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 Today in Health it, this story is a data maturity scale and the Mayo Clinic platform. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of this week in health. It. A channel dedicated to keeping Health IT staff current and engaged. Today's sponsor is Health Lyrics, which is my company.

I develop a quarterly state of health IT report that I can deliver to your team. These insights can help your leadership team, your sales, even your development team. Stay ahead of the emerging trends in Health it. For more information, check out health lyrics.com. All right. Today's story comes from health tech magazine.net, and it's based on a webinar that was done with the Mayo Clinic platform team and Google, and they discussed how the Mayo Clinic platform was used during the pandemic and where it's going and those kinds of things.

To set this up, I want to first talk about a, a data maturity model. That is sort of how I view all these platforms that are coming out, and especially around the use of data. And this is, you know, this is a I in my mind kind of thing. There's really four steps to data maturity, and these are real simple, high level stuff.

The first is you have to use your data well. Right. So the first step, you have a set of data within your health system, you have to use it. Well. The second is to integrate and use other people's data well. So you're gonna be bringing in data sets, uh, from the community and, and other organizations, and you have to be able to integrate that and use it well and generate new insights.

The, uh, the third has a new skill you have to learn and that is to use each other's data well. So it's great that you're bringing in other people's data. Alright, so now . Not only do you use it well, but you help them to use that data well as well, so they can access that data. They're generating insights and they're using it for whatever delivery they're doing on their end.

And then the final step is to invite the larger community to innovate on that data set. And that is when we invite in those developers and the, uh, colleges and universities and say, look, here's a set of APIs. Here's this data set, and it has all the right constructs around it. Uh, security, privacy, legal frameworks so that the developers can access it and do what they do best, which is innovate on top of it.

And from what I can tell at this point, Mayo is demonstrating all four of those. Here's, uh, here's a few excerpts from this, from the article. The first step involved building a solid foundational data letter. So disparate data sets could be combined in a meaningful way. There are three keys to getting the foundational data layer right.

The engineering director for Google Healthcare Cloud and Life Sciences said during the webinar, the first integrating data from multiple systems of record, both in large batch updates and incremental real-time updates. The next was harmonizing the data to common standards and schemas, such as FR, and the third was modeling data for use in AI and ML applications.

He goes on to say, bringing the right data at the right time in the right way to a busy healthcare practitioner is paramount to the overall success of AI and ML endeavors. And many developers have learned that over the years, if you can't get the data back into the workflow in a meaningful way, I. It is really just a, a useless effort that you are.

Response to the pandemic, the coalition's work comprises 15 different work streams. John Halamka, former guest of this week in health, it said from the delivery of ventilators and personal. Uh, protective equipment, PPE to treatment, protocol efficacy and vaccine development. Each work stream has different data requirements.

All told, coalition members are posting more than 700 data sets per day and using cloud hosted services to extract wisdom. From the data without accessing the underlying dataset and compromising its privacy. So those are some key elements of a platform. Looking ahead, the Mayo Clinic has two key priorities for its data, liquidity and analytics efforts.

One is to create a longitudinal patient record, which will ingest different data types and store them using the fire standard. The goal is to reflect an evolving and complete record that's more comprehensive than the traditional electronic health record you said. The other priority is to develop an AI factory that will enable both internal and third party innovation efforts to use infrastructure provisioning and computing resources that are already in place without having to worry about the technology and data access efforts.

Uh, so they can focus more on the, the use of the data and the collaboration. Uh, you know, this is a platt, this is a platform in the true sense of the word. You know, it has a defined, uh, data integration, security, privacy, legal and development framework. Uh, you know, and it's ready for organizations to plug in.

They can plug their data in and they can also innovate on top of that data. I. . This goes well beyond, well beyond the, eh, only, uh, integration mindset that, that a lot of health systems have. When you ask them about their integration plan and they say, well, you know, our EHR provider connects us with so many different health systems, this goes beyond that, and there's so many more applications to this.

You know, it really gets us to the so what? The, so what is, you know, to address a pandemic, to address social determinants of health in your community, to drive better health outcomes in your community, you have to involve organizations outside of your health system. And this is gonna require different data sets, harmonizing different data sets.

Access to those data sets in a, a secure and private way. Uh, there's no way to do that without a data platform. You have to break out of the EHR only mindset and if, if you stay in the EHR MO only mindset, that's gonna keep you at level one of the maturity. I. Model. So, uh, that's the, so what, it's time to build a platform or partner with someone who does, because where we're going in healthcare, the pandemic has shown us, uh, is gonna require a group effort.

And we've known this for a while and it's gonna require a data platform that allows us to do that. 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, health.com, or wherever you listen to podcasts.

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