235 General discussion 8 The medical bed reliably calculates the patient’s weight and automatically alerts in case of a significant increase or decrease over the course of 24 hours. The AIFluid Indicator was activated as well; this AI-assistant links the measured weights, the administered medication, and the fluid balance in the patients’ electronic record. The AI-Fluid Indicator shows a 1 kg decrease in weight under treatment with furosemide 500mg/24h, and a negative fluid balance of 2850ml. The patient’s response to the medication is effective; however, there are signs that the patient’s thirst from losing all these fluids is reducing the effect. The fluid balance does not seem to completely relate to the weight loss; the nurse could offer tips to overcome the thirst without giving in to it. The following day, the patient is a little slow; the PU-risk detector has already gone off twice, indicating no change of position during three consecutive hours. Additionally, the telemonitoring trend detection indicates an increase in ectopic activity. A nurse who just started working on this ward is taking care of this patient today. She wonders what all this could mean and queries the Nurse-AId, wherein all hospital protocols are integrated. She also consults the AI-Risk Assessments, which is daily updated, and learns that there is a high risk for delirium. Successfully implementing AI-tools into the nurses’ workflows has contributed to higher job satisfaction. Integrating these tools into their work processes enhances their clinical reasoning. Their registration burden has diminished; they even start to like data registration since AI can use most of the data to further enhance the quality of care. Nurses seem increasingly motivated to participate in co-designing future innovations and integrating evidence and innovation into practice.
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