Transcript for: #539 - Why she said no to GLP-1 money | Michelle Davey (Co-Founder & CEO, Wheel)

This is a transcript of Slice of Healthcare #539 with Michelle Davey, Co-Founder and CEO of Wheel. Please note that the transcript has been lightly edited for readability and may contain errors. Here are some useful links:

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The following is a conversation with Michelle Davey, co-founder and CEO of Wheel, the virtual care infrastructure company behind enterprise telehealth programs at scale. She started the company in 2018, when the job was still explaining to people what telemedicine was. Nine years on, Wheel runs operations for more than a hundred care programs and over three million visits a year with fewer than a hundred full time employees. This conversation is about how that happened, and about the decisions the market punished before it rewarded them.

This is the Slice of Healthcare Podcast. And now, here is Michelle Davey.

Jared S. Taylor
(00:00:32) Michelle, thanks so much for joining me on the Slice of Healthcare podcast. How are you today?

Michelle Davey
(00:00:35) I'm great. Great to be here, Jared.

Jared S. Taylor
(00:00:38) Really excited. You and I were chatting a little bit before this. I think the last time you were on the show was back in 2020, so it's definitely been a few years, and I'm really excited to catch up with you. I'd love to dive right in. You wrote a piece back in April that I thought read differently than a normal company post. You wrote that virtual care doesn't have a bad actor problem, it has a structural one. And you said you'd rather teach than point fingers. To kick things off, what has the response been four months later, and where do things go from here?

Michelle Davey
(00:01:13) I wrote that piece hot off the MEDVi article that many of us saw. There were really two responses to that article. One, healthcare insiders were pretty upset that it was featured, and upset at the amount of glamorizing of things that can happen in healthcare where people are essentially not worried about the quality of care, but more worried about the e-commerce function of how you reach more people. So there was one side there. And then there was another side that looked at it as a way for more healthcare to be accessed by patients.

Underlying all of that, regardless of which side you sat on, was this thing we've seen since 2020 and before: infrastructure companies coming to the space, very similar to Wheel, that can actually accelerate the timelines to get to market. That's not a surprise for most people in healthcare. But when I talked about the structural issues, it was really about how you self-regulate, lowercase r, infrastructure and these companies, much like all of us. How do you actually make sure they are good actors in this space and are thinking about quality of care and compliance, all of the things that are important, because we are delivering healthcare at the end of the day and there are patients at the end of that interaction with a provider. How do we actually think about that?

There was a lot of swirl at the time. I'm sure you remember the day MEDVi hit, and the two days after were a little chaotic. But what's been really interesting is that Wheel's role in healthcare, at least early on, has always been a teaching moment. From the earliest days in 2018, I was teaching people what telehealth and telemedicine even were. All the way to 2020, it was how do you stand up rapid virtual care. Now it's much more about not only how you stand up that care and scale that care, but how do you keep patients safe? How do you make sure the care being delivered is high quality? And third, how do you make sure it's great for patients and consumers, so they come back?

We've seen a lot of direct to consumer companies pushing the boundaries of that space. The large enterprises typically aren't the ones to do that, and that's where most of our customer base is. So we really use the opportunity to teach them about what is working in those models. Whether it's consumer marketing done in a different way that is compliant, how do we think about compliant marketing there? How do we think about the attachment rate to other types of services? That's a big one. You can do a lot in telehealth, but telehealth as a standalone business unit is a difficult business unit for many of our customers. So how do you have an attachment to a different care model or an owned asset? Maybe you own a pharmacy. How do you drive up patient carts? How do you think about access to the medication earlier to get to adherence? A lot of teaching moments across the segments.

We saw a lot of different responses in the industry. What I was most proud of during those times is that we've had really strong partners as well, both on the pharmacy side and the lab side, and our customers at the enterprise level who leaned in and said, there are ways to cut corners in this industry, that's not surprising, whether it's telehealth or the DME side of healthcare, where we've seen a lot as well. So how do we not do that? How do we do what's best for patients, and how do we actually use compliant healthcare and structural systems to reach more patients and push the boundaries of where healthcare has been broken for a really long time, but not in a way where patient safety is at risk?

Jared S. Taylor
(00:05:28) You made the call not to build revenue around these compounded medication programs, and you've been pretty open about that. In some cases that meant growing slower than you could have. I'd love to ask what it was like holding that line while the market was rewarding the other choice, which I know could not have been easy. I actually think you made the right decision. It was a very gray area. It was very much sink or swim for a lot of people who decided to go forward with it, almost like putting it all on black in Las Vegas. I'd love to hear your take on what it meant to hold the line, because that's a difficult decision when you're getting all this external pressure.

Michelle Davey
(00:06:11) Yes, I've absolutely had to make a lot of hard decisions over the last nine years, thinking about not only where the world is going but the longevity of the company as well. We made hard decisions in 2021, when we walked away from revenue on the SMB side of digital health because there was a cliff happening there, and we walked up to the enterprise. That was a very hard motion change inside the business. And we've had to make hard decisions around compounding for GLP-1s.

I want to be clear, I don't think compounding is bad. There is a place and a time for compounding, and there's a lot of nuance in what that means, from mass compounding to personalized care. I do think with more personalization in healthcare we're going to see more compounding come to market.

We stood firm on our decision not to build a business around compounded GLP-1s for two reasons. One, quite frankly, it wasn't something we were good at. We had not built compounding partnerships, we had not found the right suppliers or built the vetting processes. We didn't have any of that infrastructure. We could have built it over time, but by the time we would have built it, we saw that cliff coming.

Two, we did believe that the branded medications that had gone through trials were a better mass-scale fit for customers. Again, there's a place for personalization, but for most customers a branded medication was a better clinical fit.

And then third, I've learned over the years that chasing short-term revenue bumps ultimately doesn't drive the longevity of the business. You said a lot of companies got rewarded. Absolutely. A lot of companies brought in a lot of cash during those times. But what I have learned over time, especially in venture capital, is that there's a cliff in that timeline too. If you can't build a sustainable business line out of it, it actually looks worse for you in the years to come, because now you have to defend why you spent infrastructure time, why you spent capital toward that, and ultimately why you're not growing at the pace you did during a bubble.

The view we take is, where's the sustainability? How do you partner with those who are creating that sustainability for patients and for clinicians? So we really took that time while everybody else was chasing compounded revenue, even during a very gray period when supply wasn't an issue. Early on, I think it was the right choice for companies to get supply to patients. But once supply wasn't an issue and people kept chasing it, we decided to take our time and our efforts and form deeper relationships with the pharma partners, so that as supply came online for new medications we didn't have the same kinds of issues we saw coming for compounded GLP-1.

Really tough decision. How did I live with that? Ultimately I think I shut off LinkedIn for about three months, and Twitter, to stop watching people saying, I'm just full of cash over here, I'm going so fast. And really put our heads down into the business and stuck with the decisions around how we build an enduring and scalable business.

Jared S. Taylor
(00:09:48) I don't want to harp on past decisions, because as a founder you could write several books on all the decisions you've had to make. But I take great pride when I get to interview founders like yourself, because you've been through the trenches, you've experienced different markets. You were there early during the pandemic, and then you had to take a look at this GLP-1 craze. I'm with you, I do believe there's a time and place for these. I just feel like for the last couple of years it felt like how Adderall worked a while back. You're not focusing as much? Okay, let's give you some Adderall. Some people needed it, some people maybe not as much.

I'm curious, are there any other decisions? Obviously you have a lot going on in your life. You're running this company, you have your family. Are there any decisions you look back on and go back to a little bit? It can be that you made the right call, and it can also be that you made the wrong call. Are there any of those you maybe haven't talked as much about? Our founder community really appreciates it when those moments get discussed. We don't have to go into too much detail, but I'd love to hear from you.

Michelle Davey
(00:11:02) I think there are two. One is recent, so there's some recency bias to it, but I'll share it first.

One of the things we were measured on as founders, let's say in the 2020 to 2022 timeline, was how big your team was, how scaled your team was. So we were constantly in this loop of hiring. Hire more people, that means more growth. That was a metric. It wasn't something people were outwardly writing down and saying, this company is this. But every single call I got on, whether it was an investor or another startup CEO or an enterprise, was, how big is your team? How large is your team? And not the network of doctors. That makes sense, because we can show you our capacity into the network. But ultimately, how big was the core foundation of the team?

With AI and where the world is going, that has changed. Obviously we've had to make some hard decisions in the past, and I talk openly about the fact that we've had to do layoffs. Those were some of the hardest days inside of Wheel, and then how do you build out from that? But one of the decisions we've made, really in the last twelve months, is agentifying our operations. So 70% of Wheel's operations will be agentified by the end of this year. We're halfway through that journey.

We're building a lot of agent orchestration, everything from capacity modeling and forecasting to actual agents that ensure the right clinician is there for the right patient, not just deterministic routing infrastructure but intrinsic as well. And inference is running about three months out. What do we expect the world to do based on historical Wheel data and some customer data that we have as proprietary?

I go back to that because we made this decision years ago, that building a team at the pace and in the structure we were being measured by wasn't the right decision for Wheel. So internally today we're less than a hundred people full time, and we're running operations for over a hundred different care programs and over three million visits a year. We're doing that with a smaller but mightier team.

I think about that decision because those were some of the hardest days. I cannot put into words those days and how you feel internally as a founder. But seeing it from the other side, and what technology has enabled us to do and continue to scale, is pretty rewarding. A lot of founders I talk to, especially ones who are pre AI native, turning that ship AI native is a different ballgame. It's really hard. How do you help upskill people? How do you get people there? How do you rebuild your infrastructure in a cost-efficient way, because you're running a B2B2C business and you also have to rebuild it at the same time? It's not for the faint of heart, and we make hard decisions every single day because of it.

Jared S. Taylor
(00:14:23) It's really interesting you bring that up, because I remember at every conference years ago, that was the first question. It wasn't even who are you. It was, how many people work at your company? And that would be their filter process for whether they wanted to talk to you or go to the next conversation. And now you see articles like the billion dollar one person company, and then no, it's going to be the ten person billion dollar company. It keeps getting pushed back and forth. Really interesting.

You mentioned what you're doing with AI. I'd like to talk a little bit more about that, if you're cool with it. One of the things I was reading about is that you're able to hand your clinician a fully structured, AI-prepped patient profile. Really cool. I'd like to know how you keep clinician judgment central as the tooling gets better at the prep work, and anything else you want to talk about in how you're implementing AI.

Michelle Davey
(00:15:22) We actually did a really bad job of telling people all the stuff we've built with AI. It's not something we've done marketing around. It's been highly internal because of the amount of testing and governance we're doing behind the scenes.

But to give you a snapshot, one of the things we noticed change in the market, let's call it a year ago, was that patients were showing up with more context than ever. They had wearable data, they had their Apple HealthKit, they had patient health records, they had AI chats they had done with their health records, they had biomarker data from labs, they had imaging from all of these different consumer health platforms. There are plenty of people aggregating that at an API level, and then you can chat with it.

What we were finding was that people were coming into a telehealth visit, not anymore for a skincare visit, but with all of this data about themselves. They had used AI to aggregate it in some way and find some trend line. And the information asymmetry was now on the patient side. The patient had it, and the clinician didn't. So imagine coming in as a clinician and this patient has a sixteen page health record with all of these AI questions built on it, and you're sitting there going, I can't see that. I don't know what you're talking about. Is that biomarker even clinically relevant to an actual care condition, or to what you're looking to solve as a chief complaint?

So what we've done is build the ability to take that data from whatever source. We have our own partnerships. We also have clients who have wearable data and other proprietary data. We take that in. We use agents against two things: clinical guidelines, which are all built by our clinical teams, as well as our clinician decision support tools. Then we aggregate all that data and put it into context that makes sense for the clinician. Wearable data goes in one place, lab data in another, and it pulls up the information that's clinically relevant for that patient's chief complaint or overall health story. Then it nudges the clinician: here are the questions you need to follow up on.

One of the guiding principles we have here at Wheel is that clinicians still need to make the decisions. They still need to use their brain. We're not trying to remove all of the decision making, and I think that's really important, because anybody who uses AI knows that it can be wrong. It also cannot take in a lot of the context and pattern matching that a clinician has. So we use that technology to give clinicians a way to no longer be in a place where there's absolute information asymmetry on the patient side.

Not surprisingly, it also gives the patient the feeling of a much more personalized experience. They're not just looking at biomarker data alone. A clinician can talk about HRV with them for the first time, and what that means for them. How do you actually build a relationship in that moment that feels much more personalized?

We've also built clinician decision support tooling, and we've built a lot of electronic prior authorization in the GLP-1 space for very specific programs and use cases, so that the patient journey from our partners is seamless. Importantly, there are all these edge cases we've found over the years about how this insurance takes this, and if you end up at the pharmacy and you don't know that, the friction that creates.

From a decision support perspective for clinicians, because we have such a large network, it's also about how we take a clinician who maybe hasn't seen this particular case or all of this information before and really give them the insights they need, as well as questions they can follow up on, and then produce a better outcome for that patient. It's totally okay at Wheel for a clinician to say, I've never seen that before, we'll have to follow up, or I need to call in another clinician. That is totally okay, and it's in our guidelines all the time.

But the tooling we're creating is much more for helping clinicians with this information asymmetry we're seeing in the market, the aggregation of all this data, and deciding the next best action with the clinician. Some people say clinician in the loop. I agree, but I think we're still leading with the clinician, not leading with AI and then having the clinician just check the box from there.

Jared S. Taylor
(00:20:23) When it comes to clinician feedback and adoption, how do you deal with it? I'm not sure this makes up as large a percentage of your clinician base as it would have two years ago, but I still run into people today who are anti AI and say, I don't care if it makes my life easier, I don't want it. When you do come across that clinician who says, I don't like AI even if it's helping me, what do you even say to them?

Michelle Davey
(00:20:55) I think it goes back to education. Why is AI here? There are different reactions to AI. People can say, I hate AI because it's wrong when I use it.

One of the things we built into all of our AI and clinical decision support tooling is all of the references to the studies or guidelines behind why the system is telling you that something is clinically relevant or why it's important. You have the ability to actually deep dive into that information. So we're building trust along the way. We're not just writing this into the guardrails of an AI model and saying, hey, this is what we want you to do. We're building that trust based on clinical evidence, studies, and clinical guidelines that are built into the system. Helping clinicians trust the system is really, really important.

I learned this a long time ago, not even at Wheel but in my pre-Wheel days. Everybody used to say that selling software to doctors is something you shouldn't do because they don't adopt it. The truth is they don't trust it, because of how much bad software has been put in front of clinicians who were expected to adopt it blind. So really building that trust is important.

The second thing is what I said earlier about clinician in the loop. I think that can mean a lot of things, but typically it means the AI does most of the work and the clinician checks the box to prescribe. Our view is that we lead with the clinician, the AI is helping that clinician, and they're still making those decisions.

If we've explained it, and we've gone through the education, and we've tried to build the trust with the clinician, and they say, look, this just isn't for me, that's okay. We would rather know that up front, because we are building a lot of tooling here in AI. But if somebody says, hey, this isn't for me, that's okay too. There are plenty of opportunities for clinicians to still work in worlds where there isn't AI-assisted healthcare right now. I don't know that that's going to be true in five years, but it's still true now. And I do think it's our responsibility to help clinicians understand what's coming and changing about their careers as well.

The core thesis of starting Enzyme, and now Wheel, was that there was a massive supply and demand imbalance in the market. That's true. Everybody still talks about the large 80,000 physician shortage of primary care doctors in the space. So that will remain true.

How we've managed to think about that differently than, okay, let's just bring those doctors online because that scales access, is, one, yes. But two, it's really thinking about how we're predicting when patients are going to come into the virtual front door, and what type of clinician is needed at that time. Is it a nurse, an MA, or an actual physician or a specialty provider?

All of that predictive algorithm, and also modeling of what happens not only in this state, in this hour, at this moment. We're really, really good at that based on the data we have and the scale we've seen. But also, what's about to happen in three months? That can be based on real-time data we're feeding in from the CDC, it can be proprietary data we're getting from customers and customer launches, it can be market data, and it can be proprietary data we have on seasonality. We're watching flu data.

Building all of that in, I believe more than ever, especially with AI, there's going to be an incredible abundance of healthcare. More and more healthcare interactions are going to be created. We're seeing this with AI and LLMs. A great example is radiology, where everybody said AI is going to take away the radiologist. Now it's the hottest specialty to go into, because there's so much being created there. I think that's going to be true across healthcare.

So the supply and demand imbalance is going to become even greater, because remind you, we're still not making enough clinicians to keep up with the pace we're at today. When you think of that world that's abundant, with more healthcare interactions being created earlier and more often, then you're going to have to solve for that supply and demand imbalance in totally different ways. That's what we're really focused on.

Michelle Davey
(00:25:43) The second thing I'll go to is a core value. The only core value that hasn't changed here at Wheel is grit to grow, from the earliest days. Grit to grow is about the hard times being the moments where we experience the biggest growth. We face hard challenges, we face hard things together, and we see that growth together. That's nine years of running a business through multiple markets, through different phases, through the continued evolution, helping our partners across many different segments meet the consumer where they're at. That has been true from day one to now.

Even today, we talk a lot about how we like to do hard things together, because our team is constantly faced with really difficult things. Healthcare is very difficult. Evolving a company into an AI native company is very difficult. And then third, being in a startup is really difficult. So how do you do hard things with people and grow in those moments?

Jared S. Taylor
(00:26:47) What's something that's happened over the years that maybe you haven't talked about, or have only talked a little about, that you can look back on now and laugh at, but at the time it was like, this is not a great moment for the company? Or it is a great moment for the company, but that's not where it was trending at that moment in time? I'm just trying to find the one thing you maybe haven't spoken much about that you can tell us here right now.

Michelle Davey
(00:27:19) Oh man, there's probably a lot. There's so much in between the big moments, and all the small moments, both hard and great, are things we don't talk about as much. What haven't I talked about that now I can laugh at?

I've learned so much. I was just talking to somebody yesterday about how I couldn't even read a P&L. I took online classes to learn how to read a P&L. And now I've gone through major fundraises. I feel like I could read a P&L in my sleep, and I do. I dream about P&Ls, which is terrifying.

And then two, I haven't fundraised in over five years, so I don't know the fundraising market. Now I have founders call me about what it's like to acquire companies, how you set up your board structure as a founder, how you set up your trust systems and your financial systems as a founder. So I think about where I've grown to.

I bring that up because the things I probably haven't talked the most about outwardly are the founder things. I talk about it a lot with other founders. What does it mean to prepare for a board meeting? That's really hard. How do you advocate for yourself as a founder who maybe hasn't done so in years, for financial compensation and things like that?

I don't know that there are many I can laugh at. I've had two co-founders leave. We've exited two co-founders over the years. I've talked a lot about that with other founders too. So it's all these little moments that have built up over the years and given me experience as a founder, that maybe I'm most proud of, but it's the stuff I don't talk about, really.

Jared S. Taylor
(00:29:14) How did you deal with that? There are different ways, right? Whenever you lose a founder, whether by choice or it's just timing. How do you deal with that? Because there were several people in your circle who were along for the ride, and now they're not there. Talk to our audience a little bit about how you initially deal with the shock behind it, if you weren't expecting it, and then you have to go back to work. How did you deal with that?

Michelle Davey
(00:29:44) Both my co-founder leaving, and I've also had executives leave, where it felt like, what am I going to do? It's in this moment where things are hard, or even really great, and it's like, man, this is the hardest thing I've had to go through. This is my support system. These are people I've formed relationships with.

As a CEO, especially as you scale, you have fewer relationships down the line. You're most open with your C-suite and your co-founder about the hard times, about the we're in this together moments. You feel like somebody wakes up and has the same level of commitment to the business that you do. And then all of a sudden that's gone.

So a lot of it is about dealing with, who do I have to go to as an outlet now, who understands the weight I'm carrying as a founder, or the nights that I wake up and worry about this, or who can even just relate about the good times? How do you experience that together? So it is very isolating in that moment.

What I've learned about that over time is that it's normally for the best. Whether somebody's chosen to leave, there's typically something going on in their life where they're not as committed to the business anymore. And having somebody in the business who's maybe not as committed can also feel very polarizing, and you're angry, and all those other feelings come up.

And then second, I would say it takes a little bit of space. You can't just say, okay, see you later, and then let's still be best friends. That's hard. That's hard with somebody you see and talk to every single day. It's like, hey, I need a bit of space, because you're both reeling from this person detaching their identity from the business, and now I'm having to carry that weight, but also figure out what that means. So there are a lot of feelings involved, which as a founder and a CEO we're told not to talk about, because feelings and business performance don't always mix, I guess. I don't know that that's true, but that's maybe the thought.

Jared S. Taylor
(00:32:02) When you have those moments where you do lose a key executive, or like you mentioned, a founder, does it get easier the second time it happens? We talk about feelings. Do you feel it a bit less? Does it ever get easier?

Michelle Davey
(00:32:16) I think it's less that you feel less, and more that you know what to do next. I think that's the scariest thing as a founder or a CEO: what do I do next? Whether it's messages to the team, how do I pick up that workload, how do I hire the next thing? As a founder, most founders go into problem-solve mode right away. How do I solve this problem? When the problem is new and it's uncharted territory for you, that's the scary part. How am I going to get through this? What's next? How do I problem solve this? When you go through it twice or three times, you're like, I know this. Okay, here's what I go do, and here's who I call, and this is what I do.

And there are people around you too, both inside and outside the business. I always laugh, because there's one woman I called both times when I was having babies, to come help me in the business. Every time I call her, she says, I'll be there. She has her own stuff, but she comes in to stabilize the company, help run it, give some relief to the teams. Those people are worth their weight in gold. I say that because it doesn't just have to be the core team as you think about it today. Who are those other people you can talk to, whether it's your current investors or other executives, people who will show up for you?

Jared S. Taylor
(00:33:45) When you're looking to hire that next executive or key leader at your company, what's the one non-negotiable skill they must have?

Michelle Davey
(00:33:57) Right now, it's being AI native. I'm actually hiring for an executive over my operations right now, and I can tell you that somebody I would have hired six months ago, or even a year ago, is not the person I'd hire now. That profile of what matters most has changed.

Jared S. Taylor
(00:34:18) Is it more difficult with all the noise? There are lots of positives, but there's also a lot of noise regarding AI and who the best builders are and who truly is AI native in how they build and think. How do you filter through that noise when you're going through your interview process and trying to find this key role?

Michelle Davey
(00:34:37) That's a great point. And maybe AI native is just a very broad term, so we should probably break down what I mean by it.

I think two things. One, you're a builder. It may not be that you're vibe coding something on the side, although that's great if you're doing that. But you know how to use the tools, you know what they function well for and what they don't function well for, and which tools win.

And two, you're able to talk about measurable ROI and the KPIs you'd measure moving forward, and how to actually build a team. And three would be building a team that's not just humans, but humans running agents, and how you actually measure performance based on that to get to an outcome.

That is a really different way of thinking than executives of the past, where we're measuring to business KPIs and people. We have to stack people to ratios, and that will meet our outcomes. This starts with the outcome. We are reducing time to serve a patient by 30%. How do we get there? How do we build to that? Versus a KPI of, our gross margin needs to be X, and we just move things around in order to get there.

So when I say AI native, those are the three things I think about. Not that you're an inference engineer, or that you're an engineer at all. You're building, and you have the mentality and the structures and the frameworks for how to build to those outcomes, manage to those outcomes in a KPI, and then build a team that ultimately produces those outcomes.

Jared S. Taylor
(00:36:24) For whoever ends up getting hired for that position, what do you think their first ninety days will look like?

Michelle Davey
(00:36:30) I typically say thirty days of onboarding. I think that's even sped up. We have a new executive in seat and he's making decisions two weeks into the job. Things move fast at Wheel. Within ninety days, they are absolutely building and responsible for moving KPIs in the organization.

The first thirty days they're working across the teams to understand the current state of the business and where things are going. At this time in market, both healthcare and AI, and I want to back up here, AI is speeding up the world. I think we can all feel that. But healthcare is also changing drastically right now, from a regulatory perspective, from a consumer demand and patient experience perspective, from a reimbursement perspective. There's a lot of change happening there.

So by ninety days, that person has a good sense of where Wheel is and where the world we operate in is. And at ninety days, they're probably already executing on a lot of that strategy.

Jared S. Taylor
(00:37:39) Super interesting. If I asked you about the next role you hire several months down the road, that answer might be totally different too, with how fast things are progressing. Really, really interesting. Good luck hiring for that role. It can't be an easy hire either. You want to find the right person. Michelle, I want to thank you so much for a great conversation here today. Thanks for updating us on Wheel and answering some questions not just about the business, but about founder truths and what you've gone through in building this business. Really appreciate your time here today.

Michelle Davey
(00:38:14) Great to catch up. Let's not make it six years next time.

Jared S. Taylor
(00:38:18) I like it.

Michelle Davey
(00:38:18) Thanks, Jared.