There is so much outdated provider information out there.
In this episode of Bite the Orange, we are joyful to have Kit Kieling on the show. He is the founder and president of Orderly Health, a startup company that uses AI and machine learning to automate and improve provider data workflows across the healthcare ecosystem. Orderly started as a patient navigation tool, but they made a wise decision that would pivot the company and bring them to where they are today. Provider directory data across healthcare is widely inaccurate and so Orderly created software that allows providers and payers to input data in various formats and organizes it within its system. Kit talks about the importance and the value of accurate provider information within healthcare and also shares the journey he’s had with Orderly from the very beginning.
Tune in and listen to what Kit Kieling has to say about provider directory data in the healthcare industry and what his startup is doing about it!
FULL EPISODE
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Emmanuel Fombu:
Welcome to Bite the Orange. Through our conversations, we create a roadmap for the future of health with the most impactful leaders in the space. This is your host, Dr. Manny Fombu. Let's make the future of healthcare a reality together.
Emmanuel Fombu:
Good morning, good afternoon, good evening, everyone, and thank you for taking the time to join Doctor Kit Keeling, founder, and president of Orderly Health, joining us today on Bite the Orange. So welcome, Dr. Kieling.
Kit Kieling:
Thanks, Manny. It's a pleasure to be here.
Emmanuel Fombu:
Thank you, and thanks for joining us. So let's start off by, tell us something about yourself.
Kit Kieling:
Well, I'm a Leo, I like long walks in the rain. No, I, yeah, so I am, as you mentioned, I'm currently the president and co-founder of Orderly Health, but my background has been a bit of a circuitous one. I started my career in clinical medicine. I spent about 15 years on active duty in the military. Most of that time was at Walter Reed Hospital in Bethesda, Maryland. While I was on active duty, I also deployed to both Afghanistan and Iraq, where I ran all of the pediatric care for most national children. So everything from the time they showed up in the emergency room to, we mostly did very acute cases, so their hospitalization, their ICU time in the ICU. And I found that to be an incredibly rewarding experience, but also a fairly taxing one. And when I got back from my last deployment to Afghanistan, I decided to move away from clinical medicine and stay involved in healthcare, and I just wanted to move in sort of the innovation side and the business side. I spent a few years consulting with McKinsey and Company doing strategy work for larger healthcare systems and payers, some pharmaceutical work as well. And then I really wanted to dive into the startup world, so I joined Orderly Health about six years ago as employee number three or four, and have been with them, and we can talk a little bit about our pivot, but through this big pivot that we had and ultimately to where we do what we're doing right now, which is really trying to enhance how we deal with data on providers within the healthcare ecosystem. So provider directory data, the interfaces between payers and providers as it relates to that provider directory data, that's really what we're focused on now.
Emmanuel Fombu:
And that is very interesting. And I think, let's start off by, what exactly was the problem that you're trying to solve? And I, when I say Orderly, I think it is because of the military background piece, that looks like you're going to join us later down the road. So what was the big point and what was that, what is your why? What made you want to join this organization or what makes you drive?
Kit Kieling:
Yeah, that's a great, it's a great question. And what I'll tell you is originally when we founded Orderly, we had a different product. We actually had created a chatbot that was a healthcare navigator designed to help a patient work their way through the complex ecosystem that is healthcare. So essentially we had about 30 different partnerships with a number of digital health companies such that you could text our system and say, I think I have a fever. We would understand that using NLP say, Oh, this is a clinical question. We would route you to the API of our partner that did like a triage workflow and you would still interface with our system, but you would be on the back end working through one of our partners. Similarly, if you were trying to find information on healthcare insurance, we had a partner that could help you work through whether you should sign up for the ACA and what the marketplace looked like. And Manny, one of the first things that we saw was one of the most common questions being asked of our system was, I need to find a new doctor. I just moved to a new city. I just had a baby. I just blew out my knee, whatever the case might be, and we were getting pretty consistent feedback from our customers that, hey, this, this data is not correct. This doctor is actually not at this location or this doctor's actually not taking my insurance plan. And so we kind of peel that onion back, and what we found shocked, it shocked me. And what we found was that across the healthcare ecosystem provider directory data at its most basic form, I'm talking just what's a doctor's address, what's your phone number? What's your specialty? That data on average is about 55% accurate. And that obviously represented a bad customer experience for our initial customers if we're giving them bad information. But it also represented an opportunity that we thought we could tackle using machine learning, using large data sets, combining multiple sets of disparate data sources that exist, bringing them into one spot, massaging them with simple computer logic heuristics to create a more accurate set of provider directory data. And in that process we found, we signed our first big client to help improve the accuracy of their provider directory, and we uncovered a whole nother set of problems, which is when you're a large health insurer and you're working with 20 to 100 to 1000 different provider groups, they're sending in their provider rosters in myriad formats. They're coming in via Excel spreadsheets, CVS, PDFs, carrier pigeons with sticky notes, right? I mean, a little flippant, but I mean, they're coming in all of these crazy sources and they don't work with the system that the insurer has set up. So we created software that simply sits as a translation layer between payers and providers, such that with a drag and drop onto our website, they can drag their rosters in, we ingest that data, we normalize it, standardize it and turn it into the format that's required by the payer to work with their system. So basically, like I said, we have those two products. One is this translation layer that just increases the efficiency of how that workflow exists, as well as it helps us to further enhance the accuracy of our other product, which is that provider directory, which we can then leverage to sell to other folks who just need to have accurate provider directory. If you're a digital health company trying to potentially market to physicians, you need to know where they are, and so that accurate provider directory also enhances that ability to be able to just know where these doctors are.
Emmanuel Fombu:
That is quite fascinating. And I looked at your website earlier and I noticed there are two main solutions, and I think you describe both of them. The first one is the roster automation suite, right? And then you have and you have the Orderly provider directory, which is like the two main products that you offer. So first of all, data is scattered in different formats and things. So how do you get your data sets? Do you like aggregate different data sources? And that's one piece. The second part of my question is, I also noticed that I know if I look at CMS data sets or even commercial payer data sets, a lot of them are refreshed annually, but it looks like you're able to do this in real-time, right? So I'm curious about that, how you go about doing that?
Kit Kieling:
Right, and let me just start with the last question first. Yes, I mean, CMS, it's been a while. They're finally starting to realize, hey, this is a pretty big problem, so we've seen some regulatory pushes. For example, the No Surprises act, right, where, in certain groups are starting to be fined for having inaccurate information that leads patients to these out-of-network bills. So we're starting to see some of the big wheels turning to say, hey, this is actually a really big problem. But in answer to your first question, where do we get our data from? We get it from a lot of different places. When we first started, we actually had to buy some of our data. So we were looking at claims clearinghouses. Claims tend to have the most accurate data out there because physicians and groups are incentivized to get their bills paid and they have to have the claims address correct in order to make that happen. So we started with some, with purchasing some claims data. There's a lot of publicly available data out there. You know, there's the NPPE, which is the national provider and payer enumeration system out there that handles all of your NPI numbers, NPI type one and type two. There's a lot of publicly available information out there for free, and we do some harvest some of that data from the web. In addition, we have a small attestation group within our company that actually, and this is how it's been done for decades and we're trying to slowly wean ourselves off of it, is via manual attestation. The only way to know for sure at a given moment is to pick up the phone and say, is Dr. Smith, is she here? Does she practice here on Tuesdays and Thursdays? So we do a little bit of that and honestly, Manny, that latter part, the reason that we're able to scale is we use that data, that attested data to train our machine learning algorithms and to train the computer logic. So we know this is correct, let's build on that. So it's a conglomeration of all these different sources layering in some computer logic and some heuristics as well as some machine learning, and it just sort of is a cycle, that kind of flywheel that just picks up speed.
Emmanuel Fombu:
Yeah, that's quite fascinating. And it comes back to the point of machine learning and AI that used to need human input to verify the data sets because garbage in, garbage out. So you want to make sure that the input, the data sets that you're coming in as accurate as possible. So I like that whole attestation process that you have in place. So right now, who are your ideal customers?
Kit Kieling:
Well, it's, this problem is felt throughout the healthcare ecosystem. And, you know, I don't want to sound grandiose, but we are selling to all aspects. We're selling to payers, we're selling to providers, and we're selling to digital health companies that need accurate provider information. On the payer side, the biggest selling point is this, just this increased efficiency in dealing with these rosters and their, the ability to then when they publish on their website, their list of providers to be confident that that information is correct. So payers represents probably right now our biggest market share, but what's fascinating is that provider groups also have this problem and you've probably heard of, for example, network leakage. So if a provider group sees a primary care visit and wants to refer to an endocrinologist, for example, they really want to make sure they keep that patient in their network. And so we are also able to sell to providers saying, because those networks are constantly changing, they're always being there's constant negotiations, people are coming in and out of network. And so, you know, we're working on a point-of-care solution at the EHR. So at the bedside in the medical record, when you're saying refer to endocrinology, the list, it gets populated for those referrals is the most accurate and leads you to being able to keep those folks within your own network. And then finally, the digital health piece, there's a lot of entrepreneurs, a lot of innovation around there where they just need accurate information on how to contact physicians, how to market to physicians. So essentially that's, those are our three primary verticals for our sales.
Emmanuel Fombu:
That is very interesting. I know you mentioned the keeping up with the network kind of solutions, which is great because there's nothing is worse than referring someone out, and then once you leave they don't come back, right? And so being able to keep track of that, do you also measure the clinician performance?
Kit Kieling:
So we do, that is not, that is on our roadmap, and we've already started to bake in both cost and quality data. And one of the ways, it's also interesting too, Manny, like we just signed a contract with a payer in the Pacific Northwest and they're mandated by the state of Washington to have a list on their website of those providers that provide gender-affirming care. So we're able to take their claims data, scrub it for ICD ten codes that match gender-affirming care diagnoses and procedures, and then populate a list for them to say, Hey, based on claims data, these are the physicians and the providers who are doing the most gender-affirming care in your network and in your community. So there's a lot of use cases for where we can go. We do, like I said, we do have some cost and quality data, we're expanding that. We also want to make it more our bread and butter to get off the ground was just, you know, what's their fax number? Did you know that 70% of healthcare groups are still using fax machines, right?
Emmanuel Fombu:
So incredible.
Kit Kieling:
Yeah, we have to walk before we can run. But we do have a lot of plans to include even social media type of like, you know, when you ask to go to a restaurant, you ask your friends, Hey, where do you go? Wouldn't it be cool to have a social media component of this that says, I just got some, I just got done seeing Doctor Smith for a knee replacement and she was awesome, and I give her five stars. And so now you've got some social capital there to build on with respect to helping, ultimately, what we want to do is help patients find the best provider of care for them. So that is where we're headed.
Emmanuel Fombu:
Gotcha, do you also have patient side data set? So, for example, if I was a digital health startup, I know you also have clients of yours. And so if I show up, for example, and I have an app, let's say a solution for patients with diabetes or a mental health app, and I use the data sets, identify psychiatry's or identify endocrinologist, can you then tell me how many patients that doctor has? I know, you are, or are you more focused specifically on clinician data?
Kit Kieling:
Well, so we do have some, we are building up our capabilities to talk to exactly that, the volume of care, for example, not every orthopedic surgeon does that, the same amount of total knees every year, right? So if I want to have my knee done, I'd like to have somebody who's doing it pretty frequently. So we are building up that, right now, we're not handling patient information and we made a conscious decision to not, at this early stage dive into handling HIPAA information and patient information. I think as we grow and expand our infrastructure, we will likely start to include some of that, but right now we don't handle patient-specific information.
Emmanuel Fombu:
Perfect, I mean, we have a specific niche that, you are tackling a massive problem, anyway, it's in front of us. All right, you can't solve everything at once. So what has been the biggest challenge so far that you've faced?
Kit Kieling:
You mean outside of COVID, outside of the macroeconomic downturn that's happened in the last six months, outside of all of those things?
Emmanuel Fombu:
Well, maybe, maybe before that and then during that and how it does look like after that.
Kit Kieling:
Yeah, well, I'll tell you, you know, a lot of successful startups go through a pivot, but they don't often talk about how hard that pivot was. Our original product, we had revenue on that product. We had we had traction. We thought that there was going to be, the writing was on the wall, that there were some other big players entering the patient navigation space. We'd learn that Alexa was building a skill to help with that, and so we decided it wasn't the best thing for our company to continue to do that. But making a decision to walk away from a product that you've already built, you've sunk two, three years into, and is making revenue, and to completely pivot away from that, that was probably one of the hardest things that we've had to do. I think it was the right decision, but we had we basically had to start over. We had to start from scratch, you know, and build this new thing and go out to investors and say, Hey, remember that thing we told you about before? We're doing something different now. I think that not a lot of people talk about how hard pivots can really be.
Emmanuel Fombu:
Correct, so if you, looking, and that happens along with every startup, right? Sometimes you start off and the original idea you had was one thing, but it's good. What I like about what you did was using data and insights that you gathered to actually pivot your business, but looking at the same clientele, but solving a problem that they had from inside ..., which I think is, so what is the makeup of your team? Are you guys all clinicians or what is the makeup?
Kit Kieling:
No, I'm the only clinician on the team. We have, it's, we are a very tech-heavy team right now. So we have 22 employees, of which 16 are technical in some sort, whether it's product, data scientists, engineers. I mean, 10 of our 21 employees are software engineers, I mean, we're building a SAS tech product, so that's to be the case. And then we have a few folks on the go-to-market, sales, marketing strategy side, but we're looking to build that up because as you're probably aware of the sales cycle in healthcare is very long. It takes a long time to sell into a massive payer. So we're trying to beef up our go-to market team and then yeah, so that's sort of the makeup of our team right now.
Emmanuel Fombu:
Are you guys coming in and raising capital? Are you guys set? What are the next immediate steps for you guys in the next 30, 60, 90 days?
Kit Kieling:
Yeah, we will likely be hitting the, a true series A fundraise, sometime in the next few months. You know, again, we're just trying to get all of our ducks in a row. You know, you only get one chance to take a swing at the series A and want to make sure that you have everything lined up to really set yourself up for the best chance of success with that. But yeah we're looking to start fundraising here probably in Q4 or early Q1 of next year.
Emmanuel Fombu:
So with that being said, I know the wrap-up piece because I know people have very short attention spans these days. Come back for more follow-ups on this. But the question I have here now would be around what is that? I noticed when am I on the website that you have an open API or something, I just signed up for a demo that requested, but then you also had links to different APIs that people could go in and then try for themselves. Tell me about that piece and how it works. If I was trying to engage, if I was a startup or I'm a provider, or a payer listening and I'm trying to engage broadly, like what? How does that work?
Kit Kieling:
Yeah, so the best thing to do is just to go to our website, which is OrderlyHealth.com. We, on that, on the website we describe both of the products that we've talked about here, the Roster Automation Suite, as well as the Orderly provider directory. And there are links in there to both play around with, check out the things that we do. But for really, to really understand the product, the best thing to do is to, set up a demo and there's a link there that you can just click on and we'll be quickly in touch and we'd love to show you in real-time kind of what our product does, what it looks like for you as a payer. We have a sandbox you can play in and start to get a sense of what the moving parts look like and what the data sets look like, etc.
Emmanuel Fombu:
Which I did, prior to our conversation. I did sign up for a demo and I requested a demo and I'm registered, going through the whole process. And like I said, anyone, the website is pretty straightforward. You just fill out your information, name, email, address, basic contact information and request a demo. So I would like to follow up with you, Kit, as soon as I go through my demo, and hopefully, there are many opportunities for us to partner. And once again, thank you very much. Is there anything else you like to let our audience know about you and how to get in contact with you? What are the ideal customers are you looking for right now? And who would you like to, are you looking for payers to reach out right now? Do you like more startups to reach out to you? What are you looking for?
Kit Kieling:
We're sort of agnostic on that front, Manny. Like we just, our solution solves a real problem. And the folks who experience this problem that we've talked to, they say, stop talking. We understand exactly what you do and we need your help. So anybody who's struggling with managing rosters from the payer or the provider side or who just needs more accurate provider directory, please do reach out. You can reach out to me directly at kit@orderlyhealth.com. That's K I T @orderlyhealth.com. And the one last thing I want to say, Manny, is I really do appreciate the opportunity to be on this podcast. I love what you're doing I think Bite the Orange is such a great analogy for trying to innovate in healthcare, and it takes visionaries like yourself who spent a decade in higher education learning how to do bypass surgeries and saying, you know what, there's a more effective way to have a larger impact, and I just want to congratulate you on taking that step.
Emmanuel Fombu:
Thanks, Kit, and one of the parts of this is that we plan to actually have this hashtag Bite the Orange, where everyone bites the orange. Actually, take a picture of it with the new hashtag, put it on, right?
Kit Kieling:
Let me know, I'd be happy to do that.
Emmanuel Fombu:
So you just take a picture biting an orange piece and when we release the episodes, we'll all bite the orange, and not everyone gets to bite the orange, of course, because, but I believe you're going to bite the orange. And I'll reach out offline and I'll get a copy of the T-shirt and I get you a Bite the Orange t-shirt. That's what, we're going to exchange that.
Kit Kieling:
That sounds great, Manny. I really do appreciate the time. I really enjoyed this.
Emmanuel Fombu:
Thank you for listening to Bite the Orange. If you want to change healthcare with us, please contact us at info@emmanuelfombu.com, or you can visit us at EmmanuelFombu.com or BitetheOrange.com. If you like this episode and want more information about us, you can also visit us at EmmanuelFombu.com.
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About kit kieling:
Dr. Kieling is an accomplished healthcare executive with a strong clinical and academic medicine background. Prior to his current role as president and founder of Orderly Health, he spent several years as a consultant with McKinsey & Company. He got his Bachelor of Science in Psychology at the United States Air Force Academy in 1995. He got his MD at the Oregon Health Sciences University in 2001, and afterward, specialized in pediatrics.
Things You’ll Learn:
Across the healthcare ecosystem, provider directory data is about 55% accurate.
Orderly created software that allows providers and payers to input data in various formats and organizes it within its system, offering not only the roster automation suite but also a robust and accurate provider directory.
Claims tend to have the most accurate data.
Provider directory data inaccuracy not only affects patients trying to find a new doctor, but it also affects payers, provider networks, healthcare entrepreneurs, and providers themselves.
70% of healthcare groups are still using fax machines.
In the startup world, sometimes you may start with an original idea but end up pivoting to a better one.
Resources:
Connect and follow Kit Kieling on LinkedIn.
Follow Orderly Health on LinkedIn.
Visit the Orderly Health Website.
Reach out to Kit directly at kit@orderlyhealth.com.