I had a chance to catch up with Anand Iyer, Chief Strategy Officer of WellDoc at the 2016 Digital Health Summer Summit in San Francisco recently. WellDoc is one of the older digital health companies still in existence having been founded more than ten years ago. The company is a leader in the space being the first to publish results in a prestigious peer reviewed journal, the first to get FDA clearance, and one of a few digital health companies to actually get a billing code for one of it services.
The story of WellDoc contains important lessons for digital health entrepreneurs who hope to create a sustainable business. Click here to see the video of my conversation with Anand—a transcript (modified for readability) appears below.
Patricia Salber (PS): Anand, WellDoc has come a long way. Tell me again when you were founded. I think it was about ten years ago, right?
Anand Iyer (AI): I think when we last met, I didn’t have any grey hair. Anyway, we were founded at the end of 2005. It’s interesting because as I think about our evolution, it has happened in three phases. There was the phase—one that we believed in strongly—that was about research. Dr. Collins of the NIH has said, “Is the absence of evidence, the evidence of absence?” If you don’t have the evidence, then you are not a player. You are going to be absent from the fun. So, we said listen, we need evidence.
PS: But this was an unusual approach because, until recently, most of digital health was “absence of evidence.”
AI: Yes, we always say that whilst we are a digital health company, we’ve taken a life-sciences model to our existence. We said, let’s first do all of the randomized controlled studies because how can you go to an Aetna or United and ask them to reimburse you for something if it doesn’t show possible outcomes. If you want to get a medical device or pharma-like treatment, then you have to have the evidence like those guys do, right?
PS: I have to say, that’s way too rational. I was the Chief Medical Officer of a health plan and it didn’t stop other vendors from coming and asking to be reimbursed.
AI: True, true. At the same time, you want to build bricks behind you. You want to make it harder for competitors to catch up. There is nothing stopping anybody else doing clinical trials, but it will take them 3 to 5 years to get it done. And, you never know if it is going to work. If it doesn’t work, you have to publish the results anyway. At the end of the day, if WellDoc is something that’s really going to bring the patient and the doctor closer together and it’s going to come from the doctor, then you need evidence. How many studies have shown that patients will use mhealth applications if their doctor tells them to? But, the doctor is not going to tell them to use something that doesn’t work. In fact, now you’re seeing a huge tsunami of apps that are coming under extreme scrutiny from the press. They are saying, “wait a minute, you guys are doing heart monitoring and sensing, but the measures are all off.” So, doctors are saying we shouldn’t tell our patients to use them. The second phase of our journey was how do you actually make the product safe—which is the whole FDA journey for us, right?
PS: You guys got FDA approval early on, right?
AI: We were one of the first. In fact, we were the first to get FDA clearance for a real-time coaching system—nobody else had that on a mobile device. Keep in mind back then it was cleared on a Java phone because there were no smartphones back in 2006 when we contemplated this whole model. It was important for us to show that we could manage to the parameters of the FDA. All the FDA wants, rightfully so, is the product has to be safe for patients to use and you have to show that you use good manufacturing processes. That means it has to be repeatable, scalable, and measurable—all those good things.
PS: They want it to be effective too.
AI: They want it to be effective, of course, they do. That’s really what their stick is. It’s interesting, you may be aware of or an organization called the IMDRF which is the International Medical Device Regulators Forum. It is a compendia of medical device regulators from organizations in 16 countries, for example, the FDA, Health Canada, the NHS, etc. We were invited to participate as the only company, alongside of them, saying how do we draft a single mobile medical guidance document for the whole world on what they call SAAMD (Software as a Medical Device).
PS: Congratulations. Your company has been a leader in so many ways.
AI: I think it’s it’s a fortunate position to be in because we’ve learned an awful lot. Right, wrong, or indifferent, the FDA is the FDA. When you go to other countries and they see that you are FDA cleared, all of a sudden, the trust factor goes way up automatically. There may still be a little bit that you have to do, such as in-country regulations, but 90% of the journey is already done.
PS: So, that was your second phase. What was the third phase of the company?
AI: So, phase one was research, phase two to build a product platform (five nines, quality, scalable, separable—all that good stuff in the software architecture). Then, the third phase is how do you commercialize? What’s your business model? Because of our clinical trial outcomes and our FDA clearance, we were candidates to get a billing code. There are only three types of codes: procedural codes, device codes, and pharma codes for drugs. We looked at the pluses and minuses of all three and ended up going with the pharma code. It is one model we have today, but just one model. There are other models that we have that we are working with our payers on. We are now 15 or 16 months into our commercialization phase. We have a several hundred doctors in the state of Maryland and neighboring states prescribing BlueStar, several thousands of patients who are active in using it, and what’s fascinating, Pat, is the outcomes in the real world in many ways or even better than the outcomes in the clinical trials. That’s because you can actually see what real people are doing with it.
PS: Following the Pharma model, are you collecting aftermarket data in a systematic way? And, if so, are you going to make that available via a peer-reviewed journal so we can scrutinize it?
AI: Absolutely. You are touching something that is very near to my heart. I did all of my doctoral work in pattern recognition. When you think about data that were collecting and you think about what we can do with the data, I like to use 2 x 2 matrix. If I were to draw it for the camera, the horizontal axis would be the presence or absence of data; I have it or I don’t. The vertical axis is my analysis intent. That means I know what I’m looking for or I don’t. In the bottom quadrant, I have data and I know what I am looking for—we call that “I” or Informative Data. It can tell me the percentage of times women did this or how did people engage with it, who were on insulin or on oral agents? It is basic reporting, Math 101. As soon as you inform, you go north (to the upper left quadrant). You say I have the data, but I don’t know what I’m looking for. That is called “D” or Discovery. In English, the “I” quadrant answers the question what happened? The D quadrant asks why did it happen—is there a glucose trend? A testing trend? A medication trend? Or an exercise trend? Or whatever trend?
PS: What can I learn that I could possibly fix?
AI: Exactly. Next, you come down to the south quadrant. Here I know what I’m looking for, but I don’t have the data. That’s predictive. We call it Extrapolative Data or “E.” There you invoke all the mathematical modeling of predictive models. Once you do that and you’ve built a basis of understanding the relationship and causation and correlation between parameters, you go to the last quadrant which is called Adapt or “A”. The matrix spells out IDEA, the moniker for data science. You know, when you look up at the sky at night and you see a star, you say it’s a star but it could be a planet. But then, you go and look at it in the Hubble telescope and you find it is actually four galaxies. In many ways, our product BlueStar is like a Hubble telescope for a doctor to look into a patient. They can see an expansive picture of what is actually happening in a point in time. That’s when they can maximize their value to their patients and be able to say, “I know what’s wrong with you. Let’s work on this or let’s work on that so we can get you from here to there.” We know how to do it and that’s fun.
PS: It really sounds like fun. My last question is are you making money?
AI: Yes, we are. It’s a slow process, as you can imagine—getting reimbursement from the payers—but we do have a couple of reimbursement wins under our belt. That’s the continued journey for us. In many ways, what we’re learning is there are many pathways to get this monetized. One is through a reimbursement pathway. One could be, hey, at the end of the day, it’s software, right? Why can’t I just license it? When we initially cleared our product with the FDA, we got both the Rx and OTC clearances. So now we are contemplating a consumer product. How can I actually get this into the hands of consumers? Think of how many people have diabetes around the world and how many have pre-diabetes. If they can use tools like ours to keep them in the safe zone and not become an economic burden to society later on, then you’ve done something good. That’s the journey ahead of us.
PS: Well, the good news is that you have a tool for people with diabetes and so you have a huge market. The bad news for the world is that you have a huge market.
AI: We are lucky here in the United States. It’s 8% of our population. In the GCC, the Gulf Corporation Counsel (Qatar, the Emirates, etc.), it’s as high as 35%—but at least they all have cell phones.
PS: I want to close by congratulating you on all that your company has accomplished and kind of speculating on when you think WellDoc might become a case study for the Harvard Business Review on how to build and run a digital health company?
AI: I don’t know I think I think a lot of people have looked at us through various lenses and I think in many ways we have been a beacon. Truthfully, some of these things we stumbled upon—sometimes it’s better to be lucky than to be smart. Some things were very deliberate in our strategy. I think it is a combination of both of those things. It’s the classic agile thinking, right? Fail rapidly, but fail quickly. Learn from those failures and adapt your pathway going forward. Who knows whether it will be Harvard or its Forbes or anybody else—the market’s been nice to us.
PS: Well I can tell you one thing for sure, it will be on The Doctor Weighs In. Thank you very much for joining us.