BTOES Insights Official
September 21, 2020

BTOES Healthcare Live - SPEAKER SPOTLIGHT : Data Driven Decisions: How to create demand for actionable insights and develop trusted partnerships

Courtesy of BaylorScott & White Health's Maggi Savo, below is a transcript of his speaking session on 'Data Driven Decisions: How to create demand for actionable insights and develop trusted partnerships' to Build a Thriving Enterprise that took place at BTOES Healthcare Live - A Virtual Conference.



Session Information:

Data Driven Decisions: How to create demand for actionable insights and develop trusted partnerships

  • How to get to the heart of the matter
  • Discuss how to translate executive and non-technical requests into actionable insights
  • Discuss how to develop trusted partnerships to get Analytics at the table earlier
  • Throughput: one off vs standing request

Session Transcript:

Next guest, Maggie Saddle is a healthcare improvement analytics director at Baylor Scott and White. Maggie, please do join us. She is working to mature data and analytics for performance and innovator, And innovation. At Baylor Scott and White Health, ... was a healthcare improvement director, focusing on analytical maturity to achieve continuous success in value based care, a shared savings. She'll share with us methods and tips to develop demand for integrating data entry strategy. And I have to say, I was just talking to Omega beforehand. We have here this global audience with Africa, Asia era, the United States. And I just learned that Maggie is just down the street from where I live in San Antonio, those coming from Austin, Texas to the world. Thank you so much, Maggie, for sharing your expertise and journey with us today.

Thank you for having me, I'm so excited.



Heather, Scott and White Health is one of the largest non for-profit systems in Texas, and one of the largest in the United States.

We have over 50 hospitals, 7500 physicians, more than seven million patient encounters a year.

Suddenly quality alliance is the affiliated ACO for the organization.

We're a subset of providers, and overall the physician population, and we have partners in key markets that you see highlighted on the screen there, to address patient needs.

We also work together as one large, integrated network queue for at risk. They are at risk for all of our member lives, And we have an extremely successful. We continue to have success is one of the top 10 ACOs in shared savings for two years in a row now with CMS, So.

We are aggressive in what we do and quite successful.

So today, my goal is to share with you methods that the QA uses that have contributed to our data driven approach, how we've really developed trusted partnerships with between analytics and our leaders and will reveal the importance of asking questions, how to translate those requests into actionable insights. Then how we've been able to get analytics included indiscretions earlier and how we manage ... and prioritization to stay efficient.

Communication executives, clinicians' analysts, they all speak different languages, some of the words may overlap, but they're all stated, Interpreted and delivered differently.

Get comfortable and help your team get comfortable with asking what else.

Eventually a root cause will be identified.

That's it.

Keep asking questions until your description of the ath meets that if the requester.

five phrases that incorporate translation and repetition of the ads, the dialog that occurs when a request is translated back to the requester, will develop a more comprehensive set of needs. It's a really critical part. Here's a couple of examples on the screen that we use with our folks that have really worked well for us.

Screenshot - 2020-09-21T203724.645Then, Pilot Process, that works, to share both soft skills and technical tips.

As analysts learn and collect facts, Find a place to store those and support knowledge share.

For example, patient demographics are most similar at Facility AB and 1 2, 3, or technical tips like flags, stored procedures. Keep those items in an easily accessible location, and include them in your onboarding, exercises your processes, your standard workflow.

The quicker your new hires can get up to speed, the quicker their analysis, because that's what everybody really wants.

The ability to ask and answer questions is critical to learn, going back to soccer team and his method to draw out beliefs about a topic.

So, how do you teach your team kind of ask strategic questions, help them define a good question?

All questions should have a purpose, but be really careful. You don't disguise an Indian as a question.

Avoid, I don't think it should.

Or have you considered?

Keep questions clear.

Keep them simple.

Keep them brief.

Keep them thought provoking.

Avoid yes, no answer.

Define, clarify, and focus discussion.

And finally, always ask, what else?

This is a great technique, actually, picked up from a physician training course on communication.

Give this a try in your real life.

Finishing your grocery list, Ask your family.

What else, wrapping up your one-on-one with your manager.

Then what else?


Now here's a real example.

We received a request to create a Power BI that shows, what percent of our facility manju is a BSW QM.

So, for a system, with 50 hospitals, 160 plus outpatient clinics, we thought this would be a great exercise for new hires, nothing like throwing them to the fire.

Btog CTAMeeting one, questions went something like this.

What facilities doing flared up all of our hospitals, and what type of visits?

Inpatient ED, and outpatient.

Some Arvs: OK, so then you hire, It's like, Great that, That makes sense to me.

Meeting two, This intellectually look She hadn't realized that We actually have two EHRs and finding one data set that could be the source of truth was nearly impossible.

So as we discussed, visit types, data available We kept asking the inlets what pets?

In response, her questions became clear: simpler, which helped shape her approach.

When we finally got to the point that visit types were going to be identified differently, across the data service is being use of facilities being included.

She began to think about commonalty, how it could be grouped, and find a way to present very complex data in a simple to understand visual.

So going back to the need for a process to share skills and tip.

We use analysts, recaps. These are provided in our huddle are performed in adults.

An analyst recap the work they completed last week. We asked them to answer two questions.

What did you learn that can be applied in your future work?


Did you learn that you can avoid in the future?

Inner huddles, that first question, what do you learn, is usually soft X, one leader?

They think of facilities as brick and mortar, where question two is usually more technical.

Don't use patient class type to identify, visit type when you're working across multiple databases.

And so, we always end that ability to go back and end the discussion with a summary, translate the word into your own needs, You understand it better, and you develop develop a better product.

Let us stories, depend on what you're looking for. We don't all like romance novels.

We don't all live action novels, Numbers table's data. They scare even our best leaders.

The challenge we see on a daily basis is enabling our analysts to tell a story using words their audience, in digestible chunks that the audience can feel confident retelling and sharing.

Truth in, our analysts are socially awkward and interpret it.

So finding a way that they can connect with the leader and get that leader booked on the data story.

All, while empowering is socially awkward, amazingly intelligent team is critical to success.

So the steps on this slide are an ever evolving process that is continually, challenge and improved upon.

Our four step process aligns nicely with Plan Do Check, Act.


A good plan establishes a problem.

It identifies objectives, creates testable hypotheses.

Use your summary of the apps that repeating the ask in your own words to define the problem and create context.

In the beginning of my presentation, I said, Yes, your analysts to end the discussion by summarizing the ask because they understand it.

That keeps analysts' aligned to our business.

It also keeps your business partners or your audience engaged in the data story you're going to tell.

Develop your character.

We speak to employers, payers, in terms that the audience understand if you're going to speak to finance guys.

Big fan, if you're going to talk to physicians and nurses, make sure your content in your story is clinical in nature.

32Then, set expectations, identify data limitations, and be clear in what can or cannot be delivered.

do be simple visual, such as histogram, stacked bar charts, line plots. Those are all more easily understood.

Use them to craft your narrative.

OK, excuse me.

Yes, Sorry, and then crafter narrative keep your audience focused more fancy visuals.

Beall cool, your users don't have time to figure out the conclusions that you wanted them to take away.

Tell them, tell them what you told them.


Identify insights that are available to the visualization.

How many patients needed to be adherent hit Target. What patients are most likely to engage? How many patients haven't had the CPUs?

Help the team define success by identifying insights that are meaningful to the team taking action.

Don't give nurses, in saturated to classic, can't do anything with it.

Keep the insights relevant to the team, going work, and help them correctly identify, interpret the data, interpret the visual, and align what they're trying to do with the results, and the goals, to make sure everyone is on the same path for success.

X, recommend actions based on insights that are specific to your character.

So keeping that spread of the story, moving along, explain what the official shows, how it can be used in developing improvement initiative.

We're getting a iterative in our process that goes back to tell, tell them what you told them.

The most advanced analytical maturity models and capabilities leverage analytics to influence strategy and support.

Transformational improvement.

Care management is our largest headcount in the QA.

We spend a lot of time integrating their patient outreach with Quality Improvement Initiative.

For example, we use predictive modeling to identify members most likely to engage with care management.

Then prioritize those members to schedule discharge visits, follow-up visits, gapped closure, breaking down the result into bite sized chunks.

Helping our users find where they get those chunks.

Helping them connect those data insights to the improvement work they're doing, and building their confidence that they can speak to your visual has led to a very successful integration. And that's part of why we keep iterating on this plan.

It's just like the analysis that we perform, Start simple.

Ensure your users are successful.

They'll come back asking more complex questions, then build to that foundation.

Leverage your foundation and your user engagement to progress towards more predictive insights, That keeps us closely aligned to strategy, and continually improve it.

The Quality Alliance began our analytic journey, very descriptive in nature.

We, over measured 50 plus quality and utilization member measures, please, don't ever do this. It is way too many.

We provided a ton of results describing what happened, but we didn't identify why we didn't connect the opportunities to a business strategy.

Screenshot (4)Analysts were invited to meetings, were in the driver's.

As we mature analytics started being used by our executives and business partners to make decisions, expectations, increase, analysts needed to be owners, parton, and drive the use of their data.

At the same time, our business focused on a narrow set of measure of actionable metrics, that connected to our strategy.

We went from 50 plus to a manageable him, Our analytics improved their understanding of how measured for being action to the business piller or partner that they were aligned to quality utilization, Great examples.

This allows them to leverage their understanding to evaluate correlations between outreach and performance.

They presented these correlations to leaders and partnered with them to develop improvement initiatives.

Our analysts also took ownership of weeks of meetings. We increased the frequency of those meetings, many of them weekly.

Our analysts drove agendas.

They focus on education around the tools available.

Each analyst presented at least monthly to their business plan, preventing insight's, correlating outcomes, and initiatives together, identifying actionable populations.

Our analysts started developing skills behind that, the mandolin There they were becoming subject matter experts or their business partners.

They not only understand the intimate details in the data, but they're able to explain how those details work related to them.

They developed their characters. They kept the content relevant.

Then, we increase transparency of the work analytics is failing.

We prioritize work weekly.

Our analysts are fully transparent in the work they completed last week. The work they have on chart this week and the order in which they're going to complete that.

Our business partners have full authority to change prioritization, to keep analytics aligned with changing strategies and initiatives.

Change happens Daily.

Analytics needs to be part of that change so that our team always feels, right, there are a core part of what we're doing.

Currently, we've started integrating machine learning to gain efficiencies, develop clinical programs, and drive improvement since July, so, just a couple of months, since July, we have released three predictive models.

A high need, high cost risk stratification, jatra patients at risk for hospitalization.

And, it risk stratification using a wellness questionnaire.

These models are each being used to support strategic initiatives.

Mostly for our care management and our clinical excellence teams.

three models.

2.5 months.

Yes, we started working on models before July.

But, because we use weekly prioritization, we were able to keep pushing lower, busy work requests to the bot.

Keep these model development, keep leveraging machine learning, that aligned with our big strategic initiatives for the year, so, for care management, We are combining a member, breslin, their likelihood to engage and, or utilization data, to develop a staff, for prep, for practice outreach, transitions of care, and alignment of members to appropriate respects.

We are putting patients with more social determinants with our social work, We're putting people with more complex diseases aligned to our ends.

Screenshot - 2020-09-21T203724.645For our clinical excellence, we're also leveraging those same determinants and real-time cluster analysis to identify correlated data elements, two population cohorts, so that they can design programs more efficient.

How many patients overlap with major, complex diseases and multiple complex, multiple chronic diseases? What's that overlap look like?

Do they need separate clinical programs?

Tell we're leveraging machine learning today.

Our transformation has been successful because we've developed our lists in the subject matter experts, integrating them into the teams they support.

This is progressed our culture from descriptives to predictive and actionable analytics.

Our care management team, prior to ..., of course, had a workstation or analytics on their floor.

They wanted to blend in less socially awkward, super intelligent people there to help them understand, to help them connect the piece.

Our Clinical Excellence team includes analytics and our team meeting pair, truly integrated with our business.

This is also created shared expertise within our analytics.

Because we do those teach facts. We put all those lessons learned, those gotchas, those technical tips, We integrate those into our wiki where people can go find them, use them.

Then second, we continue to focus on data literacy, and teaching our business partners and leaders, how to use visualizations.

This supports and allows us to advance complexity of insights that are integrated into improvement and development work much quicker than we used to.

When technical folks think of throughput, it's around data transfer, bits per second.

What I really want you to think about is capacity, prioritization, and effective.

Use some type of time tracking.

There are tons of free apps out there, There's some cheap pay for service ones.

Big systems have time tracking, use something.

Start with simple, large, poorly defined bucket where your team can track hours.

Do you begin to evaluate how many hours are spent meetings?

You're going to cry, Then you're going to find a way to eliminate waste.

You're also gonna determine what business partner or pillars or service lines.

More requests are consuming the most hours.

You'll begin to track different fields, requesters, databases use.

This will help you put an injective number to the hour here until your teen cries Hong Kong.

What's that point, where you have too much work? People are spread too thin and you're going to start sacrificing quality just to get work done.

Time tracking helps you identify that.

32Use that to support our request for more FTEs, to scope, future work, to plan what's coming down the pipeline.

Our team functions, right?

Around 90%, which took years before my leadership to get to, and that number is really high compared to other teams. So don't be shocked when you start this, and you realize what capacity, or how much work you're doing, compared to how long it takes you to keep the lights on.

For prioritization to be successful, offer ques should have a business sponsor. The higher the better.

Ideally, somewhat, at least at the VP level, we're above with visibility to broader scope, system strategies, business line strategies.

This will help keep your work relevant and aligned.

We all hadn't worked with too many cooks in the kitchen, so keep your prioritization team as small as possible.

This will help you get a decision and have accountability.

Review and prioritize your ask, at least on a monthly basis.

This ensures that any new work added continues to align with other initiatives and strategies.

This visual, over on the right is how we prioritize one of our team's hours doing quick math, that adds up to 100.

As I mentioned, we function in 15 functions at 90%.

So my recommendation would actually be to change the hours to 85 to 90 of total hours.

Using this structure, there is a balance between building clean data and infrastructure, providing insights, customer service, the operational work to keep the lights on. It's a really important bucket as well.

This team request new work about quarterly, and we'll prioritize worker hours right around one or two quarters worth of work at a time.

And absolutely, there have been times when an urgent request will need to be addressed.

So we start ask our leaders to reprioritize.

We want to make sure our teams are always working in alignment with our leaders and system strategies.

We use people surveys, we use engaged surveys in our system.

That alignment that your team's work feels connected to system, initiates and system strategies, huge driver.

We score really well on engagement, because people understand the process and their contributions on a daily basis to move in strategist.

So the visual on the last slide showed the buckets that we use to think about time and work, find a method and a structure that works for you, where you can identify daily, monthly, or quarterly items that remain constant.

When our analysts have been supporting a request without change for multiple data refreshes or multiple months or quarters in a row, we look for a way to production Ally's that into a product.

It's transition from our analysts, to our reporting team, which frees up an analytic resource, standardizes the product, and helps improve our infrastructure.

We're all dependent on each other, and very inner, inter-related foster analytic roles, just as we are with our business partners.

We keep our ETL or our data warehouse team away from our business partners, they get to work on data, quality, infrastructure, ingesting are pair files, and integrating with our EHR. He don't need to ask the questions to our leaders.

They need to have a mean request, define deliverable, help our team be successful.

Always try to use prioritization and transparency to your advantage, then identify that iterative or phase work as early as possible.

It's those projects that come across your desk where you're like, this is kind of the thing.

Create that high level scope, Create the context, set expectations around it, then identify and know what additional requests you anticipate coming and what additional questions you're going to develop.

Then as they do move that to a parking for later phase, don't just forget about it. Be transparent with that parking lot. Let folks know what works you have waiting.

This will always keep your books focused.

Allow for new development in future phases.

And aligned to strategies?

Identifying additional request is also important, as we do. We transition our work from an analyst to a s.s.r.s. Report Writer.

That transition becomes more successful so that the Report writer isn't blindsided with Well, what about this, and I thought we discussed that They know where to find the parking lot, they know where we, where we store additional requests.

Then finally, start in the shallow end.

Don't dive headfirst your sensation, transforming data uses its heart.

So, hopefully, you've found some key takeaways around how to empower your analysts.

How to create good data stories, how to integrate data literacy, and education of your stakeholders, seem to a iterative cycle. To keep working churning along, to keep your work and your analytics progressing and aligning with strategy. So that your leaders feel as empowered as our leaders do to give analytics a seat at the table to treat them like a quality team member, care management, team member.

Those are really critical to helping your data transformation atta system.

That is my contact information, so I am more than happy to always field questions.

Maggie, thank you so much for that. We have questions that have come up during your presentation, and I'll relay those to you, so. The very first one is that, if you can back up a little bit, and just kind of give us a little bit of the overview of the journey. In terms of a timeline of where you started, You know, in the healthcare organization, where you do.

What has the journey been like for you, and for your team, where you started, how you started, and then how things have evolved in the last few years.

Yeah. We said, the quality lands. The ACO arm has a relatively new business unit in terms of our health system.

So, we've been around for about 5 or 6 years, and we really started descriptive in nature.

Mean, we were reporting performance rate after performance, right, And we weren't connecting it to any form. So it's lab data. And the trend in healthcare are leading measures, and how do you know things quicker? How are you able to predict impact?

And so, we hired the most incredible folks that we can, and we invest in them, We empower them.

The last maybe two years, really, We've made that transition to predictive in action based analytics, and really, that's been through that partnership of developing strong folks, teaching our leaders, building confidence as a team.

Screenshot (4)Very well, and the end on that, so for someone who may be starting that journey today, what would be some advice you'd give to them on how to start on the right foot?

Take off a manageable chunk.

Be careful with how much you think you can do, and how much you agree to do.

Find a nice balance between building that platform or that foundation for your analytics to be successful.

Choose measures that you can build, chu's, easy to identify, easy to understand, improvement initiatives, and allow your partnerships to really be successful.

Because you connect of results to the performance improvement opportunity, And you're sharing education, you're empowering both your partner and your analysts to be successful together, then that education in that need to progress will be a very naturally occurring event.

Very good, good advice. We, well, we have seen here, for the commentary and the questions. There's also this, this theme on the related to your team, and that, or, your core, an extended team. So, if you could talk a little bit about what the team looks like, how many people are on that team and really most important. What skillsets do you have on that team that you think is maybe the ideal mix to having a team like that?

Sure, So, I'll start down in our ETL or our data warehouse team, we have five of those folks, They are phenomenal. There is nobody in our system that knows how to process a Blue Cross Blue Shield pair file into usable data like they do.

Then, the next kind of level of our team is our report filbert.

Those folks that take a production allies products, something that hasn't had changes are needed to be changed over a period of time. And they create standard reports.

Part of what else they do, that puts them beyond just a report builder, if they identify, and they track what reports are being ... off, what filters are being used most often.

And so they can take an improved, a Power BI or Tableau product that one of our analysts has developed, and they integrate the functionality of the tool.

So now we're able to connect multiple insights across parts. We can do it for our quality folks, or post acute care, and our network.

We're integrating all of those into one big, easy to use.

Very simple visual.

So that team has Bork were fairly lean in what we do we employ and how they do and what we're able to achieve.

Our largest group are our panelists.

We use the word analyst probably a little bit in a written, to be honest. My folks are just that smart. They are more data scientists and I've got applied mathematics statistics. I've got engineering degrees as clinical degrees.

Cross the board did not one size fits all. It's really do have the right personality to bring to the table with the desire. I think It's a good word and expertise.

So, we have 4 or five folks that work on our finance, then we have five folks that work on all things, care Management, Clinical Network, post acute care.

At about like 22, honestly, very good, very good. Is, is, it comes across as a more technical group, of course, and, and I'm curious on, how do you connect that, That, the members of your team and the work that you do, with, with the business? With the business itself, the business leaders, the different business areas, and, specifically, Not just on the report and the analytics and the reporting, but does your team get involved in the execution of improvements and innovations and changes in the business that, as a result of those, reports and analytics.

Yeah, so that's super cool.

In the last year, we've had, or five analysts that have gone to go visit clinics.

They get to talk to data with their business partner.

So, facility and clinic level, or they'll go to our CMO Council, huge Council invites, and it's really because they develop that make sense of pride in their work, but also in the partnership.

And so, when, you know, our, our care management has been desk for analytics, and our, No quality guys are deeply embedded with our clinical teams.

That pride in life, let me bring my partner to the table. Let me share that work.

That's what kinda gets their presence out there, gets their foot in the door at a clinic, then usually they're terrified, honestly to do that originally because the EDA guys, right. And and but then they understand and they get to hear a nurse talk about how she uses the report every month. And they're seeing improvement in patient outcomes.

And that resonates back to why we all choose to do healthcare's to make that change and that positive outcome right and in the health of others.

And so that allows them to, I think, next and feel refreshed about what they do to get over the scary part of doctors and nurses every now and then.

Yeah. And today, when it comes to the requests that you receive or the what, what you choose to work on, how does that process work for you? Again, if you can revise that far as one more time in terms of identifying and prioritizing what you and your team work on.

Yeah, so, every pillar pretty much is led by a director and an MVP and we connect an analyst right into that group.

And so, they receive kind of requests weekly, you know, through e-mail or chat or whatever it is. Our analysts tracks all of this.

They go on the agenda under new work, received for prioritization, then they spend part of the agenda defining what that request is. It's that teach back. They translate it into their own words. OK, I heard that you're asking for this.

Clarify the need.

And then they say, OK, that, where do we want that to fall? Because I've got these items that are almost done. Do I need to stop working on them? So it's that real time, dynamic, prioritization, weekly.

If you get a big request, some of our request will take two weeks, or a month to actually stand up and get working.

That kind of becomes like, a ton block. And they're like, hey, this is how many hours I spent per week on this product to get it done, to hit your due date.

So that means you have about, you know, 20 hours of my time this week to continue with new work. What would you like me to work on first?

A cure is also there's some questions about how. How has the pandemic impacted, the work that your team does and the priorities that you, that you work on. I assume that there's so much change going on right now in the system that, that you have to react to that. Curious about what the impact has been on your end?

You know, we've been super successful. We all went virtual. We all work from home.

And, um, our embedded partnerships, honestly, with our business partners, made us and kept us successful.

And people understood that priorities were going to change. Strategies literally changed like every other day for a while.

And they knew work was going to stop work. What's kind of start.

And our business partners, they know that they can change prioritization any point in time. And so we literally have that.

Like, if, you know, eleen mentality, it was like, that's the press and stop the line, changing what we do, here's what we need to go do.

And, we have those shared, you know, really that library really robust library of tips and tricks and technical stuff. It allows folks that have never looked at inventory or a quality metric in their life, or a utilization RV you, any of that, to be able to get up to speed really quickly without taking another analyst time to learn that. So, that was super helpful.

Very good.

And one final question is, where do you see as a natural evolution of what your team does, and what do you see, you know, think next 12 months or so? What, what do you see as an evolution for the work that you do?

32Yeah, we're gonna continue down this path of machine learning and model. And we are testing out a couple of programs that have been just recently launched, and we continue to connect and provide insights.

And so, then, we just iterate back and improve our models, improve our processes, all of that, so that we can become more lean and mean around that, and also identify what data elements, what pieces we were missing, The first Go round, that would have, either help educate or help drive improvement astor.

Outstanding, Maggie, thank you so much. on behalf of our global audience. Thank you so much for taking the time to share your experience and insights, all the great work that you and your team has done, and very grateful for you, taking the time to do that for us.

Thank you, I appreciate it. Everyone, have a good rest of your day.

Thank you.

Ladies and gentlemen, this concludes they chew, vetoes healthcare Life. So, a couple of announcements. Quick announcements and for follow ups, lots of questions were asked today that I couldn't get to. Unfortunately because of the time that we have. You can still go on LinkedIn. And look at my profile, there's a Posting for beetles healthcare live. Add your comments there. Add your questions in there. We have several of our presenters who will drop in and we'll answer questions. I will do some myself based on the feedback that I get from the presenters. So feel free to use that if you, if you still want to have answers to some of your questions. Also, for tomorrow, let's take a look at what we have in the in store For tomorrow. In the very first session tomorrow, I will be leading a session on health care disruption, and how great people and organizations innovate in times of uncertainty. And the rapid change, which is what we're going through right now, Specifically, Health Care.

So, I will do a benchmark of great enduring organizations. And how did they react to crisis? I will go back to the 22,008 Financial Crisis and some other prizes and how organizations that accelerate excellence and innovation during times of great uncertainty and disruption, they take on very beneficial competitive advantages on the on the outcome. If you look 5, 10 years down the line. So we're going to examine that. I think it should be an interesting journey to go with together. After that we're going to have then Weaver from Gateway Health and he's a vice-president of Medicare and Medicaid Medicaid Quality programs and Gateway the gateway health.

And then Daniel is going to be talking about transforming the quality rating system paradigm, very interesting reveal about how things are done and some of these news, new perspectives on how a quality rating system should be addressed in healthcare.

We have Mohamed money after that and he's a senior vice-president for Transformation at Trinity Health. And and Mohamed is going to talk to us about community wellness in the midst of a pandemic and how to address the social risk factors on a community level from a healthcare perspective. So, very interesting.

Very broad view of and social view of what's going on right now, and the and how how healthcare leadership in organizations play a significant role in the unveiling of a of this pandemic that we're going through it. And we're going to wrap up the day tomorrow with a director from the Mayo Clinic. Eric ... and Eric is going to talk to us from the Mayo Clinic perspective. The impact of Cove in 19 on global health care, supply chains. I think everybody's very aware in this audience about these severe disruptions that we have had possible dislocations that we have had a healthcare supply chains.

So he this is something that the Mayo Clinic has been studying for sometime before the pandemic with several experiences in different regions of the world with supply chain disruptions. And they're going to share their learnings with ours they're going to share kind of a path forward on the related to supply chain a healthcare. So, as you can say, as you can tell, a very good variety of topics related to health care transformation, improvement and innovation that will continue tomorrow. Again, very honored to be your host, Look forward to seeing you again tomorrow for our final day, and hope that everyone has a great rest of their day. Thank you, everyone.


About the Author

more (69)-3Maggi Savo,
Healthcare Improvement Analytics Director,
BaylorScott & White Health.

Maggi Savo, Ph.D. is the Healthcare Improvement Analytics Director for the Quality Alliance at Baylor Scott and White Health. The Quality Alliance is the accountable care organization (ACO) affiliated with the health system.

In this role she leads a team to build machine learning models and implement value-based analytics that enable the Quality Alliance to achieve high quality, cost effective care. This work has contributed to tens of millions of dollars in savings and success in risk-based contracts.

Maggi holds a Ph.D. in Biomedical Engineering from the University of Texas Health Science Center in San Antonio and is a certified Project Management Professional.


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Featured Content

  • Best Achievement of Operational Excellence in Technology & Communications: IBM
  • Best Achievement of Operational Excellence in Oil & Gas, Power & Utilities: Black & Veatch
  • Best Achievement in Cultural Transformation to deliver a high performing Operational Excellence culture: NextEra Energy
Operational Excellence Frameworks and Learning Resources, Customer Experience, Digital Transformation and more introductions
  • Intelligent BPM Systems: Impact & Opportunity
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  • Six Sigma's Best Kept Secret: Motorola & The Malcolm Baldrige Awards
  • The Value-Switch for Digitalization Initiatives: Business Process Management
  • Process of Process Management: Strategy Execution in a Digital World

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