Prediction models, risk scores and decision tools are becoming a more integral part of modern medical practice. Current systems have demonstrated that early detection of patient deterioration can lead to reduced mortality risk, reduced length-of-stay, and decreased hospital costs. But to implement these systems seamlessly within hospitals outside of where they were originally developed, validation at multiple institutions with significantly different patient populations is critical. That’s where Precision Health’s Health Implementation program comes in.

In a recent study published in Critical Care Medicine, researchers from Michigan Medicine, Precision Health, the Weil Institute for Critical Care Research & Innovation, and Hurley Medical Center (Flint, MI) analyzed data from more than 11,000 hospital encounters to externally validate a predictive analytic originally developed at the Weil Institute, called PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). PICTURE is a machine learning algorithm that utilizes EHR data to predict ICU transfer or death as a proxy for patient deterioration in hospital settings.

In the study, the team examined whether the predictions made by PICTURE, developed at a single academic medical center, would generalize to a second community-oriented hospital that had significantly different patient demographics.

Read the press release here
Original study, published in Critical Care Medicine here