Reduction of Alarm Pressure at a Neonatal Intensive Care Unit : A Qualitative Study

Samenvatting

Medical equipment for measuring vital parameters in neonatal intensive care units (NICUs) is becoming more advanced, leading to an overwhelming number of alarms, of which only 20% are clinically relevant. This contributes to alarm fatigue among nurses, posing risks to patient safety and increasing burnout risk. A qualitative study explored the potential for artificial intelligence (AI) to reduce unnecessary alarms. Findings showed that many alarms could be silenced based on patient characteristics or alarm patterns, and a usable dashboard was developed to classify and analyze alarm data. AI holds promise for reducing alarm pressure by adjusting settings or leveraging patterns, with future experimentation involving a digital twin to simulate and optimize alarm settings in a safe environment.