Predictive model for optimizing prehospital transfusions in an urban EMS system.

Area of Science:

• Emergency medicine
• Health services research
• Operations research

Background:

• Prehospital transfusions can improve survival for trauma patients.
• Blood products are scarce, necessitating efficient deployment strategies.
• Mobile blood banks (MBBs) offer a potential solution for timely transfusions.

Purpose of the Study:

• To optimize the deployment of two mobile blood banks (MBBs) in Pittsburgh using computer modeling.
• To evaluate the impact of MBB deployment on transfusion times for injured patients with hypotension.
• To determine the optimal number of MBBs for servicing eligible patients.

Main Methods:

• Mixed integer programming was used to identify optimal MBB base locations.
• Discrete-event simulation modeled MBB dispatching using one year of 911 call data.
• The study focused on injured patients experiencing hypotension.

Main Results:

• MBB-administered transfusions averaged 7.2 minutes, significantly faster than hospital transfusions (36.7 minutes).
• Two MBBs could service up to 73% of eligible patients; four MBBs serviced up to 95%.
• Deploying more than four MBBs yielded diminishing returns in patient servicing.

Conclusions:

• Computer modeling is valuable for resource optimization in prehospital transfusion programs.
• Strategic MBB deployment can enhance emergency medical services and patient outcomes.
• Efficient resource allocation is crucial for maximizing the benefit of scarce blood products.