UX for Analytics using a Mental Model
This effort has evolved as a core design process for healthcare analytic applications. Given the complexity of healthcare, patients, medical providers, and healthcare administrators must understand how this system works to tackle tough health decisions together. To accomplish this across a broad portfolio of healthcare products, I developed applied techniques based on a shared healthcare mental model.
Mental models are an explanation of the user's understanding or thought process about how something works. Uncovering mental models can take many forms, from guided interviews, psycho-drawing techniques, or analysis of educational materials in a related field.
Design research for mental models in data and analytic rich applications focuses on the users understanding of:
Data Lineage- Where did this data come from? Can this data be trusted? How is the data related to my job?
Model Lineage- How did the model arrive at this answer/recommendation? Can this model be trusted? These questions apply to analytic, machine learning, and artificial intelligence models.
The mental model represents the user's understanding, and this means they can be incomplete or even incorrect. It is essential to validate the model you plan to use before making design decisions based on a specific model.
For this example, I uncovered inconsistencies in user understanding of healthcare costs and quality of care. My discovery led me to research healthcare educational materials that revealed this general mental model that became the basis for these applied techniques.
Here is how healthcare concepts of effectiveness, efficiency, and equity contribute to costs and individual wellness (below).
As I mentioned above, research revealed inconsistencies in user understanding of these concepts across different market segments. For this example, let's focus on Jack, a 55-year-old patient who needs a knee replacement and is concerned about how much this will cost.
Analytic Tool User: Data Scientist
Users of this tool generate highly accurate estimates for every local orthopedic surgeon in Jack's area; they understand that the cost of using the surgeon can widely vary based on the specific surgery facility being used.
Mental Model Inspired Design (left): Here is how we helped create a line of sight for patients like Jack for the Data Scientists to help them more effectively show him different price options.
Application of Mental Model || Care Manager
Users of this tool help patients like Jack manage the healthcare system and its benefits. Jack has previously learned from his Care Manager to be thoughtful about choosing between the Emergency Room and Urgent Care. This choice can have a significant impact on Jack's out-of-pocket medical costs.
Mental Model Inspired Design: Transformed Care Manager experience (right) helps educate patients like Jack in selecting where to get medical care (aka setting of care).
This simplified example illustrates how data and analytic literacy can further user trust in analytic, machine learning, and artificial intelligence-driven recommendations. Finally, this demonstrates how a mental model can be used to see connections that might not otherwise be apparent and build a line of sight to end-users across multiple product families.