← Becker’s Healthcare Podcast
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- [00:00]
A
Hi, everyone, this is Lucas Voss with Becker's Healthcare. Thanks so much for tuning in to the Becker's Healthcare podcast series. It's fantastic to have you. Today we're talking about advancing equality outcomes while strengthening the bottom line, certainly sometimes a tightrope and a balance to be struck. And joining me for today's discussion to discuss just that is Tessie Flood. She's the co founder and chief nursing officer at Assemble. Tessie, thanks so much for being here. It's so great to have you.
- [00:28]
B
Thank you so much for having me.
- [00:30]
A
Excited to talk about this. There's so much here. I do want to start us off with introductions really quickly for those that might not know you yet, at least if you just want to give us an overview of your work, your career in healthcare and what you currently do.
- [00:44]
B
Yeah, for sure. I'm Tessie Flood. I am the co founder and Chief nursing officer at assemble. I am a nurse leader by background and one of the few nurse founders in the AI space. Today actually started Assemble with my husband Wes, my co founder and CEO. We have a son who's turning two and three weeks now in a golden doodle, and we live in New Jersey, but Assemble is headquartered in New York City. Assemble, for those of you who don't know, is a platform for leader operations in healthcare that's powered with AI. We standardize the daily workflows to decrease burden and ultimately improve care for the patients we all serve. I have 15 years of healthcare leadership experience and really lived this problem that we're solving. The reality is that the workflow of leaders today is very much the same as it was 30 years ago. Sticky notes, emails, spreadsheets, and disconnected point solutions. As a nurse leader, I knew exactly what the outcomes were I was accountable for, but I didn't have a technology system that helped me get there. That is the gap that Assemble is built to close. We built ASSEMBLE because the leaders closest to the patients, the frontline leaders, deserve tools that are as sophisticated as the work that they do.
- [01:54]
A
And I am excited to have you on. Not just because of your experience, obviously, but again because of your perspective as a nurse leader. Having done this for so many years, you know that a lot of these solutions, a lot of these things that we hear about from a technology perspective and reducing administrative burden, et cetera, they come into the organization and then they don't really work. They're not built for the frontline realities that we're seeing today. And that's unfortunate, right? How does a clinically informed approach shape the way AI is designed and deployed within health systems. And why does that make such a big difference?
- [02:38]
B
Yeah, great question. I think first you have to understand what are the problems to be solved. Part of really understanding the problem that you're solving means understanding understanding what exists today for that workflow. So often in healthcare we have solutions come in that solve maybe one or two steps of a ten step process. So sure you're solving part of the problem, but not completely. And that's actually really led to frontline leaders spending a large amount of time managing systems instead of being able to manage outcomes. You know, healthcare organizations, they know the work they need to get done, they know the metrics they need to move. But we need thoughtful application of technology and AI that just makes that work easier, Easier, not a new complete workflow layered on top of everything else. So that's the difference between AI that gets adopted and AI that sits unused. So at assemble, we don't start with the technology, we start with the problem that our partners are looking to solve. So if that's improved communication, okay, what does a great shift huddle look like? Then we ask, well, how can AI make that easier, faster, more consistent? The sequence of approach matters here. I think at a time when we're overwhelmed by possib, there's so many great tools out there being able to focus on the main problem you're looking to solve and then look at the solutions that could potentially be a fit.
- [03:58]
A
You mentioned the workflows and the layering on top of workflows, which is certainly an issue right now for a lot of organizations. Are there specific workflows from your perspective, again, where AI really makes a difference right now, where you feel like this is it, it enhances operations, it's not adding disruption, it's truly helping the clinician do their work better.
- [04:25]
B
Yeah, great question. You know, I frequently say that there is not a dashboard shortage in health care. We have a dashboard for everything. I once had an executive share his screen during a call and he showed me 16 different tabs that he had open, each with its own unique dashboard. So the data is there, but now we've left our leaders to cobbling it together, trying to distill it down and, and really understand the story that our data is tell. So while there's not a dashboard shortage, there definitely is an information shortage. So this is where AI search and summarization can be really powerful and enable that daily data driven decision making for our leaders. So as we think about, you know, a frontline leader starting their day today, they have those 10 tabs open, maybe a sticky note, an Excel sheet, a report in their emails, some messages and teams. Now with Assemble, we replace all of that. So we change this, you know, hour of context switching and we distill it down into one prioritized view. Here's where we need your attention today. Here's why, here's what to do. Not a dashboard, but a decision. Why does that matter? Because it's more than just time savings. When leaders have the right information at the right moment, they round more consistently, they coach their teams more effectively, they catch issues before they become events. So that's where AI really earns its place and honestly demonstrates a lot of its power in clinical operations today. Getting the right information to the right leader at the right time. And that's what Assemble does.
- [05:58]
A
Now, thinking about that leader with the 16 tabs open, I feel like his ROI or her ROI would be that she or he can close said tabs. I do want to talk a little bit about the financial implications here and specifically around measuring that meaningful ROI around AI. What are some of those metrics that matter most? And how should organizations then again think about quantifying both quality improvement through technology, through AI, but also that financial impact?
- [06:33]
B
Yeah, I think what's really exciting is we're past a place of deciding whether or not there's an appetite for AI, and we really are moving into this place of evaluation. So healthcare organizations have moved past that questioning piece and they're really focused on understanding the value of implementing a new AI tool. So today's leaders are tasked with evaluating the opportunity and that means creating a business case with roi. Now, the metrics that Assemble focus on and that any, you know, healthcare vendor should really think about are what are the metrics that are important to our healthcare partners Today we think about ROI in three buckets. First is time saved. So this is not a hard dollar roi, but it's very important because it measures the time we're able to lift leaders out of high friction, low value work and allow them to focus on the high value work that drives meaningful outcomes for their organization as well as rewarding job satisfaction time for themselves. So important bucket but cannot tie to a hard dollar amount. The second bucket that we look at are quality outcomes, specifically our nurse sensitive quality indicators. The things like our patient falls, pressure injuries, central line and catheter associated infections. There's hard $roi associated here. Each hospital understands the specific amount of that cost that they incur for each of these events. And then that third bucket is also another hard dollar ROI Bucket, that's that retention bucket. You know, the cost for leader turnover is significant as well as the cost of turnover of the frontline nurses that they lead. And so when we think about the ROI and the measurement and implementing AI, it shouldn't be a leap of faith. It should be a measurable investment with a clear theory of change from behavior to outcome. And $and your partnership should have that conversation very early on to understand what is the tooling that's going to be implemented and then what's the outcome that we're, you know, going to hold each other accountable and measure together.
- [08:37]
A
You just mentioned a very important piece in all of this, which is the conversation part and making sure that folks understand the impact. They understand where this is all coming from, why certain things are happening in terms of application and why certain things get inserted into workflows in and of itself. We see still in 2026, a lot of organizations struggle with that transition of like, okay, we have something, we're trying it, we call a pilot. Right. It works maybe, but then it doesn't get applied. There's no scalability. Right. What enables some health systems to actually do scaling more quickly and realize that value that you've just described? And what are some of those common barriers that you're seeing where organizations might not be there yet and it's holding them back?
- [09:25]
B
Yeah, great question. So I think it's kind of the inverse of what we were already talking about here. The biggest barrier is not having full alignment on the problem and the expected roi. Value measurement is key in evaluating expansion and in determining whether the implementation has ultimately been successful. If you don't have the right measurement up front and you haven't chosen a problem, frankly, where you can showcase tremendous value, then when you go to evaluate a pilot, you're stuck in this pilot purgatory. Now, if you have that strong partnership and buy in from your executive and operational leaders and that framework for evaluation, when this time passes, the six months, the one year of the pilot, there's not this confusion. There's ultimately excitement about how fast can we get this across more sites of care to more users to show more impact. So I think that's really important is your problem selection as well as aligning on the correct evaluation framework.
- [10:20]
A
Tessie, it's so great to have you. So many great insights here again, from change management to the application to the pilot. We've touched on so many different things here. So great to have you. Anything else? What's that one thing you want leaders to leave here today from our conversation that may be looking at the value of AI or any specific use cases. What's that one thing you want them to leave with?
- [10:41]
B
Yeah, I think as, as our leaders today are looking outside of their institutions and they're looking for, you know, new partners, new tools to bring in to really just emphasize on developing strong partnerships. Because the relationship is more than just the product. It truly is a partnership of people. And flexibility is key. Not just flexibility in the product, which is definitely important, but in your partnership, needs change over time, things come up. You know, healthcare is a dynamic environment and it's important to have that flexibility and trust within that partnership. Lastly, I'll just say that there's a lot of conversation around efficiency right now. And at Assemble, our belief is that AI's greatest contribution to healthcare isn't just efficiency for its own sake, but really giving leaders more capacity to lead. When leaders can lead well, staff feel support reported, patients are safer, the organization performs better as a whole. And that's an outcome that is worth pursuing.
- [11:38]
A
It's understanding that trickle down effect, I think that's a very important highlight. So great to have you. Thanks so much for being here.
- [11:45]
B
Appreciate it. Thanks for inviting me.