64% Reduction in Vascular Readmissions Using Machine Learning and Big Data

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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.

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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.

Decrease in Hospital Readmissions
Million Decrease in Cost of Care
Lives Saved 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.


Midwestern Vascular Surgical Society

September 10 @ 8:00 am - September 12 @ 5:00 pm

Data Acquisition
Data Warehousing
Data Analytics
Data Reporting

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By |2020-03-22T03:26:03-05:00November 15th, 2019|Hospital Case Studies|0 Comments

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