• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to secondary sidebar
  • Skip to footer

  • Opinion
  • Health IT
    • Behavioral Health
    • Care Coordination
    • EMR/EHR
    • Interoperability
    • Patient Engagement
    • Population Health Management
    • Revenue Cycle Management
    • Social Determinants of Health
  • Digital Health
    • AI
    • Blockchain
    • Precision Medicine
    • Telehealth
    • Wearables
  • Life Sciences
  • Investments
  • M&A
  • Value-based Care
    • Accountable Care (ACOs)
    • Medicare Advantage

Geisinger, Medial EarlySign Advances to First Stage of CMS AI Health Outcomes Challenge

by Fred Pennic 11/20/2019 Leave a Comment

  • LinkedIn
  • Twitter
  • Facebook
  • Email
  • Print

Clinical Operations Makes Highest Use of Artificial Intelligence, Tufts Study Finds

– Geisinger Health was recently named by the Centers for Medicare & Medicaid Services among the 25 candidates advancing to the first stage of its AI Health Outcomes challenge.

– The CMS AI Health Outcomes challenge provides innovators with the opportunity to demonstrate how AI tools may be implemented to predict health outcomes and keep patients healthy in hopes of more AI tools being considered for potential use in CMS Innovation Center payment and service delivery models.

 Geisinger, an integrated health services organizations in the United States, and EarlySign, a pioneer in developing machine learning techniques for early detection of disease, have been recognized for their joint proposal being one of 25 accepted by the Centers for Medicare & Medicaid Services (CMS) to advance to Stage 1 of the AI Health Outcomes Challenge.  

The CMS AI Health Outcomes Challenge 

The CMS AI Health Outcomes challenge provides innovators with the opportunity to demonstrate how AI tools may be implemented to predict health outcomes and keep patients healthy in hopes of more AI tools being considered for potential use in CMS Innovation Center payment and service delivery models. More than 300 entities submitted proposed AI solutions to the Challenge.

Joint Geisinger/EarlySign Proposal 

The joint Geisinger/EarlySign proposal – Reducing Adverse Events and Avoidable Hospital Readmissions by Empowering Clinicians and Patients – will seek to apply advanced AI and Machine Learning algorithms to Medicare administrative claims data. Doing so can lead to the development of models that predict unplanned hospital and skilled nursing facility (SNF) admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, and postoperative sepsis before they occur.

“Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems,” said David Vawdrey, Ph.D., Chief Data Informatics Officer at Geisinger. “Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost, and improving clinical care provider satisfaction.”

CMS will announce Stage 2 finalists for the AI Health Outcomes Challenge in April 2020. The final awardees and grand prize winner will be revealed in September 2020.

EarlySign’s AlgoMarkers are currently helping clients identify patients at high risk for conditions such as lower GI disorders, prediabetic progression to diabetes, and downstream diabetic complications such as chronic kidney disease (CKD).  The algorithmic models developed using the company’s machine learning approach are supported by peer-reviewed research published by internationally recognized health organizations and hospitals. 

“Geisinger’s experience and substantial unified data architecture (UDA) is the perfect complement to EarlySign’s proprietary data repository and suite of AI tools, enabling the rapid development and validation of effective machine learning models” said EarlySign CEO Dr. Jeremy Orr. “These models are designed to integrate seamlessly with current clinical workflows as a decision support tool that can help improve patient outcomes and decrease healthcare costs.”

  • LinkedIn
  • Twitter
  • Facebook
  • Email
  • Print

Tagged With: AI, algorithms, Artificial Intelligence, clinical workflows, CMS, CMS Innovation Center, decision support, diabetes, Hospital Readmissions, Kidney Disease, Machine Learning, Medial EarlySign, medicaid, medicare, Patient Experience, Quadruple Aim, risk, Sepsis

Tap Native

Get in-depth healthcare technology analysis and commentary delivered straight to your email weekly

Reader Interactions

Primary Sidebar

Subscribe to HIT Consultant

Latest insightful articles delivered straight to your inbox weekly.

Submit a Tip or Pitch

Featured Interview

Reach7 Diabetes Studios Founder Chun Yong on Reimagining Chronic Care with a Concierge Medical Model

Most-Read

Epic Launches Comet: A New AI Platform to Predict Patient Health Journeys

Epic Launches Comet: A New AI Platform to Predict Patient Health Journeys

Preparing for the ‘Big Beautiful Bill’: How Digitization Can Streamline Medicaid Eligibility & Social Care Delivery

Preparing for the ‘Big Beautiful Bill’: How Digitization Can Streamline Medicaid Eligibility & Social Care Delivery

Evernorth Health Services Invests $3.5B in Shields Health Solutions

Evernorth Health Services Invests $3.5B in Shields Health Solutions

KLAS Report: Oracle Health Faces Customer Losses and Declining Satisfaction

KLAS Report: Oracle Health Faces Customer Losses and Declining Satisfaction

Tempus AI Acquires Digital Pathology Leader Paige for $81.25M

M&A:Tempus AI Acquires Digital Pathology Leader Paige for $81.25M

Mira Launches Ultra4™, the First At-Home Hormone Monitor with Lab-Quality Insights

Femtech: Mira Launches Ultra4™, the First At-Home Hormone Monitor with Lab-Quality Insights

How Healthcare CIOs Can Solve the Unstructured Data Crisis and Reduce Storage Costs

How Healthcare CIOs Can Solve the Unstructured Data Crisis and Reduce Storage Costs

Healthcare C-Suite Acknowledges AI Potential but Lacks Trust

Sage Growth Partners Report: Healthcare C-Suite Acknowledges AI Potential but Lacks Trust

EVERSANA and Waltz Health Merge to Redefine Pharmaceutical Commercialization

EVERSANA and Waltz Health Merge to Redefine Pharmaceutical Commercialization

Advancing Diabetes Care: Combating Burnout and Harnessing Technology

Advancing Diabetes Care: Combating Burnout and Harnessing Technology

Secondary Sidebar

Footer

Company

  • About Us
  • Advertise with Us
  • Reprints and Permissions
  • Submit An Op-Ed
  • Contact
  • Subscribe

Editorial Coverage

  • Opinion
  • Health IT
    • Care Coordination
    • EMR/EHR
    • Interoperability
    • Population Health Management
    • Revenue Cycle Management
  • Digital Health
    • Artificial Intelligence
    • Blockchain Tech
    • Precision Medicine
    • Telehealth
    • Wearables
  • Startups
  • Value-Based Care
    • Accountable Care
    • Medicare Advantage

Connect

Subscribe to HIT Consultant Media

Latest insightful articles delivered straight to your inbox weekly

Copyright © 2025. HIT Consultant Media. All Rights Reserved. Privacy Policy |