Jordan Cooper: We are here today with Doctor Rebecca Macheras, the chief Medical Information Officer and Vice President at Mass General Brigham Hospital, Mass.

Jordan Cooper: General Brigham is a health system based in Boston, MA for 3300 beds in 16 hospitals and 15,000 providers are Rebecca.

Jordan Cooper: Providers are Rebecca.

Jordan Cooper: Thank you so much for joining us today.

Jordan Cooper: How are you doing?

Mishuris, Rebecca G.,MD, MPH, MS: I'm great.

Mishuris, Rebecca G.,MD, MPH, MS: Thanks for having me, Jordan.

Jordan Cooper: Yes, great.

Jordan Cooper: Good.

Jordan Cooper: Glad to have you.

Jordan Cooper: So we're gonna cover a wide variety of topics today, and we're gonna start with discussion on clinician facing artificial intelligence tools.

Jordan Cooper: AI has been in the news over the last year with the emergence of chat GPT.

Jordan Cooper: Now there are large language models, also known as alms.

Jordan Cooper: I'd like to start with the discussion about not only what the hype is and what's in the news, but what's actually going on with AI in health systems today.

Mishuris, Rebecca G.,MD, MPH, MS: It's a big topic obviously, and and you know, AI has been around in healthcare for a long time.

Mishuris, Rebecca G.,MD, MPH, MS: I think the advent of generative AI is is truly novel though, and and new in the last year or so.

Mishuris, Rebecca G.,MD, MPH, MS: What we're seeing today is the use of generative AI in facilitating administrative tasks mostly is where we're starting.

Mishuris, Rebecca G.,MD, MPH, MS: Now, that's not to be minimized at all.

Mishuris, Rebecca G.,MD, MPH, MS: As clinicians, we are inundated with these administrative tasks, everything from writing our notes, which which actually has some real clinical value right to things like prior authorizations where reasonable people can disagree about whether there's clinical value in those or not, um and and things that are both clinician facing and patient facing like patient portals and and responding to patient messages through the portal.

Mishuris, Rebecca G.,MD, MPH, MS: And so we're generative.

Mishuris, Rebecca G.,MD, MPH, MS: AI is starting now is is trying to tackle some of those more administrative tasks.

Mishuris, Rebecca G.,MD, MPH, MS: I think the where everyone's hoping it will go eventually is to help us in our clinical work as well, but we are nowhere near that yet.

Jordan Cooper: So before we go into a variety of different, I think we may go into a survey of of different sort of AI tools that are available in large health systems today.

Jordan Cooper: I do wanna kind of backtrack and get context.

Jordan Cooper: A lot of the promise of electronic health records had been to automate clinician workflows to free up their time to enhance the patient provider interaction and improve health outcomes.

Jordan Cooper: And of course, the reality over the last 20 years of EHR implementation sometimes has not borne that out.

Jordan Cooper: There are many clinicians have expressed that there have been longer working hours, more administrative time and more screens getting in between the provider and the patient interaction within that context, would you care to reflect upon the AI's potential to actually deliver on the promise originally made by HR?

Mishuris, Rebecca G.,MD, MPH, MS: Yeah, I I think it actually has huge potential in ways that we have not seen to date.

Mishuris, Rebecca G.,MD, MPH, MS: So to date, we rely a lot on customization, personalization of workflows, making sure that staff are doing what they're supposed to be doing within the EHR at the appropriate times, trying to get everybody to work to the top of their license.

Mishuris, Rebecca G.,MD, MPH, MS: But what we're seeing, as you correctly pointed out, is that there's a lot of clicking, right, and it's very different than if I were handwriting a note in my Manila folder.

Mishuris, Rebecca G.,MD, MPH, MS: Umm Pre EHR and you know the the promise of EHR's was not just to digitize my Manila folder and make my handwriting more legible, although it's done that.

Mishuris, Rebecca G.,MD, MPH, MS: But it was to be able to harness data, right?

Mishuris, Rebecca G.,MD, MPH, MS: Be able to harness data to improve patient care and improve patient outcomes, improve safety, improve quality, improve equity and unfortunately we're like bogged down in the minutia of clicking and screen switching and and task switching of the EHR.

Mishuris, Rebecca G.,MD, MPH, MS: And we haven't gotten to the point yet where we can actually harness all of that data and improve care outcomes.

Mishuris, Rebecca G.,MD, MPH, MS: The fact that we're starting with administrative burden with with generative AI is actually really exciting because it means we can finally get to the rest of those things.

Mishuris, Rebecca G.,MD, MPH, MS: And so, you know, I think that we have been doing our best with trying to improve workflow within the EHR, improve training, improve optimization, you know enhancing the the technology itself.

Mishuris, Rebecca G.,MD, MPH, MS: But to really move the needle, I think we're going to see over the next, let's call it, three to five years.

Jordan Cooper: Umm.

Mishuris, Rebecca G.,MD, MPH, MS: I'm a a shift in how we're able to do that because of things like generative AI and large language models where they are going to be able to take a lot of the kind of more algorithmic work that we do out of our hands and and just be able to do it.

Mishuris, Rebecca G.,MD, MPH, MS: I think right now the technology is new enough that no one's quite comfortable enough to just let it fly.

Mishuris, Rebecca G.,MD, MPH, MS: And so we're all piloting these technologies trying to understand the impact.

Jordan Cooper: Mm-hmm.

Mishuris, Rebecca G.,MD, MPH, MS: We still very much have human in the loop with the technologies that we're piloting today because we're dealing with patients lives right and and we don't want to get it wrong.

Mishuris, Rebecca G.,MD, MPH, MS: But once we get it right, it will have changed the trajectory of how we're able to address the administrative burdens that to some extent the EHR has imposed upon us.

Mishuris, Rebecca G.,MD, MPH, MS: But but to some extent, we've actually done to ourselves in various other ways, right?

Mishuris, Rebecca G.,MD, MPH, MS: So the EHR is often the face of a lot of other things that have gone on regulatory requirements, safety checks, things which people actually really should want, right, like we wanna be safe.

Mishuris, Rebecca G.,MD, MPH, MS: We wanna provide high quality care.

Mishuris, Rebecca G.,MD, MPH, MS: Some of those regulatory requirements have come down to ensure that we are providing high quality care and ensure that we are providing equitable care.

Mishuris, Rebecca G.,MD, MPH, MS: Umm, but they are coming through the EHR and so people look at it as a an EHR burden rather than a regulatory burden or a safety burden.

Mishuris, Rebecca G.,MD, MPH, MS: And and and I guess you know calling them burdens probably isn't quite right either, right.

Mishuris, Rebecca G.,MD, MPH, MS: Like we should wanna be providing safe care.

Mishuris, Rebecca G.,MD, MPH, MS: And but it's coming through the EHR and so to the extent that we can remove some of the administrative tasks that we're doing or remove some of the things, you know some of the clicks were making to to be able to report to regulatory bodies that we've done something because now the AI can just troll the chart and figure out that I did it without me clicking a button that I did do it that will remove the barriers to providing care and the barriers to being able to get to the promise of the EHR, which is to deliver better care.

Jordan Cooper: Mm-hmm.

Jordan Cooper: Mm-hmm.

Jordan Cooper: So when you're, we're talking about a few different topics here.

Jordan Cooper: One is the potential for impact on wellbeing of the provider and as addressing provider burnout, but obviously also patient outcomes.

Jordan Cooper: And you've been talking about how we may evaluate some of that, you said maybe by having an AI eliminate the need for a provider to click a certain button in order to demonstrate that a task was completed.

Jordan Cooper: Can you speak more about the potential for impact on wellbeing of both the provider and the patient?

Jordan Cooper: And from there, I'd like to pivot into evaluation.

Jordan Cooper: Obviously you can't improve what you can't measure, so impact and well being.

Mishuris, Rebecca G.,MD, MPH, MS: Yeah, absolutely.

Mishuris, Rebecca G.,MD, MPH, MS: So.

Mishuris, Rebecca G.,MD, MPH, MS: So for me as a CMIO, this is a space I live and breathe in the intersection between technology and the clinician experience, and the intersection between technology and the patient experience.

Mishuris, Rebecca G.,MD, MPH, MS: And you know, we know that our providers are burnt out.

Mishuris, Rebecca G.,MD, MPH, MS: We know that is increasing over time for many reasons, right?

Mishuris, Rebecca G.,MD, MPH, MS: Not the least of which was the last three years of a pandemic.

Mishuris, Rebecca G.,MD, MPH, MS: And there there are a lot of drivers of burnout.

Mishuris, Rebecca G.,MD, MPH, MS: The EHR is often cited as like the top five reasons for burnout.

Mishuris, Rebecca G.,MD, MPH, MS: It's not quite true though, right?

Mishuris, Rebecca G.,MD, MPH, MS: As I was saying before, the EHR's, often the face of other things that may be contributing to burnout.

Mishuris, Rebecca G.,MD, MPH, MS: We also know that things like, you know control over 1 schedule is a huge driver burnout or lack of control.

Mishuris, Rebecca G.,MD, MPH, MS: I should say and, but the EHR is certainly one of the, let's say, called top 10 reasons for people to be burned out, and it's not the EHR itself.

Jordan Cooper: Umm.

Mishuris, Rebecca G.,MD, MPH, MS: It's the fact that I have to click the button to say that I did something that I already did right and so and what we're really interested in is whether the AI that's coming in to alleviate the administrative burden is actually going to improve.

Mishuris, Rebecca G.,MD, MPH, MS: Well being.

Mishuris, Rebecca G.,MD, MPH, MS: Is it going to reduce how much time I'm spending it in the EHR overall?

Mishuris, Rebecca G.,MD, MPH, MS: Is it going to reduce the amount of time I'm spending in the EHR in the evening when I wanna be home with my family?

Mishuris, Rebecca G.,MD, MPH, MS: Is it going to actually help me improve the care I'm delivering?

Mishuris, Rebecca G.,MD, MPH, MS: So I feel better as a clinician that I am providing high quality, safe, equitable care.

Mishuris, Rebecca G.,MD, MPH, MS: And so those are the kinds of things that we wanna be looking at.

Mishuris, Rebecca G.,MD, MPH, MS: In addition, you know, as a health system, we're also concerned about how much these things cost and whether we're going to be able to afford them.

Mishuris, Rebecca G.,MD, MPH, MS: And so is there in addition to the clinician well being return, is there a financial return to some of these?

Mishuris, Rebecca G.,MD, MPH, MS: Are we able to recoup more of the costs of caring for patients through using some of these tools on the patient side, right, there's a I already in place, right?

Mishuris, Rebecca G.,MD, MPH, MS: Plenty of people interacted with chat bots, particularly over the last three years, trying to figure out if their symptoms were COVID or not, and whether they needed to go to the hospital or not.

Jordan Cooper: Umm.

Mishuris, Rebecca G.,MD, MPH, MS: And so that's an example of of AI in practice that was improving the interaction that patients were having with our health systems.

Mishuris, Rebecca G.,MD, MPH, MS: In addition, we use, you know, umm voice recognition to route calls appropriately when patients call in.

Mishuris, Rebecca G.,MD, MPH, MS: But there's other things that we can do, you know, patients.

Mishuris, Rebecca G.,MD, MPH, MS: I'm a primary care doctor, right?

Mishuris, Rebecca G.,MD, MPH, MS: Patients ask me to fill out forms all the time.

Mishuris, Rebecca G.,MD, MPH, MS: What if I had a mechanism to automatically fill out that form based off of information that was already in your chart, right?

Mishuris, Rebecca G.,MD, MPH, MS: You would get your form filled out faster.

Mishuris, Rebecca G.,MD, MPH, MS: You would get whatever you were trying to get through submitting that form, and I as the provider wouldn't have had to spend my time filling out the form.

Mishuris, Rebecca G.,MD, MPH, MS: From I could have spent my time thinking about your really complex medical history or or something else.

Jordan Cooper: Mm-hmm.

Mishuris, Rebecca G.,MD, MPH, MS: Umm.

Mishuris, Rebecca G.,MD, MPH, MS: And so from a patient perspective, right, it's about how can we improve the efficiency and satisfaction of your interaction with their provider and with the health system in general.

Mishuris, Rebecca G.,MD, MPH, MS: I think also it has the opportunity to improve the equity of the care that we deliver, right?

Mishuris, Rebecca G.,MD, MPH, MS: If we can and look at real world outcomes, the kind of tone of medical literature and identify true personalized medicine without having to know your entire genetic code, I can actually start providing higher quality, more equitable care for you, because I will remove the biases, the kind of inherent biases we have in healthcare within an individual within the system to try and address your needs in a more equitable fashion.

Jordan Cooper: So.

Mishuris, Rebecca G.,MD, MPH, MS: So there's real potential on both sides of the coin, I think.

Jordan Cooper: On that topic, Rebecca, on the topic of equity, I wanna ask you about the quality of the data.

Jordan Cooper: There's been some reporting that a lot of LLM S have been trained on Internet data or on data that's reflective of certain races, predominantly and sexes, white men, which may have a different impact on minority patients.

Jordan Cooper: What's the effect of any inherent bias in the data that was used to train LLM.

Jordan Cooper: When it's integrated into the healthcare delivery, the delivery of healthcare.

Mishuris, Rebecca G.,MD, MPH, MS: Yeah.

Mishuris, Rebecca G.,MD, MPH, MS: So this is something we have to be really careful about and really guard against because you're right, if we train models on outcomes that were inherently biased, we are going to perpetuate those biased outcomes, those inequitable outcomes.

Mishuris, Rebecca G.,MD, MPH, MS: And that's precisely what we don't wanna do.

Mishuris, Rebecca G.,MD, MPH, MS: And so we have to do a few things.

Mishuris, Rebecca G.,MD, MPH, MS: One is to make sure we're using diverse patient data to train the algorithms in the 1st place, right?

Mishuris, Rebecca G.,MD, MPH, MS: So we can't use data from a single health system in a single small town in one part of the country.

Mishuris, Rebecca G.,MD, MPH, MS: We have to use diverse, really big data systems to to train the algorithms in the 1st place.

Mishuris, Rebecca G.,MD, MPH, MS: The second is to look at the performance of the algorithms and make sure that the performance is not inequitable, right?

Mishuris, Rebecca G.,MD, MPH, MS: So if if the algorithm suggests a certain intervention or a certain diagnostic study, is it driving outcomes that are, in the end equitable?

Mishuris, Rebecca G.,MD, MPH, MS: Or is it perpetuating inequities?

Mishuris, Rebecca G.,MD, MPH, MS: And if it's perpetuating inequities, we've got a backup and relook at the algorithm and be able to adjust the algorithm to drive equitable outcomes, even if the algorithm wants to drive inequities because of the data that's been trained on.

Jordan Cooper: So bias is one of many different risks facing health care delivery systems looking to evaluate LLM.

Jordan Cooper: S or other sort of machine learning type solutions.

Jordan Cooper: Other potential threats are HIPAA violations, with an accident that release of personal health information while training the LM, sending out the wrong message without provider review and approval or alert fatigue is a problem where instead of ameliorating the number of clicks and there's a potential that it may cause more clicks, can you speak about some of these threats and and how CIO or a CMIO listening to this episode may find an ameliorate those threads and his or her own institution?

Mishuris, Rebecca G.,MD, MPH, MS: Yeah.

Mishuris, Rebecca G.,MD, MPH, MS: And and it goes beyond that too, right?

Mishuris, Rebecca G.,MD, MPH, MS: It there are beyond just HIPAA violations, other privacy violations or possible data, you know broad data releases is certainly a fear that we all have just transparency of the algorithm and trust of the algorithm is really important for adoption.

Mishuris, Rebecca G.,MD, MPH, MS: If I can't get a provider to trust what the algorithm is telling it, then they're never going to use it.

Mishuris, Rebecca G.,MD, MPH, MS: They'll never pay attention to it, and part of that trust is knowing where it came from.

Mishuris, Rebecca G.,MD, MPH, MS: Being able to know how the algorithm was built, what data was used to to build it, how it was tested, so having a transparent system and and and the list goes on.

Mishuris, Rebecca G.,MD, MPH, MS: I think you know, as health systems are responsibility is to raise the concerns and then address them.

Mishuris, Rebecca G.,MD, MPH, MS: And so if you know to have a framework whereby we evaluate AI and and really any technology, but in today's world particularly AI against that framework where we're going to look and say is this an equitable and it is this driving an equitable outcome is the algorithm transparent, are there sufficient data security policies and practices in place where I can be, you know, at least secure that that the data is safe, kind of whatever happens to it and and the list goes on.

Mishuris, Rebecca G.,MD, MPH, MS: There are a number of professional and and national organizations, and even international organizations that are trying to come up with these frameworks.

Mishuris, Rebecca G.,MD, MPH, MS: Everything from like the White House to Sam, try to NST.

Mishuris, Rebecca G.,MD, MPH, MS: Right there, there are lots of organizations that are trying to come up with these frameworks and I think it's incumbent on us as health systems to be looking at that literature the same way we look at the clinical literature to figure out what treatment is best for hypertension today.

Mishuris, Rebecca G.,MD, MPH, MS: And we have to be looking at that literature and figuring out what systems and processes do we need to have in place at our own organizations to be secure that we are mitigating and and securing against any of those risks.

Jordan Cooper: So Rebecca, a lot of our listeners may now understand that there are some sort of abstract framework that we brought a lot of concerns, a lot of things they should look for.

Jordan Cooper: I wanna dive right into actual use cases, so mass general Brigham.

Jordan Cooper: There you have different use cases.

Jordan Cooper: The AI may be ambient listening.

Jordan Cooper: Maybe portal messages summarizing the chart.

Jordan Cooper: How do you evaluate what the lowest hanging fruit is?

Jordan Cooper: Where do we start 1st and how do we pick a vendor or solution?

Jordan Cooper: Or do we homegrown?

Jordan Cooper: My solution is that we minimize these threats and maximize these opportunity gains.

Mishuris, Rebecca G.,MD, MPH, MS: So it's a great question and your comment earlier is absolutely right, right.

Mishuris, Rebecca G.,MD, MPH, MS: I can't change what I can't measure and so and it also is going to be dependent upon your own organizations, priorities and strategy, right?

Mishuris, Rebecca G.,MD, MPH, MS: So if your organization has a strategic goal of improving clinician well being, you may look at a different piece of technology than if your organization has a goal of improving patient experience or revenue generation and the list goes on.

Mishuris, Rebecca G.,MD, MPH, MS: And so you really have to take it from your organization's perspective about what's really driving your strategy at your organization.

Mishuris, Rebecca G.,MD, MPH, MS: That being said, generally the IHI quintuple framework is a good thing to start with.

Mishuris, Rebecca G.,MD, MPH, MS: If if your organization doesn't have its own strategy, I would start there and then it part of it is also being driven by what technology is available, right we this technology is new enough that there are a few spaces where vendors are focused.

Mishuris, Rebecca G.,MD, MPH, MS: So things like robotic process automation, ambient listening that you mentioned drafting and denovo messages either to patients or summarizing a chart, there are lot you know kind of spaces within the clinical realm that that companies are starting in.

Mishuris, Rebecca G.,MD, MPH, MS: And so it's a it's a triangulation really, of what's your organizations priorities, what technology is out there and and what can you support as an organization moving forward as you move forward with these things, you then have to evaluate them, right?

Mishuris, Rebecca G.,MD, MPH, MS: What impact are they having?

Mishuris, Rebecca G.,MD, MPH, MS: Are they having the impact that you need them to have as an organization?

Mishuris, Rebecca G.,MD, MPH, MS: And so, you know, paying attention to things like, is it safe?

Mishuris, Rebecca G.,MD, MPH, MS: Is it transparent?

Mishuris, Rebecca G.,MD, MPH, MS: Is the data secure?

Mishuris, Rebecca G.,MD, MPH, MS: Those that's table stakes.

Mishuris, Rebecca G.,MD, MPH, MS: Then we have to figure out, is this actually improving clinician well being right our our my providers less burned out are they spending less time in the EHR after hours are returning messages around faster to our patients are the patients more satisfied with their experience with our health system and those are the kinds of questions you have to start asking as you actually implement these technologies to figure out if they are having the impact that that you intend them to have.

Jordan Cooper: So you actually mentioned a lot of interesting KPI just now.

Jordan Cooper: You you said specifically spending less time in the EHR after hours, our patients satisfied that's a little bit more difficult, but you can have a fetish measure for that.

Jordan Cooper: Are they getting faster responses?

Jordan Cooper: Well, that's very discreet.

Jordan Cooper: What time did we receive the message and what time did they receive a response?

Jordan Cooper: I guess I I think keep what?

Jordan Cooper: Are there any other contributing?

Jordan Cooper: Any other KPI is for clinician productivity, patient outcomes maybe you know population health management or is it all on an individual basis?

Jordan Cooper: What?

Jordan Cooper: What would you consider would be the most important KPI you can as we're evaluating AI solutions?

Mishuris, Rebecca G.,MD, MPH, MS: So so again, there's table stakes.

Mishuris, Rebecca G.,MD, MPH, MS: Is it saved?

Mishuris, Rebecca G.,MD, MPH, MS: Is it secure?

Mishuris, Rebecca G.,MD, MPH, MS: Is it equitable?

Mishuris, Rebecca G.,MD, MPH, MS: Etcetera.

Mishuris, Rebecca G.,MD, MPH, MS: And then we're talking about impact and the impact that you're going to be looking for is going to differ depending on what technology you're implementing, right?

Mishuris, Rebecca G.,MD, MPH, MS: It's gonna be different if I'm implementing ambient listening versus a call routing voice recognition system.

Mishuris, Rebecca G.,MD, MPH, MS: And so you really have to then pay attention to what's the intent of the technology.

Mishuris, Rebecca G.,MD, MPH, MS: If we're talking about clinician well being, so we're talking about something like ambient listening to support, note documentation.

Mishuris, Rebecca G.,MD, MPH, MS: I'm interested in, you know what I've talked about the how much time are you spending documenting your notes?

Mishuris, Rebecca G.,MD, MPH, MS: How much time are you spending in the EHR after hours?

Mishuris, Rebecca G.,MD, MPH, MS: Can the are the notes understandable to our patients, right.

Mishuris, Rebecca G.,MD, MPH, MS: Our patients are all reading their notes now.

Mishuris, Rebecca G.,MD, MPH, MS: Can our patients understand the notes?

Mishuris, Rebecca G.,MD, MPH, MS: What's the workflow?

Mishuris, Rebecca G.,MD, MPH, MS: Are you actually satisfied with the workflow of getting of going from the visit to the note and then things like throughput?

Mishuris, Rebecca G.,MD, MPH, MS: And that's where we're gonna have potential impact to the bottom line, right.

Mishuris, Rebecca G.,MD, MPH, MS: The revenue bottom line of the health system, so in urgent care, are you seeing more patients because you don't have to spend as long writing your notes and so you're able to see more patients in urgent care.

Mishuris, Rebecca G.,MD, MPH, MS: And so that's an impact to our financial bottom line.

Mishuris, Rebecca G.,MD, MPH, MS: It's also an impact to our patients, right, because now they can actually get the care that they came to see us for and they're not waiting 6 hours to see us.

Mishuris, Rebecca G.,MD, MPH, MS: Are we documenting better so that we within value based contracts are better representing the risk that we take on with the patient's we're seeing?

Mishuris, Rebecca G.,MD, MPH, MS: So we're better representing how sick our patients are through our improved documentation with ambient listening.

Mishuris, Rebecca G.,MD, MPH, MS: And so those are some of the other things that we're paying attention to as well.

Mishuris, Rebecca G.,MD, MPH, MS: Again, we are very early stage in all of this and and piloting it and trying to figure out what the impacts are.

Jordan Cooper: So we are approaching the end of this podcast episode.

Jordan Cooper: I like that pose.

Jordan Cooper: A final question to you, so many listeners may be thinking about timing.

Jordan Cooper: You said we're on the very, you know, we're in early days here.

Jordan Cooper: If I'm AS CMIO listening to this episode and maybe asking myself, alright, do I wanna go ahead now and be on the bleeding edge?

Jordan Cooper: Or maybe I should wait a year or two so other organizations like Mass General Brigham can do the experiments, preview of the greatest value is, and I can just waltz in.

Jordan Cooper: Once a bugs are fixed and just go in there later and not deal with all the Kingston and hassles right now, how would you answer a question like that?

Mishuris, Rebecca G.,MD, MPH, MS: Umm do you wanna be an innovator?

Mishuris, Rebecca G.,MD, MPH, MS: An early adopter or a late adopter right in the in the grand scheme of the technology wave, what's your appetite for risk?

Mishuris, Rebecca G.,MD, MPH, MS: What's your resources?

Mishuris, Rebecca G.,MD, MPH, MS: What are the resources you can bring to bear in a kind of innovative space?

Mishuris, Rebecca G.,MD, MPH, MS: Umm.

Mishuris, Rebecca G.,MD, MPH, MS: Or are you in a system that doesn't have that kind of those kinds of resources, and you do need to wait for something that's a little bit more proven because you can't afford to get it wrong, right?

Mishuris, Rebecca G.,MD, MPH, MS: I I have the privilege right now of being in an organization that has resources to bring to bear on innovation and and does that, you know, in the clinical space and also in the operational space.

Mishuris, Rebecca G.,MD, MPH, MS: I come from a system, you know.

Mishuris, Rebecca G.,MD, MPH, MS: I came from a safety net system where we didn't have those kinds of resources and couldn't necessarily be on the bleeding edge of technology.

Mishuris, Rebecca G.,MD, MPH, MS: And so it's not wrong.

Mishuris, Rebecca G.,MD, MPH, MS: Either way you go about it, but it it really has to do with your appetite and your resources.

Mishuris, Rebecca G.,MD, MPH, MS: We will all get there eventually, but it's a matter of of when, not if, I think.

Jordan Cooper: Well, thank you very much for our listeners who joined in the middle.

Jordan Cooper: This has been doctor Rebecca Mishuris, the chief Medical Information Officer and VP of Mass General Brigham Rebecca.

Jordan Cooper: I'd like to thank you very much for joining us today.