How the consumer data and analytics model works for healthcare – NewzBeta



In explaining a data and analytics operating model, it is helpful to understand the business context. As the consumer experience becomes increasingly digitized, companies have access to massive troves of data they can use to personalize the consumer experience and product offerings.

Don Peppers and Martha Rogers introduced these ideas in the early 1990s in their co-authored books, The One-to-One Future and Enterprise One-to-One. These ideas now have played out in many industries. Healthcare is next, and data and analytics are powering this transformation, contended Ryan Sousa.

Ryan Sousa is vice president of data and analytics at Pivot Point Consulting, a health IT consulting firm. Prior to his current position, Sousa was chief data and analytics officer at Seattle Children’s, where he proved out his thesis as he led the organization to be the fifth healthcare organization to achieve Stage 7 in the HIMSS Adoption Model for Analytics Maturity (AMAM).

Sousa spoke with Healthcare IT News to discuss how the data and analytics model for the consumer realm works in healthcare.

Q. You’ve worked as a data and analytics expert in several industries – retail, travel, communications, mobile, utility, finance and automotive. Please explain the data and analytics operating models and how they work in these B2C industries.

A. To understand the impact, let’s consider the retail industry. Before online retailers like Amazon, the only data brick-and-mortar retailers had access to was consumer purchase data.

With that data, leading retailers used methods like market-basket analysis to discover insights about consumer purchase behavior and used these insights to optimize the physical store experience, target relevant ads and streamline the supply chain, among other strategic initiatives. At the time, this was a powerful competitive advantage, but that was about to change.

Online retailers not only had access to the same purchase data; e-commerce captured nearly every aspect of the retail experience (virtually) – what door the consumer walked through, where they came from, what aisles they walked down, what products they looked at, what products they touched, what they put back on the shelf, how long they spent in the store and where, etc.

With this massive dataset of nearly unlimited potential, leading retailers were able to tailor the store experience for every consumer, create personalized products and services, and dramatically streamline operations through automation. This wasn’t just competitive, this was disruptive.

As you might guess, to survive and thrive in this online business landscape, retailers had to develop new capabilities to capture, integrate and leverage these massive datasets. More important, they had to learn how to innovate with data.

Where traditional companies struggled to make the transition – and many didn’t – digital-native companies thrived because their business model was optimized for innovation and growth. Let’s dig into this a little more.

Digital natives operate more like a collection of startups that function independently toward a shared vision where decision-making happens where the work is done. In these multidisciplinary teams, business and technology are so deeply integrated that there is a difference with no distinction.

The working style is “do and adapt,” trying many small ideas and then doubling down on those ideas that show promise. The delivery approach is to create value incrementally and often using a product management framework, and there is little regard for the status quo.

Perhaps the most powerful aspect of this digital operating model is the nature of motivation. People are motivated intrinsically through purpose, autonomy and mastery. This was introduced by Edward Deci in the early 1970s as the Self-Determination Theory and popularized by Daniel Pink in his book Drive. This has proven to be a powerful approach to motivation that promotes productivity, employee satisfaction and innovation.

In contrast, traditional organizations operate more like a factory using an extrinsic approach to motivation. Often technology teams see themselves as a support function to the business and even refer to their business partners as “customers.”

The working style is one of “plan and implement” with a focus on a small number of large projects. This delivery approach is more of a waterfall, leveraging a project management framework. Traditional environments also are very protective of the status quo. This operating model promotes efficiency, consistency and incremental improvement.

Fundamentally, analytics is about powering data innovation to digitally transform the consumer and employee experience, streamline operations, spotlight opportunities and accelerate research. So, there lies the opportunity in healthcare. Figuring out how to leverage the digital operating model in a traditional organization is what we did at Seattle Children’s.

Several excellent books describe the impact of the digital operating model, how it works, and how to leverage it in traditional organizations, including Competing in the Age of AI, co-authored by Marco Iansiti and Karim R. Lakhani’ and Fail Fast, Learn Faster by Randy Bean.

Q. How did you apply this model in your position at Seattle Children’s, and what worked? How did you initially know it worked?

A. We began with an action plan. This critical first step frames the data innovation program. The action plan aligns stakeholders on the current state of the environment, vision going forward and game plan for getting there. It is a living document and evolves to reflect lessons learned.

The effort to create the action plan is “time boxed.” It is never more than 90 days and is something that can be pulled together by a single person working part time. Most organizations have one or more assessments in place. So, it is best to build off of this to work to speed up the process.

If there are no assessments in play, I recommend a quick assessment using the HIMSS Analytics Adoption Model for Analytics Maturity or the International Institute for Analytics Maturity Model. These assessments will provide helpful guidance and can be used to benchmark progress.

It is important to remember that the action plan isn’t the end-all-be-all plan. It is good enough to get started and will evolve as you go. It is a starting point that generates excitement and alignment.

It also provides a practical plan where delivery happens incrementally and often creates value along the way. Elements of the action plan include business context, current state, program charter and branding, operating model and a 12-month roadmap.

At this point, you will have a core group that is excited to get started, a smaller group waiting to see and an even smaller group that is certain you will fail. It is essential to focus on the core and get to work. It is vital to start small and focus on one or two areas – ideally, one.

At Seattle Children’s, we started with a focus on quality and safety. It could have been surgical services, ambulatory, revenue cycle, supply chain, translational research, etc. The important thing is to start where you have willing business partners in an area that is visible and aligns with strategic or operational priorities.

This ensures the executive sponsors and team members are passionate and committed to the team’s mission and that they are working on something critical that will impact the business – nothing is more motivating. Equally important, the team must demonstrate visible value that justifies the investment.

Over the next three to four months, roll out your first team using the digital operating model with a maniacal focus on creating business value. I have referred to these as delivery teams as a reminder of why they exist – to deliver value.

If done right, there will be a noticeable improvement in value creation, productivity, employee satisfaction and, over time, innovation. You will use this early success to encourage other areas to join the journey.

Forming the first delivery team can be bumpy as people learn a new process and way of working. So, it is important to be patient. It took Seattle Children’s about one year to roll out four delivery teams. However, we more than doubled that number in the second year.

You can expect one year to get traction, two years to achieve acceleration, and by year three, the operating model starts to become institutionalized. I have had similar experiences in other industries outside healthcare. The speed of evolution is surprisingly similar.

I believe a major constraint is the speed at which an organization can grow its data fluency. To the degree it does vary, it is because some organizations start their journey with more data-fluent leaders.

As delivery teams evolve, success is measured by the value of the products they produce. In the early days of product maturity, success is measured by feedback from the user community. If they are raving fans, this is a good start.

The subsequent measurement of success is adoption over time. If usage continues to grow, this is a good proxy for value. The next measures of success are more quantitative, such as the elimination of surgical cancellations, detection of organ failure, recovery of OR time, revenue-cycle capture, etc.

The final measure of success is the product being leveraged by other health systems – via open source or commercial spinoff. That last measure indicates that other health systems are finding value in the product. It isn’t something one can plan for; however, nothing is more validating.

Q. In your role at Pivot Point Consulting, how can you help healthcare provider organizations like Seattle Children’s achieve similar goals?

A. Joining Pivot Point is the continuation of a journey that started seven years ago at Seattle Children’s. I remember thinking of all the wonderful things we could do for patients and families if we could only leverage what other industries have done to accelerate their data innovation journey.

The question was whether we could leverage the digital operating model within a traditional health system. To that end, we achieved success and advanced the maturity of the analytics environment from HIMSS AMAM Stage 2 to Stage 7.

More important, we implemented dozens of products that improved the quality of care, enhanced the patient experience, streamlined operations, improved replenishment, accelerated research, promoted clinical research integration, and much more. We even licensed one of the products, AdaptX, to a company that is offering it to other health systems to improve their quality-improvement efforts.

I am grateful and excited to have the opportunity to continue this journey at Pivot Point Consulting. Digital transformation has disrupted nearly every industry over the past 30 years. Now, it has taken hold in healthcare and life sciences.

We are a team of mission-driven practitioners passionate about working with client partners to leverage the power of data innovation to navigate and thrive in this rapidly evolving landscape and, more important, drive forward with lifesaving, impactful new approaches for patient care, and help accelerate research.

Q. What are the first steps for healthcare provider organizations in addressing their data and analytics programs?

A. We offer a playbook to help our client partners accelerate their analytics and AI journey. The goal is to get started quickly and show business value. This usually begins with creating and implementing a lightweight action plan to frame and start the journey, with support services depending on the need – training and education, advisory services, specialty consulting and accelerator kits.

This could include providing an interim chief data and analytics officer to lead the implementation of the action plan and the formation of the chief data and analytics officer office.

For each client partner, we tailor the playbook to accommodate the nuances of their environment and where they are in their analytics journey. The goal is quickly to frame the program, build on work that has already been done and begin to demonstrate value with the smallest investment possible.

Then work with client partners to evolve the analytic ecosystem and investment at a speed that harmonizes with the organization’s growth in data fluency.

Q. Is there anything else you’d like to cover that we may have missed?

A. Analytics and AI are transforming the world, powering data innovation to personalize the consumer experience and to deliver customized products and services. Data innovation has transformed many industries, such as finance, communications, utility, retail and travel. We now are seeing this transformation take hold in healthcare and life sciences, where there is even greater opportunity to enrich lives.

Seattle Children’s has demonstrated how a leading health system can create a robust, scalable, near-real-time analytics ecosystem that can be used to create value from the rapidly, ever-increasing volume of data.

Healthcare organizations that similarly commit to incorporating analytics into their day-to-day operations can differentiate themselves by their ability to better engage with patients, improve outcomes, streamline operations and accelerate research.

Twitter: @SiwickiHealthIT
Email the writer: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.




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