Risk interpretation
Risk interpretation was a project undertaken with the machine-learning and research department; Precision driven health. The project was focused on communicating and designing for interpretation risk scores created through machine learning algorithms, with a specific focus on readmission risk.
My role
Work with data scientists from Orion Health’s precision-driven health team to design a widget for communicating readmission risk.
The problem
More than $1 trillion in the US is wasted each year on costly administration and avoidable hospital readmissions. The precision driven health team had come up with a way to predict the risk of readmission for new patients and the challenge for us was how communicate these risks and their contributing factors to clinicians in a way that is easily interprettable and drives decision-making.
Process
Although our process varied from project to project, below outlines the process for this particular project.
Solution
The solution I came up with was designed to give clinicians understanding of the risk score glance. It would sit within a dashboard alongside other widgets.
