MilagroAI was founded to address the ongoing problems of Hospital Acquired Infections and Readmissions, issues that cause significant morbidity and mortality for patients, and increase the cost of care

In the process, we have reinvented the way that Artificial Intelligence can leverage existing clinical knowledge

Making the process real-time, contextual based, faster, more accurate and population-specific

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Solutions


Real time surveillance
Improve quality of care with hospital-wide surveillance for hospital-acquired infections (HAIs)
Early detection
Alert clinicians to patient problems such as bloodstream infection and SEPSIS before clear clinical signs are present
Real time risk score
Reduce patient complications with predictive information for HAIs and 30-day readmission


Solutions

Technology

Technology
Milagro platform continually analyzes all relevant data from the electronic medical record, monitors and other sources both structured and unstructured data.  Our innovative contextual clinical text analytics technology can interpret in real time complex clinical context and translate it into a meaningful and actionable information about the patient.
More then 70% of the real-time relevant information is locked in unstructured data sources such as hospitalization documents, Radiology summaries, surgery summaries, Flow sheets and more, and most of the structured data is episodic, noisy, sparse and irregular
Milagro technology turns all the relevant trapped and noisy data into simple, clear and actionable real-time information about the patient.


Leadership Team

Amit May-Dan

Co-Founder & CEO

Gregory Hobbs MD

CEO USA

N.K. Skip Best

SVP for Sales

Ofer Derech

VP R&D

Aviram Weiss MD

CMIO

Henry M. Kaiser PsyD

Business development

Alon Alter

Innovation Manager

Gal Nitzan

CTO and Chief Architect

Itay Peleg

Clinical algorithms and DevOps manager

Articles

Himms webinar - Eyal Zimlichman MD

Dr. Eyal Zimlichman chief medical officer at the Sheba medical center talking about using MilagroAI hospital wide real time surveillance for hospital acquired infections

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Big data TLV - Ben Gros MD

Ben Gros MD - CMIO, Sheba Medical Center presenting the Sheba Medical Center, Hospital acquired infections project

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Predictive analytics and machine learning

This article is a portion of a book titled "Challenges, Risks and Opportunities in Today's Spine World" edited by Stephen Hochschuler, MD, Frank Phillips, MD, and Richard Fessler, MD

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CLABSI demo

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Contact

  • 500 Indigo Lane, Georgetown, Texas 78628, USA
  • Dvora HaNevi'a 121, Tel Aviv-Yafo, Israel