So I asked a number of key people in healthcare.
In early August, I met with 15 executives across 15 healthcare organizations for a CHIME Focus Group to discuss this very topic. As a group, we defined what we believe self-service analytics to be. We explored experiences with deploying self-service tools and discussed related topics around governance, content definitions and collaboration/distribution. As the healthcare ecosystem continues to evolve through initiatives such as Population Health Management, Value-Based Payments and MACRA (to name just a few), the role of analytics as an integral component in the definition and successful management of these programs has become clear. The demand on BI teams has increased and along with the increase in demand has come requests for self-service analytics, so business users can ask and answer their own questions. But what truly is self-service analytics?
Defining self-service analytics is not an easy task. There are certainly critical elements and capabilities, but it comes down to different definitions for various people. How clinicians use and consume data can be very different than how an analyst would. Should people be able to bring in their own data or create their own definitions and metrics? How does this stuff stay secure in our HIPAA-compliant architecture? All great questions and our group agreed that things are still fuzzy, but the future is bright. People are increasingly asking for deeper and broader access to the data they tirelessly enter into their EHR.
As ranked by the executives, the elements of self-service analytics for data consumers (IE. Those outside of traditional IT and the BI team):
When asked who is using self-service analytics today or in the near future, I was delighted to hear that more and more clinicians are getting access. I believe it's critical if you want to be a data-driven organization that your front-line staff have access to the data and analytics in their workflows (embedded in their EHR like Epic and Cerner, with the ability to directly take action on the results). As rated:
Clearly we all have some work to do in getting more people comfortable and able to use data in their jobs. Noted outside of this collection of roles, those on the Quality Improvement teams were more apt to heavily use self-service analytics.
Cultivating Citizen Developers
We shared a real-world approach to self-service analytics from the wonderful folks at Nemours Children’s Health System. The award winning Nemours is among the most respected pediatric health care systems in the nation. The Nemours Enterprise Intelligence team has a goal to "improve the data swagger of Nemours," to drive pervasive analytics and help create a data-driven culture. Key to achieving this goal are citizen developers: business based, business intelligence developers armed with self-service analytics from Qlik.
Select individuals within the service lines (business units) receive additional training and access to data and the analytical tools. They're able to answer questions at the speed of the business and provide a much needed extension of the core business intelligence team. This frees up the core team to work on more complex data and analytics requests, like the need for predictive analytics. Keys to the success and growth of the program? The culture of data curiosity is there. Education in the tools and data literacy is mandated. The data and metrics used are consistent, governed and trusted.
Want to learn more? On September 9, Rishi Muchhala, Manager of Nemours Enterprise Intelligence team will present a deep-dive session on how they are cultivating citizen developers. CHIME members can learn more and register here: https://chimecentral.org/event/nemours-citizen-developer-strategy/
Self-Service Analytics redefined, with room for growth
The definition, roles and capabilities of self-service analytics will certainly evolve as technologies advance and people become more data literate. Let's break it down into 4 concepts:
Democratize data: Democratization of data is only possible when it is used by the majority of your organization. Self-service analytics is making the path towards this goal.
Empower business users: In this age of data explosion, if analytics tasks are confined within a limited set of people, then the organization will not be able to leverage the power of analytics. Self-service analytics empowers the business users to do their tasks themselves, breaking the Ask-Wait-Answer cycle of the common approach to analytics.
Data science team can concentrate on the core analytics tasks: By using self-service analytics, business users can perform less intensive tasks like data exploration, verification, visualization and reporting on their own. As a result, the core data science team can concentrate on more strategic and complex tasks.
Work together for better productivity: Self-service analytics users and the core data science team can work together for the best results. Business users can help themselves with self-service, and core data science team can take input from self-service analytics team for further advanced analytics or complex tasks. So it goes together as one single team to achieve the shared goals of the Triple Aim: better care, reduced costs and population health.
I encourage you to go forth and empower your data-hungry individuals with self-service analytics. Just be sure to ask them what they think that means.