Chris Shearbara is the CTO of Athena Security.
California recently proposed mandating automated weapon detection devices at hospital checkpoints by 2027. This may seem like a local initiative, but the challenges it addresses – violence and lack of staffing – are nationwide.
Automation provides relief by aiding tasks such as weapon screening, staff burdens and speeding operations. However, automation has limitations. They follow predefined rules and cannot adapt to unexpected threats.
It is AI technology that overcomes these limitations.
AI is not just about automating. Analyze, learn and adapt new challenges. For example, we can analyze abnormal patterns of behavior, such as people who are too long in restricted areas that simple automated systems may have missed. And that’s just one aspect. AI can bring to hospital security automation.
Specific use cases for AI when automating hospital security
Using AI automation in hospitals, we don’t just respond to threats. They are predicting and neutralizing them before they occur. This isn’t just security, it transforms what once seemed science fiction into everyday reality. Look at yourself:
Automated Weapon Detection
Weapon screening required multiple officers, one to manage traffic and the other to execute a secondary check when the item was flagged. This approach is slow, labor intensive and prone to human error. In fact, I’ve often heard of people exploiting these busy times to slip through things they don’t notice.
AI-powered X-ray machines automate this process, scanning luggage and individuals to detect weapons or prohibited items with unparalleled speed and accuracy. These systems reduce staffing without reducing manual checks and completely replacing regular tasks. Staff are still essential in high traffic areas to respond to AI alerts, but in low traffic zones, automation is an alternative to manual monitoring.
Controlled access with badge verification
Manually verifying visitor badges and managing access at multiple checkpoints is prone to boring and human error.
AI-driven systems streamline this process by automatically scanning badges or verifying credentials using facial recognition. This increases staff efficiency, focusing efforts on critical tasks, and ensuring only permitted access to sensitive areas.
DHS Guideline Compliance
Non-compliance is subject to an average fine of $9.6 million in hospitals 3.5 times the compliance rate. Beyond financial penalties, failure risks reputational damage and patient loss of trust.
AI simplifies compliance with regulations, including those set by DHS, by monitoring security protocols, and by following the required procedures, verifying restricted access to ensure that only certified individuals are in place. Generates real-time alerts for violations, automatically document incidents, and provides accurate records of audits.
Enhanced video surveillance
Monitoring large hospital areas for abnormal behavior or potential threats is resource intensive and prone to human fatigue. AI-powered surveillance systems help you analyze live video feeds in real time. These systems use advanced pattern recognition to accurately identify suspicious activity, unmanned objects, or potential risks.
When a threat is detected, AI will immediately send alerts to your security team, allowing for faster responses. By reducing the reliance on constant human surveillance, AI makes hospital surveillance smarter, faster and more reliable.
AI can transform hospital security, but it must address challenges such as accuracy and data security for seamless implementations.
Challenges from AI-powered automation in hospital security
When I talk about AI with hospital authorities, the first concern is almost always credibility. “Can you trust us to work accurately, treat everyone fairly, and continue to comply with regulations?” These concerns are valid. We want to implement a system that can unintentionally discriminate or invite lawsuits.
For example, consider Illinois. In the Chicago area, there is a ban on facial recognition technology that can lead to discrimination, not only due to privacy concerns, but also due to potential bias in AI programming. Let’s be clear. This is not limited to facial recognition. AI systems that violate individual rights and data privacy can face similar challenges.
So, how do we deal with this head on? It starts by training AI models on a variety of representative datasets to minimize errors and bias. Strict testing under real-world conditions is equally important for identifying and correcting potential blind spots.
Also, what about sensitive data, such as patient and visitor entry/exit records? This is where edge computing comes in. By processing data locally within hospitals, edge computing reduces exposure to cloud vulnerabilities and keeps all information safe.
However, the challenges don’t stop there. It’s not just about integrating AI into hospital security, but about rethinking existing systems as well as deploying new technologies. Many hospitals rely on outdated legacy systems, and combining these with sophisticated AI tools requires expensive and time-consuming modifications.
Certainly, these concerns are real, but they are not deal-breakers. Careful planning, open communication and rigorous testing allow hospitals to overcome these hurdles and responsibly integrate AI to create a safer and more efficient security system.
Blueprint for AI adoption in hospital security
Since most hospitals rely on third-party providers of security solutions, choosing the right AI-driven system is important to meet your needs and justify your investment.
Here’s a way to approach this:
• Select the right partner. Work with security vendors experienced with AI systems. Find a provider that provides a scalable, compliant solution by clearly documenting how AI works.
• Identify security gaps: Assess current vulnerabilities in hospitals and prioritize areas to areas where AI can provide immediate value, such as visitor management, weapon detection, or local monitoring.
• Start Small: Start pilot projects, including automating entry point security, before deploying AI across the facility.
•Team Training: Gain knowledge and skills to security staff to operate AI systems effectively and maximize the impact of technology.
• Monitoring and Improvement: Continuously evaluate AI performance using real-time feedback. Use post-vendor support implementations to ensure optimal functionality and maximum security is provided to fine-tune your system as needed.
Before it’s too late
The clock is ticking as healthcare violence escalates and orders that could shape California bills grow. Meanwhile, 65% of US hospitals struggle to maintain qualified staff, leaving a significant safety gap.
Early adoption not only helps to address these challenges, but also positions hospitals as a leader in building safety and innovation, trust and reliability.
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