大數據在災難護理全期管理的運用

The geographic and social environment has contributed to the high incidence of natural, manmade, and complex disasters in Taiwan. Since the 921 Earthquake of 1999, big data and the derived information have been collected on severe acute respiratory syndrome (2003), Typhoon Morakot (2009), Kaohsiung gas-pipe explosion (2014), the Formosa Fun Coast water park powder explosion (2015), Meinong Earthquake (2016), and Hualien Earthquake (2018) and can provide references for predisaster, intradisaster, and postdisaster nursing management. Although the definition of big data in the nursing care field requires further precision, five principles can be taken into the account when using big data in disaster nursing management: volume, velocity, variety, veracity, and value. These five V's represent the scale or size of big data; speed of generating big data; type, resources, or origins of big data; appropriateness of big data; and contribution or meaning of big data. These five principles refer to the definition, resources, and collection of big data, verification of appropriateness, and use of derived information according to characteristics rooted in the predisaster, intradisaster, and postdisaster phases, which are diverse to some extent, but interrelated. Introspection from big data and derived information, which is ＂data rich, information poor,＂ is crucial for nurses. Data must be transformed into meaningful information; data diminishes in importance over time, becoming less useful. Therefore, when employing big data in all phases of disaster nursing management, nurses who participate in disaster care should attend to concerns and learn how to define nursing-sensitive data and efficiently collect and generate appropriate and productive information to provide references for disaster nursing care.