There are limitless opportunities for machine learning in healthcare. Defined as, “a field of computer science that uses pattern recognition to identify historical relationships in large data sets using an algorithm to create a generalized model of behavior,” machine learning is one of the most important life-saving technologies ever introduced to healthcare for several reasons:
Prevents hospital acquired infections (HAIs).
Reduces hospital length of stay (LOS).
Predicts chronic disease.
Reduces one-year mortality.
Improves sepsis outcomes.
It’s no wonder health systems are eager to start leveraging machine learning to save lives, improve outcomes, and make systemwide enhancements.
They can do so by understanding machine learning basics, the importance of customized machine learning (not one-size-fits-all models) and historical data (a requirement for answering basic machine learning questions), and how machine learning helps their patients, clinical teams, and bottom lines.