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5 Reasons Why Data Fusion Holds the Key to Fall Prevention

Falls are one of the greatest risks facing seniors, often signaling the start of a continued decline. Because just a single fall incident can result in serious medical complications or even death, leaders in health tech made fall prevention a top priority in 2021 and continue to emphasize it as resident acuity rises.

At the forefront of this movement is Dele Health Tech, an innovator in fall prevention technology with a data fusion platform that functions as middleware between hardware and data stream partners. The data fusion process integrates data from a variety of sources to produce insights and projections more significant than the ones that individual data sources could produce alone.

With this data-focused approach applied to its solution, Dele Health Tech is aiming to prevent one million falls by 2024. Here are the company’s five reasons why data fusion holds the key to fall prevention.

Easy to use and cost-effective

Fall prediction and prevention systems that use data fusion are accurate, reliable, robust and cost-effective. According to Dele Health Tech Chief Technology Officer Sid Probstein, “data fusion brings together all the sources of truth instead of depending on one perspective,” eliminating the guesswork to improve workflow and efficiency organization-wide.

In addition to functionality and seamless integration, Dele Health Tech has prioritized accessibility for its partners. The need for fall prevention technology is shared across the entire care continuum, and it is being met by affordable entry points made possible through data fusion.

Reliable fall prediction systems

The high specificity and sensitivity of data fusion creates reliable fall prediction systems. This is critical because seemingly lesser risks may hold more significance in the bigger picture. A resident’s pre-existing risk factors are often known upon arrival at a new community, but when those factors overlap with other risks throughout the duration of their care, there is a compounding effect that may go unnoticed.

The more information available, the more confidence caregivers can have in understanding specific risk factors as they relate to other risks and falls in general.

Ability to capture complex data

Through machine learning, data fusion predicts falls by using contextual information about resident behavioral patterns, clinical realities, and the environment. When a resident falls, the system logs that fall with information, most importantly time and location. If the motion sensors are logging that a resident is regularly falling in her room at night, or in the bathroom, or both, then the system labels those falls as part of a pattern.

The system’s machine learning then uses this labeled data to assign significance and meaning to data points in the data fusion process, allowing caregivers to make more accurate fall predictions, which in turn allows them to proactively change the resident’s environment or schedule to prevent future falls.

“By capturing and understanding complex data, caregivers don’t have to guess when it comes to the underlying causes of falls,” Probstein says. “They can simply observe and label the data, then have the machine learn them based on the things that they see.”

Powered by artificial intelligence

Data fusion employs machine-learning algorithms in concert with predictive variables of specific fall risk factors — such as gait impairments, muscle weakness, reduced flexibility or orthostatic hypotension. When the labeled data is processed by artificial intelligence, caregivers can understand exactly what caused a fall, and make finite adjustments to keep residents safe moving forward.

AI can also help caregivers manage fall response and recovery since falls will happen in spite of the efforts to prevent them.

Collects from multiple sources

Use of data fusion from non-wearables as well as wearables and ambient sensors, user interface design, assessment of external fall risk factors and comparisons to clinical fall risk assessments help identify the risk for falling and ultimately the prevention of falling.

“Falling is a multi-factorial, multi-dimensional problem that requires a multi-disciplinary approach,” says Dele Health Tech VP of Gerontology Dr. Lydia Manning. Dele Health Tech looks for any dignified, appropriate data they can find to provide more context around potential incidents.

Research shows that when someone is on the floor for an extended period of time, several medical complications arise which can ultimately lead to death. In addition to fall prevention and the post-fall process, caregivers use real-time data capture from multiple sources to track events as they occur, thus minimizing a resident’s time on the floor and getting them into recovery faster.

“I would argue that fall prevention is more important now than it was even two years ago,” Dr. Manning says. “If you add in social isolation and lack of movement, the probability for fall risk has increased tremendously.”

Whether the result of unprecedented staff shortages or the growing acute needs of senior living residents, the risks of and outcomes from falls are much greater now than they were prior to the pandemic. Fall detection and prevention systems are critical to mitigating those pain points, with data fusion as the key to their success. 

This article is sponsored by Dele Health Tech. To learn more about how Dele Health Tech is revolutionizing health care with innovative data fusion for fall management, visit

The post 5 Reasons Why Data Fusion Holds the Key to Fall Prevention appeared first on Senior Housing News.

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