How AI Is Turning Hospital Cameras Into an Active Safety System

Hospitals have spent decades installing cameras across entrances, corridors, parking areas, and sensitive parts of their facilities, creating extensive visual records of activity inside complex healthcare environments. The limitation is that much of this footage becomes useful only after someone knows there is a reason to watch it.

When an incident occurs, security teams can return to the footage and reconstruct what happened, sometimes reviewing hours of video to understand a relatively brief event. The camera has preserved a record, but it has done little to help the hospital recognize the situation while it was developing.

“Hospitals have traditionally used video surveillance as a system to record, not a system for responses,” said Orit Dolev, Principal Product Manager at Lumana. “Teams reviewed footage after an incident. By then, the moment to act had already passed.” 

AI and the New Active Intelligence Layer

Artificial intelligence is beginning to change that role by enabling video systems to interpret activity in real time. Dolev describes the development as a move from video recording toward an “active intelligence layer,” where existing cameras become visual sensors capable of identifying events that may require attention.

“Instead of simply recording what happens, the camera becomes a sensor,” Dolev said. “A system can identify events as they occur, such as a fall, unauthorized access, unusual movement, or activity in a restricted area, and routes the alert to the right team with the surrounding video context attached. The person responding doesn’t have to go looking for what triggered the alarm. It arrives with the answer.” 

The distinction is particularly relevant in hospitals, where responsibility is spread across large facilities and teams with different roles. Security personnel may oversee hundreds of cameras, while clinical and operational staff have little reason to continuously monitor video feeds. A facility can capture enormous amounts of footage without ensuring that the right person sees an important event at the right time.

AI-powered monitoring narrows that gap by identifying defined behaviors or unusual activity and surfacing the relevant footage on its own. Fall monitoring is a clear application. The same logic extends to restricted areas, unauthorized access, and entrances, the places where something going unnoticed carries a real cost. 

Improved Reliability 

The usefulness of those alerts depends heavily on accuracy. Early video analytics developed a reputation for false alarms because relatively simple rules often struggled to interpret busy physical environments. A system that repeatedly reacts to ordinary movement creates another burden for staff and eventually loses their trust.

“False alarms are the fastest way to lose a security team’s trust,” Dolev said. “When a system generates too many irrelevant alerts, staff tune it out. The system is still running, but it is no longer protecting anyone.”

Dolev highlights that Lumana’s AI adapts to the environment observed by individual cameras, allowing the system to develop a better understanding of routine activity in a particular location. Normal movement in a hospital entrance, for example, may look very different from expected activity near a restricted storage area.

“The objective is to surface events that require attention, while filtering out routine activity and operational noise,” Dolev explained.

For her, the important benefit is reducing the amount of continuous attention required from hospital employees.

Leveraging Existing Infrastructure 

Lumana has designed its platform to add these capabilities to IP cameras that organizations may already have installed. The company’s Lumana Core device connects to an existing camera network, processes video locally, stores footage, and applies AI in real time. Cloud infrastructure provides connectivity, remote management, and additional intelligence where needed.

None of this requires replacing past investment,” Dolev stated. “The system connects to the IP cameras already on the wall, and the intelligence is added on top: real-time alerts, advanced search, centralized management, and intelligent response. That is how hospitals are stepping into the AI era.” 

That compatibility addresses a practical problem for healthcare organizations with large campuses and years of investment in physical infrastructure. Replacing an entire camera network can turn a technology upgrade into an expensive construction and procurement project. Adding an intelligence layer to existing equipment gives hospitals another path to modernization.

The same system can also make investigations more efficient. Authorized users can search for people, objects, or behaviors rather than manually reviewing hours of footage, while Lumana VMS+ allows teams to manage video through a browser or mobile device.

Compliance Considerations

Using AI to interpret hospital video also creates significant privacy and governance responsibilities. Healthcare organizations need clear policies governing who can access footage, how it is used, how long it is retained, and how actions within the system are audited.

Dolev argues that those controls have to be considered from the beginning. Systems should minimize unnecessary movement of sensitive data while providing controlled access and audit trails that support HIPAA requirements and the organization’s own compliance policies.

Moving Towards Physical AI

The technology itself is also moving beyond basic object detection. Dolev expects AI video platforms to become more capable of interpreting behavior and context while connecting with access control, alarms, and operational response systems. Edge processing, where video can be analyzed locally, will remain important in environments where speed, privacy, and reliability matter.

“The broader shift is toward Physical AI,” Dolev said. “The industry is moving from detecting objects to understanding situations. These systems do not just perceive what is happening. They help determine the right response and carry it out. The camera used to answer what happened. Increasingly, it will answer what should happen next.” 

Recommendations

For hospital executives considering an AI-powered video system, Dolev recommends beginning with a specific operational or safety problem rather than the technology.

“A powerful system has limited value if frontline teams cannot operate it easily or if it generates too many irrelevant alerts,” she said. “The goal should be measurable improvement: faster awareness, shorter investigations, fewer missed events, and more effective response across the organization.”

Hospitals already have extensive visual infrastructure. Companies such as Lumana are betting that AI can make those existing systems more useful while events are still unfolding, giving teams greater awareness across a facility without asking employees to spend their shifts watching walls of camera feeds.