Machine learning, used in the context of healthcare, is not about computers replacing doctors or rolling robots dispensing bedside care to patients. Perhaps a better term would be data-driven healthcare because it is the process of using historical patient data in a predictive model to determine the likelihood of a healthcare-related outcome. The black box that is the machine is no more than an algorithm trained by data. Most importantly, the predictions can be used by doctors to optimize decision making in real time, thus reducing readmissions, infection rates, and other complications that drive up costs and lower the quality of care.
This article explains some machine learning basics, dispels some misconceptions, and outlines five steps to its implementation:
Define the use case.
Prepare the data.
Train the model.
Make predictions on new data.
Deliver the risk score for use in clinical decision support.
Machine learning is destined to be a digital partner for physicians, executives, and health systems focused on improving clinical, financial, and operational performance. To get there, it must first be understood.