  Objectives: The aims of this study are to identify factors in physical
environments that contribute to patient falls in hospitals and to propose a
computational model to evaluate patient room designs.
  Background: The existing fall risk assessment tools have an acceptable level
of sensitivity and specificity, however, they only consider intrinsic factors
and medications, making the prediction very limited in terms of how the
physical environment contributes to fall risk.
  Methods: We provide a computational model for risk of fall based on
physical-environment and patient-motion factors. We use a trajectory
optimization approach for patient motion prediction.
  Results: We run the proposed model on four room designs as examples of
various room design categories. Results show the capabilities of the proposed
model in identifying risky locations within the room.
  Conclusions: Our study shows the potential capabilities of the proposed
model. Due to lack of enough evidence for the examined factors, it is not
possible at this point to gain robust confidence in the final evaluations. More
studies using quantitative, relational, or causal designs are recommended to
inform the proposed model for patient falls.
  Application: Developing a comprehensive fall risk model is a significant step
in understanding and solving the problem of patient falls in hospitals. It can
provide guidance for healthcare decision makers to optimize effective
interventions to reduce risk of falls while promoting safe patient mobility in
the hospital room environment. We can also use it in healthcare technologies
such as assistive robots to provide informed assistance.
