Protecting Those Who Care: An AI‑Driven Playbook for Increasing Hospital Safety
The crisis we can’t ignore
Healthcare workers represent just 10% of the U.S. labor force, yet they suffer nearly half of all reported non‑fatal injuries caused by workplace violence. Two nurses are assaulted somewhere in an acute‑care setting every hour. Events once considered unthinkable, such as the February 2025 hostage‑taking at a Pennsylvania ICU that left a police officer dead and several others wounded, now make national headlines. Protecting those who dedicate their lives to caring for others isn't just a moral imperative — it impacts patient care quality, workforce retention and hospital finances.
Traditionally, hospitals have relied on policies, staff training, security personnel and physical redesigns. While these measures are crucial, they're fundamentally reactive and often resource intensive. Systemic change requires compressing the gap between recognizing danger and responding effectively.
Understanding the escalation of violence
Violence in a patient room rarely explodes out of nowhere — it moves through a rapid yet recognizable arc. First, tension begins to build agitated movement, yelling in the room, etc. At some point, the clinician senses elevated risk; intuition and training whisper to them that the situation could turn. Finally, if nothing interrupts the trajectory, the moment of harm arrives. Sometimes those three phases compress into a heartbeat, but they are almost always present.
Real-Time Location Systems (RTLS): Lessons from an Important Beginning
Real‑time location systems (RTLS) put a wearable duress badge in every clinician’s pocket. The advantages are clear: It works anywhere a tag can transmit, from patient rooms to parking structures, with a proper investment in infrastructure, and it is highly accurate in locating who activated it and those nearby.
Real-time location systems provide wearable badges that caregivers activate in emergencies. RTLS excels at providing immediate location-specific assistance and coverage beyond patient rooms — namely, hallways, parking structures and remote areas. However, RTLS fundamentally relies on staff recognizing a threat, physically activating a badge and waiting for help. The technology can also be costly if used exclusively for safety.
AI-powered computer vision: proactive overwatch for care teams
Modern computer‑vision platforms turn the video infrastructure many hospitals already use for tele‑ICU, telesitting or virtual nursing into a continuous safety net. As a result, it offers a potentially cost-effective first step for investing in staff safety. These AI solutions offer:
● Ambient Awareness: Continuously scans patient rooms for risk indicators without requiring staff activation, enabling extra eyes, proactive intervention or virtual check-ins that defuse situations early.
● Hands-Free Intervention: Voice commands or gestures summon assistance, eliminating the friction of manually activating a safety device during tense interactions.
● Continuous Learning and Analytics: Stored or anonymized footage enables detailed root-cause analysis, transforming near-misses into actionable insights that improve future safety measures.
These capabilities shift hospital safety from reactive notification to proactive prevention. It underscores why any hospital initiative involving live videos such as virtual nursing or telesitting — must incorporate AI-driven awareness from inception rather than as an afterthought.
A Powerful Combination
Wearable duress and ambient AI are complementary and can provide an unparalleled level of intervention.
● RTLS is everywhere, AI supports patient interaction. Hallways and parking structures still benefit from location badges; patient rooms gain a silent watcher.
● Different thresholds. AI can whisper the first warning to a charge nurse or security; RTLS can shout for immediate response when staff already feel unsafe.
● Unified orchestration. Both signals can feed the same nurse‑call, overhead paging and security dispatch systems, giving leadership a single continuum of escalating response.
Hospitals can anticipate before escalation and intervene in faster, more direct ways.
From static risk scores to live, AI-driven situational awareness, mitigation, and learning
Hospitals already note behavioral flags in the chart and train staff to be aware of certain conditions and behaviors, but risk can change hour by hour. AI brings moment‑in‑time context.
The context of a high-risk patient is critical at each interaction with staff — a patient who has been pacing and shouting for the past 30 minutes is different from the same patient calmly watching TV. The latter can now lead to a virtual nurse checking in via camera to gauge tone, calm the patient or decide to have staff enter only with backup.
Finally, AI and/or video provide context in what happened leading up to an escalation, providing valuable instruction for an ever-learning system to prevent such events.
Compassion plus analytics: redefining hospital safety
Technology alone does not create a culture of safety, but it amplifies human capacity and empathy with data‑driven watchfulness. RTLS badges reassure clinicians that help will find them quickly. AI‑powered computer vision gives them a partner that notices what is happening before danger erupts. Earlier intervention lets us do more than record violence — it helps prevent it.
By embracing proactive, future-centric technology for safety, leaders send a clear message to every nurse, aide and physician. For hospitals it's an altruistic message and one that serves their financial reality: “Your well‑being is as critical as the patients you care for.”
Narinder Singh is the CEO and co-founder of LookDeep Health, a leader in using VisionAI to help nurses and doctors in hospitals better care for patients.
