Problem Tech Solves

In light of staffing shortages, declining margins, inflation, there is a burning need to transform how care is delivered in the acute care setting to reduce errors, reduce unnecessary utilization, reduce cost of care, improve outcomes and make it easy for providers to do more with less. Over the last decade, the US healthcare system went from paper records to electronic health records, which now provides a digital infrastructure where massive amounts of data is being captured. However, tapping into these high dimensional, messy, clinical data sets to increase the capacity of the providers has proven to be very difficult. Unlike other AI approaches to harnessing these data sets for more timely clinical decision support, Bayesian is a collection of many models from a wide array of rich data types (structured/unstructured) each being able to be tuned in concert with one another to capture the heterogeneous presentation of different diseases. This ever-evolving synthesis of models produces better inferences and clinical insights because the data and algorithms at its core are not static. It incorporates state-of-the-art techniques for improving precision, ongoing monitoring for tackling bias and performance deterioration due to dataset shifts, and tuning while addressing a number of challenges common in EHR datasets. This automation gives clinicians context on what’s happening within the entire patient population, eliminating time consuming, manual activities and more rapidly identifying life-threatening complications for patients at-risk of adverse events.