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DOI:10.1097/ACO.0000000000000663 Postoperative mortality and morbidity remain major challenges. In a European cohort study, Pearse et al. [1] observed that 4% patients did not survive to leave hospital. Much more patients encounter postoperative complications including pulmonary, cardiovascular, neurological complications, and renal failure; some experience chronic postsurgical pain. Most of these complications develop during the early postoperative period when patients have left the recovery room. It is common knowledge that postoperative complications should be detected and treated as early as possible. Ghaferi et al. [2] suggest that improving the care that patients receive once complications have occurred is crucial for reducing mortality. They conclude that strategies for ensuring the timely recognition and effective management of these complications are all-dominant. During their operation and stay in the recovery ward, qualified medical personnel monitor vital functions of patients continuously and recognize and treat deviations, such as intravascular hypovolemia, hypothermia, anemia, hypercapnia, hypoxia, or pain. In contrast, monitoring and qualified expertise is usually available quite infrequently as soon as patients have left the recovery ward. The question arises as to how we can improve prediction and detection of complications. Intriguing developments such as remote monitoring, electronic health records analytics, Big Data, smart algorithms to calculate early warning scores, artificial intelligence, and machine learning for clinical decision support open new possibilities that might enable us to identify postoperative patients at risk much earlier, which in turn might help us to make the best decisions. For instance, it would be highly beneficial if we could reliably detect sepsis developing in patients at a much earlier stage during the postoperative period. In this context, this issue on Technology, Education and Safety of the journal Current Opinion in Anesthesiology addresses some of the challenging topics relevant for the early postoperative period as well as for pain therapy.