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

TL;DR

This study introduces a computational model that evaluates patient fall risk in hospital rooms by analyzing environmental and motion factors, aiming to improve fall prevention strategies.

Contribution

The paper presents a novel computational approach incorporating physical environment and patient motion analysis to assess fall risk, expanding beyond traditional intrinsic factor-based tools.

Findings

Model successfully identifies risky locations in different room designs.

Demonstrates potential of the model to inform safer hospital room layouts.

Highlights need for further validation with more data.

Abstract

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.