Clinical trials

Clinical rounds in the intensive care unit (ICU) involve substantial manual documentation. Retrieving the correct protocol text and structuring notes at the bedside is time-consuming and may contribute to variation in documentation quality. Modern artificial intelligence (AI) can help structure existing information and automate protocol look-ups within a restricted, manually selected document set. The tool evaluated in this study acts as an AI-based informational assistant for clinicians. It (1) pre-populates a standardized physical-exam and daily-rounds format, (2) prepares a concise ICU course/overview using predefined formatting, and (3) retrieves relevant passages from protocols to enable rapid consistency checks by the clinician. The AI-based informational assistant does not provide treatment recommendations or patient-specific advice; all outputs require clinician verification and clinical responsibility remains with the physician.

Increasing patient complexity, staffing shortages, and administrative burdens have intensified nurses' workloads, contributing to burnout and reduced job satisfaction. These challenges were particularly evident in the intensive care units (ICU) during the COVID-19 outbreak. With the growing healthcare demand in front of us, it is essential to understand and manage perceived workload effectively to maintain high-quality care and promote staff well-being. A real-time overview of nursing workload may facilitate the identification of patients requiring additional support and enable more effective distribution of workload among nurses during shifts. A dashboard has therefore been developed for use in the ICU to provide an overview of the patients at the unit and their corresponding nursing workload. To assess whether this dashboard is fit for purpose, this study aims to evaluate nurses' perspectives on its implementation, focusing on acceptance, adoptability, appropriateness, and fidelity. Additionally, the model for the calculation of the nursing workload will be assessed in terms of its alignment with ICU nurses' perceived workload and its potential for automation with artificial intelligence (AI).