-making. It may be a technology buy, but it’s for an end-user purpose. John Lynn: Yeah. That’s interesting. Well, I mean, you know, obviously, AI is essential. We’re seeing that in healthcare organizations. We’re seeing it in your organization as you leverage it to improve your tools. But what else is MediQuant focused on in 2025 aside from AI? Jim Jacobs: You know, helping our hospital systems get to more work faster is sort of a theme. So, we’ve launched a product around application rationalization. The idea being, how do you prioritize and roadmap what your opportunities are? John Lynch: I heard someone say that application rationalization isn’t about getting rid of apps as much as focusing the organization on where you want to go, which I thought that was like the cool way to phrase it. Jim Jacobs: It’s a much better way to frame it because maybe you stop paying maintenance on an older system. So, you don’t have the latest security patches. That might be a high priority. If you’re not paying maintenance, there’s not an ROI necessarily, but there’s a risk mitigation. Are you complying with information blocking? Do you need this data in a patient portal? There’s a lot more to application rationalization than just saying what’s the most expensive software we have, or just because we went live on our new EHR, what was the old one, and let’s get rid of that. That’s a simple, obvious one. There’s a much more sophisticated rubric to it. And then internally, we need to make sure, especially with complicated systems where hospitals are buying or divesting, how we can help them get their own ROIs faster. Because they need to get that cost savings. They need to turn off older systems. They need the data out for the physicians. So doing things better, faster, prioritizing, we’re even helping, staff hospitals are bringing partners to bear, so they actually can, in total, have to maybe bring in a third party, but they get a faster result, which for them is a better realization of the return. So, all things about getting the return for the customer faster. John Lynch: That’s awesome. Jim, I appreciate all the work you’re doing to help improve healthcare organizations. It’s such an important space with great ROI. You know? The legacy archiving and dealing with your data across these disparate systems makes sense. But I love that you’re transitioning from just archiving it to now empowering that data to solve some of these problems that AI is going to help us with. We appreciate you sitting down and talking with us, and thanks, everyone, for watching and listening. If you want to find more great healthcare IT content like this, be sure to check it out. www.healthcareITtoday.com or search for Health Care IT Today on your favorite podcast application.