Rigid algorithms need human intuition.
Learn how we infused the age-old adage of, 'an ounce of prevention is worth a pound of
cure' into a healthcare predictive model.
Businesses and operating organizations have significant needs to predict future events.
A common question and source of friction is whether to rely on the instincts of the organization
or turn over the keys to an algorithm to predict events.
For example, should you trust your sales team that knows the nuances of your customers inside
and out or a machine that can analyze a significant amount of data to predict next quarter's revenue?
The answer can be both, and we demonstrate this with the D2 Hawkeye Case.
Specifically, we show how to infuse the intuitive idea that, 'an ounce of prevention is worth
a pound of cure' into a predictive algorithm.
We first do this by recognizing that outcomes are not all the same.
We refer to these instances where outcomes are not the same as asymmetrical.
For example, in the United States legal system, it is assumed that an innocent person locked
in prison is a significantly worse outcome than a guilty person free on the streets.
In our healthcare example, we define and enumerate these types of asymmetrical outcomes.
Specifically, we recognize that failing to predict a healthy patient who suddenly gets
very sick carries more weight than failing to predict a sick patient who quickly becomes
healthy.
Second, we build our predictive model that respects these asymmetric outcomes and seeks
to minimize error.
Third, we define interventions based on our model.
Because we have built in outcome asymmetry, our model guides us to take a number of preventative
measures with both healthy and sick patients.
Finally, we validate our results and determine the value-at-stake for choosing to use or
not use our model.