The Permanente Medical Group

The calling for innovation is perennial with added urgency for healthcare organizations. With the penned-up demand for services, staffing shortages, and risks of economic downturn, care delivery organizations must look for ways to deliver care more efficiently and cost effectively.

Yet healthcare is notorious for its resistance to change. Complexities and significant investment needs aside, changes in healthcare often require altering long-standing practices and mindsets. Many healthcare professionals have years of experience and training in their field and changing the way they do things is often a tough sell.

Often smaller scaled innovations start successfully, but after a short pilot, the program finds it difficult to operationalize. Mischaracterization of the needs or demand for change, insufficiency in addressing the unmet needs, and inability to hardwire operations with support of data and/or technology are all common root causes.

Implementing lessons learned elsewhere as a way to innovate works well when both the problem and solution are well understood-but that kind of foresight is seldom available to healthcare operations. The pandemic showed us just how much scrambling healthcare organizations had to do to rise to the occasion. Three years later, tremendous unknown persist but are very different from those of early 2020.

Analytics and design thinking can work well together to support innovation in gray spaces, where general understanding coexists with plenty of unknowns. As healthcare transitions from volume to value and strives to service more with less resources, there are ample applications of gray space innovations.

Design thinking is a human-centered problem-solving approach that focuses on understanding the needs of user and stakeholders to create innovative solutions. It involves a series of steps that includes defining the problem, gathering information, generating ideas, prototyping solutions, and testing and refining those solutions.

Unlike top-down implementation of protocols that many are familiar with in healthcare, design thinking starts with understanding people’s unmet needs, both physical and emotional. Physical needs point to the essential functions the innovation must bring forward and in a way that is practical, scalable, and durable. That could include many technology tools such as a search function for key resources, as well as non-tech components such as training for new skills. At the same time, design thinking also recognizes the importance of addressing the emotional needs. This means factoring in cognitive biases such as tendency toward default settings, digging into the sources of anxiety and joys, and reframing them into unmet needs to be addressed when developing solutions. By taking into account the emotional needs of people, design thinking can lead to solutions that not only have useful functions, but also create more intuitive experience and deeper connections with people

 Successful applications of design thinking have to be guided by insights and feedback, a space naturally suited for analytics. Together they make the two sides of a coin 

Successful applications of design thinking have to be guided by insights and feedback, a space naturally suited for analytics. Together they make the two sides of a coin.

During the early stage of design thinking, information gathering lays the foundation to define the unmet needs. Even though a variety of methods such as ethnography, interviews, and quantitative analysis are used, the systematic approach of analytics helps tease out generalizable traits and priorities of the unmet needs.

We also rely on analytics to evaluate the effectiveness of solutions developed using design thinking. By tracking key performance metrics as well as participants’ behavior indicators, organizations can determine whether the solutions are meeting the intended goals and how likely the changes will stick. This feedback loop in turn guides the next iterative design for improved solutions.

Conversely, design thinking can be a good partner in extending the use of analytics in innovation.

Data collection is a common challenge with innovation, as evidence is often obscure, scattered, and circumstantial, especially during the early stages of innovation. Led by human-centeredness, design thinking can develop end user surveys and other data collection tools that better capture the perspectives and experiences of stakeholders. This can help to ensure that the data collected is more representative and accurate, which, in turn, can improve the quality of the insights yielded.

Communicating actionable insights is another good place for the joint deployment of design thinking and analytics when it comes to innovation. Healthcare is built on data-based learnings. Leading messaging with insights is often preferred, yet not always done right. It is hard to win the constant competition for attention from the overwhelming amount of available data, and you’ll have to overcome people’s natural tendency to overlook unfamiliar signals. Design thinking can help make insights stand out and appear more user-friendly and intuitive to decision makers. This could involve a creation of a data visualization dashboard, or the picking of a handful performance indicators, out of several dozens, that truly matter.

Together, design thinking and analytics can be a powerful pair for driving innovation in healthcare. By combining the user-centered approach of design thinking with the data-driven insights of analytics, healthcare organizations can develop innovative solutions that are tailored to the needs of users and stakeholders, and that are supported by evidence and data.