← Becker’s Healthcare Podcast
Becker’s Healthcare Podcast
Brenna T. Loufek, Director of AI Regulatory and Quality at Mayo Clinic
00:18:24
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- [00:02]
Brenna Lofek
@ Athenahealth, we know your ambulatory practice wants healthier a healthier business, healthier care teams and healthier patients. But the complexities of modern healthcare tech make it hard for you and your care teams to focus on what matters most. That's where athenahealth can help our AI native all in one solutions reduce administrative burdens, streamline billing and payments, and deliver critical insights when clinicians need it most. That means fewer clicks, more time for patients, and stronger bottom Practicing medicine is complex, but running a practice can be that Much simpler with Athenahealth. See how simpler is healthier at athenahealth.com.
- [00:44]
Scott King
Hello everyone, this is Scott King with the Becker's Healthcare Podcast. Thrilled today to be joined by Brenna Lofek, Director, AI Regulatory and Quality center for Digital Health at Mayo Clinic. Brenna, how you doing today?
- [00:57]
Brenna Lofek
I'm doing great. Happy to be here.
- [00:59]
Scott King
Thanks so much for joining us. We have a lot of big, you know, tech questions and dealing with healthcare to get healthcare to get to, but if you can please just share a little bit about your background and your work, that'd be great.
- [01:10]
Brenna Lofek
Certainly. I lead AI Regulatory and Quality Strategy at Mayo Clinic in the center for Digital Health where our mission is to safely translate innovation into clinical practice. My work centers on building the infrastructure that allows AI technologies to be deployed responsibly. We develop governance frameworks, ensuring regulatory compliance and establishing evaluation process for digital health tools is across the entire enterprise. My background spans both the medical device industry and healthcare delivery, which gives me this really cool dual lens for for balancing innovation with patient safety and ethical responsibility. From an academic perspective. I received my Master's from Johns Hopkins in Patient safety and healthcare quality and I'm currently a doctoral student at that same institution focusing on the intersections of health policy and AI. My research is related to the translational practices across healthcare organizations in applying laws, regulations and standards to AI enabled digital health technologies.
- [02:25]
Scott King
Well that's awesome. Thanks so much for sharing your great background info. We're going to get a lot to your role. I want to ask you about specifically everything that's going on digitally and with tech and in health care. First, I want to you know our our research has told us that nearly half of medical practices reporting using AI in some capacity last year and obviously remains a key topic for health IT leaders. From your perspective, what are the use cases that are making a difference right now and how are you leveraging them in your organization?
- [02:57]
Brenna Lofek
Yeah, in my opinion, use of AI can really be broken down into two key areas within healthcare organizations. The first One are technologies that are doing administrative support related tasks, and then the other area would be tools that are doing clinical decision support. Administrative support tools are going to be technologies that are helping with workflow tasks within a healthcare organization, but they're not directly doing any sort of diagnosis, treatment, cure or mitigation of any diseases or conditions within patients. Whereas the other category of tools, clinical decision support software, those are doing those activities and there are many use cases that are making a difference for the practice providers and patients. In both of these categories, in the admin support space, we see technologies that are aiding in scheduling and communication, which automate previously manual and tedious tasks. These are immensely impactful because they help not only with time savings, but also with burdensome tasks that are healthcare professionals find annoying or not really worthwhile or beneficial in the actual care delivery actions which are the core of all healthcare institutions, mission, visions and values. In the clinical decision support space, we see technologies that are either directly or indirectly assisting healthcare professionals with diagnosis and treatment of their patients. And these really span everything from tools that are leveraging information found within the electronic health record to do detection of patients that are at risk of different diseases or conditions. Maybe they would benefit from some additional screening or interacting with information in that electronic health record to give recommendations around treatments or diagnoses. I believe that around 60% of healthcare professionals are using a tool called open evidence, which is assisting with different differential diagnoses happening. And that is just highly beneficial for healthcare professionals. We also see more advanced technologies doing really brilliant things where they're analyzing medical images, signals or patterns to do early detection and diagnosis, or to automate different workflow steps that take a lot of time by our healthcare professionals or that are not always done consistently. And these are tools that are interacting with images, signals or patterns to do things like segment medical images and do calculations of distance or volumetric measurements. There are also technologies that are interacting with patterns or signals, like in cardiology, where at Mayo Clinic we have tools that analyze ECGs to flag patients that may be at risk of different diseases or conditions. And so these technologies, the clinical decision support tools, really help move the needle on patient care delivery. And we're really excited about these tools at Mayo Clinic.
- [06:34]
Scott King
Yeah, a lot of those uses you just mentioned are incredibly interesting. I feel like we're hearing about new tools and new uses every day now. Just with your role and I guess what you've seen and maybe even what you've heard. Do you remember ever healthcare having so much cutting edge technology involved and how important tech is in this moment in time to healthcare. Like, have you ever heard that before in the industry?
- [07:01]
Brenna Lofek
No. I think that the paradigm has really changed in the last five years. Whereas we saw a lot of the med tech innovation coming out of startup companies or established organizations like Medtronic or Johnson and Johnson, where they were the ones really developing the technology, often working in collaboration with, with health care institutions and providers. But they were the legal manufacturers of these tools. They were the ones identifying the opportunity and being the ones legally responsible for developing, implementing, commercializing these different products. But as I said, the paradigm has really shifted. Now where AI enabled technologies are in the hands of health care professionals themselves. They are able to identify local problems that could be solved or local opportunities that could be addressed with AI enabled digital health tools. And they, in collaboration, often with data scientists, can develop these tools and implement them into their practice. So we have this space of booming innovation, but also highly localized development of technologies that are solving problems that the healthcare professionals themselves are seeing. And this is just going to continue to grow with generative AI and the accessibility of using established large language models to apply it to different activities within your organization. So in many ways this is a massive boom, incredible expansion. But it's also a real shift in the types of individuals that are identifying opportunities, building tools, testing them and deploying them. Where historically we saw a real shift, a real separation of those that were doing those activities and then those that were actually using the technology. This is a really exciting time to be in healthcare and helping innovators. Healthcare professionals take tools from discovery or ideation to actual implementation into clinical practice. But it also opens up the door for a number of ethical considerations that we haven't thought of before. And it also puts health care institutions in the position of having to apply different governance activities that they historically have not been responsible for.
- [09:36]
Scott King
Yeah, I'm sure you just touched on it a little bit. But yeah, obviously new challenges come with these tools and new tech being introduced. If you had to like focus on your top piece of advice for leaders as they introduce this new technology or kind of work it out, what would that be?
- [09:53]
Brenna Lofek
Yeah, so your governance within your health care organization should be seen as a mechanism that accelerates and prioritizes innovation. It should not be perceived as a roadblock. At Mayo Clinic, we use a risk based assessment framework that allows us to evaluate these internally developed solutions, but also vended solutions quickly while maintaining rigor for that development and testing process that is proportionate to the risk of the tool. I previously mentioned that in my opinion there's really two main categories of AI enabled technologies. There's those that are in the admin support space and then there are those that are directly or indirectly doing clinical decision making or aiding. We don't want to apply the same level of rigor around requirements within the software development lifecycle, specifically with how we demonstrate quantitative or qualitative measure of safety, efficacy and ethical use to tools that are being used for admin support that are doing basic scheduling compared with tools that are analyzing medical images and doing early detection of patients with pancreatic cancer. Those are two really polar opposite examples. But you need to make sure that the governance infrastructure that you develop accounts for that range of diversity of technologies that you certainly will be implementing into clinical practice. But you shouldn't apply the same level of rigor for those two. Under our Chief AI Implementation Officer Mickey Tripathy here at Mayo Clinic, we are currently developing top down policies that standardize those evidence requirements so our teams know in advance what is needed for safety and compliance. That transparency really shortens the path from concept to clinical use and implementation in the practice. Innovation must move at the speed of trust and this is the trust with the practice providers, patients and the public. At Mayo Clinic we encourage experimentation through suggested tiered deployment that allows for evaluating safety, equity and value before scaling occurs. This approach allows us to be bold in trying new technologies while maintaining the discipline that patients expect from a world class healthcare institution and sticking with it.
- [12:32]
Scott King
I think that's fantastic. And sticking with advice, you know, a lot of the use cases you work out, you know what's coming in, you work with it. But the thing, the unknown, you know, seeing all the advancements we've had recently, like you said, the last five years has been monumental. But you know, just thinking of what could be out there down the road. What's your top piece of advice for leaders as they prepare for further advancements in technology and kind of weighing those with the rising demands for care?
- [13:00]
Brenna Lofek
I think the future belongs to those organizations that effectively operationalize governance, establish multidisciplinary review structures early and ensure decisions about AI deployment are data driven, transparent and clinically grounded. It is essential that you embed your organization's relative risk tolerance into this governance structure. What we've done here at Mayo Clinic may not be directly applicable to your health care organization because maybe you have a different risk tolerance. Technology can't transform care unless people trust and understand it. Leaders should prioritize digital literacy, cross functional collaboration and clinician engagement just as much as the technical infrastructure. I really think that culture is the true enabler of responsible AI or really in any innovative technology. Every AI or digital tool must serve a clear clinical or operational purpose and we should measure for outcomes that support that. This tool is achieving that through post deployment monitoring activities. Lastly, my advice is to align and innovation with your institution's mission, whether that's advancing healing, equity or access for your patients, and then hold technology to the same ethical standards as the practice of medicine itself.
- [14:35]
Scott King
Thank you so much. And the last thing I want to ask you is obviously all this tech that we're working with and applying in health care, you and your team are. It takes great leadership, right? So I just want to ask you on a human level, how have you evolved as a leader?
- [14:54]
Brenna Lofek
So I started at Mayo Clinic as a manager of the same space in regulatory and quality where I was coming from the medtech industry really with the primary goal of getting technology into the commercialized space. So my experience was really in submitting products to FDA and understanding what that pathway to commercialization would be. My formal and formative regulatory years were at Medtronic in their Cardiac rhythm and Heart Failure division and here at Mayo Clinic. My job is not so much to pursue the commercialization of these different technologies, but to see how we can build those roadmaps from research to implementation into clinical practice in a local way. As a leader, I have really grown and expanded my understanding of what regulatory and quality means when building roadmaps or pathways to implementation, where I've really shifted my mindset from the end goal of getting FDA clearance or approval for these technologies that are then going to be marketed to building pathways more generically for broader AI enabled digital health technologies to be implemented into clinical practice, but not necessarily commercialized. I've had to root myself in the clinician's perspective of understanding what it really takes to develop safe, effective and ethical products while leveraging information from laws, regulations and standards, but really relying on their expertise to understand how and when can we develop AI technologies to solve these local problems. I have grown from what I would say is a pure formal regulatory expert to being someone who is more of a generalist in translation science. I think that this is where we are going to see opportunity and growth in leaders with effective implementation of AI, where we build leaders and expert contributors who understand the regulatory ecosystem system and what is written in laws and standards, but they take a generalist approach that really leans on the clinician's expertise to understand what it takes to pragmatically translate these tools into clinical practice.
- [17:30]
Scott King
Brenda, thank you so much for joining us on the podcast. I think it was a great conversation. I learned a lot personally. I know everyone listening did, and I hope to work with you again soon.
- [17:38]
Brenna Lofek
Thank you so much. At athenahealth, we know your ambulatory practice wants healthier a healthier business, healthier care teams, and healthier patients. But the complexities of modern healthcare tech make it hard for you and your care teams to focus on what matters most. That's where athenahealth can help our AI native all in one solutions reduce administrative burdens, streamline billing and payments, and deliver critical insights when clinicians need it most. That means fewer clicks, more time for patients, and stronger bottom lines. Practicing medicine is complex, but running a practice can be that much simpler with athenahealth. See how simpler is healthier at athenaheal.