Real-Time Personal Protective Equipment (PPE) Compliance and Clinical Tool Monitoring Using Generative AI: A Novel Approach for Adaptive and Automated Healthcare Surveillance.

BACKGROUND

Hospital-acquired infections (HAIs) remain a critical patient safety concern, affecting one in 31 hospitalized patients daily. Non-compliance with personal protective equipment (PPE) protocols is a preventable driver. Current monitoring methods, such as manual audits and closed-circuit television (CCTV), are limited by delays, inconsistency, and reactivity. Traditional artificial intelligence (AI) systems are rigid and require retraining when protocols change.

OBJECTIVE

To construct and evaluate a generative AI-driven compliance monitoring system, built with Google Gemini (Mountain View, CA, USA) on Raspberry Pi (Cambridge, UK) hardware that translates hospital rulebooks or free-text prompts into real-time enforcement logic without retraining.

METHODS

The system integrated Gemini, OpenCV (Dover, DE, USA) and Streamlit (San Francisco, CA, USA) to convert natural language rules into executable logic. Performance was tested in 168 mannequin-based trials under varied conditions (skin tones, orientations, and object presence). Outcomes were compared with reference labels using accuracy, recall, specificity, F1 score, and Cohen's Kappa.

RESULTS

The system achieved 95.8% accuracy, 91.0% recall, 100% specificity, F1 = 0.95, and Cohen's Kappa = 0.92. Performance was consistent across mannequin skin tones and between rulebook-derived and free-text prompts, with no false positives recorded.

CONCLUSION

This generative AI compliance system demonstrated strong accuracy, adaptability, and cost efficiency. Integration into hospital workflows could enable proactive real-time monitoring of evolving safety protocols, improving compliance and reducing costs relative to current methods.