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HomeMedicalImage AnalysisAI and Nursing home safety: How do they intersect

AI and Nursing home safety: How do they intersect

By Nilay Parikh, Co-founder and CEO, Be Global Safety

Safety is one of several reasons people decide that elders with chronic health problems are better off in nursing homes than in their own homes.

However, the safety advantage of nursing homes over the home has not been clearly demonstrated. In fact, 20% of accidents involving the elderly occur in nursing homes¹

A large number of nursing facilities are dangerously short-staffed. But the labor cost of trained nursing staff is a major deterrence to adequate staffing in nursing homes. As a result, patients do not get enough care and attention as required.

According to a survey, half of the nursing homes had inadequate staffing (3.53 hours per resident day or less), with at least a quarter having very low staffing (3.18 hours per resident day or fewer), compared to the suggested minimum of 4.1 hours per patient day.

In fact nursing homes have a greater risk of occupational disease and injury for workers than coal mines according to the Bureau of Labor statistics. It declared that nursing homes are the most dangerous workplaces in the United States due to the number of occupational fatalities like manual material handling.

Heavy manual handling is one of the leading causes of injuries among caregivers in nursing homes

Most businesses and industries have guidelines for manual lifting. In some cases, workers do not manually lift stable things weighing up to 50 pounds or less. The case is different in nursing homes. Some resident patients may weigh more than 250 pounds and may be obese; caregivers cannot forklift the weight as construction workers would. A nurse may have lifted around 1.8 tons in a typical 8-hour workday.

This is why manual handling is the underlying cause of musculoskeletal disorders (MSDs) in nursing home employees. In fact, nursing homes have the highest report of sprains, strains, and tears, and back injuries,.

As a result, employees in a short-staffed nursing home are overexerted, fatigued, and are unable to discharge patient care as needed. These issues snowball into a row of safety policy violations in many nursing homes.

In 2015, roughly 93 percent of nursing facilities in America received at least one inspection deficiency penalty¹º, and one in every five got major quality violation flaws in the same year. These violations cost millions in civil monetary penalties every year ¹¹ and one of the primary causes of these adversaries is an unsafe patient environment.

An unsafe environment causes more slips and fall among elderly residents than muscle weakness

Slip and fall accidents are the direct cause of approximately 1800 nursing home resident deaths every year. But, while muscle weakness might seem the obvious issue, round 27% of all accidents in nursing homes are the result of physical obstacles located in the rooms and hallways of the facility.

This issue is so prevalent that over 3 million older people are treated in emergency departments for fall injuries every year¹³.

Guaranteeing patient safety in nursing homes necessitates a structure that simultaneously ensures personnel safety.

Fall and trips, manual handling, musculoskeletal disorders can all be prevented if residents and staff have increased awareness of environmental hazards-which causes more injury than the fragility of residents.

What if similar success can be achieved with innovative solutions like image analysis technology; even cheaper and more effective?

Leveraging image analysis technology for patient and employee safety awareness

Image analysis, often known as imagery analysis, is the process of extracting useful information from pictures, mostly digital images, or video, using image processing techniques. Image analysis tasks can range from as simple as reading bar-coded tags to as complex as recognizing hazardous objects on the pathways.

Object tracking technology can forecast slips, fall and trip, and bedsores injuries

An elderly person’s walking pattern can change in response to the physical task being performed and the person’s mental state. Researchers have shown that by using computer vision, a person’s walking pattern can be monitored to identify the person’s level of risk for falling¹⁶. If the elderly person shows an abnormal walking pattern, the system can be used to alert caregivers that the elderly person may be at risk of falling. Or notify the staff immediately when someone falls down.

AI-powered image analytics can improve a caregiver’s ergonomic practices

Through the application of AI-driven image analytics, staff can get personalized and data-backed ergonomic recommendations based on the gathered data.

For instance, when a care professional tries to raise a patient or a heavy object around the facility, image analysis can use 3D analysis to detect the weight of exertion, suggest correct lifting posture or notify other colleagues for help.

Machine learning module of image analysis can detect if the nurse is struggling to lift an object or if the object’s weight is beyond recommended weight. Every workplace has its peculiar hazards. The implementation of image analysis technology with such insight can help administrators understand the frequencies and risk factors of the hazard.

The system studies the patterns of injury over time and learns new changes. Effectively, the machine learning program is trained to combat these hazards.

Most nursing homes use security cameras to monitor the parking lots, and areas of entry and exit. Image analysis technology brings this security function on par with safety since nursing home administrators can enhance the existing security cameras with AI sensors that would perform

safety functions. Therefore, implementing image analysis technology is a scalable and cost- effective way to detect and prevent injuries in nursing homes.

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