The Broker's Voice: Dr. John Duldner

All right. Welcome back to the Broker's Voice. I am excited to have Dr. John Daldner with Connecticare join me. John, welcome to Broker's Voice.

Morning. Thanks for having me.

You bet. So people probably just heard Dr. John Dalner, and there you are with your white lab coat. If you're watching the video, you are the founder of Connecticare, which we're going to get into. It's a really cool analytics platform for the health insurance industry. But you're also a practicing physician, so why don't you. Let's kick it off by you sharing your backstory.

Sure. Well, I've been practicing medicine now for 27 years. 27 years and one month.

You know, it's been a long career. Kind of started actually when I enlisted the Marine Corps out of high school. And that's actually where I decided to become a doctor. I've always had a kind of a non traditional approach to things. And in college, got into research, continued that into medical school, and took a lot of time off during medical school, during the clinical years to do research. Then during my residency and then on into my fellowship in stroke and cerebrovascular disease, had this crazy idea that I should probably get a degree in statistics because it would make me better understanding data and analytics and data representation and then outcomes. So for me, data and research and analytics has kind of been part of me for at least as long as I've been a documentary, also been a technophile, which we all grew up with the Internet and we think back about the Dewey Decimal System and now we're to the point where we can pretty much get information instantaneously. We can empower ourselves, and that technology is actually translated over what we can do in medicine as well. But as far as connecting care, I look back and say in retrospect, a lot of this that I had done for the last 27 years has kind of really positioned me perfectly to be able to work in connecting care. And that's what we've basically developed and using all those skills for that. Yeah.

And you have a pretty, you know, personal story as to what led you to build connect care in the first place. And that goes back to your personal experience taking care of your father during his time of illness. Why don't you share that story? And really what led you to ultimately realizing that there has to be a better way to do this?

Yeah. So, you know, having been a Marine and doing emergency medicine, trauma and critical care, to me got a very kind of a hard charger approach to medicine. But Then a number of years back, I had the unfortunate occurrence to actually diagnose my dad with cancer. He was having some pain on the right side of his chest and finally took him in, got a CAT scan. And I think that was probably one of the worst days of my life when I looked at the CT scan image and just saw these lesions in his liver, which are obviously never good. But I just remember that day. And you know that that day was probably the second worst day of my life. And the first worst day was actually the day he died.

But I remember about connecting care, his care, everywhere along the way, we had a very difficult time managing the information, communicating, getting the data, getting the numbers, getting all this into one compartmentalized approach, and actually having the physicians communicate with us. I remember to this day, it was October 12th, 7:38 in the morning. I was just leaving. The care was so bad so often that we actually, our family stayed with my dad 247 so that we could watch over him because there were 17 errors. And unfortunately, it wasn't actually the cancer that he died from. It was the complications related to it and medical errors that he just couldn't come back from. So. And that was really the genesis. So my father's diagnosis, that would really address medicine, its care, its delivery, all aspects of it in a very different way.

Yeah, and you bring up a really good point. You and I have talked about this at length offline. You know, I think a lot of people in our industry think, in the health insurance industry, think the problem is healthcare, right? They're out to fix the healthcare system. And you and I have also talked about doctors aren't bad people. But there's a problem when people think about connected care and the problem it solves. I think a lot of people would think it's. Well, your data analytics, you know, it's. It's better reporting what is that problem connected care is really solving at the end of the day.

So the, the biggest problem we solve is bad information, right? And bad information is where a lot of things are masked. And what is it about the bad information? So the information itself can be bad. Not having the information can be bad. Inaccurate information could be bad. Any information that's either unavailable, unacceptable, or unsatisfactory is where the problems of healthcare really lie. It's not so much in the system, which is part of the problem, but it's really about bad information. And the difference for connecting care is we take that bad information and translate it into information that's actually usable and can work on improving Health eliminating some diseases as well as better managing the individuals.

Yeah, I love what you guys are doing from that standpoint because I think again, I think the health insurance industry, we're often villainize, right. The doctors, the people who are making the calls on the consumer. And at the end of the day, it's typically the information they're making those decisions off of that is leading to the bad diagnosis, the bad decisions and so forth. You know, if you don't mind, you know, let now let's just go into Connected Care. You know, I think what I've seen so far from the program is it's. You've got a really unique differentiator, couple differentiators in it for sure.

Let's talk about what connected care is because I think again, our industry loves to commoditize things.

Sure.

And so they'll see what you're doing and saying, oh, it's data analytics, but that's just the beginning.

So. Yeah. So again, the reason we're different is it's just not analytics, it's just about numbers. It's overall. If you really think about data in healthcare, all that data relates around a single person and that person is needing care. So what we're doing already with the data and why it's bad information is we're separating the person from the mechanical aspect of the coding and the, the payments and those things when we're really losing focus. And so what Connecting Care does is it maintains that focus. And the way we do that and why we eliminate bad information in healthcare transactions is we put a doctor actually in the health plan so we have a full team of clinical people to support that. But our difference really is just not about the numbers, but correcting the bad information, replacing it with the right information and then being able to communicate, which is ultimately the biggest problem in not, not just the healthcare industry, but every industry is communication. So people have to know where I can get the right care, the right price, and it's gonna do me the right way.

Yeah, no, I think what you just said is spot on. Cause as a former advisor myself, you know, I can't tell you how many times you've met with a data analytics platform. Right. And every new platform tells you their data is better than everybody else that, that we, we had, we get access to, to certain elements of the data, nobody else does. And after a while you just kind of get sick and tired of hearing that. It's like, how in the world are you any better? And you and I have talked about this. Better doesn't win anymore. Different wins. And I think what makes you different is there's. I, I don't know of any other analytics platform out there that literally gives you a doctor reading your data and making the calls. Now, some people listening to this are going, okay, I need to know more about this doctor and the plan type thing. John, are you John Dalton? Dr. John Dalner, literally reading the data and working with the consumers in these health plans to help them make better decisions?

Yeah, right. Right. To every line of code. So there's always a doctor. It's either myself or one of my colleagues. Primarily me, because I'm the. You know, for me, data is like being a kid at Christmas. Anytime I get to look at it, you know, it's a great day. So, yeah, every single aspect of. From the data. Right. To how we deliver to the patient, the employee, however you want to label that person, that person who's receiving and needing care. We have a doctor that touches the data at every level.

That's awesome. Talk to me a little bit. Like, give me a couple examples, if you don't mind. Maybe some case studies where you've been able to, through reading data. Been able to either. I think you have a really good case study where it's been caught too late. You know, an example of data where a cancer or something was unfortunately caught too late. But also a case study where, because of your ability to read that data as a practicing physician, you've been able to help. You know, I think our industry, again, is focused on reducing the cost of care. So we're not avoiding the episode. We're just going to reduce the cost at which it is purchased. But you have a couple case studies where you're actually helping people avoid the big ticket items to begin with.

Yeah.

I don't think there's a single data set that we look at, you know, in a consultative way or for any of our clients, where we're not finding errors, omissions, you know, tons of things where we could say, listen, how was this Mr. Why wasn't this Mr. It should have gotten looked at, or, hey, we can provide a better service here. And the case was where it was a young lady in her 40s who hadn't really been to the gynecologist in a couple years because she had not needed it. Right. She'd gone through early menopause. In the interim, her gynecologist had retired, so she'd kind of been lost to follow up. And then a couple years later, she ended up having some bleeding and took her in and at that point she was ultimately diagnosed with surgery, stage three cervical cancer. And the problem with that is that cervical cancer is very easy to screen, but worse in this person's condition. She didn't understand that the simple fact that she'd had cellular atypia in the past, that she was always at higher risk, she had gotten it treated when she was younger and figured, well, I was treated and treatment doesn't always equal cure. And so stage three cancer, she'd gone through treatment, it was a very expensive treatment, a lot of interventions, definitely a high cost claimant. But again, the cost aside is that person's life was impacted in a very negative way. And if she'd have known better, if someone was looking at their claims data and recognized, hey, this 40 something year old female didn't get her screenings like she should have, it could have probably been caught either. Again, still in the atypical phase, maybe stage one, maybe stage two, but instead we're at stage three, which cerebral cancer in the third stage is a terrible disease and difficult to treat, but not that it's not treatable. But again, this could have all been avoided with an ounce of prevention. Would have been tons of cure.

Yeah, okay. Again, I think our industry, one thing I've always noticed, and I had to be careful myself, is as an advisor or an industry partner representing a product that might be considered a cost containment product. Right. To help to solve the healthcare cost issue, we toe the line sometimes of almost starting to play doctor a little bit. And we got to be careful with that. And that's what I think you do. What makes you guys so different than everything else is you are an actual physician helping make these calls versus a tech cruncher, numbers cruncher behind the data, trying to make these calls for people. A lot of these analytic programs out there are, are often started by tech people, right? Tech people that are going to solve a big problem we all see in the country, which is healthcare. You're coming at it from a completely different approach, which is the practicing physician who has seen the mistakes, has seen the bad information for the last 20, 25 years and now built a tech platform, an analytics platform to help solve that, which I think is just absolutely phenomenal. I want to ask you something I didn't intend to ask you, so I'm going to throw a curve ball at you for a second. You put a post out there recently.

Cicero's famous quote, trust but verify. And I think Ronald Reagan also used that one in the 80s when it comes to data like this that our advisors are using to put in front of their clients to help them make more informed, strategic decisions about their health insurance. What's your philosophy behind trust Verify? What should they be careful of?

I mean, the laundry list is massive. So the things to be careful with is, again, how is the data presented? And so when you're looking at your analytics for your clients or for anyone that's consuming or using health care data to make a decision, you got to understand that data itself is useless until it becomes information. So the first thing you got to understand is who's actually looking at the data. And like you said, we are actually physicians looking at every aspect of the data, but who's actually combing through the data. Not just a report, but report is what's happened. You know, it's in. You know, we all know what happened. Who won last year's World Series or the. I'm a big soccer fan. So you know, who won the women's Euros? Those. Those things are already in the past in the rearview mirror. We like to take what we saw, we drove by it and continue to look forward. So we have. It's called a proprietary data grinder. And so the question you gotta ask is, who is grinding your data? The second thing is, how are they actually looking at the data? Are they just giving you reports, just spreadsheets? Well, at connecting care, again, with our data grinder, we grind up data and transmit it and translate it to information. The third thing is what's actually happening with the data. And this is where there's a huge disconnect, is that just because you have that information.

If you're static about it, then all that's going to happen is that data is going to continue to accumulate. There's going to be no change in that. So if you expect a different outcome.

You'Ll be sad because what you were expecting to happen just didn't happen by a natural occurrence. So someone actually has to act on the data. And that's the other part of connecting care. So we've got our data grinder, but we have our platform with apps that we can deal with patients directly however they consume their information and help guide them to the care that they need, knowing what their history is, what their claims history, everything about them. So we're actually an ally for them. It's almost like having a doctor in the family. And the health plan actually gets a doctor in the plan. And because we do that, we can offer care at a better cost, but a better quality. Right. And we all know that if you Improve the quality, cost savings will follow.

Great point. I think again, there's often an emphasis on the cost and not enough emphasis on the quality and little did. By the way, if you're watching this, listening in, most people don't know Mr. John Daltner is also a former college soccer player. And so for the soccer players listening in, Spencer Smith, I'm calling you out, make sure you connect with John here. No, you're absolutely right, John. I can remember being an advisor ten plus years ago, working with our self funded clients and that problem of bad information. You know, if you're an advisor and you're getting reports from your TPA or your stop loss or whoever's, whatever analytic platform is providing you with this information, if it's bad information. All I did 10 years ago as an advisor was relay bad information to my clients and I look back and I go, was I really helping them make good strategic decisions? If I was just regurgitating the information I was given, which again, if it's bad information, I'm just regurgitating bad information.

Yeah, it's a lot like when you're running track, right, and you're passing that baton on, no one ever actually looks at the baton, right? Because when the person who's next to run that part of the relay, they're actually not looking back, they're looking forward and they're just taking what they have in their hand and running with it. And so in those situations.

We'Ve all heard the old adage, garbage in, garbage out, but garbage is just going to keep going on that track. And at some point you have to look at the data differently. And again, the point about your trust but verify is you can't trust the data unless it's actually been verified in a really rigorous way. Not the simple fact that yes, one plus one equals two, but was this code appropriate? Was this code not appropriate? You know, we've done a lot of consultations for analytics just to say, hey, let's verify that data set that you have, that it's one actually accurate. Two, there's not missing data or there isn't coding abnormalities, which we find a lot with whether or not it's going to procedure codes put in the revenue code slot. There's a ton of data inaccuracies and if you're not really looking at those on a line by line or a column by column basis, you can't verify that what you're actually passing that baton on to your brokers, your TPAs and ultimately the health plan, and then ultimately More importantly, the individual member or a patient, you don't know what you're passing on. And so you really have to have someone in an external fashion look at the data and verify. Yeah, one plus one still is two. And we're not all of a sudden getting a 2.5.

So if I'm an advisor right now, we talked about the Trust but Verify. What part, what date, what should I be looking for? So you know, I'm an advisor, I get analytics from you name the company that provides it. These days I have self funded clients. Is there anything specific I should be looking for when I'm getting my data or where it's coming from? What advice would you give John?

Well, I mean the first advice is, you know, you have to ask yourself, do you have the skill set to actually look at the data in the way it should be looked at? So if you don't, if you don't have an analytics background, but you're pretty savvy, then you're probably pretty comfortable looking at numbers and passing the information on. But what you really have to look for are inaccuracies. Things like when they put up benchmarks. Do those benchmarks actually reflect what's truly in population? Health literature is what they're talking about actually evidence based on. We base everything we do on up to date literature. We use medical references, we use our medical expertise, we use the expertise of our colleagues. So when we put all that and we're looking at information.

We make sure that those preliminary considerations are there. But it's some of the stuff that some of the analytics platforms will put out there. If they give you a list of 30 or 40 numbers, what do all those mean and are they actually relevant? So our data reporting is different because we actually don't give you a laundry list of information. We give you the information that's actually pertinent, relevant one to the plan, more importantly to the individual consuming those care needs. So when you want to just glance over those, you know, benchmarks are a great way to start. And we just did a report and you know, there's, they've indicated that the obesity rate was 5 out of a thousand, which if you think about that number, you're saying that there's only five obese people in the United States out of every thousand people you looked at. So if you go to a football game, any event, and you look around, you say, wow, there's only five people that meet the obesity category. We know that's wrong because even the CDC reports obesity rates as high as 40% this year. So when you start seeing that those numbers are just put into place, you really have to be suspicious that every other number in that data set was probably just thrown in there algorithmically by a program and it wasn't looked at by a doctor, by a nurse or any other person who actually cares about what that number looks like versus just generating that report.

Yeah, I mean, so I just had a thought and I want you to verify it for me. If I'm an advisor and I have connected care built into one of my clients self funded plans, do I have the opportunity to actually have you, Dr. John Dahlner, alongside with me even if it's on the phone going through that data with my client?

Absolutely. So we're full service, we're not something you put on the shelf. And hey, you can go back to. We are integral and always active. And so with our broker relationships now and our advisor relationships now, we meet as often as they want or we request meetings when we find something that hey, we need to make an intervention and let everybody know about. Again it's about the information and communication. So we're always there to help. Doesn't matter if, if it's showing up on site and saying listen, I need this support or hey, I got a question about either if it's a Covid thing or a disease issue, your HR person, the broker staff, anybody can either call us, text us, email us, we're always at the other end because again it's just like the doctor should be, should be available if you have a question, particularly if it's about your healthcare.

I love that. I want everybody to hear what he just said. Imagine you have a self funded client, it's time to go through their data and you literally have a doctor on your team helping you and your client go through that data. That's a differentiator. So John, if we have piqued the interest of an advisor today and they're like we need to get a hold of this guy right now. What are the best ways to get a hold of you?

So you know a great way is LinkedIn. You just look at John E. Doldner or look at the data doc, that's me. You can look at the website connect and care.com they can email me at john connect care.com or they can just call me.

I have your email right here below my box. So yes, if you guys want to get ahold of John, there's his email right there. Go check him out on LinkedIn. Like he said, the data guys, this is a differentiator. You know as Q4 rolls around you're going to be having some conversations with your existing self funded clients. Maybe you're trying to push a fully insured client to self funding. Why not build a health plan that has a doctor baked inside. I don't know about anything better than that. So John, I want to thank you. This was awesome. Great information.

Thanks Andy.

And for everybody else listening in please do yourself a favor, get a hold of John today. What they're doing to help clarify create better data analytics by being the data doc and reading the data for you is something I haven't seen very much in this industry. So John that was awesome. Everybody else, you know what happens. You mix clarity with confidence. You go do massive things. Do yourself one massive favor this fourth, fourth quarter and get a hold of John. All right Take care guys.

Thanks Andy. Have a good day.