Falls pose a substantial risk to elderly individuals, especially those over 65, often leading to severe consequences. This project investigates the potential of the tēmi robot for fall detection in care facilities and its integration into a simulated clinical workplace system. The prototype employs the YOLOv8 image recognition model to detect fallen individuals during patrols, transmitting incident data to a simulated clinical system via Fast Healthcare Interoperability Resources (FHIR). While initial tests delivered promising results, enhancements in image recognition accuracy are required for effective real-world deployment.