Unfair machine learning will disenfranchise customers and colleagues.
A group of physicians found a cure.
While ridesharing companies like Uber and Lyft leverage algorithms to schedule millions
of rides per day, the vast majority of physician scheduling is done by hand.
Most often, a physician division chief or a veteran nurse will own the task of balancing
critical metrics of patient outcomes, physician welfare, and revenue maximization.
This method never provides an optimal solution.
The perceived logic is that algorithms cannot incorporate the human judgement required to
provide a fair schedule.
A group of doctors at the Brigham and Women's hospital in Boston proved this is not true.
To create a schedule of physicians operating in an extended campus construct that maximizes
revenue and is fair to each physician, they executed the following four steps:
First, they gathered the facts.
Specifically, they identified projected patient requirements and individual physician billing
data.
Second, they gathered the desires of each physician.
Through surveys, they quantified each physician's ideal schedule.
Third, they provided this information in plain language to a data and analytics team that
then conducted a mixed integer optimization.
Fourth, they evaluated the results of the analysis and selected a schedule from a range
of options that not only felt better to the group of physicians, but it also increased
revenue by 6%.
To summarize: Fair machine learning will excite customers and colleagues, and It's good for
business.