Research & Development World

  • R&D World Home
  • Topics
    • Aerospace
    • Automotive
    • Biotech
    • Careers
    • Chemistry
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Software
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
    • Semiconductors
  • R&D Market Pulse
  • R&D 100
    • Call for Nominations: The 2025 R&D 100 Awards
    • R&D 100 Awards Event
    • R&D 100 Submissions
    • Winner Archive
    • Explore the 2024 R&D 100 award winners and finalists
  • Resources
    • Research Reports
    • Digital Issues
    • R&D Index
    • Subscribe
    • Video
    • Webinars
  • Global Funding Forecast
  • Top Labs
  • Advertise
  • SUBSCRIBE

UMN Researchers Develop Algorithm to Improve Care Delivery to Seriously Ill Patients

By University of Minnesota Medical School | July 6, 2018

The level of communication between patient and physician can make a monumental difference, specifically in the case of seriously ill hospitalized patients. Researchers at the University of Minnesota have found a way to better identify these patients with the hopes of better facilitating “end-of-life” or specialized conversations and care.

Studies have shown that seriously ill and frail hospitalized patients are frequently subjected to unnecessary, invasive procedures that do not enhance the quality of life. Surveys amongst seriously ill hospitalized patients have identified better end of life planning as an area of potential improvement for hospitals.

“The idea was to come up with an algorithm that would identify those patients from among 100,000 seriously ill patients who might die within a year,” explained Nishant Sahni, MD, MS, Adjunct Assistant Professor in the Department of Medicine, University of Minnesota Medical School. “The hope is when a patient is leaving the hospital, the physician will get a notification that the patient is high risk and needs those specific conversations and care, which would empower patients to make more informed decisions regarding their medical care.”

“Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study” of which Sahni was the main author, was recently published in the Journal of General Internal Medicine.

The data was gathered from nearly 60,000 hospitalizations from six hospitals over four years. It can be used to accurately estimate the risk of 1-year mortality within a cohort of multi-condition hospitalized patients.

In addition to empowering seriously ill patients to make more informed health care choices, this model could help clinicians reduce unnecessary invasive procedures on patients who are not likely to benefit from them. This is an important consideration as the number of Americans ages 65 and older is expected to reach more than 98 million by 2060 – putting an increased strain on increasingly limited Medicare and Medicaid funding.

“We want to make sure that as a health care system –we are providing our patients with appropriate and cost effective care” said Sahni.

This could be a big step for health care systems, although Sahni acknowledges there is still work to be done. While the applications for the algorithms are endless, the next step is determining how best to use and learn from them.

Related Articles Read More >

regulatory
As FDA moves builds out ‘Elsa,’ this AI compliance CEO underscores that need for a hybrid AI approach
Open-source Boltz-2 can speed binding-affinity predictions 1,000-fold
New Gemini 2.5 Pro model achieves top-tier science and coding performance while costing 1/8th the price of OpenAI’s o3
Berkeley Lab’s Dell and NVIDIA-powered ‘Doudna’ supercomputer to enable real-time data access for 11,000 researchers
rd newsletter
EXPAND YOUR KNOWLEDGE AND STAY CONNECTED
Get the latest info on technologies, trends, and strategies in Research & Development.
RD 25 Power Index

R&D World Digital Issues

Fall 2024 issue

Browse the most current issue of R&D World and back issues in an easy to use high quality format. Clip, share and download with the leading R&D magazine today.

Research & Development World
  • Subscribe to R&D World Magazine
  • Enews Sign Up
  • Contact Us
  • About Us
  • Drug Discovery & Development
  • Pharmaceutical Processing
  • Global Funding Forecast

Copyright © 2025 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search R&D World

  • R&D World Home
  • Topics
    • Aerospace
    • Automotive
    • Biotech
    • Careers
    • Chemistry
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Software
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
    • Semiconductors
  • R&D Market Pulse
  • R&D 100
    • Call for Nominations: The 2025 R&D 100 Awards
    • R&D 100 Awards Event
    • R&D 100 Submissions
    • Winner Archive
    • Explore the 2024 R&D 100 award winners and finalists
  • Resources
    • Research Reports
    • Digital Issues
    • R&D Index
    • Subscribe
    • Video
    • Webinars
  • Global Funding Forecast
  • Top Labs
  • Advertise
  • SUBSCRIBE