Case Study

Before Agamon, more than 1 in 4 follow-up recommendations were going undetected

Overview

The radiology department of a leading healthcare provider in Midwestern USA, wanted to create a more effective workflow that would better capture findings and follow-up recommendations, increase follow-up adherence, and reduce the risk of malpractice liability.

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Before Agamon, more than 1 in 4 follow-up recommendations were going undetected
Before Agamon, more than 1 in 4 follow-up recommendations were going undetected
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What our customers say…

“Agamon has built advanced AI and NLP that assess the significance of findings extracted from unstructured medical text, starting with radiology reports. This helps us make sure no important clinical recommendation ever falls through the cracks.”

Dr. Daniel Siegal

Dr. Daniel Siegal

Vice Chair of Radiology, Henry Ford Health System

“Agamon helps us gain meaningful insights on our performance, clinical decisions and patient conditions. This allows us to quickly react to different conditions and ultimately deliver faster & better care for our patients.״

Dr. Michal Guindy

Dr. Michal Guindy

Assuta Medical Centers

"Agamon addresses a clear need in the market to structure and analyze healthcare data with the help of machine learning and to turn it into meaningful real-world evidence."

Dr. Sebastian Guth

Dr. Sebastian Guth

President, Bayer Pharmaceuticals

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