Since Symphony Care Network's introduction to DataRobot in 2016, the Illinois-based transitional care and assisted living provider has used the automated machine learning platform for a number of applications, including one that uses data on falls to predict readmission cases. After seeing research indicating that if certain patients fall, they are likely to fall again, Taylor and his team built a model with DataRobot that can help predict if a patient is expected to fall. The model incorporates data on the type of medication a patient is on and if the drug makes a patient more susceptible to falls, such as by making them dizzy. It also takes in data on patients' conditions and whether a patient has fallen before, among other data. The DataRobot-built model can mine patient data and identify patients who are likely to fall.

Back in 2016, Nathan Patrick Taylor, then a clinical informatics consultant for post-acute care provider Symphony Care Network, was skeptical of automated machine learning in healthcare. The technology, still considered new today, was all but unknown four years ago. Automated machine learning had a "mystique" to it, Taylor said, with vendors claiming it could expedite the building and deploying machine learning models by automating time-consuming processes. Some people assume that it can completely automate the creation of machine learning models, removing the need for a data scientist entirely, he said. But he thought that was unrealistic, so, when a colleague texted him to check out a DataRobot booth at a conference, Taylor wasn't sure what to expect. "I rolled my eyes," he said, skeptical that DataRobot, then a four-year-old startup vendor of automated machine learning, likely couldn't accurately automate what a data scientist does.