Shield Health Care Workers from Workplace Violence with AI Tech
Health care and social assistance workers are five times more likely than employees overall to experience a workplace violence-related injury, according to the U.S. Bureau of Labor Statistics. Nonfatal incidents nearly doubled between 2011 and 2018. In a 2025 survey by National Nurses United, 82% of nurses reported at least one violent incident in the past year, and nearly half saw an increase on their unit.
Emergency and trauma departments sit at the epicenter. Long waits, uncertainty, and strained resources fuel frustration that can turn into aggression toward staff. Beyond security and clinical response, this is a workforce issue - one HR must lead to protect people, stabilize teams, and support patient safety.
Why this matters for HR
Violence drives anxiety, burnout, and turnover. It erodes confidence and care quality. It's expensive, too. U.S. hospitals faced more than $513 million in staffing-related costs tied to workplace violence in 2023, according to the American Hospital Association.
HR leaders can partner with clinical, security, and IT teams to reduce risk, tighten policies, and build a culture that backs staff when incidents happen - and ideally, stops them before they start.
Where AI Fits: Predict, Detect, De-escalate
The missing link in many safety programs is foresight. AI can flag risk earlier by combining patient history, prior encounters, and public data with real-time signals from cameras, microphones, and sensors. It can spot patterns like overcrowding, prolonged wait times, escalating voices, or aggressive postures - then alert the right people before things spiral.
One Los Angeles-area hospital deployed gun-detection tools, real-time aggressive-behavior detection, and facial recognition to identify banned or disgruntled individuals and respond to law enforcement BOLO alerts. Once a high-risk situation is flagged, staff can follow established protocols to intervene and de-escalate.
"It can't make final judgment-based decisions, but it can alert staff to risk, provide guidance, and recommend the best course of action during a crisis situation," said Scott Snyder, chief digital officer at EVERSANA. He noted that accuracy improves when models are trained on relevant internal data.
What the research shows
Researchers at the University of Washington and Johns Hopkins trained deep learning models on clinical notes to predict which patients might become violent within three days. The models correctly forecasted 7 to 8 out of 10 incidents; trained human experts hit 5 out of 10. The takeaway: AI can sharpen foresight, while people make the final call.
HR's Leadership Role: From Policy to Practice
Policy and governance
- Define the purpose and limits of AI use: what it will monitor, where it applies, and how it supports staff safety.
- List data sources the system may access (e.g., clinical notes, video/audio, incident reports) and set strict retention and deletion rules.
- Embed consent and privacy protections for patients and employees; validate compliance with local and national laws.
- Set roles and permissions: who receives alerts, who decides on deployment, and who can override AI recommendations.
Protocol development
- Establish clear escalation paths for AI alerts, including thresholds and response times.
- Define which roles can review, approve, and document interventions.
- Protect patient rights while prioritizing staff safety; include steps for respectful, bias-aware de-escalation.
- Require documentation and post-incident reviews for all AI-identified events to improve the model and the process.
Training and workforce readiness
- Explain what the tech can and cannot do, how alerts work, and how it protects staff and patients.
- Teach hard skills (using the system, responding to alerts) and soft skills (calm communication, situational awareness, evaluating AI output).
- Run competency checks, refreshers, and drills; capture frontline feedback to refine policies and models.
A Practical 90-Day Plan
- Weeks 1-2: Form a cross-functional working group (HR, nursing, security, IT, legal, compliance). Agree on objectives, scope, and success metrics.
- Weeks 3-6: Map high-risk areas and workflows. Select pilot use cases (e.g., ED waiting area aggression alerts). Draft data, consent, and access policies.
- Weeks 7-10: Configure AI alerts with conservative thresholds. Train charge nurses, security, and supervisors. Run simulations and tabletop exercises.
- Weeks 11-12: Launch a limited pilot with 24/7 on-call support. Review incidents weekly. Adjust thresholds, scripts, and staffing as needed.
Addressing Concerns: Privacy, Bias, and Trust
Strong guardrails reduce risk. "Establishing guardrails and protocols in these solutions can help minimize biases and errors," Snyder said. Keep humans in the loop to avoid false labels or inappropriate action.
- Bias checks: Test models with diverse data, audit false positives/negatives, and rotate independent reviewers.
- Transparency: Tell staff and patients where AI is used, what is recorded, and how it supports safety.
- Governance: Create an oversight committee to approve use cases, review incidents, and retire tools that underperform or raise risk.
Strengthening Safety Through Human-Centered AI
AI won't replace security or clinical judgment. It gives teams earlier warning and clearer options under pressure. HR's role is to set the rules, train the workforce, and make sure the tech serves people - not the other way around.
Do this well, and caregivers can focus on care with fewer interruptions, fewer injuries, and fewer good people leaving the field.
Helpful resources
- U.S. Bureau of Labor Statistics: Workplace injury and violence data
- American Hospital Association: Reports and guidance
- Complete AI Training: AI courses by job function
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