Using a machine-learning approach, we created, tested, and patented an algorithm that accurately predicts elderly falling. Additionally, using the algorithm our research in a community-based environment we demonstrated the ability to prevent injurious falls. Our algorithm allows clinicians to screen for individual fall risk, with suggestions for appropriate interventions. A significant part of our fall- prevention program incorporates a critical risk factor— specific drugs and drug dosages. This algorithm not only was able to accurately predict a patient’s risk of falling but demonstrated a cost benefit by lower healthcare utilization, reducing hospitalization and providing overall economic savings. Specifically, we reduced injurious falls and saved health care providers $2.40 for every $1.00 invested. Our long-term goal builds on the foundation we developed with the creation of this machine-learning algorithm by expanding this approach into a computerized clinical decision support system (CCDSS), which clinicians can use to offer tailored, specific suggestions for fall prevention to their patients. CCDSS is a technology that uses patient-specific data to provide relevant medical knowledge at the point of care. It is an important quality improvement intervention, and the implementation of CCDSS is growing substantially.
Journal: TechConnect Briefs
Volume: TechConnect Briefs 2021
Published: October 18, 2021
Pages: 111 - 113
Industry sectors: Advanced Materials & Manufacturing | Medical & Biotech
Topics: Advanced Manufacturing, Materials Characterization & Imaging