We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




AI Predicts Risk of Readmission for Heart Failure Patients

By HospiMedica International staff writers
Posted on 19 Dec 2017
Print article
Hitachi, Ltd. (Tokyo, Japan) and Partners Connected Health (Somerville, MA, USA) have collaborated to develop artificial intelligence (AI) technology, which can very accurately predict the risk of hospital readmissions within 30 days among patients with heart failure. The AI technology helps choose appropriate patients to participate in a readmission prevention program after hospital discharge and provides an explanation as to why the patients have been identified as high risk ones.

Partners Connected Health, at Partners HealthCare, is leveraging information technology – mobile phones, tablets, wearables, sensors and remote health monitoring tools – to deliver quality patient care outside of traditional medical settings. The Connected Health team creates and deploys mobile technologies in a number of patient populations and care settings, and is conducting innovative research studies to test the effectiveness of mobile health technologies in various clinical applications, including medication adherence, care coordination, chronic disease management, prevention and wellness.

Hitachi's new AI technology uses deep learning to construct the risk prediction model. The company’s technology for risk prediction analyzes the results presented by deep learning and extracting the several dozens of actionable factors for each patient from the vast amount of data collected from heart failure patients. Through a standard statistical approach based on this risk prediction model, the extracted factors are used to calculate the risk of hospital readmission, and the relevance of the factors is calculated.

As part of a study, the Partners Connected Health Innovation team simulated the readmission prediction program among heart failure patients participating in the Partners Connected Cardiac Care Program (CCCP), a remote monitoring and education program designed to improve the management of heart failure patients at risk for hospitalization. These results were compared to data from approximately 12,000 heart failure patients hospitalized and discharged from the Partners HealthCare hospital network in 2014 and 2015. The analysis showed the prediction algorithm achieved a high accuracy of approximately AUC 0.71, and can significantly reduce the number of patient readmissions. (AUC, area under the curve, is a measure of prediction model performance with an ideal value range from 0 to 1.) As a result, an additional amount of approximately USD 7,000 savings per patient per year can be expected among the cohort of CCCP patients.

The technology is an example of explainable AI, a new term currently defined as enabling machines to explain their decisions and actions to human users, and enabling them to understand, appropriately trust and effectively manage AI tools, while maintaining a high level of prediction accuracy. Hitachi and the Partners Connected Health Innovation team plan to jointly conduct a prospective study to evaluate the prediction program by clinicians, and study how to integrate this within clinical workflows. By using this new AI technology, Hitachi will provide solutions for the medical field, including solutions for insurance and pharmaceutical companies, emergency services, and other healthcare services where prediction-based on medical data can be utilized.

"Traditional machine learning can help us predict events, but as end-users, we can't tell why the machine is predicting something a certain way," said Kamal Jethwani, MD, MPH, Senior Director, Partners Connected Health Innovation. "With this innovation, doctors and nurses using the algorithm will be able to tell exactly why a certain patient is at high risk for hospital admission, and what they can do about it. We want to enable our providers to act on this information, which is a step beyond the state-of-the-art today, in terms of machine learning algorithms."

Gold Member
12-Channel ECG
CM1200B
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Vital Signs Monitor
M3/M3A

Print article

Channels

Critical Care

view channel
Image: Researchers have developed a novel risk score for cardiovascular complications after bone marrow transplant (Photo courtesy of 123RF)

Novel Tool Predicts Cardiovascular Risks after Bone Marrow Transplantation

Every year, thousands of people undergo bone marrow transplants to potentially cure serious diseases like leukemia, lymphoma, and immune deficiency disorders. While these transplants can be lifesaving,... Read more

Surgical Techniques

view channel
Image: The Early Bird Bleed Monitoring System provides visual and audible indicators of the onset and progression of bleeding events (Photo courtesy of Saranas)

Novel Technology Monitors and Lowers Bleeding Complications in Patients Undergoing Heart Procedures

Bleeding complications at the femoral access site can significantly hamper recovery, affecting the success of procedures, patient satisfaction, and overall healthcare costs. It is crucial for surgeons... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The new eye-safe laser technology can diagnose traumatic brain injury (Photo courtesy of 123RF)

Novel Diagnostic Hand-Held Device Detects Known Biomarkers for Traumatic Brain Injury

The growing need for prompt and efficient diagnosis of traumatic brain injury (TBI), a major cause of mortality globally, has spurred the development of innovative diagnostic technologies.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.