AI-Driven Falls Risk Assessment Case Study

This case study outlines a collaboration between the National Institutes of Health (NIH), University of Massachusetts-Amherst, and ECG aimed at reducing falls among older adults through the use of artificial intelligence (AI), computer vision, and virtual caregiving. It highlights the significant impact of falls on the elderly population, noting that over 25% of Americans aged 65 and older experience falls annually, leading to substantial healthcare costs. The initiative leverages the AWS Health Data Accelerator to develop ECG’s FAITH module, which assesses falls risk by analyzing various data sources, including video data, chronic health information, and functional assessments. The case study details how this approach allows for real-time, at-home assessments, fundamentally changing the traditional model of falls risk evaluation. It also discusses the scalability of the solution, which aims to make falls risk assessments universally available, thereby improving access to care and potentially reducing hospitalizations and healthcare costs.