Demand Analysis and Preparedness Experience for Logistic Supply Requirements of Disaster Medical Assistance Teams: A Case Study of Hualien DMAT

Summary: In Hualien, Taiwan, the mountainous terrain covers 90% of the area, and medical resources are concentrated in urban centers, which makes emergency response in disaster situations challenging. Natural disasters occur frequently, and Hualien County urgently needs to establish and develop a disaster medical rescue team. In 2018, the Hualien County Health Bureau formed a disaster medical team to address local needs. However, accurately estimating logistical supply requirements remains a significant challenge, as insufficient supplies may delay response, while overstocking leads to resource waste. To improve the accuracy of advanced preparation of logistical supplies, Hualien’s major disaster data in the past ten years were reviewed. A major disaster is an incident involving more than 30 injured people. The two most significant incidents were the 2018 earthquake (293 injuries) and the 2021 train accident (220 injuries), of which over 90% were trauma victims. Based on these data, supply and demand are estimated using the Historical Data Projection method adapted to local conditions. Emergency nurse practitioners experienced in responding to these disasters completed a questionnaire about supply types and quantities. Through this analysis, the four most in-demand supplies were identified: 4x4 gauze pads (221 packs), cotton swabs (222 packs), 4-inch elastic bandages (43 packs), and 6-inch elastic bandages (28 packs). This study demonstrates that the high demand for gauze and swabs underscores the critical need for wound cleaning and dressing in trauma-heavy scenarios. Literature suggests that precise pre-arrangement of logistical supplies reduces resource waste and improves cost-effectiveness. The Historical Data Projection aligns with Hualien’s needs, providing reliable guidelines for resource allocation. Ongoing data collection and AI integration will further enhance the accuracy of supply forecasting, ensuring efficient disaster response.