Alex is the Co-Founder and CEO of Nabla. Nabla is an all-in-one digital solution with an AI-based assistant for healthcare to help free up healthcare providers to do what they are trained to do. That is spending more time with their patients.

Alex is a pioneer in the AI/ML space. He has had two very successful startups acquired by Nuance (VirtuOz) and Facebook (Wit.ai) respectively. At Facebook, he worked on their AI team developing conversational AI.

If you are a clinic or healthcare startup, check out Nabla or email Alex at alex@nabla.com to set up a meeting and discuss your use case. There is no reason to build something from the ground up when you have such a great resource!

Links:

* Nabla

* Follow Alex on Twitter and Linkedin

Transcript

Zain: We got Alex from Nabla here. I'm really excited for this conversation. Alex, thank you for coming on. For those who don't know you, would you mind giving us a little introduction?

Alex: Hi everyone. Yes, sure. So my name is Alex. Alex Lebrun. I started Nabla three years ago for Nabla. I started two startups in the machine learning space. The first one was acquired by Nuance Communications in 2012 and the second one was acquired by Facebook in 2015. So after that I spent three years with Facebook at Facebook AI research, working mostly on conversational AI. And three years ago we left Facebook Research to apply what we've learned to ask care of Naba.

Zain: That's awesome. That's amazing. So what can you tell me about what is Nabla? Can you just give us a little background about what you guys are building with Nabla?

Alex: So we are building AI based assistance for doctors. So we all know that doctors waste a lot of time doing things that they shouldn't be doing and that they don't have enough time for us patients. And so this is a problem we are trying to solve with Nabla. So for instance, if you have a consultation with your physician, let's say it's a remote consultation, they would look at you in the video and on the side, the Nabla assistant would take care of many things that they hate to do. Points on the clinical documentation, writing the report about the consultation, updating your patient records, programming the flood review, maybe writing some prescription, doing some stuff called coding or insurance. And so we are trying to automate all these things so that doctors can spend more time on the actual care and empathy with the patients.

Zain: All of that sounds amazing. I think that's what I mean, what you're bringing up is what I think like AI and ML would be like great for me personally, the connection with the patient is what's the most important in healthcare. And you don't want to replace that, you just want to augment it. And it sounds like that's what you guys are trying to do.

Alex: Yeah, it's funny because I've seen some companies trying to replace doctors with health care professionals with chatbots or things like that. And I spent the first ten years of my life building chatbots for customer service very early on, before it was a pool. And I perfectly know the limits. Chat bots can be very powerful in some context, but I also know the limits and healthcare is typically a setting where I don't think chatbots a good idea. Okay. Anyway, we are far from being able to build this today, and even if we work at all, it is really a good idea. Not sure. So the idea, but actually the technology to build an assistant that makes the Fort life easier, it's very close to technology. Build chatbots. It's the same models that are listing.

Zain: Yeah, no, for sure. What is the back end of novel. From what I understand, it's kind of like a CRM. Is that correct?

Alex: Yeah. So once we say we are building an AIbased assistant, the question is how do you bring these products to the healthcare providers? And so we started by building digital care platform that really looks like a CRM. Like it's like a modern CRM.

Zain: Yes.

Alex: It handles both asynchronous communication with patients, text, and also synchronous communications with video consultations. And it has everything you find in a good CRM system. So to remember important things about your patients, creating some tasks and collaborating as a medical team, because you may have different kind of nurses, specialist nutritionists, working together on one patient. So all these things are obvious if you are in CRMs, but they are not obvious at all in the healthcare provider world today. And so we built that and then added our machine learning assistant in this tool so that it comes with the AI assistant doing the documentation and other tests.

Zain: That's awesome. I even wrote about it. I think CRM, if you have like a back end of the CRM, it's like the perfect tool for health care. Because like you said, it provides you everything, right? It can write all your notes. And the point of a CRM is as many touch points as possible with your customers. But in our case, it's the patient. And it just makes life easier versus what we're doing now. It's like we have all these other systems kind of coming in together and none of us really work well. So I've always wondered why in the beginning, EHRs were just not CRM and then people just built off of that because it would have made our lives so much easier. At least on our end.

Alex: I have the answer to this question, I think. So EHR are not designed it around because EHRs were designed to build insurance payers. And this was the only goal of EHR. It's around coding because documentation to support your claims and how you code them. And so it's really a financial goal or anything. And then you try the plug eventually a few things for patient relationship, but it's not the main this is not the main goal, the original goal. So somebody should build an EHR. Certainly many people tried, but then it's difficult to have people change their EHR process.

Zain: Yeah, I can tell you using, EHR? I mean, they are. That's why they were built, right? That's the genesis of the HR epic. All them were built for billing. And I can tell you using it, using them, it definitely feels like they were not built for patient care. We're built for billing. And there's a lot of issues that come along with it, which we don't have to really get into that, but that's amazing. So where do you see AI and ML in healthcare kind of going like what do you see as like the biggest roadblocks and then how would you get across those roadblocks?

Alex: There are lots of applications. There is maybe the first real world problem that AI solves in health care was around imaging, analyzing x rays or ECGs or other because this is very close to the original playground problems that researchers worked on in the it's a surprise that it started with these kind of pattern matching problems and now it's totally in production. There are FDA clearance for the augmented Xray. And so this was I think the first wave of ML impacted Escare. And now we are entering the second wave where it's less obvious. In health care, I think 80% of the product is communication between the patients and the medical professionals. And so if you really want to impact healthcare delivery, you need to be involved in this conversation. And so this is where conversational AI comes into play. How can you be helpful in this conversation and automated parts of it without re...

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