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

Area of Science:

• Healthcare design
• Patient safety
• Computational modeling

Background:

• Existing fall risk tools primarily focus on intrinsic patient factors and medications.
• These tools have limitations in assessing the impact of the physical environment on fall risk.

Purpose of the Study:

• To develop a computational model for evaluating patient room design's influence on fall risk.
• To identify specific room features and layouts that contribute to patient stability and mitigate fall incidents.

Main Methods:

• A computational model (algorithm) was developed using literature data.
• Trajectory optimization was employed for patient motion prediction.
• The model incorporates static factors (lighting, flooring, support objects, bathroom doors) and dynamic factors (patient movement).

Main Results:

• The preliminary model was tested on four typical patient room configurations.
• Results demonstrated the model's capability to identify fall risks associated with different room designs.
• The model successfully highlighted the influence of room layout and features on patient fall risk.

Conclusions:

• The proposed model offers a novel, evidence-based approach for evaluating room design and estimating patient fall risk.
• Challenges in data heterogeneity were identified, impacting the understanding of the built environment's role in fall mitigation.
• Further validation with actual patient fall data is recommended for model refinement.