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Becker’s Healthcare Podcast
AI, Contract Intelligence, and Revenue Transformation in Healthcare with Mohnish Mohanty
00:21:25
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- [00:00]
A
This is Scott Becker with the Becker's Healthcare podcast. I'm thrilled today to be joined by a brilliant founder. We're joined today by Mohnish Mohanty. And Mohnish has spent time at Carta, at Docusign, and now he's the CEO and founder of IntelliSense. He's also been a Menlo park fellow. A truly brilliant person. Mohnish, thrilled to be with you today. Can you take a moment and introduce yourself and tell us sort of about the founding and how you got there to founding and growing intellisense?
- [00:33]
B
Yeah. Scott, first of all, thank you for having me on your podcast. I think I've shared with you before, but Becker's is one of the first things I read every morning, so it's great to be on the show. To answer your question, maybe just going back down memory lane here a little bit. I started out in medical devices, then went to consulting, whereas doing cloud. So when, when cloud was in its AI era currently, when people weren't really sure what to do with it, that's when I really entered the workforce as consulting for Accenture Deloitte, helping healthcare organizations optimize contracts to cash workflows. And then I joined DocuSign and led the core ESIGN platform as we were going public there and then did a couple startups and most recently had a product for cap table at Carta and then started intellisen. We actually started doing contracts intelligence across all major industries. We were looking at government and we were looking at supply chain finance, use cases across government, healthcare, et cetera. And then we got pulled into healthcare and aggressively got pulled into healthcare. And so, yeah, that's what we're doing now. We're helping hospitals increase their margins by creating a new category called contract verified revenue.
- [02:03]
A
And Monish, you've had background at some of the great companies that built over the last decade or so. Carta, DocuSign, you also were a fellow at Menlo Ventures. You spent some time in consulting at Deloitte. I mean, brilliant, brilliant career. Talk a little bit about what are you most focused on and excited about now and where do you see sort of the AI, you know, working into what you're doing, where is it moving and where is it moving incredibly fast?
- [02:36]
B
Yeah, yeah, absolutely, yeah. You know, I'm particularly bullish about AI. It's been building AI applications for over six, seven years now, building, you know, B2B enterprise for over 15 years. But one of the things that I'm definitely excited about AI around, and we've all heard this in Organizations, when you have a great idea or when you want to go work on these big, hairy, audacious goals, sometimes you bring it up to your leadership team, or sometimes a CEO brings it up and says, hey, look, we should go and work on this thing. Then the organization rallies around it and then eventually they say, hey, you know what, we don't have the time or resources to go tackle that, but it's a great idea and let's table it for the future. And it's always around time and resources, right? And that's always the equation that we need to balance. But one of the things that I'm excited about within health care is we finally have this moment where we get to tap into an infinite number of resources and really shorten those cycles. And so we get to really focus on those outcomes that we've always wanted to. So I think organizations that are really going to be successful with AI
- [03:55]
A
are
- [03:55]
B
going to be the ones that really focus on, hey, you know what, we have infinite resources, we have all these agents and tools at our disposal. What are the outcomes we really want to drive towards and how can we build those quickly? And I think that reframe really changes how we think about AI today, which is, hey, you know what, let's use AI to speed things up. But really let's start thinking about not just how do we build faster horses, but how do we build a car
- [04:25]
A
and talk about that intentionality. We talk a lot about automation and efficiency. But where do you actually see meaningful return on investment as people dive into AI spend and AI use cases? Where do you see real return on investment?
- [04:42]
B
Yeah, I think, Scott, you've definitely addressed this in some of the publications that you've posted before. But one of the things that we can do with AI is things that were structurally impossible to do before. Of course we have chatbots, we have better chatbots, we have all of this automation and better summaries. But when you start to look at certain industries, and we're very focused on healthcare, specifically excited about AI generally. But the healthcare knowledge layer has been frozen in PDFs or it's been frozen in a fee schedule somewhere or some policy document that's published every month, and it's been frozen in these documents. And so whether they're contracts, policies, prior auth criteria, fee schedules, whatever, we haven't been able to tap into that knowledge base. And so when you really think about it, there is no system of truth in healthcare. And when you're trying to drive return on investment, one of the things that you're gonna have to eventually answer is, hey, how are we really going to 10x our output here and not just, let's not go spend a bunch on AI and then really come out and say we just had a 1.5x improvement. But really, how do we think about ROI more strategically and more intentionally? I like that you use that word. And one of the areas that we're really getting pulled into is driving ROI from a net patient revenue perspective, really tapping into this knowledge layer that's been frozen and using that to really create a new category for us where we're helping hospitals recover more of that revenue by tapping into all of this unstructured data.
- [06:32]
A
Thank you. And take a moment. Don't. When you see health systems in companies that are adapting well right now versus those that are struggling, whether with or without AI, is it based on that intentionality? Is it based on their execution in teams or their vision? You mentioned the 10x concept, and I love this theory of thinking of things that can move 2x versus 10x. And there's obviously a huge difference. Where are companies doing this well and where are they not doing it well?
- [07:04]
B
Yeah, yeah, it's a great question. And I think, you know, we're. We're in the second inning here with AI, so, you know, time will tell. But one of the things that we're seeing here is that, you know, if you're looking at rebuilding workflows around AI, really thinking about how do we, you know, we've been doing an activity, let's say revenue reconciliation, for instance. Typically a lot of the revrec tools that have come out and have been around, hey, we've been helping this person in finance achieve this outcome. And we're trying to do, we're trying to build this product that solves this job to be done. And a lot of the tools that we saw in the last 10 years, and this kind of plays into the ethos of how a lot of companies are also building AI, is, hey, how do we go take those things and just add and bolt on AI to it? And I think that doesn't work because ultimately it just becomes a feature that's never used. Your pricing structure changes, goes up, and then I think eventually comes out as churn. But in healthcare specifically, I think a lot of that defensibility and a lot of that value really comes from rebuilding those workflows. And I think we have to start asking ourselves, what is the outcome that we inevitably want? And we have to start from the outcome and then work backwards. And so if we want to go increase that patient revenue. We should go look at, hey, what are all the elements that haven't been addressed? Are we actually making sure we're getting paid what we're contractually owed? Are we actually looking at fee schedules? Are we negotiating these things? Right. And are we getting into a space where the relationship between providers and payers are acrimonious and continue to get more and more acrimonious, or are we actually building something that's creating a better outcome for the entire system? Are we creating something that's a better outcome for the organization, not just optimizing around an individual? I think when you start to think in outcomes where you get to solve the real problems and not just bolt on these AI tools. But I think. Just a couple thoughts there, but I'd love to get your thoughts on this too. What have you seen out there that's a great example versus just a mediocre example of value?
- [09:37]
A
No, I think that's a great question. I mean, what we see so far is very much goes back to some of the thoughts that you had and some of them that I love from an author I'm spacing out on, which is this 2x versus 10x concept, you know, where people are applying AI to big ticket areas, to revenue cycle, to ambient listening to predictive analytics. You know, they're not playing small ball, they're playing big ball and going after big things with intentionality. They're making a big, big impact. And I think a lot of the drivers becomes so much of this is very easy for so much of us to use AI for a lot of small tasks, for a lot of small tasks. I mean, I find myself, you know, daily using ChatGPT for a dozen things. But that's a huge difference than using it at enterprise and figuring how to use it as enterprise solutions. So when I see Cedar Sinai brilliant system training 6,000 other people in AI use cases and how to sort of like find more ways to use AI, I think that's fantastic. And then coupling that with constantly looking at intentionally what are these sort of bigger areas where we could have that big impact, that makes a big difference. I'm an advisor to a company called Clearing Them in the Supply chain area. Fantastic company, founded by Steve Liu. And I find what they're doing fasting because it's a big enough target area that it's worth investing in and really trying to find enterprise impacts on it. And I think that's a lot of it. You know, you're looking at, you know, things where there's enough juice for the squeeze, enough cost benefit that you're really focused on. And those things are harder than the little things that a lot of us do on a daily basis now. So I think it's that, that perspective, a lot of it's to cost benefit where the big opportunities and really. And it takes real focus and real teamwork and brain power to really attack those enterprise solutions.
- [11:42]
B
Yeah, couldn't agree more. Yeah, exactly. I think you made a really good distinction there. The 2x is automating workflows around things that we already have. Whether it's scribing and intake, just assistance and just better dashboards. I think that definitely moves the needle and that's necessary and we should always make those improvements. But that 10x unlock really comes from doing things that no human team could actually do. No rules engine that was structurally designed one time 10 years ago could ever do at scale. When you start to look at that dividing line, it's unstructured reasoning. How do you actually take all of this required reading and interpretation and then do this at scale? Something that if we had unlimited resources, we could actually do. If you were to go look at what a CFO does when you start to kind of look at some of these tasks, just kind of bringing it back to intelligent a little bit. A lot of AI and RCM today is 2x and that's good. It's faster denial letters, smarter dashboards. We decided to choose this really hard problem in this 10x lane, kind of on purpose, which is how do you reason over the contract itself? How do you enforce it against all the payments that were made? Looking at every single denial and then looking at the contractual data and the policy data to make sure these systems are getting paid what they're owed is the difference between going out of business or having a healthy margin and having a successful system here. Bring it back to roi. This shows up in tens of millions, not just single digit productivity gains.
- [13:30]
A
No, and I love that. I think that's exactly right. Monique, let me ask you this question. You know, when you think about balancing innovation versus trying to execute on things, how do you, how do you look at that with companies, with systems, with customers? This balance between constantly looking at new ideas, then put into new things versus actually having to make it work and being able to execute on it, how do you balance those things? Innovation versus execution.
- [13:57]
B
Yeah, Scott, you pointed this out. With my career, I've been a product guy for over 10 years. Being in product management, I am team innovation here. But I actually started My career in consulting, one of the first things I got exposed to in the enterprise is operational complexity and having an appreciation for these organizations and these processes being difficult. And so I got to have more of an appreciation around the structures that are in place that sometimes seem like the walls that we need to climb to get some innovation in.
- [14:44]
A
But
- [14:46]
B
it's there for a reason. And when we look at healthcare as an industry specifically, it's, it's kind of earned its skepticism in many ways, as you can attest to. So the bar for trust is high and it should be. And so when you look at operational complexity, it's there for a reason. And I think when we have a lot of these point solutions, I'm more optimistic about point solutions than these large platforms because I think they're going to be able to do things a lot faster and add value a lot faster. So when you start to look at innovation, it's really looking at individual problems first and then building and then painting that bigger picture. But yeah, I think the complexity, I don't think it's a bug. I think that's the feature, that's the moat. If it were easy, these large enterprises would have solved it 10 years ago. I like the complexity because I think it helps us gain an appreciation for how that business is run.
- [15:50]
A
No, I love that. I think that makes a lot of sense. Talk about this constant conflict between point solutions done really, really well versus customers wanting to not work with so many different vendors and so many different point solutions. How do you sort of think about that squaring that the point solutions, which are often incredibly well done versus customers not wanting to work with a thousand different point solutions. How do you think about that in the evolution of your company or other companies?
- [16:20]
B
Yeah, I'll be lying if I didn't say I would love to be the size of Salesforce or I'd be a Salesforce. I think everybody wants to be a platform and everybody wants to be this giant system that controls all of this data, all the workflows, have all the users on the system and really solve all of the institutional problems that exist. But the truth is there are challenges there. And I think whether it's a startup or a mid sized company or an enterprise company building an AI, we have to earn the right to really be a platform. And so when organizations are evaluating, when CFOs are looking at all the tools that exist and doing a rationalization around, why should we get this tool versus something that looks very similar, we have to ask ourselves, finally, what is the outcome that we're trying to go towards. And is this the company that's going to help us with that? Oftentimes what I see with large ecosystem players and these large platforms is the unwillingness to actually customize. And when you're dealing with specific industries like in logistics, supply chain and healthcare, and you don't customize that, that's a red flag because these organizations have been built on a lot of customization. These organizations are special in their own regard. They have their own special sauce, they have their own ethos and culture of how they do things. And we have to be able to configure and customize. I do think that we have to solve problems at the end of the day and we have to solve bigger problems and earn the right to solve bigger problems for customers. And I think speed matters, being able to be nimble matters. And so I think when leaders are making these calls and procurement is looking at a tool, we should really be thinking about, hey, are these guys in it for the long term? Are they going to work with us and the boundaries that we work in? And do they have the aspirations we have and grow with us?
- [18:35]
A
Monish, thank you very much. Talk about as you move into the second half of 2026, what are you most focused and excited about?
- [18:45]
B
You know, when we started IntelliSense, I'll tell you this, one of the things that definitely inspired me is definitely my time at DocuSign, I was, you know, we were building contract management tools and all of that. And I started to look at how do we really use the deep dark data of these agreements and contracts to really drive process workflow. And then we shifted into healthcare, solved a different problem. But along that journey, one of the things that we really found is our mission and just something that our team is gung ho about every single morning. Just last night, our team, a few people on our team were up till midnight debating fee for service schedules and whatnot. And I think this morning they Got up at 6am Just continuing this conversation. And I think everybody in the company was just genuinely excited to go solve this problem. For hospitals, we found our mission in being able to actually go help drive net patient revenue up. And so we want to help the good guys win. But we don't want to do this in an acrimonious way. We want to really. The category that we're creating is this contract verified revenue. So if you look at one of the metrics that really matters to health systems is clean claims rate, we think contract verified revenue is going to be the new clean claims rate and that's the category that we're building and our way to get to that is creating this enforceability layer of what are you owed, what are you paid? How do we make sure it's right? How do we make sure that you're growing with the network, you're growing in the geographies that you need to and you're being paid fairly and really creating that enforceability layer that doesn't exist today. We're not just trying to 2x rcm. We're not just trying to look at managed care and say hey here's a better contract, here's some insights. We're really creating that layer that's going to create a better healthcare just industry for everybody. I'm excited for that because I think it'll help drive the patient outcomes. It's going to help drive just building a great business and making sure we have, you know, more coming in top line than just from being able to see more patients.
- [21:07]
A
Mohnish, just fantastic to visit with you. An amazing leader again. Mohnish Mohanty CEO Founder Intellisent thank you so much for joining us on the Beckers Healthcare podcast. Thank you very much.
- [21:20]
B
Thank you Scott, appreciate your time here.