Mount Sinai puts machine learning to work for quality and safety

Robbie Freeman, vice president of clinical innovation at New York's Mount Sinai Health System began his career working at the bedside, so he has an intimate appreciation of the real-world value of patient safety projects – and importance of ensuring key data is gathered and made actionable with optimal workflows. "I'm a registered nurse, and I think working with patients and spending a lot of time on data entry is what kind of led us to this real focus on clinical workflows and delivering additional value," said Freeman, speaking Wednesday at the HIMSS Machine Learning & AI for Healthcare Digital Summit about some of Mount Sinai's recent automation initiatives. In an earlier, pre-digital age, many of the flow sheets and assessments collected during a nursing assessment, or other clinical information entered into the chart, might not have been "used or even necessarily looked at," he said. But in recent years, "they've become very valuable in the world of predictive analytics. There's a lot of information in those flow sheets that we can tap into for these models."