Today in health, it, the growing power of cloud providers amid this AI boom. My name is bill Russell. I'm a former CIO for a 16 hospital system and creator of this week health. A set of channels dedicated to keeping health it staff current and engaged. We want to thank our show sponsors who are investing in developing the next generation of health leaders, short test artist site, parlance and service. Now check them out at this week. health.com.

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, probably twice a month. , because it's a common mistake. We have made it over and over again. And, , I will. I say some. Controversial things. , from time to time on this show and I get emails about them. , for example, I believe we are getting to a point where a single EHR provider has too much market share in our, ,

, industry. And I've said that a couple of times, , not that I think that they should stop doing what they're doing or, or slow down or that it's their fault. I think that many of the other EHR providers out there have done such a poor job that they naturally have, , grabbed more market share. I think they are very.

, vendor are very, , client focused organization. And if you do that well, and you provide the services that you need to provide and you build a great community around it, you're going to succeed. So I I've said those things. I've said that the AI model specifically around computer vision that are locked into a specific hardware and cameras and those kinds of things.

, is another trap that we're falling into. And, , the cloud providers is another one where we have to be very cognizant of vendor. Lock-in. And we we've talked about abstraction models. Which gives you the ability to move in and out of certain cloud providers. But, , regardless of all the things that we were saying and all the warnings.

We, , we are. It's the same trap that we're. , presented with over and over again. So what's going on now? Well, you have artificial intelligence is taking off and we now joke about it when we have conversations with CEOs. And we have our 2 29 project meetings and whatnot, people will say, , okay, I'll, I'll say generative AI. Well, if you count the number of times, gender of AI said in those meetings, it's probably.

, I don't know, 20, 25 times a day. Because it is. It has such potential in our space has potential in revenue cycle has potential in coding has potential in documentation. And those are just the tip of the iceberg. I believe. So we have these AI models and then you have the, , AI makers and we have open AI.

, anthropic and cohere have led the charge in developing proprietary large language models. And I'm now quoting from the article. That companies are using to boost efficiency in areas like accounting and writing code. And some other things, tie ups between model makers and major cloud companies include open AI and Microsoft Azure.

Anthropic and cohere with Google cloud and machine learning, startup hugging face with Amazon web services, Databricks, a data storage and management company agreed to by generative AI, startup, mosaic ML in June. And then they start talking to these, , CEOs for many businesses, the primary choice isn't which AI model to use, but whether they stay within the AI ecosystem offered by their cloud providers.

And I'm hearing that a lot as well. If you are a Microsoft cloud customer, You are going to be looking at Chatsy PT and open AI. If you are a Google cloud customer, you're going to be looking at the tools that they're bringing to bear. , around machine learning, AI and generative. , Generative AI as well. So, , and if you are an Amazon web services, you're going to be looking to the partners that they are bringing to bear.

Still, if a company chooses a single AI ecosystem, it could risk vendor lock-in within the provider's environment, a set of services said. , the chief technology officer of Euston based BMC software. This paradigm is a recurring one where the businesses, it system software and data all sit within one digital platform and it could become more pronounced as companies look.

For help in the generative AI space companies say the problem with vendor lock-in, especially among cloud providers, is that they have difficulty moving their data to other platforms. Lose negotiating power with other vendors and must rely on one provider to keep it services online and secure. So that's the thing we're looking at. It's interesting. I'm going to deviate from the article now. I mean, there's, there's a bunch of CIO is talking about how they're hedging and they're looking at multiple models and, , what they're doing and there's some good ones in here.

Johnson and Johnson. , is in here. , a one. AI 21 labs. , let's see. , Anyway, a bunch of different CEOs. , speak into it outside of our industry, but still relevant for the discussion that we're having around vendor lock-in and whatnot. Here's what I'm hearing and think about again. The, , the challenges that, that they rattled off, you lose negotiating power.

Have difficulty difficulty moving the data to other platforms and must rely on one pro provider to keep its services online and secure. Not to mention the fact that, , innovation, right? So you're relying on that partner to be innovative. If, for example, open AI and chat GPT. Ends up taking a significant lead and you're locked into a different AI.

A different cloud provider. You may not have access to those things because your data and other things are in that cloud provider. So there's a lot of. A lot of challenges. Here's what I'm hearing in healthcare. I'm hearing a lot of people go with their existing cloud providers. I have not heard of anyone building out their own.

, models, but I have heard of people. , there's really two ideas around AI. Are you going to hear this in an interview I've done with several CEOs. One is what we're calling the easy button. , AI and the easy button AI are those models that you can just go buy, implement. And away you go, that's, , the chat GPTs of the world.

And the other models that are out there, , it's the, , It's it's essentially by it. And use it almost the way it is. Then there is the not so easy button. And these are the specific applications, those models that you're going to train, those things that you're going to integrate into your EHR and those kinds of things, , for that, you're going to need to develop some skills, some different skills than we've had in the past.

Some software development skills, maybe some agile. , project management, , some, , product management skills and those kinds of things. , and again, it's probably in a lot of cases, it's a new muscle that we would have in healthcare. By the way when I'm looking at healthcare again, I stratify this. If you're a small player, you're going to go with the easy button stuff.

And if you are, let's say 2 billion or less, you're probably going with the easy button. You're going to partner with Microsoft. You're going to go with Azure. You're going to go with a chat GPT. And if you are partnered with Google, you're going to partner. You get. You get the picture. , and so if you're 2 billion or less, I think that is your strategy. It's like pick your player. That's the direction you're going. The challenge you're going to have is you're going to lose negotiating power over time.

And hopefully you are adept at negotiating long-term contracts. And I think there's value in doing as long a term contract as you can. , if you're going to be locked in, you might as well be locked in and you might as well get the best deal you possibly can. And then the, , other thing I would say around that, not only the negotiating skills.

But also the ability to manage your use of the various things. A lot of these things are metered type solution. So you want to keep an eye on how much of each one you're using. If you're a larger player you're going to be using, , potentially one or two different models there there's. Again, you can do contracts. You can use best of breed.

And hopefully you're more adept with your data and moving it into various platforms and training it again. The key thing that we're looking at is how do you protect your data? How do you ensure that your data is not used to train a model that you don't want it to train and is used to train the models effectively that you want to train and use internally?

So there's a lot of things to consider here. I am concerned about the power that the cloud providers. I currently have. And I'm hearing it all over the industry of 10% increase here. 15% increase here at 10% increase here. , just across the board, the software contracts. That we have that are coming due are seeing a fairly sizeable increase.

, with some higher than others, but regardless our negotiating power has gotten less and less as we go along. So we have to keep that in mind. We have to worry about vendor lock-in. If you move your data into something, you always have to ask yourself, how can you get your data out? If you move your compute in, how do you get it out? , not only in, , just, just, , basic business practices, but also BCDR if your cloud provider goes down,

What is your disaster recovery business continuity. It's another thing I worry about as well. I worry a lot. It sounds like. But, , but yes, I am worried about vendor lock-in I think the, so what is, I believe we should be as well as we move more and more of our chips into a few players, make sure they are true partners. Make sure you have good contracts and good relationships. And.

, also, , plan for the relationship going sour. And not that it will, not that it should, but if it does. You should have a plan in place. All right. That's all for today. If you know of someone that might benefit from our channel, please forward them a note. They can subscribe on our website this week.

Dot com. Or wherever you listen to podcasts. We want to thank our channel sponsors who are investing in our mission to develop the next generation of health leaders. Short test artist, site, 📍 parlance, and service. Now check them out at this week. health.com. Slash today. Thanks for listening. That's all for now.