Optimize Your Medical Device FDA Submissions with Imaging Analytics


  • Share on LinkedIn

Total review times for 510(k) submissions have increased by more than 55 percent since 2005 because of poor quality submissions by medical device manufacturers, according to a July 2011 FDA report. Tim Kulbago, President & CEO of ImageIQ, tells us how to increase funding and improve regulatory approvals with extremely powerful imaging data.

Interview Highlights with Tim Kulbago

  • How ImageIQ turns subjective opinion into hard data and visual results.
  • Examples of how imaging and analytics have made the difference in medical device approvals by the FDA.
  • Can image analytics really help to reduce the length and cost of pre-market approvals and enhance post-market surveillance?
  • Improving physician-adoption through enhanced image analytics.
  • Tim’s advice for other medical device doers.
  • And much more!

This Is What You Can Do Next

1) You can listen to the interview with Tim Kulbago right now:

2) You can also download the mp3 file of the interview by clicking here.

3) Don’t forget – you can listen to this interview and all of the other Medsider interviews via iTunes.  And if you get a chance, leave us an honest rating and review on iTunes. It really helps out.

4) Read the following transcripts from my interview with Tim Kulbago.  Also, feel free to download the transcripts by clicking here.

Who is Tim Kulbago?

Tim Kulbago is the President and CEO of Image IQ, a Cleveland Clinic Innovations company that specializes in utilizing software engineering, high-resolution 2D/3D/4D imaging, and custom-tailored image analysis and visualization to support a vast array of biomedical preclinical and clinical specialties. Tim has over 14 years of experience in the medical imaging industry. Most recently he was responsible for two businesses within Merge Healthcare (NASDAQ: MRGE). Tim was formerly the Chief Technology Officer of RIS Logic, a Solon Ohio based start-up company which created an industry-leading information system for radiology practices. Before joining the medical industry, Tim spent several years as a Research Assistant at the Applied Research Laboratory at The Pennsylvania State University, developing virtual reality training and visualization systems. Tim holds a Bachelor of Science degree from John Carroll University in Cleveland Ohio with studies in Computer Science, Physics and Math, and was honored with their “Distinguished Physics Alumnus Award” in 2004. He has a Master’s Degree in Acoustics from The Pennsylvania State University.

Read the Interview with Tim Kulbago

Hey everyone, Scott Nelson here.  Just a quick message before we get started.  If you’re a medical device company, you probably realize that paying direct reps is often worth it but definitely comes at a high cost – big salary, company car, expensive insurance, etc., etc.  Well, Covasc may be an option to consider.  They have a unique network of affiliates that don’t require salaries or cars.  These affiliates excel clinically and have existing physician relationships at the local level.  The end result?  Increased market penetration, less cost and higher margins.  Check out covasc.com if you’re interested.  That’s C-O- V as in Victor- A- S- C dot com.  And if you’re interested in joining the Covasc network of affiliates on the sales side, go to covasc.com for more information.  Now, here’s your program.

Scott Nelson:    Hello, everyone.  It’s Scott Nelson, and welcome to Medsider, home of the free medical device MBA, and on today’s call we have Tim Kulbago, and Tim’s going to help me correct that pronunciation if it needs to be corrected, but Tim is the CEO of Image IQ, which is a contract research organization that helps researchers and biotech companies, medtech companies, etc. increase funding and improve regulatory approvals with extremely powerful image data.

Now, I’ll readily admit that I took some of that copy from Tim’s website, and it sounds somewhat vague, my little description, so Tim let’s start there.  Why don’t you give us a brief introduction to Image IQ, and maybe in layman’s terms the problems that you help your clients solve, and then we’ll dig in to maybe an example story.  So, welcome to the call, Tim.  Thanks for coming on, and let’s start there.

Tim Kulbago:    Oh, thanks Scott, yeah, and you got my last name correct, so that was awesome.  Very good.  So, Image IQ has an interesting history.  We are actually a spin-out of the Cleveland Clinic, the group that once Image IQ was inside the Cleveland Clinic serving their researchers and their clinicians for 10 years, and over the past year and a half or so they were approached by a lot of folks outside the Cleveland Clinic to provide their services to them, and the Cleveland Clinic said, “Well, this is a great opportunity for us to transfer the technology outside of the clinic into a commercial entity.”

So, we have a nice heritage and a wonderful heritage, and we are now a stand-alone organization that does, Scott, kind of what you said.  We do image analytics for folks, and probably the best way I could describe it is through a little bit of a story.

Scott Nelson:    Okay.

Tim Kulbago:    Let me take a crack at this.

Scott Nelson:    That’s great.

Tim Kulbago:    I bet a lot of folks who are listening to this have probably at some point in their life gotten a speeding ticket, and you know that sinking feeling when the lights go on behind you and you’re like, “Oh, man.”  That just tanks, right?

Scott Nelson:    Oh yeah.

Tim Kulbago:    And you know, the officer walks up to the side of your car and what’s the first question you ask him?

Scott Nelson:    Maybe apologize first if you’re feeling like you want to avoid the ticket, right? [Laughs]

Tim Kulbago:    That’s right.  You could apologize first.

Scott Nelson:    Apologize first, and maybe, “How fast was I going?” or “What’s the problem, officer?”  Yeah.

Tim Kulbago:    Exactly.  “How fast was I going?  What’s the problem officer?”  And if the officer said to you, “Well, you know what?  Yeah, I got you speeding, dude.  You know, on a scale of like 1 to 5, you were going a 4, maybe even a 4-1/2.”  You’re like, “What?”  Right?

Scott Nelson:    [Chuckles] That’s not me.  That’s not me.  Yeah.

Tim Kulbago:    Or if the officer said to you, “You know, hey, look, you were definitely going faster than the guy behind you.”  You’d be like, “What are you talking about?”

Scott Nelson:    Right.  Pretty vague, yeah.

Tim Kulbago:    Pretty vague, and if he said that, your reaction would probably be something like, “Well, show me the radar gun.”

Scott Nelson:    Right, exactly.

Tim Kulbago:    Like, “I want to see how fast I was going.”  Well, that’s what Image IQ does for imaging in the bioscience base, right?  So, what happens a lot is people will look at images, whether they’re radiology images or digital light images, dermatology or images of your eye or slides.  Even slides.  There’s lots of images in healthcare and in biosciences, and a lot of times people, professionals, are making judgments on that and doing somewhat subjective, relative, vague interpretations of those images, you know?

Scott Nelson:    Yup.

Tim Kulbago:    “Yeah, this looks like this is grown from last time.  It looks like there are about 30 cells that are transpecting the gene,” whatever it is.  Well, we’re like the radar gun.

Scott Nelson:    Gotcha.

Tim Kulbago:    We come out and we actually take that image, run it through some custom software and produce real data.  We tell you how fast you are going. So, does that explain…?

Scott Nelson:    Gotcha.  Yeah, I know.  That’s actually a great example.  I mean, in doing some of the research for this interview, when you first started out with that example of a speeding ticket, I almost right away knew where you were going.  So that’s a perfect example.  It’s almost, if I can say that again, Image IQ is the radar gun for certain data sets, almost. [Chuckles]

Tim Kulbago:    Yup.  Yeah, exactly.  We don’t replace the police officer, right?

Scott Nelson:    Right.

Tim Kulbago:    So we don’t replace radiologists and people who are looking through microscopes or doing like that.  What we can do is give them quantified data so that they can write the ticket more confidently.

Scott Nelson:    Gotcha.  Okay.

Tim Kulbago:    To extend the analogy probably just a little too far.

Scott Nelson:    Right. [Laughs] No, that makes sense, and I’m expecting or I’m hoping actually that the audience understands that analogy because it’s definitely a good one.  So, let’s dig in to that a little bit more.  I mean, why don’t we just dig into this a little bit more and understand that component of that acting as a radar gun just to use your example.  So, the gold standard now in a typical situation is if you’re, like I know one of the case studies that’s on your website, and I would encourage everyone to look at some of the case studies on the Image IQ website.

But one of those that I read about was an example of an orthopedic screw implant, and the gold standard being that a tradition radiologist would look at an image and find out how much bone growth there is post implant, etc.  Is the radiologist’s opinion still the gold standard, and how do you work with that or how do you fit into that equation in working, you know, if they are still considered the gold standard, how do you fit into that equation?

Tim Kulbago:    Sure, yeah.  So to answer your question, absolutely, the radiologist is still the gold standard.  The way we fit into the equation is usually a helping application or a helping quantification.  So, in that particular example, we had a client who was doing a resorbable ACL screw, and they had a lot of questions over multiple time points about the performance of the screw and the environment the screw was in.  They not only wanted to know how the screw was performing but they wanted to understand that the tunnel that the screw was in, they wanted to understand the tunnel diameter, the volume of that tunnel, they wanted to understand whether new bone growth was going into the tunnel as the screw resorbed into the body.

Scott Nelson:    Okay.

Tim Kulbago:    Right?  And so, you know, they actually approached, and the Image IQ team sat at the table with the radiology team that was going to do this evaluation, and the radiology team basically said, “Look, you know, we can do it.  It’s going to be, you’ve got dozens of patients and you’ve got multiple time points.  This is going to take us a long time.  We’re going to have several different radiologists do it, and you know, it’s going to be a little bit varied.  There’s going to be variability in our radiology team as to how we read this and, you know, we’ll get you some quantified data, and it’s going to take a while, and we really don’t want to do any of the work until the entire file is over.”

Scott Nelson:    Okay.

Tim Kulbago:    “Because then we can look at all the images at once,” right?  So, you had a client here, right, who was sitting here going, “Okay, you’re going to give me relatively variable data.”

Scott Nelson:    Mm-hmm.

Tim Kulbago:    “I’ve got to wait till the end of my study [chuckles] to figure out what’s going on.”

Scott Nelson:    Right.

Tim Kulbago:    “And it’s going to take a long time,” i.e., in the client’s mind, expensive.

Scott Nelson:    Mm-hmm, yeah.

Tim Kulbago:    Right?

Scott Nelson:    Yeah, no doubt.

Tim Kulbago:    Exactly.  So, what we did is we said, “Hey, how about we do this?  Let’s take the images, let’s take a look at it, and we, with our engineering team,” and this is the magic, I like to call it the [8:33], right?

Scott Nelson:    Mm-hmm.

Tim Kulbago:    This is what we’re going to do.  They said, “Look, we’re going to take a look at the images,” and they were able to start a whole series of different, you know, very sophisticated image processing algorithms.  First and foremost, take multiple time point images, images acquired right after surgery, six months later and 12 months later, and register them so that in every image that we know we’re in the same physical place.

Scott Nelson:    Okay.

Tim Kulbago:    And that is something that usually happens in a radiologist’s head, right?

Scott Nelson:    Right, right.

Tim Kulbago:    So we were able to do that and visually now show the radiologist, “Look, when you’re looking at the screw in the image from right after surgery, the one you’re looking at six months later, these two go together.  These are the same physical place, same coordinate system, if you want,” right?  And that was step one.

Once we did that, that in and of itself was very powerful, but then once we did that, we were able to, through the use of, it was a CT study, we were able to actually tell the difference in color, pseudocolor, of the screw, new bone growth, the tunnel where the screw was, the void, the volume, and show that to the radiologist, and now, they could start making very accurate measurements and it approved their accuracy.

I mean, it was kind of neat, at least in this particular study, is we were able to run that program however often they wanted, so when a new patient came in at the six-month time point, because not all these patients have the surgery on the same day, right?  Everything’s kind of…

Scott Nelson:    Sure.

Tim Kulbago:    It trickles in, right?  It trickles in. [Chuckles]

Scott Nelson:    Right.

Tim Kulbago:    So when we were doing the screw, you know, we get the next time point, six months after surgery, and we can run the software, let the radiologists very quickly make their determination, and show folks how things were going so that they could choose to continue the study or not continue the study or add more time points if they felt they weren’t getting the right data.  It actually really improved their ability to check the effectiveness of their device.

Scott Nelson:    Yup.

Tim Kulbago:    And ultimately, they got quantified data.  They actually got a spreadsheet with tunnel volume and screw resorption rate and screw volume, and just going, “Yeah, well, we think it’s looking good,” you know?

Scott Nelson:    Okay.

Tim Kulbago:    We get back to your radar.

Scott Nelson:    Gotcha.  So, I know that, it seemed like the voice cut out there for a second, but I think you just mentioned something that I wanted to cover because the first, back to that radar gun example that you mentioned before, that speaks to more of the regulatory approval enhancement that your clients get by using your services, kind of the regulatory aspect.

Tim Kulbago:    Right.

Scott Nelson:    But you also just mentioned more of the safety and efficacy aspects as well.  I mean, your services, they don’t just impact the enhancing regulatory approval times, but it also, correct me if I’m wrong, but you’re also talking about enhancing, you know, and particularly that orthopedic company’s engineers are able to use your imaging services and imaging analytics to look at safety and efficacy of the certain device, correct?

Tim Kulbago:    Yeah, that’s a really good point.  Yeah.  As a matter of fact, it’s important in all stages whether it’s preclinical or clinical or even, we’re doing some postmarket studies with some folks where they want to get that data and understand the safety and efficacy, and it’s hard.  You know, it’s hard to get the data.  It’s hard to do the analytics.  One of the studies we have is actually very interesting because, as I’m sure you know and your listeners probably know as well, there’s a lot of buzz right now around this pain pump situation and is it or is it not causing cartilage degradation, right?

Scott Nelson:    Okay.

Tim Kulbago:    And we’ve actually done some studies where we’ve shown that we can objectively measure cartilage thickness, right?

Scott Nelson:    Yeah.

Tim Kulbago:    And that helps you understand what’s going on, and it isn’t specific necessarily to that device, but it’s something that people need a question answered for, and right now it’s hard to get the answer to that question.

Scott Nelson:    Sure, okay.  Okay.  And that goes back to that, almost that data quality aspect that we spoke about.  So, I mean, you’re really covering in terms of, like when I look at Image IQ and what you folks have to offer, it’s not only that enhancing data quality, kind of the proof in the pudding.  It’s also insight into that actual device, you know, the safety and efficacy components that we just mentioned, but also, you know, the time and the cost benefits associated with regulatory approvals that would, you know, something that would otherwise take an inordinate amount of time.  You’re definitely speeding up that process.  Is that safe to say?

Tim Kulbago:    Absolutely, yeah, and you know, if you go back to it, I think you said that really well because if you look at the situation like right now in the FDA, right, from a regulatory perspective?

Scott Nelson:    Mm-hmm.

Tim Kulbago:    Those folks, they see thousands, literally thousands of applications a year.  I think there are 4000 submissions a year, and of the 510(k)s that go through the FDA, 20% of them go through three review cycles.

Scott Nelson:    Okay.

Tim Kulbago:    And a lot of the time, a lot of the reason they’re going through the review cycles is the folks at the FDA who are working their butt off and trying to understand, you know, just all sorts of different medical technologies that are being brought their way, a lot of times they’re trying to understand, right?  “Look, you need to explain this better to me.  I don’t understand what you’re trying to do,” or “I don’t understand how your data is showing that this is a safe and effective device,” because that’s their mission, right?

Scott Nelson:    Yup.

Tim Kulbago:    So what we’ve seen is that, you know, when you incorporate imaging into that, it helps explain it, and you know, for anybody who’s in your audience, you know, you think about it and say, “Okay, well, 20% of my 510(k)s go through three turns.”  Imagine if you could eliminate one turn – more bandwidth for the FDA, less cost and faster time to market to you, and feeling more confident about your product.  So it’s kind of like, it’s got all the right feel to it in terms of helping the device manufacturers develop products more quickly, more safely, and know what’s going on.

Scott Nelson:    Right, right.  Well, you know, that’s a good segue because I know there’s been a lot of buzz, too, in regards to kind of the recent IOM report that was released about the FDA’s 510(k) process, you know, and the FDA’s responses.  The number of submissions has increased enormously over the last years, and the quality of those submissions has been downgraded, hence the backlog, you know, similar to [chuckles] the backlog in the Patent Office.  I know that’s some recent buzz, too, the patent reform stuff.

So, let’s dig into that a little bit more, kind of that whole process and how imaging, you know, Image IQ and your analytics, is used to help in preclinical and clinical studies as well as postmarket approval.  Can you speak to that at all?

Tim Kulbago:    Sure.  Sure, absolutely.  Absolutely.  So, I think if you look at it, there are a few steps that folks go through, and even if you start this in the obviously slightly less expensive space of a preclinical study…

Scott Nelson:    Mm-hmm.

Tim Kulbago:    What we’ve been able to do for a lot of our customers is help them explain their preclinical data and bridge the gap between the preclinical world and the clinical world.  That’s a tough leap for folks to make especially in the FDA, who ultimately are going to have to say, “Yes, go ahead and initiate your clinical trial.”

Scott Nelson:    Okay.

Tim Kulbago:    So, for example, I mean, I’ll give you an example.

Scott Nelson:    Yeah.

Tim Kulbago:    We’ve been working with a group that is building kind of an interesting bone scaffolding kind of thing, you know.  They’re inserting that into an area where bone is gone, and they want help from bone growth.

Scott Nelson:    Okay.

Tim Kulbago:    So, some of their original work is obviously in the preclinical space, and they can be much more intense with their imaging, and the way that most of the time this is done is through histology slides.  You know, you look at slides, and what they do with their slides is they will take an area of interest, a volume of interest, this material, and they’ll slice it up real thin and they’ll put it on a microscope.  You know, sometimes they can only look at 10, maybe 20% of all the data that they have in this volume because you can slice a lot of things out of a volume.  You need a lot of slides.

Scott Nelson:    Mm-hmm.

Tim Kulbago:    So what they try to do is they engage a biostatistician and they say, “Well, hey, how much of this should we look at?”  And the biostatistician works with them and comes up with a number that you hope is statistically significant.

Scott Nelson:    Right.

Tim Kulbago:    Right?  Well, what we’ve been able to do with some of our imaging technology is we’ll just image the whole volume, the whole thing, and do the analysis on the whole thing.

Scott Nelson:    Okay.

Tim Kulbago:    So, there’s no subsampling, there’s no anything.  Literally, you do the whole thing.  You automate it with software, you get professionals to check it and validate it.  There’s a whole validation process that happens that doesn’t play real well on PowerPoints and sales presentations, if you will. [Laughs]

Scott Nelson:    Sure.

Tim Kulbago:    Because it’s not a lot of fun, but it is actually the basis of the science we do, is you have to have good validation and good controls, and then you build it up and you can run the software on this whole volume, and then to go to the FDA and your submission and say, “I covered 100% of my preclinical samples.”

Scott Nelson:    Yup.

Tim Kulbago:    At some level, what you can expect from the FDA, and this is what we actually, we had a webinar that talked about this a little bit.

Scott Nelson:    Mm-hmm.

Tim Kulbago:    And one of the folks on the webinar, one of our panelists, worked at the FDA, and her comment was, “Well, if folks at the FDA hear that you covered 100% of your samples and you have all the data, they’re just going to check the box and move on.”

Scott Nelson:    Oh, that’s amazing.

Tim Kulbago:    They appreciate that thoroughness, right?

Scott Nelson:    Mm-hmm.

Tim Kulbago:    And they don’t have to engage a biostatistician and start asking questions like, “Was this the right amount of data analyzed or not?” because they have a responsibility to do that.  And so if you just tell them, “Look, we’ve just looked at it all.”

Scott Nelson:    Yeah.

Tim Kulbago:    Then, they can move on.  It makes it quicker for them, and it makes them feel like you’re being very responsible in your judgment on what you’re doing.

Scott Nelson:    So that’s…

Tim Kulbago:    Does that kind of explain it?

Scott Nelson:    No, that helps a lot, and it’s almost like, from the FDA standpoint, it’s like the Image IQ seal of approval [chuckles], you know, like when they’re looking at ABC Companies…

Tim Kulbago:    They’ll never tell us that, of course.

Scott Nelson:    Right, right.  Yeah, that’s my own kind of [chuckles] coined phrase, but it’s almost like how I’m seeing that in your story that you just told about, you know, that that person is working at the FDA, if they see that that data said, you look at 100% of the patient population size or whatever it may be, that’s huge.  To that point, that status quo versus like using kind of a comprehensive image analytics and image data, is it hard to, maybe you can pinpoint how much time that really saves?  So, almost like if you were trying to sell me on your services, is that hard to identify, or can you give me an average time of time savings and cost savings associated with something like that?

Tim Kulbago:    Sure.  Yeah, sure.  Absolutely.  Unfortunately, the real answer is it depends.

Scott Nelson:    Right.

Tim Kulbago:    Part of the Image IQ business model is what we don’t do—this is kind of interesting.  Let me tell you what we don’t do.  We don’t actually sell software.  What we do is sell services because, I’ll use another analogy.  We’ll go away from our radar gun analogy, and then I’ll answer your time savings question.

Scott Nelson:    Yeah.

Tim Kulbago:    There are a lot of very advanced visualization packages on the market today that people can buy, right?

Scott Nelson:    Right.

Tim Kulbago:    And we kind of look at those as they’re very powerful, and it’s kind of like jumping in the cockpit of an F-18, right?  If you saw an F-18 sitting on the runway, you could probably jump in the cockpit, you’d probably find the throttle, and you could probably get it off the ground, and from that point on, nothing good is going to happen…

Scott Nelson:    Right.

Tim Kulbago:    …if you’re not a qualified pilot, right? [Laughs]

Scott Nelson:    Right.

Tim Kulbago:    And the imaging analytic software on your own is kind of the same story.  You can download, you can get the software, a lot of them you can buy online, you download them, it loads your images up, and it’s really cool how the images are on your screen, and everything goes bad from there.  You don’t have proper controls.  You don’t have proper validation.  You don’t understand the morphological filters.  That’s a big word, but you know, I mean…

Scott Nelson:    Yeah.

Tim Kulbago:    You’ll get yourself in a lot of trouble, and so to answer your question about cost savings is, yes, we actually have a person who does the work with you and help you through it.

Scott Nelson:    Okay.

Tim Kulbago:    That kind of stuff.  But in some cases, we’ve saved people up to 90%.  I’ll give you a perfect example.  We had a group, they were doing gene expression.  They were looking at cells through microscopes, and they were running a project to figure out how many of their cells are properly expressing a gene, right?

Scott Nelson:    Okay.

Tim Kulbago:    You have to look through a microscope, you put stains on them and stuff like this.  So they’d run their transpection experiment, and then they would take two weeks of a good number, you know, at least five people looking through microscopes trying to figure out how many of these genes are being expressed.

Scott Nelson:    Okay.

Tim Kulbago:    Then they would get a percentage, like, “How’d we do?  How good was it?”  Well, we came in and said, “Look, there’s a better way.  We can automate this for you.”  And again, we put the software together, validated it, wrote the software and ran it on this, and they went from taking, let’s call it five people, five people a week, two weeks, it took them 10 working days to just finish the results of the experiment to one hour.  Okay, now, how do you qualify that cost savings?

Scott Nelson:    Sure.

Tim Kulbago:    I mean, it’s hard to put that in a sales and marketing document because in that case, it was a 90-plus percent cost saving in general.

Scott Nelson:    Right.

Tim Kulbago:    On average, the cost savings is more like, we see about 20 to 40% cost savings over doing things manually and, you know, kind of traditionally.

Scott Nelson:    Right, which is…

Tim Kulbago:    So, the 90% is kind of [23:12].

Scott Nelson:    Gotcha.  Well, I mean, to your point though, a 20 to 30% cost savings, I’m thinking, and I don’t have the data in front of me, but the cost of doing a preclinical or even a clinical study, even if you throw in like postmarket surveillance studies, the overall cost is enormous, so a 20 to 30% savings is a huge [chuckles] sum when you look at actual dollar figures.

Tim Kulbago:    True.

Scott Nelson:    I would presume, correct?

Tim Kulbago:    Yeah, absolutely, especially when imaging is a core part of the trial.  You know, there are a lot of other parts to the trial that have cost.  You know, the other thing that we’re looking at, and who knows how this is going to play out in Washington…

Scott Nelson:    Mm-hmm.

Tim Kulbago:    But there is this medical device tax coming up.  I think it’s 2.3%, right?

Scott Nelson:    Right.

Tim Kulbago:    You may have heard of that.  Well, you know, the medical device companies are going to have to find a way to recoup that somehow because they’re being charged that.

Scott Nelson:    Mm-hmm.

Tim Kulbago:    So, you know, one way to do that we think is to integrate more advanced technologies like Image IQ does into your R and D process, in your manufacturing process.  We can hit the whole thing, and you know…

Scott Nelson:    Right.

Tim Kulbago:    …not only reduce cost of trials but now you’ve got more gas in the tank to do R and D and innovate.

Scott Nelson:    Right, right.  No, that makes a ton of sense.  Honest, I didn’t even think about that, but looking at other ways to become leaner, this definitely seems like a promising approach for sure.  So, I certainly understand and I’m hoping the audience understands how Image IQ and using comprehensive imaging analytics helps achieve faster regulatory approvals, you know, and enhance overall medical device approvals through the FDA.

Can you speak at all to, because I know in that webinar that you mentioned, one of the things I think that was covered on the webinar was improved adoption by physicians.  Can you explain that in a little bit more detail?

Tim Kulbago:    Sure.  Yeah, well, that’s a great question.  Yeah, good catch.  That’s really interesting.  So, in today’s environment, many devices go through their approval process which you know has got a couple of [25:25 paths to it], but one of the ways is what they call the ever popular predicate device.

Scott Nelson:    Right.

Tim Kulbago:    Right?  So, our device is just like something else.  It’s been approved and it’s substantially equivalent, and therefore, right?

Scott Nelson:    Yeah.

Tim Kulbago:    Well, that’s all well and good, and it’s fine, and it’s the way things are.  But sometimes, you need to be able to show your device is better than the competitor’s [chuckles], right?

Scott Nelson:    Yup.

Tim Kulbago:    Which really has nothing to do with regulatory approval.  It has to do with sales marketing, competing and showing that you have a better mousetrap, for lack of a better way to say it.

Scott Nelson:    Right.  Okay.

Tim Kulbago:    And so we can actually work with some customers where, and you’ve been in the med device industry for a very long time as have I, and you know, it’s very easy to build a marketing brochure that says, “Look, we’re better,” you know?

Scott Nelson:    Sure.

Tim Kulbago:    That’s kind of an easy part about it.  But if you can bring objective quantitative data to show that your device performs better, you know, the medical community, people go into medicine to heal people.

Scott Nelson:    Right.

Tim Kulbago:    And they see something that can make them a better physician, a better caregiver, then they’re going to listen, and so we’ve actually done that with imaging analytics because we aren’t in the marketing department or even in the R and D department of the medical device.  They outsource that to us and say, “Well, look, can you help us run a postmarket understanding of how we’re doing because we didn’t have to do that as part of our regulatory thing.  We’d like to do it separately.”

Scott Nelson:    Sure.

Tim Kulbago:    So that’s how we fit in to that piece of the lifecycle.  Does that make sense?

Scott Nelson:    Yeah, yeah.  It makes a ton of sense, and the example I’m thinking of in my head is when I was selling peripheral stents, I know that the company, without naming names, the company that I was working for used a core lab to look at, I think for some of the actual in-clinical data as well as the postmarket clinical data, and I remember from a sales standpoint, I think the entire sales force agreed that it would be awesome to have some actual images of how this certain stent held up in a certain vessel versus another stent, or not even versus another stent, just how it holds up based on certain anatomies.  So I can completely understand that having various images that show the superior efficacy of a certain device would greatly enhance sales, you know, in marketing.  Definitely.

Tim Kulbago:    Absolutely, and look, you know, the best sales folks in med device have so much fun is you’re ultimately helping physicians help patients.

Scott Nelson:    Right, right.

Tim Kulbago:    So, it’s a very educational process, right?

Scott Nelson:    Mm-hmm.

Tim Kulbago:    And so we look at that, I think you hit the nail on the head, it’s a perfect way to say it, is that if we can help educate someone more clearly with a picture or a movie or a visualization and the data that goes with it, then, boy, that helps everybody, right?  It helps move medicine forward, it helps people sell and share [28:36], and competition’s healthy.  It forces everyone to prove that they are moving the ball forward.

Scott Nelson:    Without a doubt.  Without a doubt.  I wholeheartedly agree.  So, I know we’re running short on time, Tim, so let’s wrap it up with a little bit about, if you can tell a little bit about yourself.  I know you have a very impressive résumé in the medical device world.  A lot of your time has been within kind of the imaging niche, correct?

Tim Kulbago:    Yes, yeah.

Scott Nelson:    Yeah.

Tim Kulbago:    The 30-second version is I’ve had the good fortune of spending almost 15 years in radiology.

Scott Nelson:    Okay.

Tim Kulbago:    Radiology information systems and tax systems, and I was the chief technology officer at a wonderful company, Merge Healthcare, for a long, long time.  They’re growing and they’re doing a wonderful thing and, you know, we got to a point where I needed to move to my next thing, so I joined the Image IQ team, but I’ve been doing radiology software and technology for 15 years, and so I really enjoy doing that.  Cleveland is home for me, so working with the Cleveland Clinic Company is very important to me, personally.

Scott Nelson:    Right.  Mm-hmm.

Tim Kulbago:    And so we’re really excited about how Image IQ is shaping up.

Scott Nelson:    Yup, cool.  I know I’ve heard a lot about kind of that Cleveland Clinic’s, I guess, somewhat blazing some trails and how they’re spinning off different technologies, and so it’s interesting to hear, I mean, yeah, I guess you’re a perfect example of that kind of philosophy, right?

Tim Kulbago:    Yeah, there’s a real strong commitment at the clinic to taking the work that’s done by the core researchers and some really world-class physicians and trying to find a way to get that out to the rest of the world.  They are blazing some trails with that, and you know it’s not an easy thing to do for sure, but they’ve got a great team here that works on that, and yeah, we’re one of the poster children of that being successful, and hopefully, continuing to be successful.

Scott Nelson:    Gotcha.  Yeah, very cool.  And I would like to kind of finish off these interviews with a little bit of advice from you being what I consider a medical device stakeholder mentor, if you will.  I don’t know if you have kids or how old they are even, but if you had a son or a daughter, or maybe even a close family friend that would come and say, “Hey, Tim, I know you’ve had a successful medical device career, what one or two things could you tell me that would kind of either put me on the right track or maybe keep me on the right track or put me in the right direction.”  Can you offer a few words of wisdom?

Tim Kulbago:    Sure, yeah.  I have four kids, so I have fortunately done that a few times.

Scott Nelson:    Yeah. [Chuckles]

Tim Kulbago:    I think the number one thing is remember when you’re making a medical device that ultimately you’re helping improve the lives of real people and real patients, right?

Scott Nelson:    Okay.

Tim Kulbago:    And that, I think, if you remember that, it’s motivational and it helps you realize you’re making a difference.  And so keeping that in the forefront, that there’s a business that goes around this, and yes we all want to get paid and grow companies and stuff like that, but ultimately you’re helping make people’s lives better.  Absolutely, I would tell you that, number one.

Scott Nelson:    Mm-hmm.

Tim Kulbago:    And number two, I would say, as silly as it sounds, you know, remember that there are a lot of really smart folks out there and it’s really important to listen.  Listen to ideas that are around you, because so many times people get very hung up on, “Well, I know the right way,” and I’ve learned more often than not that listening first is a really important skill that you need to have, especially when you’re dealing with devices like we deal with on a regular basis.  They’re important, they need to work well, so keep your ears open and keep the passion about helping improve people’s lives.

Scott Nelson:    Very good, very good.  I like that.  That’s one skill that probably isn’t mentioned as often as maybe it should be, the skill of listening, especially in this world when the people that make up kind of the medical device world are usually really, really bright people just like yourself.  So, good stuff.  Good stuff.  Lastly, Tim, for those listening that want to learn more about Image IQ, learn more about yourself, the services that you folks offer, where would you like to direct them to?

Tim Kulbago:    Oh, the [32:56] thing is our website, of course.  It’s image-iq.com, and you can find us, you can Google us.  We have a tendency to stroke towards the top of the Google world, and we’ve got, like you said, a number of case studies on there, video case studies, a capabilities database that outlines all the kinds of imaging we’ve done, and there’s a lovely contact form that emails our team, and we’d be happy to just talk to you and learn about how we can help you, because that’s what we’re in the business of doing.

Scott Nelson:    Right.  So that’s image hypen iq, image-iq.com.

Tim Kulbago:    You got it.

Scott Nelson:    Right, and I would wholeheartedly agree.  Obviously, I have no personal bias towards Image IQ, but that video, and I’m certainly no image expert by no means [chuckles].  I can’t say that I have 14-plus years of experience in the medical imaging world, but those video case studies are very cool.  They help someone like me that really kind of get my hands around what you guys have to offer and kind of the unique aspects of your data analytics.  So, good stuff.  I can’t thank you enough, Tim, for coming on the program, so thanks once again.

Tim Kulbago:    Well, thanks for having me.  It was great, a lot of fun.

Scott Nelson:    Alright.  Thanks everyone for listening.  Take care. (Music Plays)

 

Learn From the Best

The best stories from the world's brightest medtech and healthtech thought leaders. Subscribe for free and never miss an issue.

100% privacy guaranteed. Your information will not be shared.