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Objectives: The aims of this study are to identify factors in physicalenvironments that contribute to patient falls in hospitals and to propose acomputational model to evaluate patient room designs.  Background: The existing fall risk assessment tools have an acceptable levelof sensitivity and specificity, however, they only consider intrinsic factorsand medications, making the prediction very limited in terms of how thephysical environment contributes to fall risk.  Methods: We provide a computational model for risk of fall based onphysical-environment and patient-motion factors. We use a trajectoryoptimization approach for patient motion prediction.  Results: We run the proposed model on four room designs as examples ofvarious room design categories. Results show the capabilities of the proposedmodel in identifying risky locations within the room.  Conclusions: Our study shows the potential capabilities of the proposedmodel. Due to lack of enough evidence for the examined factors, it is notpossible at this point to gain robust confidence in the final evaluations. Morestudies using quantitative, relational, or causal designs are recommended toinform the proposed model for patient falls.  Application: Developing a comprehensive fall risk model is a significant stepin understanding and solving the problem of patient falls in hospitals. It canprovide guidance for healthcare decision makers to optimize effectiveinterventions to reduce risk of falls while promoting safe patient mobility inthe hospital room environment. We can also use it in healthcare technologiessuch as assistive robots to provide informed assistance.

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design