Techno-economic evaluation of an ontology-based nurse call system via discrete event simulations

Current nurse call systems hinder the efficiency of nurses as the systems are not aware of the type of requested help and the context in which their help is required. To tackle these issues, we have developed an ontology-based nurse call system that automatically takes the patients' and caregivers' profiles and context into account when assigning calls to nurses by modelling this information in an ontology, i.e., a formal domain model. For example, current tasks of the nurses and trust relationship with patients are considered while allocating calls to caregivers. Focus is not only on creating a higher quality patient care, but also on distributing the workload more evenly over all caregivers. However, not in all hospital departments such a smart nurse call system will have a significant impact, e.g., geriatric versus emergency care. To gain insights into the total impact of a smart nurse call system, a dedicated discrete event simulation (DES) model is presented that tests its performance. Based on realistic nurse call logs and information gathered at representative hospital departments through interviews and observations, the simulation model allows optimizing decisions, modelled as rules based on the information captured in the ontology, to allocate calls to the best suited nurse. Several scenarios with a varying number of calls, staff members, etc. are tested to be able to define the effectiveness and the (dis)advantages of the ontology-based system with respect to the current one. In conclusion, recommendations are made towards improving the currently employed nurse call systems in hospitals.