Predict Serious Fall-Related Injuries

A fall is defined as an event which results in a person coming to rest inadvertently on the ground or floor or other lower level. Fall-related injuries may be fatal or non-fatal (1) though most are non-fatal. For example, of children in the People's Republic of China, for every death due to a fall, there are 4 cases of permanent disability, 13 cases requiring hospitalization for more than 10 days, 24 cases requiring hospitalization for 1–9 days and 690 cases seeking medical care or missing work/school.

Size of the Problem

• 900,000 hospitalizations in 2018 were due to fall-related injuries (3).
• 32,000 deaths were attributed to fall-related injuries in 2018 (3).
• $50 billion is the annual cost of fall-related medical spending (5).
• ‍88 older adults die every day from fall-related injuries (3).

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Serious fall-related injuries profoundly affect the lives of older adults, and falls are the leading cause of fatal and nonfatal injuries among adults age 65 and over (2). Approximately one in four older adults falls each year and nearly 36 million falls were reported in 2018, resulting in more than 900,000 hospitalizations and 32,000 deaths (3).These numbers are expected to balloon as this older population continues to grow. Death rates from falls have already increased roughly 30% in the last decade, and the current $50 billion fall-related, annual costs are expected to climb accordingly (4,5). Moreover, falling once doubles the chance of falling again; even individuals that fall but do not sustain serious injuries are at an elevated risk for subsequent falls that may require medical attention (6). Yet, falls are not reliably reported, despite their prevalence and the dangers they pose. 72% of patients who had received care for a fall-related injury did not report it when asked by their physician, leaving nearly three in four patients without the initiation of fall prevention activities (7).

Fortunately, predictive analytics can enable practitioners to proactively identify high-risk patients in a timely manner and start conversations about the myriad of preventative measures available. From there, practitioners can leverage AI-based insights to create intervention strategies that may include individually-tailored combinations of preventative measures, such as strength and flexibility training, medication review, assistive devices, and home modifications.

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• Medical Claims: Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.
• e-Prescribing Data: Data from electronic prescriptions detailing key information about medications, dosage, patient instructions for frequency and timing, and available refills.
• Remote Monitoring Data: capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).

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