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This version of NSU News has been archived as of February 28, 2019. To search through archived articles, visit nova.edu/search. To access the new version of NSU News, visit news.nova.edu.
This version of SharkBytes has been archived as of February 28, 2019. To search through archived articles, visit nova.edu/search. To access the new version of SharkBytes, visit sharkbytes.nova.edu.
NSU Researcher Receives U.S. Patent for Developing Fall Prevention Model
Patrick Hardigan, Ph.D., executive director for NSU Health Professions Division research was recently honored by NSU officials for receiving a U.S. Patent. (left to right): Gary S. Margules, Sc.D., Nova Southeastern University (NSU) vice president for research and technology transfer; Fred Lippman, R.Ph., Ed.D., chancellor of NSU’s Health Professions Division; Ralph V. Rogers Jr., Ph.D., NSU provost and executive vice president for academic affairs; Hardigan; and George L. Hanbury II, Ph.D., NSU president and CEO
Each year, one in every three adults age 65 and older falls, according to the Centers for Disease Control (CDC). These falls are the leading cause of fatal and nonfatal injuries among older adults, resulting in approximately $30 billion in direct medical costs per year.
In an effort to prevent these unnecessary injuries and deaths, Patrick Hardigan, Ph.D., executive director for Health Professions Division research at Nova Southeastern University (NSU), set out to develop a model used to help predict the effect of medication and dosage on injurious falling. He recently received a U.S. Patent for an algorithm he developed called the “Statistical Model for Predicting Falling in Humans,” also known as the “Fall Model.”
“Our goal is to develop a multidisciplinary fall prevention program and ultimately reduce the number of deaths and serious injuries due to falls,” said Hardigan.
The Fall Model uses a robust amount of unidentified patient information from the state of Florida and other public sources such as height, weight and age, combined with each respective patient’s clinical diagnosis and prescription medication regimen to determine the patient’s likelihood of falling. The result is a “likely” or “not likely” determination.
This model will eventually be computerized in an easy-to-use template for use in health care settings, including hospitals, pharmacies, nursing homes and clinics. This computerized application will provide individual risk profiles for falling that will enable health professionals to implement personalized fall prevention strategies. Providers will be able to enter patient data to determine if alternate drugs should be prescribed or dosages altered, and/or whether a patient should be under stricter observation or undergo physical therapy or other form of preventative measure to reduce the risk of the patient falling.
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