From framework to frontline: Embedding enterprise risk management into emergency department fall prevention

Area of Science:

• Healthcare Management
• Patient Safety
• Emergency Medicine

Background:

• Fall-related injuries pose significant risks in healthcare settings.
• Existing frameworks for enterprise risk management (ERM) can be adapted for fall prevention.
• Emergency departments (EDs) present unique challenges and opportunities for fall risk assessment.

Purpose of the Study:

• To extend ERM frameworks for fall prevention specifically to emergency department settings.
• To highlight the benefits of early, individualized fall risk assessment at triage.
• To address the financial implications of falls within or near EDs.

Main Methods:

• Leveraging existing patient data accessible at triage (e.g., anticoagulant use, cognitive impairment).
• Integrating high-risk indicators into machine learning tools (e.g., Rothman index, fall triage score).
• Utilizing risk matrices and heat maps for safety prioritization in dynamic ED environments.

Main Results:

• Individualized fall risk assessment at triage is strategically advantageous in EDs.
• Accessible data can be effectively utilized by machine learning tools for fall risk prediction.
• Falls originating in or near EDs can increase length of stay and incur non-reimbursable costs.

Conclusions:

• ERM-based fall prevention strategies are scalable and applicable to dynamic ED environments.
• Prospective validation of these ERM approaches in Level 1 trauma centers is recommended.
• Collaboration is invited to test the framework's effectiveness in real-world emergency settings.