Spatial Artificial Intelligence: The Future of Proactive Fall Prevention

Imagine a room where the walls, instead of barriers, are silent observers. Subtly tracking movements, predicting risk, and intervening before issues occur. For the millions of individuals who are at risk of debilitating falls due to aging, illness, or other vulnerabilities, this isn’t science fiction. It’s the promise of spatial artificial intelligence.  

What is Spatial Artificial Intelligence?

Spatial artificial intelligence (AI) refers to technology that uses machine learning to understand, analyze, and navigate three-dimensional (3D) environments. Like other AI applications, spatial intelligence has enormous potential, particularly within sectors like urban planning, logistics, and healthcare. 

Visual-Spatial Intelligence vs. Spatial AI: What’s the Difference? 

Visual-spatial intelligence refers to a human’s ability to create mental representations of physical spaces through the perception, analysis, and manipulation of visual information. This is inherently a human skill, often present in those who pursue careers in areas like architecture, engineering, and landscape design. 

Spatial artificial intelligence, on the other hand, relies on technology and data analysis to derive an understanding of physical environments. 

In short, visual-spatial intelligence is a human capability, while spatial AI is a technology. 

Defining Spatial AI in Healthcare

Spatial AI has enormous potential in many industries, but perhaps none more important than what it’s expected to achieve within healthcare. 

In her April 2024 TED Talk, Fei-Fei Li, founding director of the Stanford Institute for Human-Centered AI, shared her lab’s focus on applying AI to help solve some of healthcare’s most pressing challenges: relieving staff burnout and improving patient outcomes. From piloting smart sensors in partner hospitals that can detect improper handwashing by clinicians, to keeping track of surgical instruments, Li’s team is working to develop AI technologies that can serve an “extra set of hands” or “an extra set of eyes” for clinicians in healthcare settings — resources that allow caretakers to focus on patients and provide higher quality care. 

Like others researching this technology, Li's team is pushing the boundaries of spatial AI to develop impactful healthcare solutions. As researchers continue to refine spatial AI technologies, we anticipate that these applications will become more commonplace — and even essential — in hospitals and post-acute care facilities throughout the world.  

Advantages of Spatial Artificial Intelligence in Health Care Settings

Many health systems are already using AI-based tools to help streamline tasks like basic charting, clinician note-taking, prior authorizations, finding billing codes, and issuing discharge instructions. While these use cases highlight the efficiency and productivity benefits of AI in healthcare, the technology’s biggest potential lies in enhancing proactive patient safety and improving patient outcomes through spatial awareness and analysis. 

How Spatial Artificial Intelligence Works for Proactive Fall Prevention

700,000 to one million patient falls occur in hospitals in the United States each year, resulting in roughly 250,000 injuries and as many as 11,000 deaths annually. Falls that result in serious injury or death due to a lack of proper fall-prevention protocols are known as “never event falls.” This classification, given by the Centers for Medicare & Medicaid Services (CMS), means that hospitals will not be reimbursed for additional costs as a result of these types of falls.   Health systems must work to mitigate this risk to improve patient outcomes, cut costs, and enhance operational efficiency. Beyond the obvious benefit of patient safety, using spatial AI for fall prevention protects health systems financially — with the cost of treating injuries from falls in older adults projected to reach over $101 billion by 2030. 

Many traditional fall-prevention measures like pressure pads and virtual sitters are unreliable, prone to human error, and most importantly, cannot predict falls before they occur. Bed-alarm devices return an average of 20-30 false alarms per bed per day, with detection accuracy as low as 15%.  

Proactive fall prevention technology, in contrast, uses spatial AI to predict falls before they happen with up to 98% accuracy. By taking this “predict-to-prevent” approach, clinical care teams can maintain focus on treating conditions patients came with, rather than caring for post-intake injuries that could have been avoided.  

Key Components of Spatial Artificial Intelligence

Like other forms of AI, spatial artificial intelligence is comprised of several key components critical to its functioning that fall under one of the following three categories: 

1. Perception

A spatial AI system cannot function without a deep understanding of the physical environment in which it's expected to operate. It gains this geo-spatial awareness in one of two ways: with vision systems such as cameras, or depth-sensing technologies like LIDAR (light detection and ranging).  Camera monitoring isn’t as accurate and comes with risks, including patient privacy breaches and potential HIPAA violations.  

Infrared depth-sensor monitoring with LIDAR technology can replace the use of in-room cameras for added accuracy and patient privacy. Just as LIDAR sensors on a car can alert drivers to upcoming obstacles they should avoid, this technology — coupled with machine learning — can alert staff to a potential fall before it occurs.  

2. Reasoning

Once the data is collected it must be interpreted to glean any meaningful information. Like perception, reasoning can be approached in a few ways: via data-interpretation methods that use algorithms to analyze data and identify shapes, structures, and patterns; or by machine learning.  

Machine learning and machine vision are two distinct but complementary components of spatial AI. Machine vision refers to a computer’s ability to interpret and understand its environment, such as recognizing a pedestrian in a crosswalk using LIDAR or camera data. Machine learning, on the other hand, uses algorithms to continuously analyze data, recognize patterns, and make decisions based on information provided by machine vision. In healthcare, these technologies work together to enable spatial AI to perform tasks like predicting a patient’s intent to exit their bed 30-65 seconds before they actually do, by processing and acting on visual and contextual data in real-time.  

3. Execution

Following perception and reasoning, the next component to spatial AI is execution. Depending on the use case, this can take the form of path planning, which uses algorithms to determine the best action or path forward; or through physical manipulation using robotics. For fall prevention in acute- and post-acute care settings, execution might look like an alarm that automatically alerts staff of a patient’s intent to exit their bed a full minute before they do. It could also take the form of audio prompts that are played to the patient for support until additional actions can be taken.  

Challenges and Considerations of Using Spatial AI in Healthcare

As one of the most regulated industries (and for good reason), healthcare should approach adopting spatial AI just as they would any other technology, with a full understanding of its challenges. Here are a few of the most critical, along with considerations health systems should keep in mind when assessing solutions: 

• Data Privacy and Security: Any technology that collects sensitive patient data in the U.S. must comply with HIPAA regulations. When assessing solutions, look for vendors that are transparent about their data-privacy and security protocols.  

• In-Room Patient Privacy: Many fall-prevention options rely on cameras to inform their machine learning capabilities, which are fraught with privacy and regulatory concerns. Solutions that use infrared sensor technology in place of cameras are not only more privacy-focused, they are also more accurate.  

• False Positives and Alarm Fatigue: Systems that are oversensitive or lack proper machine-learning protocols frequently generate false alarms. Solutions built on highly-trained AI algorithms and machine learning can provide accuracy up to 98%. 

• Cost and ROI: Adopting any new technology comes at a cost, and ROI is an important consideration when assessing spatial AI solutions. Rather than focus solely on the up-front costs, consider the long-term return on investment (ROI) potential. Solutions that use sensors instead of cameras, for instance, are less likely to result in privacy-related lawsuits or costly HIPAA violations. Similarly, technology that proactively predicts falls before they happen can save health systems hundreds of thousands of dollars in costs associated with inpatient falls.  

• Scalability and Maintenance: To get the most out of any tech investment, it’s important to adopt solutions that can scale and are supported by comprehensive maintenance support. Look for vendors capable of supporting growth at all stages along with top-tier customer service and maintenance.   

How VirtuSense Solutions Leverage Spatial AI for Early Detection and Intervention

Spatial artificial intelligence is already being used in multiple facets of healthcare to improve patient outcomes and clinician burnout — and VirtuSense has been a pioneer in this space for over 10 years. 

Our patient safety and fall-prevention solutions are currently implemented in hundreds of facilities across healthcare specialties — from skilled nursing and assisted living communities to hospitals and health systems to primary care offices and physical therapy centers. To date, our technology has impacted over one million lives, prevented over 100,000 falls, and saved healthcare facilities nearly $800 million.  

VSTAlert, our flagship proactive fall prevention solution, is enabling post-acute care facilities to reduce falls by 85%, reduce falls with injury by 96%, and eliminate 95% of false alarms. Powered by spatial AI technology equipped with advanced infrared LiDAR sensors instead of cameras, VSTAlert maintains patient safety while preserving their privacy and dignity — all while keeping health systems 100% HIPAA compliant.   

VSTOne is a smart automation hub that’s transforming patient care within hospital rooms. Acting as a virtual assistant, VSTOne integrates seamlessly — and privately — into a patient’s environment. Designed as a spatial AI solution, VSTOne can monitor patient safety, track vitals, and connect them with virtual nurses and specialists using the same LiDAR sensors and machine learning capabilities that are essential to proactive patient care. 

VSTBalance, our automated fall-risk assessment solution, leverages spatial artificial intelligence to objectively identify the three leading indicators of fall risk: balance, gait, and function. Like VSTAlert, VSTBalance only uses infrared sensors to assess residents, enabling quick, highly detailed, and accurate assessments. VSTBalance is currently used by more than 6,000 clinicians, with 85% of patients reporting mobility improvements. In skilled nursing facilities, falls have been reduced by 73% overall.

Conclusion

There are many spatial artificial intelligence examples in healthcare that are shaping the hospital room of the future. Proactive fall prevention is expected to be one of the most impactful applications of spatial AI in the industry. Acute- and post-acute care facilities can benefit from adopting solutions like VSTAlert and VSTOne in both the short and long term.  

Discover the impact fall prevention technology powered by spatial AI can make on patient outcomes, safety, and clinician burnout. Request a demo to learn more.