Episode #155. Healthcare’s AI Inflection Point: Why Everything Is About to Accelerate, with Nilesh Bhandari, Chairman and CEO at the Advanced Health Academy

Summary

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Risk Never Sleeps – Episode #155

Healthcare’s AI Inflection Point: Why Everything Is About to Accelerate
Guest: Nilesh Bhandari, Chairman & CEO, Advanced Health Academy
Host: Ed Gaudet (joined by Saul, co-host, and Mark Gaudet)
Date: December 9, 2025

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Episode Overview

This episode dives into the accelerating transformation of healthcare through artificial intelligence, focusing on why the industry has reached an AI “inflection point.” Host Ed Gaudet and co-host Saul are joined by Nilesh Bhandari, a serial healthcare entrepreneur and Chairman & CEO of Advanced Health Academy. Together, they explore how AI’s application in medicine is shifting from slow, fragmented adoption to an era of widespread excitement, practical deployments, and clinician-led innovation. The discussion is filled with insights on effective use of data, efficiency hurdles, new AI tools—and the real risks and rewards of this next phase.

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Key Discussion Points & Insights

1. The Unprecedented Buzz and Participation Around Healthcare AI

[01:31–03:47]

• Nilesh’s Take:
  • For the first time, excitement about AI in medicine is driving innovation at every level, “not just at the level of the innovator or the AI scientist, but all the way up to the boardroom.”
  • Unlike the past, multiple groups (clinicians, executives, scientists) are now collectively pushing boundaries and sharing knowledge.
• Memorable Moment:
  • Nilesh describes the “yin and yang” of clinicians and AI scientists finally “digging into” model development together, rather than talking past one another.

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2. Efficiency Stagnation in the EHR Era and the Promise of AI

[04:00–06:21]

• Eye-Opening Statistic:
  • “Over the last generation—20 years—we’ve lived in the EHR era… and the efficiency gains [in healthcare] have been negative.” (Nilesh, [04:28])
  • While other industries saw efficiency gains up to 15% annually, healthcare experienced net losses, partly because digital records remained disconnected from workflow improvement.
• Path Forward:
  • Foundational investments in EHR are now enabling rapid, AI-driven workflow improvements.
  • "Now every clinician, based on their specialty, should have a very specific view... to make their job that much easier and faster." (Nilesh, [05:55])

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3. The Personalized, Adaptive Future of Medical Practice

[06:21–07:54]

• By 2030, clinicians will interact with a healthcare system that adapts to and learns from their individual behaviors, bringing personalization and efficiency.
• Quote:
  • "All growth in nature is exponential. It may appear linear, but it’s all exponential. And we are..." (Nilesh, [07:54])
  • Ed and Saul immediately call this the “quote of the event.”

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4. Exponentially Accelerating AI Innovation—Inside and Outside Healthcare

[08:07–09:06]

• The pace of change is highlighted by improvements in domains like AI video generation:
  • “Three years ago, [AI-generated] solutions were substandard, awful... today, we’re way closer than we’ve ever been.” (Nilesh, [08:30])

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5. Nilesh’s Companies: Real-World AI Tools for Patient Safety

[09:12–13:23]

a. Advanced Health Academy: Demystifying Lab Results with Deterministic Algorithms

• Created tools that analyze lab results, providing demographic-based, standardized explanations for patients and actionable, consolidated data for clinicians.
• Quote:
  • “Our biggest algorithm… is looking at lab results and providing a comprehensive but demographics-based interpretation… not just based on the individual provider.” (Nilesh, [09:53])
• Impact: Patients better engage in care; providers see cross-panel data easily; diagnosis coding and reimbursement accuracy improve.
• Example: Surfacing ICD-10 codes automatically helps ensure full documentation and correct billing—an efficiency win for health systems.

b. Drug-Lab Interaction Checking

• Addresses the gap: “Why is there no drug-lab check?” (Nilesh, [12:37])
• Combines recent lab results and drug prescribing for better safety, avoiding alert fatigue by emphasizing relevant warnings.

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6. The Bigger Picture: Deterministic AI Models, Risk, and Whole-Person Care

[13:43–16:03]

• Strong focus on “chain of reasoning” to ensure AI outputs are transparent and reliable (against hallucinations or model drift).
• At Thymos Health, Nilesh is building multidisciplinary, AI-driven care teams for autoimmune diseases:
  • “It is the only specialist for autoimmune care that cuts across all 150 autoimmune diseases. In autoimmune… there's no quarterback of the situation. That’s what our CEO has done for 15 years... We've created all the wraparound care.” (Nilesh, [14:45])
• The challenge: integrating clinical, psychological, emotional, and epigenetic insights in real-time—a massively complex but necessary endeavor.

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7. The Lightning Round: Memorable Personal Insights

[16:18–20:16]

a. When Did AI Hit the Mainstream?

• Nilesh: “The thing that changed three years ago was, I think, LLMs and what that meant for the world and the openness to it... The acceptance and the usage has just been accelerating.” ([16:39–17:15])
• Most profound sign: "My parents are asking me about AI. That was the moment I said, wait a minute." (Nilesh, [17:20])

b. Riskiest Thing Done?

• Kitesurfing in Tarifa, Spain, as a way to overcome fear of heights.
  • “If you get the right gust...when I get that fear of heights, I tense up—which is the exact opposite of what you want to do.” (Nilesh, [18:28])

c. Advice for Younger Self:

• “Don't be in a rush, take your time... Think of [your journey] in terms of decades... and to keep taking more risks along the way because that's how we learn." (Nilesh, [19:05])

d. Desert Island Albums:

• “Zeppelin IV, Ed Sheeran’s ‘Play’... Bollywood soundtrack... something EDM, probably Afrobeats.” (Nilesh, paraphrased from [19:36–20:16])

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Standout Quotes & Key Timestamps

• “For the first time… excitement about innovating in AI in medicine… at every level… let’s go try a number of things.” – Nilesh Bhandari [01:31]
• “Over the last 20 years, the efficiency gains [in healthcare] have been negative.” – Nilesh Bhandari [04:28]
• “All growth in nature is exponential. It may appear linear, but it’s all exponential.” – Nilesh Bhandari [07:54]
• “Patients can finally understand what the lab results mean… that’s a huge win for the consumers.” – Ed Gaudet [10:32–10:53]
• “My parents are asking me about AI. That was the moment I said, wait a minute.” – Nilesh Bhandari [17:20]

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How to Connect

• Contact Nilesh: [e-mail] | LinkedIn (“not that many Nilesh Bhandaris out there!”) [21:05]

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Final Thoughts

The episode gives an energizing, insider’s perspective on how healthcare is past its digital “foundation-building” days and entering an age of exponential AI-driven improvement—led by clinicians, operational leaders, and technologists working together. It highlights both the technical advances (from deterministic algorithms to ambient data integration) and the vital cultural shift now permeating the industry.

If you want to understand or help steer the next wave of healthcare innovation, this episode is a must-listen.

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Transcript

[A]

Foreign.

[B]

Welcome to Risk Never Sleeps, where we meet and get to know the people delivering patient care and protecting patient safety. I'm your host, Ed Gaudet.

[C]

Hello, everybody. Welcome back to the AI Med Insight series. I'm so excited to be back here with my co host, Ed Gaudette.

[A]

Hey, Saul, that was a great session.

[C]

We're just having fun here, man. We're having fun AI and also the people behind AI, which is something you do well on your show, Ed. And today we have none other than Nilesh Bhandari with us today. He's a serial entrepreneur and just an incredible person. Nilesh, welcome to the podcast.

[D]

Well, thanks so much for having me here. Yeah, you got a great lineup. You always have a great show. So I'm excited to hopefully contribute or decontride, depending on the way the conversation goes.

[A]

We'll see if you can keep up.

[D]

We'll try.

[C]

And by the way, I do want to give a shout out to Mark Gaudette, who's with us. Oh, no show too.

[A]

My older brother.

[D]

Hello, gents.

[C]

Glad you could join us.

[A]

Opening line, we bring this guy into the podcast.

[D]

Actually, I didn't bring him in.

[A]

You brought him in.

[D]

Absolutely. If I want true stories about you growing up, what better way?

[A]

All right, we're going to keep him.

[D]

We've all had the awkward days in our lives.

[A]

Start with you.

So what have you seen so far, this show that's blown your mind?

[D]

I think the biggest thing I've seen is the level of excitement about innovating in AI in medicine. Historically, I feel like it's been coming along, the kicking and dragging. There's been small pockets. This is the first time I think I've seen multifunctionally the level of excitement to try a number of things, not, hey, go take proven AI solutions and implement. Let's go try a number of things. That, to me, is taking us to a whole next level of innovation and drive at every level. Not just at the innovator, not the AI scientist or the data scientist, the chief medical information officer or the analytics officer, but now all the way up to the boardroom. Let's go try different things. And it may sit silently in the background, but if it can help the providers clinically, which, by the way, that was always been a much further futures element. Not just administrative, but we can help clinically. We can help every aspect of the patient experience and the provider experience, we're starting to see more and more of those tools come together.

[A]

Yeah, I think you're spot on. It's been Incredible evolution of this event. I think like 5, 600 people standing room only during the keynote today. It's been incredible. I wasn't expecting that many clinicians here to learn, like you said, and understand how to apply AI to medicine. It's incredible.

[D]

And the other part that I find to be really interesting is that it's a combination.

Of yin and yang. And what I mean by that is historically you've got clinicians, whether it be academic or out in practice, sort of commercially, in sort of very rarely in the same rooms, but you have those in the same room as well as deep AI scientists in the same room. And the conversation usually goes right by each other. This time, actually, it's been much more fun to actually dig into how were the models developed, what does it actually look like and contribute back and forth. And that water cooler conversation has been, in my opinion, a ton of fun because it's a number of folks interested on both sides with the depth information to say, hey, how do we apply this appropriately across both elements?

[A]

I think that's right. For once we see the doctors and nurses and other clinicians leading innovation, not waiting for it to bring it to them.

[D]

Wait, it brings innovation.

Interesting stat I saw. Interesting stat that I saw recently. It was told to me that over the last generation 20 years, we've lived in the EHR era. If you think about the last 20 years, the level of efficiency gains that have been had across every industry, what's your guess? Roughly how much? 15% annually, probably. Yeah, right. What do you think it's been in health care?

[C]

I would be guessing, I don't know, 5% negative.

[D]

Over the last 20 years, the efficiency gains have been negative. Really? And now with the type of attitudes we have and the applicability of AI and the openness towards it, we're testing at a minimum and a lot of administrative tasks, a lot of other areas where it's already been implemented in a big way, we're going to see a very large improvement on efficiency.

[A]

Interesting. I have to check out that. Do you know where that study. Do you remember the study, where it came from or.

[D]

Always asking the hard questions. I can follow up with you afterwards because I was given up to me from a Stanford professor who we were having a conversation deeply about this.

[C]

Yeah, put it in the show notes, guys.

[A]

Yeah, no, it's really an important point.

[D]

If it's.

[A]

I'm assuming it's true. I mean, what does that say about our level of progress, bringing EHRs to market really, to solve the problem around efficiency and Better care and better outcomes. Right.

[D]

20 years ago, I don't believe that was the idea that initially it was the idea of let's create the foundation, lay out the concrete, let's make sure the plumbing's in place, let's make sure the electrical is in place, let's make sure the beams are up. Now I think we're finally putting finishing touches on it to actually make the home livable. And so I think early on EHRs were really truly a system of record. They were not focused on workflow management in general. That has changed in the last seven to 10 years. And I think we're going to see a lot more of that in the next few years because the system of record is going to sit underneath. But now every clinician, based on their specialty, should have a very specific view. Every person, whatever their role is across the ecosystem, should have their own view that is specific to make their life, their job that much easier and faster. And now, okay, we can call it AI, but with technology in general, that is now possible. But that was not possible until we got all the foundations set first. So I think we're going to see massive improvements in efficiency in the next five years.

[A]

So take us to 20, 30. How are things going to be different?

[D]

Imagine that every clinician is going to have their own view of the system, personalized view. They're going to define it. If they're going to take a few minutes, that take a long time to define it. And then over time, as they're doing new things, the system is going to learn. Wait, so and so is checking A or B or C and implement those pieces in to make it easier to bring that to the surface.

[A]

The system is going to self heal and optimize based on the clinician's behaviors. That's what you're saying.

[D]

I would not use some heel there.

[C]

But I will say it will the end user, right?

[D]

It will show the end user the information they need to be faster in their process, wherever their process may be. And the hard part there is making sure that the process is not so fixed by individual that they're still able to see broad enough and test different things. So it's going to make sure it regulates appropriately how much is it driving the exact process the provider takes every time versus what are the additional things to bring in that's considered best in class across all the providers of that type to have them try different things.

[A]

I like this notion of adjusting to fill in the gaps for the provider.

[D]

Right.

[A]

Because every provider does it differently. They do it their own way. Right. So some are better than others in this area. Some leave gaps here. But if the systems learn our behaviors and can actually automatically fill in the gaps. Right. What could that mean to health care? And is 2030 too soon for that?

[D]

Hardly.

[A]

You don't think so?

[C]

Yeah.

[D]

All growth in nature is exponential. It may appear linear, but it's all exponential. And we are.

[A]

My favorite quote of the event, by the way. Seriously, we're pulling that out.

[C]

That's pull it out.

[D]

Yeah.

[A]

It's so true.

[D]

Absolutely. Yeah. And so we're starting to see more and more tools that are out. Let's take us outside of medicine for a moment. Video generation and the use of AI to generate video. One could argue that three years ago the solutions were substandard, awful. And I would say today, I'm not sure we've crossed that uncanny barrier of appearance, but we are way closer than we've ever been.

[A]

Absolutely. Fidelity is not quite there, but it's damn closer than it was three years ago.

[D]

And that pace of change I'm seeing, I think about. Okay, great videos are there today that are amazing for social media. 1030 second clips. 10 second 30 second clips. But if I want to do a training video to explain a concept that takes more than 30 seconds, which, let's be clear, most complex topics in the world take more than 30 seconds for someone to understand. Either it's done as a bridge with a number of different videos or it takes a longer video to go generate. There's no great tools today for that. That is changing right now as we speak.

[A]

Now, is this one of the companies you're working with?

[D]

No talk. What are you doing today?

[C]

Yeah, you're working at.

[D]

Well, there is a few things I'm working on. I've seen gaps in the healthcare ecosystem and that's where I'm focused. So first company I started about five and a half years ago, called Vast Health Academy, we are creating algorithms that are deterministic in nature, not AI or not ML based, where we are trying to find different areas where we can drive much greater efficiencies, where it hits all the stakeholders, the patients, the providers, the health systems and the payers to help all. Our biggest algorithm out there is looking at lab results and providing a comprehensive but demographics based interpretation of those lab results such that we can standardize that and it's not based on the individual provider, whatever specialty they have, focus on it, but it brings it all together. That has been incredibly successful. We've got roughly about 500 million lab results that we've run over the last two years. Wow. So that has helped a lot of health systems.

[C]

And what are you finding? Like, what's the big win for these health systems?

[D]

There's a big win and a small win. The smallest win on behalf of our customers. I'm not talking about myself. The smallest win is the patients can finally understand what the lab results mean in a way that's very easy and helps drive that patient engagement and really drives forward the type of behavior change. We're looking for them to drive each other.

[A]

Oh, that scares me. I can't lie to my wife anymore about what my numbers mean.

[D]

Right.

[A]

I'm going to stop eating pepperoni.

[C]

Finally.

[A]

I don't like this world you're bringing us.

[C]

Well, listen, I mean, the question is.

[D]

What do we do with that risk? Right. It's up to us. And I'm not saying that people can define how they want to handle that risk on their own, but understand that.

[A]

No, I love that because the markers are all different depending on what you're looking at, which is so why. Right. And normalizing that in language that people generally understand, that's a huge win for the consumers.

[D]

So that's one side. The other side, providers love it because in the ehr, every panel is a different screen. The old days, we used to get faxes.

[A]

Yeah.

[D]

Still get faxes. Let's be clear. E faxes now. Yeah. We used to be able to spread those papers out and be able to cross correlate across and understand we're looking at. We used to know the patients much better. We can use that logical relationship with them. Those things have changed quite drastically. And as that happens, what we're able to do instead is say, wait, let's take all the medical calculators and let's take the global science. Bring it to bear so that the takeaways are right there, right front and center. And they are personalized. That patient's demographic, sex, age, race, ethnicity. Because we know that matters.

Is the risk profile changes as a result. So that's big piece. The biggest win though, in this one algorithm is that we're able to surface the diagnosis codes, the ICD10 codes from the lab results themselves. If you think about a provider, all of us, we've got time to probably note the top three to five things in the patient chart. And it's not because we're lazy. It's because we're focused on what matters the most. That patient. But issues 5 to X never make it in there. It goes into consideration. Set. But it's not necessarily documented appropriately. So we're able to bring those out and help health systems provider groups, different tools, bring that out and make sure that's there and get appropriately reimbursed for that as well. Because that is part of the thinking. So that's a big win.

[A]

Interesting.

[D]

Another solution, we've got completely different. We've got drug drug databases, interaction checks, we've got drug allergy checks. Why is there no drug lab check? Someone's prescribing a medication. Let's look at the recent lab results to say, wait a minute, is this drug an exacerbated issue that's already showing a high level of dysfunction in the labs? If so, let's do the cost benefit analysis of that medication or let's think about the dosing of that medication. This should be happening. But these things are sitting in two different screens in the hr. If you're thinking about dosing or prescription assessment, care plan, we're not thinking about that piece. And we don't want to alert too much. We know what that looks like. We look at an alert heavy world. So how do we make sure we bring in just the right element? So that's another example of it.

[C]

Some great examples.

[A]

Really good. Yeah. Reducing that noise in the signal to drive efficiency and effectiveness.

[C]

And why aren't we looking at that data right now? I mean it seems like it's something that we should. So something that we should all be thinking about. Why are you here? And one thing that you would want to share with our listeners that couldn't be here.

[D]

A couple of reasons. Number one, we've got an abundance of data. We're learning how to drive insights from that data. We're using different algorithms, a lot of AI and ML tools to train on that data to drive insights.

Understanding the chain of reasoning around how we got to that is incredibly important. And as Advanced Health Academy, I will say that we are providing deterministic rule sets to a number of folks that are here to help them at least drive understanding for some core basic foundational elements that go into their AI models to ensure that they understand. There's no monol drift, there's no hallucination, there's no issues around it. Lab results is a perfect example of that. Some of these other sort of cross cross functional checks are very important. That's where bullion and bears, that's one area. Second, completely different company looking at company called Thymos Health.

[C]

I didn't hear.

[D]

I'm here. So we are here.

[A]

Of course they're here. Saul, what's wrong with you?

[D]

It is the only specialist for autoimmune care that cuts across all 150 autoimmune diseases. No can manage medical management, care, support, care, navigation. We see this in oncology a lot, but the oncologist drives it. And then there's care, navigation, peripheral services. But in autoimmune of the 150, there's a dozen different specialties where it hits. There's no oncologist, no quarterback of the situation. That's what our CEO has done for 15 years. She has now created this. We've created all the wraparound source with it and we are launching soon. But let me tell you, the hardest part I see here is we've created all types of different AI models for this. Many are deterministic to get to the clinical part, of course, but beyond that we think about a whole person approach. Where have we seen great models out there to assess someone's psychology, base psychology in real time. One's emotions baseline in real time. One's epigenetic factors baseline in real time. And let's assume we have those models, which we don't today. How do we apply that with the clinical in an integrated way? That's the work we're working on there at Thymos Health in introducing this. And so that's the other reason why I'm here. I'm talking to other researchers about how do we integrate these pieces together because it's imperfect. There's no prior art.

[A]

Really interesting.

[C]

Yeah. So look, Nilesh, I think we've got. I don't know, Ed, do you want.

[D]

To do your question lightning round?

I feel like I've been preaching, but I want to bring Mark into the conversation.

[A]

No, no, no, no. He's doing a Weekend at Bernie's here. I don't know what's going on.

[D]

I do have a question for.

Looking back. What was that one moment in the last three years where you said to yourself, oh, wow, okay, AI, this stuff is here to stay. So I've been in machine learning since the 90s. I have my master's in it.

[A]

Bayesian Neural Networks.

[D]

Very much so.

[A]

Yeah, me too. 98.

[D]

97.

[A]

Oh, 97.

[D]

Yeah. Did a lot across the service based industries, across hospitals, consumer banking, when that was something. Yeah. And a host of other areas. The thing that changed three years ago was, I think, LLMs and what that meant for the world and the openness to it. Now one could argue that they were introduced at a point where they were very immature. Still, OpenAI did a brilliant job in terms of the marking of how well they were. Gemini fell flat on their face. They were not that dissimilar actually where they were at that point in time when that happened. Since then, the acceptance and the usage has just been accelerating.

[A]

It has been incredible.

[D]

That acceptance, I think has created an openness that I don't think has existed broadly ever before. No, I think my parents are asking me about AI. That was the moment. I said, wait a minute.

[C]

Good moment.

[A]

My parents were.

[D]

Yeah, they had no idea what I got my master's in. That's right, that's right.

[A]

But they asked you about AI.

[C]

That was the moment.

[A]

Wait, that was the moment.

[D]

That's great.

[A]

That's like that question, is my son smart enough? Does he know AI?

[D]

It's my parents and I'm Indian. I'll never be smart enough. Let's be clear. That's what I mean.

[A]

So they're tests you. So, son, tell us about AI.

[C]

I think just given the time, we'll have to do one lightning round. Question.

[D]

One.

[C]

Yeah.

[B]

Okay.

[A]

What's the riskiest thing you've ever done? I'm going to ask him another one too. He's so interesting.

[D]

Kitesurf. So I've got a fear of heightsurf and cool. The hardest part in kite surfing is when we get a little bit scared. Yeah, I don't got the. I don't get the washboard ass. Obviously. This is years ago.

[A]

Where did you do it?

[D]

I learned in Tarifa, Spain.

[A]

Oh, nice.

[D]

No wetsuit required. Which is lovely.

[A]

Yeah.

[D]

The hardest part in learning is I have a fear of heights.

[A]

You get pretty high on those.

[D]

Oh. If you get the right gust and you hit the right. And the hardest part is when I get that fear of heights, I tense up until you pull in. Which is the exact opposite. What you want to do, you want to let go of the sale. Yes. To take the air out of the sails and come right back down. Yeah. So yeah, I'm a wimp. I mean, I've gone up to the edge of cliffs and whatever. I've cliff dived and cliff jumped before but to get over this fear. But the kite surfing thing for me was the thing that scared me the most along the way. I mean, I love it today. Don't get good enough. Crazy, busy building things.

[A]

But yeah, you go back in time and see your 20 year old self. What would you say?

[D]

Don't be in a rush, take your time. If I don't think about my career in terms of a couple years at a time, but Think of it in terms of decades and not career in terms of just knowledge acquisition. But think of the cumulative knowledge acquisition and skills capabilities, learning over time. I think that's where the real value is also. And to keep taking more risks along the way because that's how we learn along the way.

[A]

Movies or music?

[C]

Both.

[A]

Why you can't choose top five records you'd bring on a desert island.

[D]

Oh, man, it's all starting to sweat now.

[A]

I was like, we got.

[D]

We gotta do that one. Zeppelin 4.

[A]

Oh, nice.

[C]

California.

[A]

A little Going to California. Nice.

[D]

I'll do Ed Sheeran.

[A]

Oh, all right, perfect.

[D]

Probably play. I like that album more than I thought I would. Oh, play. It's been growing on me. Yeah. There is a movie soundtrack that's Indian Bollywood that I love.

[A]

Bollywood. I know it.

[D]

Yeah. A small little genre. And then fifth would probably be. If I'm on a desert island, I got to stay motivated. There'd probably be some sort of edm, sort of pick me up along the way, but probably Afrobeats, if I had to guess, just because I like the variety.

[A]

Nice.

[D]

Yeah.

[A]

Well, you're a very interesting man.

[C]

Eclectic.

[A]

Very eclectic.

[C]

Yeah, I know.

[A]

I'm glad you came here today.

[D]

I'm glad you guys are here. This is really helpful. I think just one plug for folks who've ever get a chance to come to the AI in Medicine conference. Oh, it is incredible because I will just say this is not a vendor conference. This is actually seeing what's real out there today.

[A]

That's right.

[D]

What's really happening, what's being developed today. It is so much fun. If you're someone like me who loves building brand new stuff, there's no prior art. This has been amazing.

[A]

It's not even like a conference. It's an academic event, right?

[D]

Yeah.

[A]

Which is nice. I mean, there's a couple vendors floating around here, but for the most part, yeah.

[D]

Yeah.

[C]

It's all good stuff. Nilesh, if you want people to get in touch with you, where can they reach out?

[D]

Email me directly. I've got no problem with that. Email nileshealthacademy.com or just look me up on LinkedIn. There's not that many Nilesh Bhandaris out there. Got the good fortune of not having a very common name. All right, reach out to me anytime. Happy to help however I can. Love it.

[A]

Thank you.

[C]

Thank you being with us.

[D]

Have a good one.

[B]

Thanks for listening to Risknip Sleeps for the show. Notes, resources and more information and how to transform the protection of patient safety. Visit [e-mail] that's C-E N S I N E T dot com. I'm your host, Ed Gaudet. And until next time, stay vigilant because risk never sleeps.