The evidence shows that the need for emergency evacuation in hospitals has arisen. Designing an emergency evacuation decision making tool increases the confidence of hospital managers in the decision made. Therefore, this study was aimed at the development, and the psychometric properties, of the decision-making scale for emergency hospital evacuation in disasters.

This study was done in 2 phases of qualitative study and literature review and designing and psychometric properties of the instrument. After development of the primary item pool, the psychometric properties of the questionnaire were evaluated. In this regard, face and content validity, internal consistency (Alpha’s Cronbach), reliability (ICC), and stability were assessed.

In the validity stage of the instrument, 4 items were removed. Also, 4 items were modified and 2 items were merged. The number of items was thus decreased to 64. After CVI calculation, 5 items were removed, 4 items were modified, and 2 items were merged. As a result of this, the number of items decreased to 58 items. The scale has good reliability and stability.

It seems that the instrument could be useful in decision-making for emergency hospital evacuation in disasters.

A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following hospital fire using machine-learning algorithms.

In this study, the real emergency evacuation duration of 190 patients admitted to a hospital was predicted in a simulation based on the following 8 factors: the number of hospital floors, patient preparation and transfer time, distance to the safe location, as well as patient’s weight, age, sex, and movement capability. To design and validate the model, we used statistical models of machine learning, including Support Vector Machines Random Forest, Naive Bayes Classifier, and Artificial Neural Network.

Data analysis showed that based on the Area Under the Curve, precision, and sensitivity values of 99.5%, 92.4%, and 92.1%, respectively, the Random Forest model showed a better performance compared to other models for predicting the duration of hospital emergency evacuation during fire.

Predicting evacuation duration can provide managers with accurate information and true analyses of these events. Therefore, health policy makers and managers can promote preparedness and responsiveness during fire by predicting evacuation duration and developing appropriate plans using machine learning models.