The Hospital Finance Podcast

In this episode, Scott Schrader Head - Healthcare Provider at Firstsource discusses reshaping revenue cycle strategy for healthcare's next era.

In this episode, Venkat Mocherla, Co-Founder and President of Midstream Health discusses the margin hospitals already earned but never see.

← Back to All Podcasts From CFO to Cancer Research — A Conversation About Challenging Assumptions In this episode, Ted Giovanis, President of the Jayne Koskinas Ted Giovanis Foundation for Health and Policy, discusses from CFO to cancer research, a conversation about challenging assumptions. Highlights of this episode include: What drives hospital CFOs to keep taking on entirely new challenges One thing about how healthcare operates today that we should fundamentally rethink A question about healthcare we’re still not asking Return on investment when it comes to scientific research that may take years to pay off Which is more expensive for healthcare, trying something new that might fail or continuing to do things the way we’ve always done them? What lessons to apply for improving healthcare and cancer research Subscribe Today! Kelly Wisness: Hi, this is Kelly Wisness. Welcome back to the award-winning Hospital Finance Podcast. We’re pleased to welcome Ted Giovanis. Ted has been the president of the Jayne Koskinas Ted Giovanis Foundation for Health and Policy, which conducts research in metastatic breast cancer and health policy. He is also a professional race driver and team owner in the International Motorsports Association Racing Series. Examples of health policy research that their foundation has conducted include research on the value of hospital tax exemptions relative to hospital-provided charity free care, how to explain health plan prices and quality information to individuals with varying degrees of numeracy and literacy, and defining meaningful quality measures, including the weighting of those measures into aggregate measures for communication to patients. While in practice, Ted was involved in several legislative efforts that resulted in the establishment and implementation of several payment mechanisms, including the Medicare Geographic Review Board and Section 508 reclassification process, the policy for the payment of the certified registered nurse anesthetists, and the long-term care payment systems for various states. Most notably, Ted discovered and prosecuted the Medicare budget neutrality error, which was the basis of the rural floor budget neutrality appeal and related settlements that at the time was the largest settlement Medicare had lost. In this episode, we’re discussing from CFO to cancer research, a conversation about challenging assumptions. Welcome, and thank you for joining us, Ted. Ted Giovanis: Oh, thanks, Kelly. Thanks. Good to be here. Kelly: So, let’s go ahead and jump in. So, you’ve been a hospital CFO. You’ve helped shape healthcare policy, been part of a landmark court case, written books, and now race professionally and fund cancer research. What drives you to keep taking on entirely new challenges? Ted: Well, that’s a really good question. So, I can tell you as far back as I can remember, I’ve had this drive to achieve. Maybe it was the fact I was an only child, and we didn’t have any money. But to achieve, you need to be very goal-oriented. And in this sense, I’m the typical Capricorn. I set goals and I locomote towards them. I started in healthcare and finance and worked my way up, setting goals all the way along the line. And somewhere around 1978, I became acquainted with a guy named Mike Vance, who was an influential thought leader and author. And he was the one that instilled in me a commitment to a lifetime of continuous learning and growth. And ever since then, that’s sort of what I’ve been doing. That curiosity has led me to ask a lot of questions that others wouldn’t ask, which brought me to the lawsuit, ironically, and has also led me to cancer research. So, asking questions prompts you to try to find the answers. And if you don’t know the answers, you talk to other people and you try to find them from them. So, you look up to people that can give you the answers. So as a bonus, as it turns out, as you age, learning more and staying mentally active is a big plus. A long time ago, Henry Ford once said, “Anyone who stops growing is old, whether that happens at 20 or 80.” I just turned 80, and I don’t plan on stopping anytime soon. And Henry Ford also said, “Anyone who keeps growing becomes more valuable, regardless of physical capacity.” So, I hope I’m becoming and learning and growing and becoming more valuable. Kelly: Yeah, I love that committing to a lifetime of learning and growth. That is great advice. After decades in healthcare finance, what’s one thing about how healthcare operates today that you think we should fundamentally rethink? Ted: Well, I think there’s one thing we need to rethink. It’s actually how to lead there’s a lot of people in healthcare today that are talking about being patient-centered, patient-centric, but too often they don’t think patient-centric in the way they make decisions. So, we need leaders willing to challenge the status quo. And more importantly, willing to be introspective, look within oneself of how they can improve. And remember, remember the definition of insanity is doing the same things over and over again and expecting something different. And I think that’s what we do a little bit. I support now much research. And so, I can use that as a little bit of an example. The goal of most healthcare research is ultimately on our side, is to help patients, to find cures, to determine better treatments. If we look at healthcare organizations, there’s things that they can do to lead and to streamline things, including in the administrative cost area, in the near term. To me, indirect costs are something that we can manage now. In many cases, they’ve got out of control. There’s a lot more to do all the way along the line, but I think indirect costs are something that we can deal with to get started. Kelly: No, I love what you said about challenging the status quo. I think that is really important, especially in healthcare. Ted, you’ve often said that breakthroughs start with asking better questions. What’s a question about healthcare that you think we’re still not asking? Ted: That’s a good question. The question we’re really not asking is, do we really understand how X or how something works, or are we assuming too much? I mentioned before, putting patients at the center, that to do that well, we need to understand what those patients are experiencing virtually at every level, from diagnosis, treatment, to billing, and follow-ups. So, for me, I’m looking specifically at breast cancer and within breast cancer, specifically at metastasis. How does it spread? Why does it spread? And we’re down into the minutiae of intercellular communications. But as a country, we don’t fund research in basic science to find out these kinds of things and to find out how things work. We tend to ask questions we think we know the answers to, but rarely do we throw money behind something that we truly don’t know or understand. And so, I think we’ve got that a little backwards. If we know how things work, then we can model it and we can experiment in the model to identify a path to diagnosis, treatments, and prevention. We really need to change our thinking. And I’ll paraphrase it in racing. There’s a concept called ocular driving. You drive where you look and where you can see. The farther you can see ahead and anticipate, that’s where you’ll go. The same thing is in healthcare with our thinking. We need to change our frame of reference and our thinking. And so that’s sort of the framework that will let us design patient flows of the future. And those patient flows need to be adaptable. Because the one thing is for sure, care will change again within the next decade. And so, adaptability and resilience is going to be key. Kelly: Yeah, no, I love what you said about we have to change our thinking. I think that’s very true. As a former CFO, how do you think about return on investment when it comes to scientific research that may take years to pay off? Ted: I’ve learned that the best return on investment comes from understanding how things work first and then leveraging on that knowledge. That’s true in racing and it’s true in research. And I’m sure it’s true in healthcare organizations and in the provision of care. In racing, it’s how you get an edge on your competition. At first, you look for the bigger things, and of course, you take care of them. But over time, it’s the small adjustments, the steady improvements that combine to improve your performance. We take the same approach to research. We invest in foundational science and talented people who ask the right questions or challenge the status quo. It’s investing early before answers are no, and that’s important. Those investments may take some time to pay off, but they create the knowledge that foster future breakthroughs. And we need to be good financial stewards. It’s about making investments today that make some things possible tomorrow. So that’s sort of the mantra that we’ve adopted in our foundation, knowing how it works. Kelly: That makes a lot of sense. So, which is more expensive for healthcare? Trying something new that might fail or continuing to do things the way we’ve always done them? Ted: I think, Kelly, first of all, it comes back to maybe it’s another problem with our thinking, and that we are afraid of failure. Finding out you’re wrong isn’t a failure. It’s information. It’s learning. It tells you where to adjust before you’ve invested years heading in the wrong direction. So, research, metaphorically, is like moving a football down the field. You can’t score without first moving the ball and without investment at the beginning of a drive, so to speak, you’ll never reach the end zone at the other end. Every study, every experiment, every lesson learned hits another first nail. So, if you want to look at that metaphorically, that’

In this episode, Beverly Neal-Clinton, Marine and Co-Founder of servehealrecover.com discusses revenue recovery that pays for itself by fixing hidden leaks.

← Back to All Podcasts Locking Down Corporate AI Usage Webinar In this episode, Jason Nadal, Besler Holdings’ and Sypher Security’s Information Security Officer, will provide us with a glimpse into the next Hospital Finance Academy Webinar, “Locking Down Corporate AI Usage,” live on Wednesday, October 7th, at 1 PM ET. Highlights of this episode include: How companies deal with artificial intelligence and how they should make the decision to embrace this technology What the benefits and detriments of AI usage are What tools out there to help protect your company and your data What other challenges you have in keeping aware of AI usage How you manage third parties not directly under your control The key takeaways Subscribe Today! Kelly Wisness: Hi, this is Kelly Wisness. Welcome back to the award-winning Hospital Finance Podcast. We’re pleased to welcome back Jason Nadal, Besler Holdings’ and Sypher Security’s Information Security Officer. In this episode, Jason will provide us with a glimpse into our next Hospital Finance Academy Webinar, “Locking Down Corporate AI Usage,” live on Wednesday, October 7th, at 1 PM Eastern Time. Welcome back, and thank you for joining us, Jason. Jason Nadal: Hi. Thanks for having me. Kelly: All right. Well, let’s go ahead and jump in. So, we know artificial intelligence is everywhere now. How should companies deal with this and make the decision to embrace this technology? Jason: Well, if you run a company right now, somebody on your team is likely already using AI. So ideally, that would be via an approved path that you have already set up, but maybe it’s just a browser tab they opened up at lunch, putting something in Google and getting AI results. Either way, the question is not, “Should we use AI?” It’s already embedded in a lot of the approved corporate tools that are already out there. So, the question is, “Do we know what we allow, what we forbid, and how do we keep that promise when the tools keep changing under our feet?” So those are the kinds of conversations that security and IT folks have when leadership asks for a practical plan around AI. So, over the next 10 minutes or so, I’ll walk through some of the benefits and risks. We’ll get into that a bit deeper during the webinar that’s upcoming, but this will give you a methodology of control you can actually run, some of the categories of tooling that support it, and the challenge of feature creep in tools that you’ve already approved. And we’re also going to look at how a vendor and open-source software assessment has to catch up. Kelly: Yeah. I mean, it is really everywhere. It’s embedded in everything now, it seems like. [laughter] Yeah. It’s a lot to keep up with. So, what are the benefits and detriments of AI usage? Jason: Sure. There’s a lot of benefits, and there’s definitely a lot of detriments as well. So, AI earns its value when it shortens the work that you’re already doing, especially that work that used to waste a lot of your time through just tedium, repeated tasks, and things like that. So, I find it really shines at drafting, summarizing, searching or researching, coding assists, and also triaging helpdesk tickets. So those are real-time gains when the data and the use case match the risk. So, in healthcare and adjacent work, that might be a little less clear. So, a nurse or revenue cycle analyst can summarize a long chart note, draft a patient letter, or speed up some prior auth paperwork. A cost report or appeals team can find patterns faster. So done carefully, ideally with this human oversight as to what’s produced, that is time returned to care and to accurate billing. But the downside is just as concrete. So, paste the wrong note into a public chatbot, and you may have moved protected health information, PHI, or something else confidential or sensitive, outside your control. So, a helpful summarizer can invent a fact, called hallucinations, that then gets treated like clinical or financial truth. There’s really a huge downside to that if it’s not checked over by humans with clinical experience. An automation that writes into scheduling, ticketing, or an EHR-adjacent system can turn a bad model answer into a real patient or operations problem. Outside of healthcare, the pattern is much the same. So, marketing could paste customer lists into a public model. Finance could paste financial forecasts. Engineering could paste source code or API keys, maybe in a hurry or just this one time, and leak information that you don’t really intend to leak. Legal can paste contract language. You get the benefit of speed, but the detriment of leaking your data, over trusting the AI, and a trail that, once it’s out there, you can’t really tidy it up after the fact. So, a key point to establishing governance is having frank discussions of what data is acceptable to this risk if it were to leak. I really can’t say that one enough. Kelly: Yeah, I mean, I know that in marketing we use it quite a bit, but I mean, you all help us really understand what we’re really in for there. So, I know there’s a lot to keep in mind. So how do you lock this down without pretending people will stop being curious? Jason: So yeah, you do need this methodology. The first thing I’d say, write an AI usage statement. This is your company’s position on how AI is used. Let it have plain language that’s easily readable. And this company position will include what interactions are allowed and what are not. Who’s able to use what classes of AI? What data classes may never leave the building? It may not just be PHI or employee data. It could be financials, as we said before, or just confidential mergers and acquisition information. So, what AI interactions require a human in the loop? What happens when someone isn’t sure? They should always be trained to ask before they just paste data in somewhere. Publish this conspicuously, train on it, and make sure that people know what’s appropriate to do with their AI. Refresh it when the landscape moves. So, if you don’t have a written position, every employee is going to have their own assumptions on what is good or bad. That’s not really empowerment. That’s just risk that’s out there unmanaged. I’d also recommend don’t write this usage statement from scratch. See what others in the industry, especially your industry, are doing, and tailor what’s out there to yourself. Secondly, you should assess the AI that is in question, the AI that you want to use in any given situation. When a third party uses AI in a product that you buy, treat that as part of vendor due diligence. Ask the vendor, “Where are these models running?” Ask whether the prompts or the outputs of those prompts are retained with them or are they used for training. Ask about sub-processors. Maybe that vendor is using somebody else to do their AI work. Ask also whether AI features can write back into your system. Don’t just accept a blanket, “We’re AI-powered,” as a checkbox. You need enough clarity to decide, yes, this is an acceptable risk. No, this isn’t going to work for us. Or yes, but we need these conditions in place to feel good about the risk of using this tool. And keep logs. Review those regularly. What is not AI today might become AI tomorrow. I’m sure all of us have seen an application that we’ve used online that just, after approved, suddenly has new AI functionality. So, train your users to notice new AI functionality added to the products that they already use. Third, you want to control access to your systems. And there are different types of access that you need to be aware of all three of these and looking into where AI touches all of these. First off, private access. This is ideal if you’re using sensitive data. Your employees would then be using AI that is hosted locally within company boundaries. Private model and private instance, your network. You can still govern who can use it, what data they may feed it, and how you log that use. Just because it’s private doesn’t mean it’s unsupervised, but at least you can keep the inner boundaries of where that data can go. Next up would be public access, people reaching third-party AI sites out in the open internet. So, you decide who can get there under what policy and how that policy stays current when new sites appear every week. So, blocking everything forever, that’s rarely an answer. But blind open access is worse. Pick that controlled path and make sure you maintain who has proper access to it. And internal access is different from private. So, this is AI that’s baked into platforms you’ve already approved. Productivity suites, especially CRMs, ticketing, EHR add-ons, collaboration tools, all of these are things that we’ve seen ripe targets to add AI functionality to. Again, the purpose is to make them easier to use, but you need to make sure you maintain the risk of that if it wasn’t there when you initially assessed that vendor. So, it could be somebody just flipped a feature flag or a vendor released a sidebar assistant. It’s not really a new public chatbot to evaluate from scratch, but there could be changes to what models are being used and the risks associated with that. So, you should treat it as a change to an approved system. You should have that same change management, the same risk review that you do for a new vendor, the same data classification questions. So, if you’re only policing public websites, you’re going to miss that AI that arrived inside your door because you’ve already let it in. Fourth, control AI access to your data. The data classification is very important. Label what your data type is. Is this file public? Is this one confidential? Is this one regulated because it’s PHI? Ask the impact question out loud. If this data type was released, what would be the impact to our company and to its reputation? So that fr