Data and the use of data is where health systems are going to start to differentiate themselves. Today we talk about creating a culture to enable growth in this area.


Today in health, it three steps for creating a data to value ecosystem. My name is bill Russell. I'm a former CIO for a 16 hospital system and creator of this week in health. It 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, Gordian dynamics, Quill health tau site.

Nuance and current health, check them out at this week. health.com/today, by the way. If you want to have a story, or you want me to comment on a story? , just go ahead and send it over to me, send it to hello. At this [email protected] would love to do that. , for you and with you, that would be fantastic. All right. Today's story comes from cio.com.

It is by Dan Sievers, who is a CIO. And it is about the three steps for creating a data to value ecosystem. This is so important. I'm going to cover this in my, so what, but I think this is the area that healthcare systems are going to be able to differentiate themselves. Over the next three years.

And actually I felt this way for the last five years. Anyone who can really get their arms around data is going to separate themselves from the field. And we're seeing this with, with the usual players, right. You're seeing, , Mayo do some amazing work. , with an around data. And really push the envelope forward. And so you see those, those leading companies who really have thought through the architecture have thought through the staffing and skills.

And putting the right platform together. , starting to take a lead. And, and I think that's going to continue, but we are going to see some other players who get their arms around this. Move forward. So let me give you a little, a little taste of this. , of this article.

Although many organizations are utilizing AI and machine learning tools as coordinators in their data analytics projects and AI spending worldwide continues to rise. The hard truth is that most data science projects are doomed to fail. How's that for a provocative. Opening sentence. , there are several reasons for these failures ranging from inherent complexity of AIML initiatives, which is true, and the persistent lack of skilled talent.

DC global survey of more than:

This is, , this whole drive towards data and the use of analytics. Is pervasive around the world in every industry. So it goes on making matters worse. While most companies routinely maintain large amounts of data. It is often stockpiled in functional silos and not easily accessed. Or used across these boundaries.

Advances in cloud computing, data engineering tools and machine learning algorithms are also coming faster than products and new processes can be deployed. Then there are the competitive challenges that come from both traditional channels and new disruptive technologies.

To overcome this reality and create new value for customers and shareholders. It leaders must create a community and culture that can accelerate and sustain.

The growth of data science and analytics throughout the company. And it's important. I. He, he goes on to talk about this and I'll touch on a minute. But creating a culture that understands the value of data and how to use data is important. And you can't do that by just hiring a few key people. You have to cultivate anytime you hear the word culture.

It's incumbent upon you to bring people in, to educate people, to bring people along and to have them. , elevate their level of competency and understanding of this and bring other people along the best case scenario. At some point, when you have people all the way up and down the organization, understanding the power of data, asking for data.

And utilizing data, , in areas. I haven't even thought of before. , now you've created that culture. And that is, , that's going to be imperative now in order to create that culture, you have to have some wins and you have to have some platforms and those kinds of things. , so let's go on to some of the things he says here. , one, he goes on to the, the scarcity of resources and because of the scarcity of resources, you're going to want to focus in on highest ROI, quickest time to value ratio.

, when you unleashed the power of data within your organization and you, you get those platforms going. You're going to have people hitting you from all sides. Hey, I want this data and this data and I want to use it to do this. And, and if you give me this data, I'm going to be able to do this return or increase these outcomes. , that's a good problem to have, but you only have finite resources . So. , you do have to identify the quick wins, continue to show ROI. Look for the highest ROI you can possibly can.

So he recommends a center of competency. Be established so that those resources can be shared. , but he talks about one of the challenges is we often put that, that group under it. And oftentimes that results in a misalignment of goals and

Grades delays and ultimately, , increased project failures. And so he talks about a hybrid organizational approach that is necessary so that you keep that data science team close to where the data is actually being used. And they actually act as a bridge between the, , the it and the infrastructure.

And the processes that need to be in place for the data. And the business itself. So it is an interesting hybrid. , group and a hybrid approach. And so they need to be client facing and really understand the business at a very deep level. All right. So let's get to these three things. So setting sights on ROI success, the following foundational goals are critical for developing a strategic plan and process for competency and sustainable enablement.

Number one, create a center of competency projects, often fail because they are developed in isolation without consideration for the entire life cycle of a model, as well as a digital thread lineage and data pipeline requirements. People may hold onto or high data and information. Out of the belief that it may help them personally, this attitude impedes the potential for creating value. When looking for deeper insights, it's important. Understand that data science and analytics are a team sport, creating a center of competency that focuses on collaboration.

Education and inclusion will help build trust between functional organizations. And I think this is important. I mean, the center of competency just flat out makes a lot of sense, but the leadership of that is so important. It needs to have a, someone at the helm that understands building culture that understands.

, inclusion that has a, a heart and a desire for education and taking the organization forward. So something to consider there as you're building out that a center of competency and the leadership of that. All right. Number two, extend data and design literacy efforts. Create a virtual community across the entire enterprise for fielding questions from the most basic concepts to the most complex data science and design thinking constructs.

As part of the center of competency, this resource hub will drive development and administration of curated plans of study ranging from the onboarding of analytics skill level to more advanced data science certifications. This hub will also be a focal point for training and certification of new data scientists.

Within all the functional domains across the company, the goal is to create a cross-functional community that provides support for everyone in their data literacy journey. You know, we did this back at St. Joseph's. So it's so important to have this resource available and for them to understand. , that they are standing this up for the entire organization.

When they have that mindset. They will, , really approach this differently instead of thinking, oh, we're about, , identifying the tools or data governance. They will understand that their role is to enable a team of people to do, to go beyond. It's interesting. When we set up our first. A data governance team. I was asking people to be on it.

And they were just looking at me, like, I don't know anything about this. Why would I be on this? And these were key business leaders. And I started to talk to them about, , how data could be used in their area and how it could be used more effectively. And they started to talk about the things they were passionate about and I'm like, and, and help, , by, by just sparking the conversation, they immediately understood.

The value of being on that group and on that committee. And by the way, in healthcare, we're really good at this. We already have this round, the EHR, and it looks kind of similar around the, around the data side. Right. We have. , the intake of projects and the, , development of the right tools and the right platform and analytics, isn't buying a thing. And then, oh, you're set.

Just like the EHR is not buying one thing and then you're set. It is buying one thing and then buying different modules along the way. And it is, , not only the different models along the way, but how you implement those things and how you, how you, , operationalize those things as well. So it's a very similar project. So we have some experience in doing this. So remember to extend the data and design literacy efforts across the entire organization, and then number three, create a cross-functional and diverse team.

Of strategic thinkers. This provides a company-wide platform for sharing ideas and identifying projects. With the highest potential. It also enables team members to leverage each other's skills and domain knowledge, to co-create new value for customers and shareholders. And he goes on to talk about KPIs and ROI for projects and those kinds of things. And that just gets to what I was saying earlier, which is we've done this, we've done this around the EHR.

So when you're thinking about standing up your data efforts, ask yourself is your data efforts as robust. As your clinical informatics efforts as your EHR efforts are at your organization. And if they're not, they probably should be. At this point, you're collecting an awful lot of great information about the patients and you're doing surveys, you're doing all this great stuff. Why are you doing it? If you can't utilize the information to benefit the community that you serve.

And so we need to be investing in as much in that area. And that's really my, so what on this. It's it's more of a call to action data. And the use of data in healthcare is going to become a core differentiator between health systems. Over the next three years, you, you cannot invest enough time, money, and resources in advancing your capabilities in this space.

, you know, I'm excited that, that this year we're going to have Charles Boise. Who's an accomplished data scientist. , joining us six or seven times on the news day show that'll air every Monday on this channel. And we have Angela Russell also joining us. She's going to be doing a show on our town hall channel.

Which is a different, a subscription on iTunes. But she's going to be doing a channel focused on this space as well. So, , you know, this is not an easy area to get, right. And I expect there's going to be many missteps, but even if it's two steps forward, one step back. I hope to see some real progress.

In our use of data in healthcare, that's all for today. If you know 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, apple, Google, overcast, Spotify, Stitcher. You get the picture. We are everywhere.

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