Iterative Design of a Decision Support System for Fall Risk Detection in residential care facilities

Abstract Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make decisions under psychological, emotional and physical stresses. Machine learning methods and in particular, Decision Support Systems (DSS) can be used to support nurses with improved information (e.g. visualization of individual health risks) for decisions that are not easy to make. As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. We explain our user-centered design process based on ISO 9241-210-2019. User feedback is obtained at each phase so that nurses can identify problems at an early stage. The result is a conceptual design, visualized as a click dummy, to identify individual fall risks and predict preventive measures to reduce the risk of falling.