The Game-Changing Women of Healthcare | Miruna Sasu: Decoding the Data with COTA

Meg welcomes Miruna Sasu, President and CEO of COTA, an RWE service founded by doctors, engineers, and data scientists to create clarity from fragmented and often inaccessible real-world data to provide a comprehensive picture of cancer that can be used to advance carepaths and research.

Meg and Miruna discuss utilizing patient-generated data, difficulties with EMR, the drug development and clinical trial processes, as well as ethical considerations and patient burden. Miruna reflects on her own personal experience with cancer diagnoses for both of her primary caregivers: her grandparents. She also shares her mentorship experience and provides advice to get involved and take control of your care as a patient.

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Episode Credits:

The Game-Changing Women of Healthcare is a production of The Krinsky Company.

Hosted by Meg Escobosa.

Produced by Meg Escobosa, Calvin Marty, Chelsea Ho, Medina Sabic, and Wendy Nielsen.

Edited, engineered, and mixed by Calvin Marty.

All music composed and performed by Calvin Marty.

Creators & Guests

Host

Meg Escobosa

Meg Escobosa has 15 years of innovation consulting experience, focusing on the unique challenges of healthcare since 2012. For The Krinsky Company, Meg leads client engagements overseeing advisory board design, creation and management. She also leads industry research, expert recruitment and trend analysis to support corporate innovation initiatives centered on the future of healthcare. Her background in innovation and strategy consulting began at IdeaScope Associates where she was involved all aspects of strategic innovation initiatives including understanding the voice of the customer, industry research and aligning the executive team to invest in promising strategic growth opportunities. Meg received her BA in Latin American Studies from Trinity College in Hartford and her MBA in sustainable management from the pioneering Master’s degree program, Presidio Graduate School. She is also on the board of a non-profit foundation focused on researching and developing technology to support a sustainable society. She lives in San Francisco with her husband and two teenage daughters.

Producer

Calvin Marty

A man of many hats, Calvin Marty is a Podcast Producer, Editor, Engineer, Voice Actor, Actor, Composer, Singer/Songwriter, Musician, and Tennis Enthusiast. Calvin produces, engineers, edits, mixes, and scores The Game-Changing Women of Healthcare. Calvin is also the creator of the 2020 podcast, irRegular People, among others. Find his music under the names Calvin Marty, Billy Dubbs, Nature Show, and The Sunken Ship. Over his long career as an actor, Calvin's has voiced many Radio and TV commercials for a wide-range of companies and products and has appeared in small on-camera roles on shows such as Chicago Fire and Empire.

What is The Game-Changing Women of Healthcare?

The Game-Changing Women of Healthcare is a podcast featuring exceptional women making an impact in healthcare today. We celebrate our guests’ accomplishments, setbacks, and the lessons they've learned throughout their careers. We dig into the many healthcare issues we face today and how these innovative leaders are working to solve them. Join host Meg Escobosa in conversation with some of the many brilliant, courageous women on the front lines of the future of health.

Miruna Sasu: My grandfather got diagnosed with stage four lung cancer. That was really devastating to me. It was just horrible. I thought my world was gonna end. But my brother went to this daycare across the street. It was a convent so the nuns there had a sister who was going through a clinical trial that was meant for stage four lung cancer patients. My grandfather got into it. It saved his life. So I hope to be in a place where I can do that for others.

Meg Escobosa: Welcome to the Game Changing Women of Healthcare, a podcast featuring exceptional women making an impact in healthcare today. We celebrate our guest’s accomplishments, setbacks, and the lessons they've learned throughout their careers. We dig into the many healthcare issues we face today and how these innovative leaders are working to solve them.

I'm Meg Escobosa. Join me in conversation with some of the many brilliant, courageous women on the front lines of the future of health.

Welcome back to The Game Changing Women of Healthcare. I'm your host, Meg Escabosa. Today on the show we have Miruna Sasu, president and CEO of COTA, a company providing proprietary technology, advanced analytics and expertise to organize complex data, including real-world data to provide a comprehensive picture of cancer that can be used to advance care and research.

Hello Miruna, welcome to the show.

Miruna Sasu: Hi, Meg. Thanks so much. It's great to be here.

Meg Escobosa: We're thrilled. We have come across this notion of real world data a bunch over the last few years, and I've thought what we could do first is just establishing what it is, and then let's get into what COTA does.

You know, real-world data are the data relating to patient health status or the delivery of care. It can come from the electronic health record, claims and billing activities, product and disease registries. Patient-generated data including home-use devices and data gathered from other sources that can form health status.

Would you agree with that definition, or what else might you add to this, or how might you reframe how we think about real world-data from COTA's perspective?

Miruna Sasu: Yeah, I would absolutely agree with all of those categorizations. I would say each of them has different use cases, so you can do different things with the different portions that you just mentioned.

COTA specifically works on electronic medical records, which is a set of use cases that are rather deep, so you can think about electronic medical records being able to be used for things like patient journey activity. Research work, discovery of medicines, as well as developing medicines like for example, putting instead of a control arm in an oncology trial, doing a control arm in a data set.

You can actually do that with electronic medical records, whereas with the others it's a little bit more tough to do. Those categories are perfect and I'm really excited to be talking about all of them.

Meg Escobosa: Terrific. Well, tell us who is COTA? What are you guys doing and who are your clients?

Miruna Sasu: Sure. COTA is a real-world data company in oncology. We specialize particularly in oncology, and what we do is we take electronic medical records from academic centers, from community centers, and we make sense of them. And what that really means is we take that information and we put it in data sets that are structured and organized in such a way that people can actually analyze them at scale at a population level. So we have data sets in different diseases in oncology because as you know, oncology isn't just one disease. It's categorized by hematologic disease versus solid tumors. And then within each of those different categories, there are organ systems, and so each one of those diseases is a subset of disease. And so you have to be very, very careful on how you take the datasets and work with them because those patients are very different from each other. So COTA actually has datasets that pertain to each individual disease type. So what we do is we go into the electronic medical record, we take this data, we put it in a data set, we structure all of the data points. We go into the doctor's notes, which is very, very difficult to do, and pick up data, like for example, what stage of disease is it.

We take that, we structure it, and then once you have that beautiful data set together, we license it out to, for example, pharma companies, biotech companies, and we also provide it back to healthcare providers so that they can do research in it, so they can take all of these patient-level information and perform analysis on top of it to know things like, how did this type of person do in a particular category of treatment? Or did it work to expose this patient to chemotherapy and then to something else like immunotherapy? Did it extend their life? Did it not? What happened? What types of adverse events did they have and why? This is the type of information that we have in our data sets that we can analyze.

So we provide the data and we also do the analysis.

Meg Escobosa: I'm curious, you know, why cancer? Why did you focus and what's the unique benefit of real-world data in cancer care? Because I know an individual's treatment path is so important, you know, where do you start? Do you start with radiation?

Do you start with surgery? Do you start with chemo? And that all has very big impacts on their outcomes. Can you share why cancer?

Miruna Sasu: Absolutely. So COTA was founded by oncologists and this is the area they knew best, but in addition to that, part of the reason is because oncology is serious. It's a very, very serious set of diseases that used to be terminal.

Now, I think I would say that we have enough therapeutic options to hopefully not make it so, but depending on staging of disease, it was very, very, serious diagnosis. And so because of that, COTA focused on trying to cure cancer through data.

Meg Escobosa: Wow. So you've got provider clients, you've got pharma companies and payers that you work with. For the drug makers, how do they use real world data and how does it affect the drug development process?

Miruna Sasu: Sure. So I actually happen to have a really good standpoint on this because I was a drug developer prior to coming to COTA. And I've worked with most companies, especially in the oncology space, most companies that still exist in this space, including COTA, to make real-world data a reality for drug development.

So there are three areas that I would say are really impacted by real-world data in drug development. The first is discovery so discovering new molecules or looking at molecules and how they should work in a set of patients. The second is development of clinical trials, and the third is post-marketing and commercialization of a drug.

Historically, real-world data, as you mentioned, has been claims and billing information. That information has been used for a very, very long time in commercialization and post-marketing, but real-world data like electronic medical records have not been because it's very difficult to curate that data.

Claims - you get the claims and you don't have to do anything to them. Electronic medical records, you really have to go in and make sure that all of the elements are in there. You pull out what's important from doctor's notes, which are paragraphs and sometimes thousands of pages of paragraphs for a patient's history.

So you take all of that information and with the three different parts that I just mentioned, what you can do in discovery is actually test different molecules on a set of patients that are in a database. That's number one. Then in the development space, you can have what's called external control arms, and that means that you don't have to put a patient on a control arm.

What? Who wants to be on a control arm? Right. Nobody, you wanna be treated with the exploratory medicine. And so what we can do now is utilize electronic medical records and patients who have been through the therapy that you are trying to put on that control arm and test that against treatment so that way you can push more patients into the treatment arm to see if you know they would benefit from that treatment.

And then in post-marketing commercialization, you can get rid of some studies by doing them through a data set, you don't have to actually go out and enroll these patients and make them come to the site because you can follow them in electronic medical records. You can just look and do research in those records that those people are going in for treatment anyway so you can actually look at that instead of making them do all of this other stuff for an actual study.

Meg Escobosa: One thing that you just immediately prompted for me is up until now, electronic medical record data has been really difficult to curate, and what do you think has made it possible to access that data?

I know that structured/unstructured data is a challenge, and the unstructured is those doctor's notes that you referenced, are there technologies that you guys have taken advantage of that allow you to do that? And are there other technologies that you wish you had that would help you to achieve more?

Miruna Sasu: Yes. So first and foremost, the type of data that is in electronic medical records has to be curated, at this moment, with a variety of different methodologies. First methodology is you put it through a technical work stream. So technology actually goes in and picks up all of the structured data.

In the unstructured fields, a human has to touch that data because even if a machine goes in and decides, “Okay, this is the stage of disease that this doctor put in here,” you don't actually know for sure until a human person goes in and says, “Yeah, okay. That doctor did say that it was this stage of disease in this medical record,” because a machine will pick up funky things like the doctor's note could say, “Not stage four”, and if the machine picks up stage four, then it's actually the opposite. So the technology can do a lot, but there's a point where it stops and someone has to apply logic to it because we don't have a brain in terms of the technology.

So we say “artificial intelligence” a lot. I would say it's “artificial toddlerhood”, and it's nobody's fault, right? We've advanced it so, so much. But there's a lot that it can't do because it can't apply logic. So if I were to say, “What, what do I want?,” you know, I want Jarvis from Ironman. That's what I want. And that's who we all can benefit from.

Meg Escobosa: So yeah, there's still a huge human component of the work that you all are doing to harvest the insights and data from the electronic medical record.

Miruna Sasu: Absolutely. And I think there will be for a while.

Meg Escobosa: So human researchers and analysts out there, fear not.

Miruna Sasu: Fear not, fear not. This is going to be a needed component for a very, very long time.

Meg Escobosa: Another thing that I have learned, or believe is true, is the idea that using real-world data, helps drug makers reduce the time to development and potentially reduce costs associated with developing drugs.

Is that true? Is that just a myth? What, what are some of those payoffs that you get or the drug makers get from using this kind of data?

Miruna Sasu: Yeah, it is true. There is a lot of efficiency that can be built into the drug development machine with data. In the oncology space, there have been a lot of efficiencies gained from real-world data. There's a caveat here, if the company doesn't have infrastructure to be able to bring the right data in at the right time and analyze the data properly, that may not be realized.

So a lot of folks think about, “Oh, I'm just gonna buy a bunch of data.” If you don't have a bunch of scientists that can organize the data as to the type of data that goes to the appropriate part of the drug development continuum and actually do the analysis that applies to a point in time, it doesn't work.

So I've done it successfully. We've built that machine internally to be able to screen the type of data that you need for the type of question you're asking at the right time, and to build it into the process.

Because pharma companies, biotech companies, they're like, “I've done this historically. I need additional people to do this on top of what I'm doing.” That is not the right answer. The right answer is, “I need additional people. To do this a part, as a part of the process that I'm currently working on. To infuse it into the blood work of the whole ecosystem so that, what I've done at J&J, I was in charge of clinical trials, feasibility and data science. So what we did was upon startup of every study in oncology at the least, we actually took and screened and brought in data sets to look at diversity in clinical trials.

Where can we go find those patients? Well, the answer to that is in a dataset. In a large dataset that you can license, you can find that information and then you can target your clinical trial to those patient populations. It used to be about finding the site that you can go to. I would argue that it's actually finding the patient and then going to them because that is how you don't put burden on that patient and the site, and that is how you can capture that patient in their element.

These people are sick. These people are very sick. You don't want them traveling a hundred miles to the next academic site. Plus, they don't want to be doing that. They wanna spend time with their families. They have just been given a very difficult diagnosis. The last thing you wanna do is have them go through all these hoops to enter your clinical trial.

Meg Escobosa: So are you saying that you are offering a way to get involved in clinical trials beyond the data, or is it literally just taking existing electronic medical record data and identifying patients that way?

Miruna Sasu: We'd like to be. COTA is still relatively junior to this particular use case, but a pharmaceutical company or biotech company can take the data that we offer and look for the diversity of patients.

Meg Escobosa: Design their study based on that.

Miruna Sasu: Exactly. And that's what I used to do at J&J is instead of putting, sort of saying, “Hey COTA, can you do this for me?”, I would say, “Okay. Give me the data, I'm gonna look internally, and then I'm gonna go out and do this because I have this clinical operations group of people that does this work”, but I'm going to guide them in the direction of which I'd like to go with this data.

Meg Escobosa: That's amazing. Well, and I know that the whole way that clinical trials are being done is going through an innovation as well, just this idea of digital clinical trials or studies,allowing patients to check in from home and things like that so that's a, that's a totally different element, but this whole field is desperate for ways to reduce costs so that we can speed the evolution of the drugs and get them out to market.

Miruna Sasu: Absolutely.

Meg Escobosa: And ensure they're effective. Of course.

Miruna Sasu: And safe. So safe and effective is a, is a really key piece.

Meg Escobosa: Absolutely. So, yes. I'm curious how you got into this field, in general. Like, bring us back, I know you did an undergrad at Colby, pursued a PhD almost immediately. Tell us what was behind your, you know, ambition in your career back then, and how you made the transition to where you are today.

Miruna Sasu: Sure. I'm happy to. It's a bit of a funny story actually. So I did do my undergrad at Colby. I was, to be perfectly frank, I couldn't hack it in an animal lab. I just couldn't sacrifice the animals.

It was really, really tough for me and I could never get over it. And so I actually did my PhD in a plant biology setting where a lot of my work was in disease modeling. So I actually have two doctorate degrees, one in biology and one in statistics. In part because I couldn't hack it in the animal lab, I just, I honestly couldn't sacrifice the animals.

So what I would've said first is I'm a biologist at heart with a really solid set of statistical tools, which is why the data piece of what I do really speaks to me.

I was born in Romania and my mother, when she came here with me, we immigrated here in 1991, after the Berlin Wall fell. When we came here, my mother's degree was not recognized in the US. And so she actually ended up having to work on an assembly line and going back to school. So while she was doing that, my grandfather and my grandmother moved here to take care of my brother and me.

And my grandfather, almost a year into him being here, got diagnosed with stage four lung cancer.

Meg Escobosa: I’m so sorry.

Miruna Sasu: Thank you. It’s actually a good story, which led me to where I am today, but that was really devastating to me. I remember coming back from the hospital with that diagnosis and I think I vomited almost immediately. I thought my world was gonna end, but my brother went to this daycare across the street. It was a convent so the nuns there had a sister who was going through a clinical trial that was meant for stage four lung cancer patients. They recommended it to us. My grandfather got into it. He screened in. It saved his life.

So when that happened to us, I said, “You know what, I wanna do that for other people. It's my life's calling.” I felt like it was a miracle. It was truly a miracle. Stage four lung cancer is really, really advanced, and he lost probably 90% of his lung capacity through it, but he survived. And he survived and passed away of old age a year and a half ago.

Meg Escobosa: Oh my goodness. Amazing.

Miruna Sasu: It was incredible, and so I know what it's like to go through the clinical trial story. We were very lucky that he screened in.

Meg Escobosa: That's the biggest challenge, absolutely. Meeting the criteria.

Miruna Sasu: Absolutely. And we were also lucky that he was placed on a treatment arm because if he was part of a control arm, the same thing might not have happened. That trial was run by BMS, Bristol Myers Squibb, and so that is why I went and worked there for my first job out of the doctorate degree.

Meg Escobosa: That is really unique. Coming full circle with your family.

Miruna Sasu: Yeah. So I hope to be in a place where I can do that for others.

Meg Escobosa: I wanna go back to this notion of reading doctor's notes. So I understand that COTA has just recently gotten into working with Google on their natural language processing technology. I'd love to hear how you guys are taking advantage of that and how it's helping you with your ambitions.

Miruna Sasu: Sure. I'm happy to speak about that. It's something I've been looking at for a long time, actually. In my role at BMS and J&J, we seeded companies and we worked with companies. To bring electronic medical records to the forefront in these data sets. And at the time you could really only do the obstruction and curation of this data through people.

So it was all, you know, people reading these records and actually entering them into a data set. Now, technology can help us funnel in the data that is structured already. You know, all of those dropdowns that doctors have to go through and they, you know, they find very annoying, all of those beautiful dropdowns are actually gold to a researcher.

So, you know, the demographics of a patient, you can funnel that directly into a data set. The parts that you can't funnel in are things that are found, for example, in a PDF document that's a attached to a patient's record, that's text, or in the doctor's notes, because with every record and anytime that you've ever been to a doctor and you've had a visit, they have to enter a whole bunch of information into a paragraph. And so that information sort of sits there untapped. And until now, you had to have a person read the whole thing, and you can imagine, you know, a person has tens of years of history that's like thousands of pages. So now what we can do with natural language processing is actually do a fair bit of screening of that text and highlight. So you can actually highlight through natural language processing where the person doing the abstraction should go, where their eye should go.

The next generation of this work is to actually have the natural language processing algorithm go through and pull the information out itself so that the person actually doesn't have to read at all. They just have to kind of like, be like, “Okay, that is correct, or that is not correct.” So they're just checking.

And then the last iteration of this is that the program takes it from the record itself, and no one has to check. That is quite far away. But what we're doing with Google is that interim step. There are companies that have done this work and have said, “This is not possible right now. We can't do it.” We believe that we can. We have actually proven it in a couple of disease instances where our models are trying to get relatively precise. And it has been working for us. So the technology is getting to a place where we are doing this faster and easier and better every day, and Google is helping to amplify that with us. And so we're really, really excited about the partnership that we just commenced and are going forward. And so we're really hoping to see some benefit, not just on the accuracy, but also on the speed by which we can do this because ultimately, we wanna do this as fast as possible.

Meg Escobosa: Where are you finding the greatest traction in your client base?

Miruna Sasu: It's more the drug development world. So it's more biotech, pharma, biopharma. I love our payers. I've gotta say that, but they're not moving fast. I think that the model that is currently there is fine and it works and it works for them.

It’s actually, pharma, biopharma, biotech that is looking to speed up the trials and bring more efficiency into their clinical operations because as you know, R&D costs a whole lot of money and a whole lot of time, and patients don't have the time.

So part of my role at BMS and J&J was how do I get this drug to the right person faster? And to do that, you can use data. And you can use analytics so that it's not a guessing game anymore. It's not a, “Well, let's just throw the net out there to patients that might want to join the trial and screen them from there.” You can have an educated guess as to who might benefit and who you should actually try to enroll.

Meg Escobosa: Are there any messages to patients, like how can patients help themselves get informed and find the trials that need them to participate?

Miruna Sasu: I love this question. So I wanna assure patients, I mean, my grandfather was a patient; in fact, my grandmother was also a patient. She unfortunately did not make it. She had non-Hodgkin's lymphoma. She had a good five year run, but unfortunately she succumbed to the disease. But I want to tell you the experience that I went through was a very good one. And not just because it saved my grandfather's life, but also because the company that Bristol Myers Squibb, that we went through the clinical trial with, was very caring about how they treated their patients.

And I think part of the issue for patients is there's not a big trust sort of level there, and it's also very confusing. So you often don't really get the protocol behind the study. You often don't really understand what's going on, and sometimes, and a lot of times it's really hard to enroll because you don't know where to go.

Does your oncologist even do this? And so I would say you gotta get informed. You gotta understand what is out there because your oncologist, we love them, but they're people, right? And so they may only know what they know. And so try to get as informed as possible or have a loved one help to get informed, as informed as possible, as to what exploratory medicines might exist out there. And take it within your hands to go out there and see and ask, “Hey, what about this new immunotherapy? Hey, what about this new cell therapy that's out there? Can we try that? Is there any reason I'm not an eligible patient? Or if I am eligible, where can I go?” And I know that's really hard to do because after you've had the diagnosis, it's emotional, stressful, hard. You may have to travel, and it's not the best, but it can save your life or it can save your loved one's life.

Go to clinicaltrials.gov, see if there's something out there. If not, ask your oncologist. Talk to that person and try, you know, really try, because I believe that there is a right set of treatments and a sequence of treatments for everyone. I believe that.

Meg Escobosa: That's very encouraging and hopefully people will be able to take advantage of that with your resources, with COTA's services, I wonder if that encourages drug makers to explore. Do more discovery, do more work to come up with new medicine. Do you feel like that is one of the impacts that you're aiming for is to just enable that?

Miruna Sasu: Yes, absolutely. I think that some drug developers have already started utilizing the data for these things. If you look across the landscape of oncology, a lot of times you are seeing that these treatments are targeted towards specific biomarkers, and I would argue that that is a direct implication of how we use data, because if we didn't look at data, we would not know that biomarkers are really helpful in how we treat.

We would not know that a specific biomarker that is on a particular tumor can be targeted from the immunotherapy realm to the, you know, sort of cell biology, cell therapy-type of realm. And so it has already helped and there's so much more than we can do. You've heard this concept of sort of digital twins, that can mean a lot of things, right?

So digital twins can mean a digital, organic twin, like a set of organs on a plate. It can also mean a fake patient in a data set. So there are companies out there that have slides that basically mimic an organ system. So you can test. It's amazing. You can test a particular drug or a set of drugs and see how it works in an actual organism that is on a disc, on a plate, essentially, which is amazing.

It’s physical. Sometimes it’s made up of technology, right? It's not like a living organism. The other part of this is a synthetic patient that looks and feels like a real patient that you can test different parts in a cyberspace. That also exists. Not everybody's taking advantage of it. COTA doesn't currently have those, but it's something that we could make. Currently, we just take electronic medical records. We make sense of them, put them in data sets, but it's something that we could statistically go after, because we have all of these example patients in our databases.

And so drug development or discovery, the discovery side of drug development has so many possibilities. I could see it blossoming over the next few years.

Meg Escobosa: Wow. So, where would you like to see COTA go and what do you think is possible with advancing drug discovery so that there are more tools in the tool belt for the oncology world to treat and address cancer patients' needs?

Miruna Sasu: So clinical genomics, what we call clinical genomics data is incredibly important and does not currently exist end-to-end. Genomics data comes from when you screen a tumor for either a whole exome or whole genome sequencing, or a really deep set of biomarkers. That data actually exists because a lot of oncologists order those types of assays so they know what kind of cancer they're treating, and so they know what targets, in terms of drugs, that they can bring to the system. So they know what targets to bring in and so that data sits out there in either PDFs or some other, you know, some other mechanism.

We can pull that in and match it to the patient's record. So you know the demographics about the patient, you know their previous history, you know, their diagnostics, you know, you know all of that. And also, you know, this genomics information, well, that is a plethora of information that you didn't otherwise have to do researching.

So that is the first step for COTA is we're gonna go, we're gonna go and develop this type of data. We already have a partner. In fact, I'm really excited to announce that this partner signed with us this week.

Meg Escobosa: Oh, congratulations.

Miruna Sasu: Thank you so much. We're really excited about it. I can't release it quite yet, but it's okay.

Meg Escobosa: So we'll be listening. We'll be listening.

Miruna Sasu: Need to, need to listen. We're gonna make an announcement for sure. It's a phenomenal partnership that's gonna give us some really deep genomics information that we can match with our clinical data. So we can make a holistic system and we can really understand the patient in a holistic way.

Meg Escobosa: Talk to me about your career, sort of any mentors that you have had, and how you think about mentorship given now that you're in the CEO role and you're in an inner leadership position, curious about your experience with mentorship?

Miruna Sasu: Mentorship is a really big part of who I am and I have some mentors that I consider family, and part of that is they, you know, I've been given some really tough love before, which has shaped me and shaped my career.

I've been told a lot of things by a lot of people, and one thing that I would say is that feedback is rhetorical. That's one.

Meg Escobosa: I'm gonna write that down.

Miruna Sasu: And the other is everyone has a lens. I think the most important part of what I've heard from my mentors, over time is, “Be yourself and be genuine”, and that is something that I have had to really embody myself because I am a woman, I'm a relatively young woman in this space. And I am a relatively young woman in leadership. And it was sometimes it was really hard to be myself, but if you are not, everyone can tell.
So I often felt like I should be tough and I should show that I'm tough and guess what? I'm a big squishy, squish ball on the inside, and sometimes I have to be tough, but I don't like it.

And I do think that feedback comes with, you know, another thing that you mentioned about mentorship, feedback, right? So I am a mentor to both men and women of, you know, various places in their careers. They often ask me, “Well, what do you think as I'm going into, you know, leadership roles, how should I handle that?”

I always go, “It's about people.” Leadership roles. Yes, you have to know what to do and how to do it, but it's about people. The greatest accomplishment of your life as a leader is developing another leader, and being there for them. And enabling them and empowering them to also make a difference. That's how you amplify your impact. And so it's always about, “well, how do you do that?” And I say, “You have to give feedback.” And people say, “Good and bad?” And I go, “There is no bad feedback. It's constructive and I'm gonna give you constructive feedback with my lens. So you have to put it into the context of your lens.”

Meg Escobosa: That's a really, really nice way of thinking about it because, you know, it takes away the potential fear of criticism or whatever the anxiety might be that feedback presents. That's really nice advice.

Miruna, this has been such a pleasure to connect with you, hear your story, hear about COTA. We love the work you're up to. Good luck and we'll be looking for the big news and big announcements coming out.

Miruna Sasu: Thank you so much, Meg. It's been such a pleasure to be here and I really appreciate the opportunity.

Meg Escobosa: Thanks for joining us for the Game Changing Women of Healthcare, a production of The Krinsky Company. Today's episode was produced by Calvin Marty, Chelsea Ho, Medina Sabich, Wendy Nielsen and me, Meg Escobosa. This podcast is engineered, edited, mixed and scored by Calvin Marty. If you enjoy the show, please consider leaving a rating and review wherever you get your podcasts.

It really does make a difference, and share the show with your friends and colleagues. If you have any questions, comments, or guest suggestions. Please email me at meg@thekrinskyco.com and you can visit us on the web at thekrinskyco.com.