So I think there's a couple of major experiments out there, but I honestly can't say I can talk about what I think are the headwinds for a lot of them, but I think it's tricky for example. So, there's a cue, which is basically the merger of data with ____ services, right?
Yup, yup.

And then, but the other major experiment is, umm its uh its uh hows it what's the name? Syneos.

yup.

And there a merger of sorta pre and post-market Everything you would wanna do in terms of services. Two different approaches in a sense. I don't know which ones are gonna win, but I can see problems for both of them. I think they're struggling exactly the same way that farmers are struggling, they're trying to do different things. But I don't see any natural any when I started that it was blue sky. People needed so many services. And and you can charge a lot now, it's much more sophisticated. And I don't see that sort of blue sky run anywhere. Umm And so, the traditional model of the zero, which is... We'll put people on the ground, you pay us and we'll put either clinical research associates to gather the data or the salesmen to go do stuff and all that. There's no margin there anymore. So they there looking one solution is, Well, we're gonna use data, and we're gonna apply sophisticated things to that, another model is, "Well we're gonna capture, we're gonna be everything that you would wanna be. But I don't know that either of them. I think there's problems with both of them, For example, Iqvia.

Okay.

There was an article in Forbes a little while ago, I think May something, I don't know if you saw that, but I can't remember the name of it, but the guy talked about whether the price can support what they've done can support, the stock price. And he made a few very cogent argument there about the difficulties of trying to combine it. Fail, like they did. And then the privacy issues with the data and what happens if the regulations get tighter on private use of data and stuff like that.

mhm.


for Iqvia, or example, I would think that's gotta be number one at the top of their list is to figure out how to... You say You're gonna apply all this data to services. How exactly is that gonna happen you know?

So in your position? Just focusing in on a Iqvia in your position. Since the merger been a year and a half or so, you haven't really seen any difference in the Quintiles offering, is that can you maybe describe any difference you might have seen, or maybe some of the issues that you think could come up with this combination.

I know that there was stuff in patient selection, where they applied some machine learning and data to, I think they improved patient selection in maybe beyond, so they were able to demonstrate a more rapid trial... Start instead of maybe eight months, it was two months or something like that. I don't know the specifics on that one. But I have heard of success there, but when you think about the range of zero services, just if you take any particular splice drug safety is an example, you have to report adverse events, but you also have to work with regulations, and you have to interface with the government and then you have to submit reports. There's all these different areas, which theoretically are very right for improvement. But to me, the devils in the details its like... You have to there's no way around the hard work of getting domain experts who also understand machine learning and looking at what's there and trying to figure out where you can apply stuff. And there's absolutely a lot of potential, but there's plenty of places to go wrong to you get guys who, if you don't have the attention of your scarce resource of machine learning, guys, if you have domain experts that can't think out of the box, and change the business model in order to apply the machine learning and use the data, if you... One of the classic problems in trying to apply data to an area is it looks like you have a whole bunch of data, but when you get into the governance of that data, for example, who really owns it? Who do you have to get approval from in order to use that data, you start getting in to the different programs, that that data is from, or the restrictions that were originally put on it or they use that restrict us from other things. And by the time you're done, a lot of times you end up with far less data than you thought you had. So this idea of just having data, one of my questions is, what does that really mean? Can the data be applied? Can you use it, do you have to go back and renegotiate agreements and things like that? So those sorts of things I see are serious challenges for anybody. Obviously, this is basically, I'm talking about all of healthcare, but so if you're gonna say, We're gonna improve clinical trials, for example, we're gonna run a virtual clinical trial, so the patient can run.