Decision Support System Helps Hospitals Prevent Heart Failure Readmissions

Technology #33648

Questions about this technology? Ask a Technology Manager

Download Printable PDF

Image Gallery
Categories
Researchers
Thomas Wan, Ph.D.
External Link (www.cohpa.ucf.edu)
Varadraj Gurupur, Ph.D.
External Link (www.cohpa.ucf.edu)
Managed By
John Miner
Assistant Director 407.882.1136
Patent Protection

Provisional Patent Application Filed

Low-cost, innovative support tool enables care managers to implement interventions that prevent readmissions and improve the health outcomes of patients with heart failure

UCF researchers have invented a decision support tool for enhancing patient care and reducing hospital readmissions related to heart failure (HF). The innovative support tool identifies and analyzes patient-centric human factors that affect hospital readmissions of patients diagnosed with heart failure. Results generated by the tool enable medical practitioners and patients to develop and use interventions that mitigate the risks of readmission. The tool analyzes items such as choice, rest, environment, nutrition, habits, activity and other human factors that affect a patient’s health outcomes. 

According to the American Heart Association, heart failure is a leading cause of hospitalization and costs the nation about $32 billion annually. Readmission for congestive HF is the most common reason for returning to the hospital, and up to 25 percent of HF patients do so within 30 days. Additionally, hospitals face stiff penalties from Medicare for high readmission rates. In response to these issues, the UCF team developed a decision support system that uses empirical data and statistical methods to reduce hospital readmissions of heart failure patients. The first of its kind in readmission prevention, the tool generates knowledge-based information that medical practitioners can easily use to improve patient care and avoid costly outcomes, such as added hospitalization.

Technical Details

The invention comprises a novel algorithm that identifies the significance of human factors related to hospital readmissions of heart failure patients. The algorithm incorporates structural equation modeling and meta-analysis to obtain the associated probability value for individual human factors that help to reduce readmissions of heart failure patients. In one example application, the invention uses data from a systematic review and meta-analysis of clinical trial studies on heart failure hospitalization and care management strategies. The system extracts and generates relevant data, rating the significance of human factors that influence heart failure patients’ knowledge, motivation, attitude, preventive practices, and health outcomes. Care managers use the results to determine the clinical interventions and practices needed to help a particular heart failure patient avoid rehospitalization.

Benefits

  • Inexpensive, simple and easy to use
  • Enhances patient care and reduces hospital readmissions due to heart failure
  • Increases the efficiency and effectiveness of care delivery and lowers hospital costs

Applications

  • Chronic disease (heart failure) management
  • Web-based health education for patients and caregivers
  • Track/monitor and analyze patient care outcomes