In an era of constant disruption in the healthcare industry, Medrio is doubling down on a mission of stability, efficiency and long-term impact. In a new interview with Fierce Biotech’s Chris Hayden, Medrio CEO Nicole Latimer shares how the company is helping clinical researchers streamline their operations and deliver life-saving therapies faster—without losing focus in a fast-evolving landscape.
Latimer, who brings three decades of healthcare and life sciences experience, discusses the importance of creating strong foundations for clinical trials. From prioritizing data standardization to offering user-friendly tech with expert guidance, Medrio aims to equip sponsors and research sites with the tools they need to scale efficiently.
She also highlights the growing role of AI and automation in accelerating trial processes, while cautioning that not all trends are worth chasing. “We stay laser-focused on solving our customers' needs,” Latimer said, noting that Medrio evaluates new technologies, like generative AI, through the lens of real-world clinical utility.
With five of the 96 FDA approvals in 2024 supported by Medrio software, the company’s impact continues to grow. Latimer emphasizes that true stability comes from long-term relationships, fiscal discipline, and a willingness to evolve alongside customers.
The conversation touches on some of the industry’s biggest challenges—like scaling trials and integrating data from EHRs and wearable devices—and looks ahead to the next two decades of innovation in clinical research.
For more insights into Medrio’s approach and the future of clinical trials, watch the full interview with Nicole Latimer.
Chris Hayden:
Hello, everyone. Thank you for joining me today. My name is Chris Hayden. I'm a producer here at Fierce Biotech. Today I'm talking to Nicole Latimer. She's the chief executive Officer at Medrio, and today we're going to be talking about how to create a stable foundation in clinical trials and opening the door to innovation. Over the last 20 years, Medrio has supported sponsors, CROs and clinical research sites with technology to help empower their clinical trials. Stable, predictable growth is not only the ethos that Medrio lives by but one they champion for customers. Nicole, I would love it if you could just take a few minutes to introduce yourself to the audience.
Nicole Latimer:
Absolutely. It's so good to be here, Chris. Thanks for having me. I'm Nicole Latimer, as Chris said, I'm the CEO of Medrio and we are a company that makes software to help life sciences companies perform clinical research more efficiently and effectively. I've been in both the healthcare and life sciences industry across the last 30 years and love working for Medrio, primarily because we have the opportunity to fulfill our mission of saving hundreds of millions of lives on an annual basis. We play a very small part in clinical research, but the part we play helps to put life-saving therapies into the hands of patients much faster.
Chris Hayden:
Yeah, I love it. That's the beauty of healthcare, isn't it? There's so many little pieces that go into it, so I think that's fascinating. Just to get started a little bit, something that we probably don't have to tell most people listening, but the healthcare industry is always changing.
Nicole Latimer:
That is true. That's very true.
Chris Hayden:
Yeah. Medrio opened their doors over two decades ago and you've got over 30 years of experience navigating this healthcare industry. Yeah, so I think this will be an important conversation for our audience. I'd like to just jump right into the questions if we could, and this is an interesting question, especially given where we stand in 2025. It's an industry full of disruptions and there's just so many disruptions and how do you stay focused on solving the real challenges?
Nicole Latimer:
This is interesting, Chris, because this is a conversation we actually had at our standup meeting, our full company standup meeting today, which is there are a lot of distractions out there. There are distractions in the industry, there's distractions in the world, and we concentrate on staying focused on the mission that I talked about. Our mission is to save a hundred million annual lives. We do that by helping life sciences companies to streamline their clinical research, create very accurate data that supports their ability to raise additional funding to get regulatory approval as well as to commercialize their therapies.
In other words, we're helping them to get those lifesaving therapies into the hands of patients faster. I share stories about our customer companies having success with using our software, finishing their trials, using the data from those trials to get regulatory approval, sharing some of the success stories about how they've been able to commercialize and save patients' lives, and really focusing people in on "Why do we get out of bed every day? Why do we do this?" It really helps with minimizing some of those external distractions and it helps with thinking through how do you handle the change, how do you bring changes in and incorporate them in a way that stays true to the mission that you have.
Chris Hayden:
Building off of that, and I think that's an absolutely great point, and I love the "Why do we get out of bed every morning?" It's not just to come in and send a couple of emails every day. There's more to it than that. I think that's great. I'd be curious to hear from you though, and I love that you just had a stand-up meeting with your company, so how can an organization maintain stability while also evolving with the industry?
Nicole Latimer:
You're absolutely, right, it is a challenge that we have to maintain what we do every day, but we also have to evolve with the industry. A couple of things that we do that helps us with this. The first is we create long-term relationships. For Medrio, when we work with our customers, we are doing it for the long-term. We love to work with customers who are just starting out in their clinical trials. They're going into the clinic for the first time, they're trying out their therapy for the first time in humans. It's a great opportunity for us to provide guidance from the expertise and the experience that we've had over the last couple of decades.
It also then gives us an opportunity to stay with them throughout their clinical research journey. As you know, very often clinical research is a seven to 15 year journey for many organizations, from the moment you go into the clinic for the first time to the time you get approval. We know we want to be working with them over that long period of time and supporting them to make sure that they can be as efficient and effective as possible.
Second thing we do is we also focus in on standardization and efficiencies. We think that a lot of clinical research has the possibility of being overly complicated or overly delayed. We take it upon ourselves to be providing best practices and ideas with our customers on how they can be more efficient, what they can do that's more effective, sharing with them the techniques and the tips that other people have used in order to streamline their clinical research. For our sponsors, very often this means streamlining and establishing their data, creating standards on how they're going to collect that data so that from study to study, they are reducing redundancies, they're creating those standards, they're making it easier for them to compare their results from study to study. We really see those as repeatable processes, repeatable standards that make their overall research journey that much more efficient.
Then the third thing we do is we really want to equip the team and our customers to be empowered to make decisions, to do what they need to do, and that's part of what we call our guided autonomy approach. We make software that's easy enough to use that our customers can use it themselves, but we also have a wide variety of experts, people with decades of experience who can work with our customers to ensure that they are optimizing the use of that technology, that they understand the best practices in how to streamline their research efforts and can be tapped into it at any time for advice and counsel on how to overcome any issues that might arise during the research process.
Chris Hayden:
That's great. It brings up another... It leads us into our next question here. You mentioned standardization, efficiency, effectiveness, and I love that. It makes me think of the financial stability. What strategies have been most effective in maintaining financial stability while continuing to deliver value?
Nicole Latimer:
It's hard. This is a risk-based industry, right? We are doing research and by definition in research, you don't know what you don't know. There are going to be changes and you're taking on risk. I actually just ran a March Madness bracket through LinkedIn where we looked at what are the biggest roadblocks to drug approval. The number one thing that came through in our bracket was funding, the challenge with getting funding. Fundamentally in this industry, having financial stability, whether you're a biotech that's out seeking funding so you can conduct your research or you're a technology company like Medrio, where you've got to have a solid financial groundwork to be able to be around for 20 years, you know that the good financial, good economic stewardship is critical to surviving here. We really look at making sure that we are using our economic resources well, not only for ourselves but also for our customers.
We do a couple of things. We stay laser focused on solving our customer needs and that when trends come up, we look at those trends and we determine are they actually going to help with resolving or serving those customer needs? A great example is AI. There is no limit to how you could use generative AI in clinical research, but a lot of it is not necessarily going to meet the needs of our customers. Our core customers at Medrio have said that speed and flexibility are the most important things that we can deliver in terms of our technology. When we're applying AI, we're doing so in a way that expedites the clinical research. We're applying it in a way that allows us to automate the database builds, which is a precursor for clinical research, and we're using AI to enable our reporting.
Using machine learning to pull together disparate data sets from the research to provide very easy to use reports on demand, and most importantly, allowing you to create reports through a prompt just like you would chatGPT, but using that prompt to generate a report rather than having to spend the time dragging and dropping different fields in a traditional report building mechanism.
Chris Hayden:
Yeah, that sounds very helpful. I love it.
Nicole Latimer:
Another critical part of how we maintain our financial stability is by not just addressing those customer needs but also evolving as our customers' needs evolve. The great thing about some of the pioneering and mid-sized life sciences companies we're working with is that they have the potential to be big Pharma 10 years from now. What we know is that their needs today are different than their needs are going to be 5, 7, 10 years from now. But we can grow with them. We can serve them now in their early phases, and for many of them we are continuing to serve them into later phases.
As a matter of fact, five out of the 96 FDA approvals, and I'm talking medical devices, biologics and new drugs that were approved in 2024, Medrio supported the phase three or pivotal studies for five out of the 96. We are continuing to evolve with our organizations continuing to support them as they grow. When you have customers who work with you for a decade or more, that's the core to stability. That allows you to not only be around for true decades, but to continue to invest in new technologies like AI to evolve and improve the technology that we provide.
Chris Hayden:
I love that. It's interesting, Nicole, you mentioned it is a 17 to 15 year runway for a lot of these products, and it just makes me think when you mentioned generative AI seven years ago, generative AI really was not a thing yet. AI was glorified machine learning. It's interesting that you're able to walk through these evolutionary steps with them, so that's fascinating. Those conversations must be interesting.
Nicole Latimer:
It's a conservative industry and a lot of it is guided by pretty strong regulatory processes, and so it's hard to know as you're making investments, are these investments that are eventually going to be widespread, well adopted innovations or are they flashes in the pan that people are not going to adopt? I think if you went back to 2020 in the heat of the pandemic and all anyone wanted to talk about was decentralized clinical trials. They are incredibly valuable and they expand the reach for patients to be participating. They can be incredibly cost-effective, but they're not the panacea that they seemed to be five years ago. I think part of what we do to ensure our financial stability, to ensure that we'll be here for another 20 years is understanding those trends, understanding do they truly meet the needs of a wide variety of customers? Are they likely to become prevalent ways that will work within clinical research? If they are, they meet all that criteria, we then invest heavily into it and incorporate it into our technology.
Chris Hayden:
That's great. Now, I want to change lanes a little bit, Nicole, if we can, and talk a little bit about scaling clinical trials just because it's such a challenge. It's such a hurdle, I think, for a lot of these smaller companies. Scaling clinical trials efficiently is a major challenge throughout. What do you want those running trials to know to make it a little bit easier?
Nicole Latimer:
To make it easier to scale clinical trials, it really has to do with how you set those trials up from the beginning. Yeah, it's the foundation and if you have a large portfolio of therapies that you're going to test, there are two things that you can do to make it a lot easier to scale. The first is data standardization and process standardization. Creating ways to capture the data, so your electronic clinical research form, your ECRF, having some standard forms that you're going to use in every trial makes it easier to standardize that data and to then compare it as you continue to scale your trials, so really critical to say, "What is the data I want to capture in every trial, and then how do I standardize it into a form and capture it exactly the same way for all of the trials I have going forward?"
It also helps to then standardize your processes, and one of the processes that we think is critically important is looking through your data and understanding which data is truly necessary, absolute must have and what doesn't quite meet that standard. I bring this up because in our research and in our experience, what we've seen is that over the last six to seven years, there's been a 35% increase in the number of clinical variables associated with phase one studies. The earliest and supposedly simplest phase, there's 35% more data you're now capturing. It's adding to the complexity, it's adding to the time, it's adding to the cost, and we see our sponsors streamlining their effort.
They're bringing in their medical monitors, they're in clinical operations, they're bringing in data management. They're even bringing in their commercial colleagues and saying, "Let's look at all the data that we are thinking about and let's hold it to that incredibly high standard of do we truly need it? Do we need it to help us with raising our next round of funding? Do we need it for regulatory approval? Do we need it for commercializing our therapy once it is approved? How critical is it to those steps?"
We fully understand that when you're going through clinical research, you are building the second most valuable asset for the life sciences company. The most valuable asset they have is the actual therapy that they're testing. The second most valuable is the clinical research data that results from your research because you are going to use it for so many different things, whether it's funding, or approval or commercialization, but it doesn't mean you need to boil the ocean in terms of data.
When you hold it to that standard, when you have all the critical parts of the organization reviewing that information and boiling it down to just those pieces that are truly necessary, that's what helps with scaling, because nothing sinks your portfolio than a bloated trial where you're collecting far too much data and you can't recruit enough patients and you end up with tons of delays, because you're cleaning up data that ultimately isn't going to be used for anything.
Chris Hayden:
It makes sense. I like that and I love the standardization piece as well. I think that's so important, too. It's something I've always been curious about how far standardization goes. That's probably another webinar, that's probably another conversation for another day, Nicole. But looking ahead for our last question, I think it's always fun to look ahead. How can clinical research continue to evolve over the next 20 years, which I know is a pretty big bite, and what strategies will help ensure stability amongst all this change?
Nicole Latimer:
When I think about how clinical research evolves over the next 20 years, there are two things that strike me as must haves, things that absolutely have to happen for this industry. The first is greater technology adoption. It's amazing to me today that we have many organizations who are still incorporating paper-based data collection as part of their overall clinical research process. Paper, as you know, is prone to error and can get lost. There can be stray marks that are uninterpretable. There are legibility issues with people's handwriting. We don't have any of the benefit of time stamps or passwords to say truly attribute that the data was entered by the person we think it was entered by or that it was entered at the time that we think it was. Paper today is still a second rate, second level of quality of data, when it's compared to electronic capture.
As I said, we still have people who are using it. We see that for consent forms. eConsent today allows you to provide multimedia sharing of information which has been proven to help patients better comprehend what they are actually signing up for. It helps not only to ensure that they know what they're signing up for, but because they better understand it, they're more likely to stick with the trial. It helps with retention rates, and yet we still have people who believe that paper-based consent is easier, or more cost-effective, or more the standard way of getting consent from trial participants. There's some very basics around just plain clinical trial technology adoption. We've got to migrate to this is higher quality data. It gives us time stamps, it gives us attribution, it allows us to know exactly who touched the data, where, when, and how. That's where I would start.
It's got to happen because that's going to make it much more efficient and higher quality data. The second thing I would think about is the ability to bring in source data. Right now, we've got a lot of double data entry where information and data is coming into a source. People are then pulling up that source, looking at it and transcribing that information into the clinical trial data systems. That then requires verification and monitoring and introduces the possibility of errors. We've got to really think about how do we look at some of that source data, not only to enhance the quality, but also to make it much more efficient. There are studies out there saying that 75% of all the data that is required in a clinical trial is already captured in the electronic health record. That being able to port over information from electronic health records into an EDC or CDMS that is going to substantially reduce the amount of information, the double data entry that sites or CROs would have to do.
That's one thing that we should think about. There's also the source data of using devices and sensors. Do you want to ask a patient to fill out how many minutes of exercise did they do each day? How many steps did they take, whether or not they actually ate certain foods according to the protocol? Or do you want to have devices and sensors like their watch or a Fitbit or other things that can help to electronically track this information? Again, with timestamps, giving you greater accuracy around that information and not having to question it or judge it, but rather knowing it's coming over automatically from these sensors and devices and helping you with getting a better sense of how is this therapy actually impacting or improving the life of the patient.
Chris Hayden:
Yeah, I like that. I know we've all exaggerated our steps before Nicole, so I think that is a good idea.
Nicole Latimer:
We've all undercounted our calories.
Chris Hayden:
Yes, exactly. It was only a half a serving. Yeah, I think that's great. It's interesting, EHRs are such a third rail in so many of these conversations because they have the information in there and it's just so hard sometimes to get that data out of the EHR. It's a fascinating conversation as well. Probably again, a conversation for another day, but an interesting one as well. Well, Nicole, thanks so much for joining today, I really appreciate your time and your expertise.
Nicole Latimer:
Thank you so much. It's pleasure talking with you.
Chris Hayden:
Absolutely. Thank you.