臺灣的地理與社會環境發生天然、人為與複合式災難的機會極高，自1999年921大地震以來，在2003年SARS、2009年莫拉克風災、2014年高雄氣爆、2015年八仙塵爆、2016年美濃地震，以及2018年花蓮地震等災難後所蒐集到的大數據以及衍生資訊，可以做為日後對應災難前期準備、中期應變、後期復原的參考。雖然大數據在護理照護領域的定義仍待更明確的共識，但學者所提出五項定義標準: volume, velocity, variety, veracity與value，可作為對應災難護理運用大數據的參考。此五個「V」分別代表大數據的規模、大數據產生的速度、大數據不同的資料形態來源或起因、大數據的正確性、大數據的價值；此五項原則，在災難中不論是大數據的定義、來源、收集方式，正確性的驗證，以及衍生訊息與運用價值，都是因應著災難前、中、後期的特性，雖有所變異卻又息息相關。當面對大數據與衍生資料的運用上，DRIP (Data Rich, Information Poor)值得大家的省思；若未有效將資料轉換成有意義資訊時，就像drip英文字義一樣，當資料隨著時間一點一滴流逝而無法有效運用時，再大的數據也將不具意義。也因此運用大數據在災難護理全期管理時，如何界定災難護理敏感性的數據，並快速蒐集與衍生出正確而有效訊息，提供災難護理照護決策判斷依據，應是所有參與災難照護的護理人員應該關注與學習的課題。

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.