64% Reduction in Vascular Readmissions Using Machine Learning and Big Data
Healthcare Organization Case Study
Patients with peripheral artery disease (PAD) have among the highest rates of avoidable hospital readmissions. This leads to a high rate of complications and healthcare costs.
COVID-19 is the disease caused by novel coronavirus. Given that the virus is new and moves fast, the data we gather and utilize about this virus and the resulting disease needs to move just as [...]
During this ongoing and unprecedented COVID-19 pandemic, the news is ever-changing and ever-evolving. Conditions are hard to keep up with, and it’s left many feeling confused and lost in what was already a stressful situation. [...]
Surgisphere worked with a large hospital system in Chicago to develop a series of data-driven benchmarks and data integration through improved interoperability. We then applied sophisticated machine learning tools to predict which patients were most likely to be readmitted. This led to the development of new clinical management algorithms.
Through an industry-leading predictive model, this hospital system was able to reduce readmissions for their vascular patients by 64%, leading to $1.2 million in direct cost reduction and saving an estimated 18 lives per year.
This successful pilot test is now being expanded throughout the entire hospital system and is expected to lead to more than $4 million in savings throughout FY 2020.