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Bill Russell: Welcome to this week in health it. Where we discuss the news information and emerging thought with leaders from across the healthcare industry It's Friday, February 23rd. This week do we need a national unique patient identifier AI in the clinical setting and how to get started with artificial intelligence project at your health system this podcast is brought to you by Health lyrics a leader in digital transformation in healthcare.
This is episode number seven my name is Bill Russell recovering Healthcare CIO writer and consultant with the previously mentioned Health lyrics today. I'm joined by a great friend of mine and a wicked smart data scientist Charles' Boicey Wicked smart. That's for my friends on the in the Boston area Charles's Chief Innovation officer for clearer sense.
Healthcare analytics organization specializing in bringing Big Data Technologies to Health Care prior to clear science Charles' was the Enterprise analytics architect for Stony Brook medicine in his role. He developed the analytics infrastructure to serve the clinical and operational and quality of research needs of the organization.
He was a founding member of the team that developed the Health and Human Services award-winning application now trending to assist in early detection of disease outbreaks utilizing social media feeds Charles' holds at the MS in. Technology management from Stevens Institute of Technology and is the president of the American nursing informatics Association good morning Charles and welcome to the show.
Charles Boicey: Good morning, Bill Good To Be With You.
Bill Russell: Wow you have so many bios out on the web. I hope that one was current you do a lot of speaking in a lot of different things is that one pretty current. Yes, pretty current with the exception. I know past president of the American versity implemented Association past president.
Charles Boicey: Oh yeah, and I felt I've got one to add for you. I'm not a professor at Stony Brook Medicineyou. I help them develop out there applied analytics master's program, so I fell three classes, and I teach three classes there wow so so can we start calling you professor? Yeah, but you have to use it at like an assistant level Professor not going to the full fledge.
Bill Russell: Yes the professor. I you know the now. We've worked together for a while in fact. We were at competing health organizations, and I was so impressed with your work. I brought you into to talk to our team, and then eventually to the leadership about what could be done with big data and big data analytics and.
And I've tried to hire you on several occasions, you're the hardest person to hire. You just you're so loyal to the people you're with so I you know I've been stilted a couple times, but I still enjoy our friendship because you really cause me to think about this about these topics of machine learning,
AI and big data, so one of the things we do is we like to ask our host to give us an idea of what they are currently working on or what they're excited about so what have you what do you have going on these days?
Charles Boicey: Sure, and so there's a couple of things. I'll kind of start out from you know the academic perspective or education it applies the F in the academic environment, but a lot of the work that I do of you know with clear. Sense is actually education if you think about where you know Health Care. You'll far as analytics machine learning AI what not many of us are still in a spreadsheet stage right some of those who progressed to data Marts and so forth with some visualization, but very few of made the leap into these big data Technologies.
You know Advanced analytics Ai and still torso a lot of what I do what I really had excited about doing you know is educating clients. Prospective clients and as you know for the last eight years or so I've been Evangelize the you know this throughout the throughout the community and what not. on me.
Yeah on the professional side some of the things that we're doing with our clients that I'm really really excited about are in areas of operation of operations as well as it being clinical practice on the operation side. It will take a look at like this a patient access if you will we all have a problem with you know keeping you know the clinics.
You know full in a capacity and whatnot so a lot of the work that we've done lately is developing out models that you know to take into consideration patients future or past attendance habits. We take a look at the weather. We take a look at traffic patterns. We take a look at a lot of different experience factors.
Not all into you know a model and then give our give our clients the ability you know univ the day. These are the folks that are likely not to show up tomorrow, but not just that give them a list of those that are likely be able to fill fill in those slots. If those folks. You know indeed don't show it so they're able to make those phone calls at night to you know get a definitive.
Yes or no. I'm going to be there if it's a no response somebody else in and we keep our clinics. Also. That's pretty exciting on the operational side. Of on the clinical side again. You know just working on the you know situational awareness type applications. You know loading up. You know Lloyd machine learning environment with you know all the physiology all of the you know the laboratory data physiology data, and really identifying patients are likely to crash in the next 30 to 60 minutes so again we get into the AI side of things today.
We'll talk about AI versus what? Call intelligences. So so those are the kinds of some of the things that you know that we're working on better. A pretty excited it has to be practical and we'll get into that little bit too,
Bill Russell: so we're just we're still scratching the surface. I remember one of the first use cases.
You gave me was hasn't CIO. I was sort of struggling with EMR implementation in our doctors for. Um we're struggling to figure out how to utilize the EMR effectively and you told me about how you use data science to collect all this infrared Big Data collect all this information and use machine learning and data science determine which doctors were actually having.
I difficulties utilizing the EMR you were actually able to identify them by name so that you could really do targeted Education and Training of those doctors based on how they were getting lost in the system. How many clicks because we have all that data right every click that happens in the Mr.
We're tracking it. You just said there's value in this data will figure out how to use, and I thought that was an interesting use case you still that was plan yes to using that use chaos that?
Charles Boicey: Yeah, that's the HIPAA law and so forth and you know an interesting you know an interesting side note on that was and we did exactly what you described, but when you tell somebody on the shoulder because you know where they are at a particular time, and then you know say hey we noticed you know in the last couple hours.
You're doing these kinds of things and look like you're struggling a little bit and that you know kind of flipped them out initially by after. They come down you. Can you can have that intervention really kind of help them on their way and then try. Got them going it going forward. We had we had somebody we had a we had a clinician that was actually getting 150 a lot of notices alarms a day in which is quite extraordinary and then once sitting down with was Emily.
We kind of helped him walk it through and they got you know much less you know alerts and so far, so yeah, there's no simple logs. There's tons of data in there. Yeah, yeah obviously
Bill Russell: there absolutely is a cultural aspect of this and we'll get to that in two in the second segment when we get there.
Let's get to the news you, and I didn't know you and I can have really long conversations. I'm going to try to keep this to a half-hour. I doubt that but let's let's see what we can do so we'll take a look at the news. Here's what we do charles' and I have each led to the story to discuss, and I'm going to take us off the story.
I picked is from the New England Journal of Medicine catalyst. Has time for Unique patient identifier for the US? So it's a 30-minute show, so I'm going to not sure all the credentials, but a couple of a couple of names of note. Who are the authors of this are Pete sued David Bates John Wonka CIO Beth Israel Deaconess David Bates CIO, Brigham Women's Hospital and Aziz Sheikh who's a professor in the University of Edinburgh?
Here's a little synopsis of this story. It's time to revisit congresses fears about the unique patient identifiers and Institute of system that will enable more complete and accurate patient records so a unique patient identifier was proposed as part of HIPAA but was shot down for products that concerns the primary reason for their argument.
Is that a national unique identifier at least a better care, so here's another quilt when accurate information is attached to the right patient data accesses timely and appropriate care reduced and health information exchange becomes easier. Within organizations as well as between so and they also go on to talk about how some states have already implemented this the state of Nevada and Minnesota.
And they say you know we can see how those go and scale them up. They close with this so with billions of dollars having been spent on EHR implementations the Healthcare System must vigorously investigate more efficient ways to connect fragmented patient data an effort that is increasingly relevant to the as the US moves from fee-for-service to value-based care, so I.
So true. I'm going to go a little bit of a rant here because I think this I think they accurately capture the perspective of a physician, but I'm not sure that's the right lens to be looking at this so you know their argument is. Let's create a longitudinal patient record so that we have all the information at the point of care.
Great, no one's going to argue with that. I think you and I would both agree that a truly complete longitudinal patient record would improve care, but here's where my path sort of diverges with where they're coming from and I believe we should put the medical record in the hands of the patient not the health system.
If you really want to change health care. We have to free the data and putting the patient at the center of the equation instead of the health system or or Pharma or pairs or or the EHR providers or even research? Years it is really going to do that so when I throw that out. I typically get three three kind of push backs.
There's there's the argument against giving the patient data that Judy falter was was caught at spazzing, but she's not the only one there's plenty of Physicians who have said that and they essentially say you know what the patients wouldn't know what to do with the data if we gave it to. And you know there's just a certain level of arrogance that goes along with making that statement.
I may say that to to my five-year-old. I don't have a fire, but I may say it to a five year old, but never really to a grown adult another argument. Is that it will expose data to theft, and you know that sort of has a level of hypocrisy to it because in 2017. I just going to give you some stats real quick so in 2017 Healthcare had 477 breaches and 5.7 5.6.
Million records were lost and that followed a 2016 that saw 450 breaches and 27.3 million records lost and the article actually pulled those from said we're making progress because we went from 450 breaches in 2016 to 477 so we're slowing down the rate 27.3 million records lost you know. It's it's it's crazy.
You know I have a stack of identity protection offers on my desk from various Health Providers and seriously my credit card has never been stolen from Apple. We've seen models from really smart people who show that this is a viable off option like the blue button initiative from Park, and I know I'm ranting here.
I'm going to this second, and I know that really puts me over the edge on this. Is you know you just can't have it and I know that HIPAA says that we can get our medical record and in most cases. You know we'll get it in paper or Worse will get it as unstructured data, and and the reason that's worse is they don't even do.
As'ad courtesy of putting it on on paper and paying for the ink and the toner they make us do it because the next healthcare provider. We have to figure out a way to get it in whatever form they give it to us to them. You know and we've talked about this. It's not like we can't share discrete data.
Elements. We've had the technology since the 90s and the health systems either choose to prioritize not prioritize data sharing or they don't have the appropriate skills or they don't have the right incentives to get this done, so. Yeah, when I'd rather see here, and I'm ranting on what I don't like about it what I'd rather see is sort of a change in our thinking of a patient-centric approach, which says let's get let's get the medical record in the hands of the consumer so epic concern, or suspend your fees for developers and implementers and allow that data to flow out into the into devices that can actually be mobile with the consumer because the consumer is.
Only constant at the point of care. I'd like to see us move from from hl7 to apis. I'd like to see a new model where we have maybe a whole person profile. You know our health record our fitness or food are purchasing information so that people data scientists like yourself can really do some things with it, but also that the consumer can benefit right so the consumer is consumer can say I want to participate in this.
In this study or or quite frankly they can sell the information a lot of Health Systems. Do end up selling the information either directly or indirectly through through third parties. That's a long rant, but I know it's hard to follow Ran, So. Let's change. This up a little bit your data scientists and talk to me about how the patient identifiers would make your life easier as a data scientist or.
What would you be able to do if the federal government mandated and identifier that perhaps you can't do today?
Charles Boicey: well. Couple things first I'm going to agree with what you just said even though. I hate to and we didn't have any we didn't have any pre discussions on this, but you know. I you know apples apples going to leave the way we know they are they're doing it right now.
They've been working on for quite some time. Of the the data absolutely in the hands of the patient we have you know Technologies allow for that. You know block during being being one of them. They'll let the penal patient decided in terms of you know potential emergencies and so forth is there a need for a you know an identifier for data science no, but I think you know kind of stepping back.
Is there a need for regional. You know health information exchange there actually could be a national health. Information exchange that we could actually do in surrogate apply empi to those patients based on a whole bunch of various you know characteristics that you know really on the line understands how that's done doesn't have to be mandated know it can be done they can be done as a surrogate so I think that you know I think one right.
To I like to see let's get away from Regional exchanges and then regarding. The data science you know profiling whatnot so work that we're doing at University, California remaining with the Institute of future fridge future health is exactly what you describe what building what we call Persona codes, so that basically is your is your profile and its unique.
Not because it's Unique with a number attached to it because it's Unique in all its characteristics that is you and that doesn't eat your profile as much different than anybody else's profile. It's a combination of physiology. It's a combination of you know what labs have been attributed to you.
You're either eating patterns or your exercise patterns. There's a whole bunch of ways that we can identify you as you without you know imposing a you know. The national identifier to so from that data science aspect. Yeah absolutely it sure would make it easier, but the scientists are supposed to work around issues like that and so that's kind of you know how I would approach
Bill Russell: that it's interesting sort of flies in the face of the we're going distributed with black chain over everyone almost agrees the over the next five years will go to distributed ledgers and records and whatnot.
And this sort of flies in the face that says they close keep it centralized. Let's you know let's create an index that we can utilize one on
Charles Boicey: now different consumer is a right. You know consumer has been will continue to you know eat into Healthcare. I think for the better, and you know we'll have to do it the consumers.
You know consumer Works
Bill Russell: absolutely right, so let's let's kick to the Second Story here, and this is your story so take it away.
Charles Boicey: OK, this is on the topic of AI and I think we'll be able to get a little bit controversial here. I think I'm going to bring that last segment let you give a little bit more so this this article is by Michael art January 30th in Health Data, and AI is disrupting clinical practice, so I'll check isn't implemented matters and absolutely and.
I'll Jen to get into it, but I'll go with a story really quick so back in the late 80's early 90's at at LA County USC. I'm you know I'm also in your not trauma nurse and whatnot we did a lot of predictive models and so far. They look with the doctor William Shoemaker who started the Society of Critical Care Medicine.
We actually go predictive models that for patients in trauma. That would predict depending on the therapy what the outcome would be we made a really big mistake that can we call it prescriptive and. X the collisions went nuts they don't want a machine telling them what to do, and this is really what this article is all about.
This is you know this is almost. Oh my gosh this of almost thirty years later, right? So again with a I we can build out you know beautiful models that. I would like to say can assist I call intelligent assistance with you. I don't like the idea of using this technology to tell somebody like to do I'd rather produce a cognitive trigger.
This is what is described in the in the article if I can give you a heads up that something's going on that you may not have been aware. It aware of. That's fantastic then you can you know make a clinical decision and move forward, but so here's here's a couple of quotes. An answer do is skepticism and that's what we definitely encountered back, then you know still plenty of positions of clinicians have skepticism despair, if not outright hostility.
And you know a couple other ones is really it's interesting you really can't force these issues if you come up with these great models one night. You really can't cram it down. Anybody's throat. You. Can't say surprise. You know here's what the diagnosis is somebody has slammed that fist down and say hey.
Forget you. I'm going to go back to what I've been doing for the last thirty years, so there's an adoption, so how do you get how do you did that adoption and really this article points out that there needs to be an adoption and the Really the way you do it. You don't Black Box any stuff many folks out there.
Have you know their models that other proprietary models, and you know this and the other thing you've got to show how you got to you know how you got to where you got what did only two used. What way she attributed to them you know? What was a neural network that was employed was a random Forest?
What did you go through all the way to the process to get to the point that you're at now, and how accurate is how can I accurate is it? You know you have an Roc curve the show you know that you know various matrices and so forth of what's the Precision? What's the recall you have to be able to demonstrate that and you have to be able to demonstrate it was their data.
And you really can't make statements like you know you know this model work everywhere because they won't do a very good job specific well Works in Southern California. Isn't going to work as well in Sarasota, Florida. It's going to need some tweaking because of the demographic nature and even some of the the external factors, so I think you know Mike put it put a really nice.
You know package together. Yes, you know saying yes, you know a high is important, but we need to kind of go through it in a you know adoptive. Kind of way not just you know kind of throw it out there, and you know where does it? Where does it fit in best you know don't try to make a pain? That's all that you know trying to solve problems that are you know already out there
Bill Russell: absolutely the you know so and this is now the job of the leader either the informatics leader or the data science leader or Chief Information officer its cultural change.
And it's really interesting because you know you sort of mentioned, so let's first of all let's give. So this is really it's an article from Healthcare it news, so it is a plug for the machine learning and AI sessions of HIMSS in Vegas in a couple of weeks March 5th the project manager. They quote the bunch in this is jeff Axt, Sir project manager and system analyst.
In the IT department of the hospital for special special care in New Britain, Connecticut, and he does say you know if you go into an apartment and say surprise. This is the diagnosis from a machine. You're just I mean you're just going to it is a visceral reaction, but I think that's also why someone like yourself has been successful.
You know you have that that clinical background and being in the ER and really understanding. How it how these things. Play out and how technology is adopted we almost need more clinicians to get into this space so that they can they can help people to make those transitions of saying you know I understand that the AI model isn't perfect, but neither is a human human isn't perfect either right and so if the two can figure out how each other and as you say you know those cognitive.
The triggers that that help you both become better that the clinicians are training the AI to be better in the AIS is helping the clinician who's you know busy running from Patient to Patient to see something that maybe they didn't see and and that that transition is going to be interesting so anything else you want to say about this article.
We're going to jump right back into a i in the next segment, but anything else you want to say about this.
Charles Boicey: No, that's fine. It will continue that will continue, and I'd like to you know kind of bring in my students take on it as well Center next segment. That would be interesting right so the second segment.
Bill Russell: We typically talk about leadership or Tech talk and clearly we're going to jump into a i and so give us a couple more use cases around Ai and health care. What do you see it? Well actually let me. Let me step back. Uh are you doing work outside of the US with a I at this point?
Charles Boicey: Yeah, absolutely we're working in the UK was in the mental health Arena so in in the UK the number one cause of death for males under 50 suicide, and we're taking a little bit different approach and that the concept is to identify.
Those at risk, but you know we're never going to be able to I don't believe will be able to actually determine where lightning is going to strike. We're going to take a you know. We'll let you know where the funder storms are and then those that might be affected by those thunderstorms, and you know you can you know take the necessary action so the idea is to really understand.
How some of the factors that are involved in you know somebody you know making a choice on that so this. Were you know big data and in you know the data science comes into play because we got to bring in you know social media for that. We've got to bring in the temperature panels. You got to bring in past.
You know suicide patterns. We've got a with consent. Bring in the various patients. You know social social feeds and whatnot so you think about bringing all that in and then let the folks that are following you know those patients. You know giving them a heads up, so you'd hey these are the folks that are you know you know successfully any particular point in time and that really changes.
You know as the days go by so enough information so that you know they can you know reach out and make sure closer okay, and whatnot that is a little bit different approach.
Bill Russell: That's fascinating then. Actually pretty relevant given the Florida school shooting that and I actually wrote this down, and you know lightning and thunderstorms.
We're probably not going to be able to predict that this student at this time is going to go into this school and do this this action, but but the whole idea of thunderstorm. There's enough activity going on that you might want to take shelter or you might want to look into something so data Sciences isn't that the point of saying?
You know it's this person at this time, but it is at the point of saying. Hey there's. There's a storm forming over here. We might want to might want to get in front of that out. Is there a difference in the UK versus the US in terms of adoption. I mean, are they more prone in the UK or are we more prone in the US to be adopting a I take models.
Charles Boicey: I think it's pretty much the same it meets with the initial skepticism which is important because it keeps us on our toes, so it's it's it's not you know tell me show me you know you know prove it to me
Bill Russell: So real quick. Give me three AI Health Care models in health care that that that you've seen that are better effective, so you gave us a mental health give us give us a couple more real quick.
Charles Boicey: Sure, I think we did mental health the patient deterioration that I talked about earlier patients that are likely that you know crash patients that are likely to enter a sepsis pathway so treatment can be you know begun you know earlier outside of that. We are looking at you know deep learning machine learning finding those those patients that are likely to be and we're doing this for staying in particular that are likely to be open toe.
It problem, so you think about all the different data points that you can bring in and do those triangulations, or what not you know identifying. I think even from the data science then necessarily an AI, but it data science. You know finding those those patients within our populations that are pre-diabetic are there are hypertensive yet.
I'm diagnosed the other UK on the other UK project is an identifying patients that are have a fib that are not being treated. Just four stroke, so there's a lot you can do with the data that we have you know you know initially I think that's how data science makes its initial wins, and yeah,
Bill Russell: so I mean so you have you have Watson and we've seen some some crash 'n burn type scenarios with Watson.
Specifically MD Anderson was a was a crashing bird kind of thing and when we talked about it when I've talked about it with others. They said you know it was the quality of the data. We couldn't get the data to the point of actually being able to do things. We wanted to do was that because there is a data quality problem, or is that because we're not choosing the right use cases
Charles Boicey: sure so IBM does some wonderful things that in a I want you to particular was with a system developed to answer questions.
Bill Russell: Yeah, like Jeopardy.
Charles Boicey: I'll keep it that you know. I'll keep it as simple as that so you can ask a question if the if the answer is within the confines of Watson. It's not there then there's no response. Yeah. There's a lot more. There's a lot more again. This is where. I would I really think that it's really important that we think about how these Technologies can assist us.
As you look different intelligence,
Bill Russell: so what what's gives an idea of some of the some of the good data sources that you're utilizing. I assume you know bedside data is pretty consistent right. It's it's sending you the I mean that data is very consistent, and you're consuming probably as much of that bedside data that you can possibly can whether other sources of.
Of high-quality data that you're utilizing.
Charles Boicey: Yeah, sure so anything off of physiological monitor ventilator smartphones, so you think it would you know anesthesia machines they all are accurate pretty accurate and so they are accurately stated you know every now, and then you'll get a weird signal somebody will turn a stopcock, and they get a CDP of you know 13.
It jumps up to 300. Or an arterial Imus is occluded in you know that jumps up those can be taken care of with them within a system. You know laboratory data coming in pathology all of the universe or system. You know you know very clean especially you know if it's been ontologically right size, but we can make we can we can fix that but.
It really is you know what what data? You need to do. Whatever. It is you're going to do with it. If you try to chase 100%. You're going to be in trouble and within the realm of data science we can make we can make. We can help out if things aren't you know totally perfect? There's things that we can do there's methods to account for missing data, or data?
That's you know outlier or out of range or not expected
Bill Russell: At the risk of going a little bit over on this episode, then how does that? How does the health system get started
Charles Boicey: with their sure sure sure so umm. commitment. To do the education process to really understand what it is you want to do before you jump into it.
I see Healthcare organizations that higher data science teams that we don't know what a data scientist is going to give you my definition. I say I participate and data science. I do not call myself a data scientist, and I'll tell you why the rigor of a Ph.D program. Prepares you for the regular that's required for data science.
It's absolutely essential because you can get sloppy you can get lazy you can you know jump off that you know the long track and you can really lead an organization. You know down the wrong path folks like myself are very assistant to data scientists if scientists on my team are Ph.D. Folks. I work with them.
We get to where we need to go so I think that. Before you got there higher the team make sure it is you understand what you're hiring what you want them for and then maybe bring in some folks to help you you know put that team together because if you don't know what data science is really all about what it is scientist is you can be six months down the road.
Oh now. You know lost six months, so that's kind of a. As much time as you can on on education
Bill Russell: yeah, I don't say I made I made some mistakes there. I brought in some data scientists, and they were immediately gobbled up in the and pushed back down into the the day-to-day analyst, and yeah, it was an awful waste.
It took me about six months to to extract them out of some of those projects and get them too. Focus on a little higher level things so
Charles Boicey: ended and built it from the interview perspective. Just one little tidbit if you're if you're you're interviewing again scientists, and you don't understand what the heck they're saying then you probably are interview data scientist.
Bill Russell: Yes, that's probably true. You know they they did the distinction. I found was they don't answer questions they tell you which questions you should be asking. And that's correct it. They look at the data, and the data informs them and it's amazing the number of tools. They have good ones having their and their bag so time to close show favorite social media push to the week.
I'll start it off this is from William Walters, and it's just a it's a comic strip. And it has a gentleman sitting across from a receptionist and the receptionist saying to him you cannot list your iPhone as your primary care physician so charles' for you. What's your favorite social media post?
Charles Boicey: Sure my social media posts today comes from a colleague of mine Brian Brian Norton Brian Norris colleagues work together for the years, but his Twitter. Handle is geek underscore nurse, and he put out this week fellow nurses. We need to elevate our seat at the machine learning AI tables bring that clinical digital Acumen to bear.
I think we need a nursing Coalition of nursing data scientists folks driving the digital age forward with a shout out to my. Self and Judy Murphy with at IBM
Bill Russell: awesome, so that's I assume you're going to be at HIMSS you're going to be a HIMSS.
Charles Boicey: I'll definitely be there as well as all my students will be there and they and I the last kind of shouting part.
They cited on mostly Millennials. They cited on the side of a i r. You know the systems telling people what to do versus distance assisting people. I just want to get that out too. Yeah, I tried
Bill Russell: I trust the machine where that I trust the person had to do that. I would love to I'm going to be at him as well, and I would love to catch up with you and your students.
That would be a lot of fun. So that's all for now you can follow charles' at N2 informatics RN on Twitter, and me at the patient CIO, and don't forget to follow the show on Twitter as well this weekend HIIT and check out our new website and charles' where you called your calling in from Jacksonville.
Yeah, call me from Jacksonville, Florida, Clarence at percent right so I will one of the things we do on the on the website. You'll notice is the image is usually a Skyline of where the guest is calling in from so we will have a Skyline of Jacksonville Jacksonville has a Skyline I soup. Yeah, take Jax Beach or maybe just a beach show that's probably good way to get ya.
Well if you like to show, please take a few seconds give us a review on iTunes and Google play. That's all for now please come back every Friday. Where we will do this again with another great thought leader the next week Doctor David Benson was going to be here two weeks David Baker CIO for Pacific Dental.
I think that will be an interesting conversation because that's also the week man, and so that's all for now. Thanks for joining.