What we can learn from Big Data about factors influencing perioperative outcomeLiem, Victor G.B.; Hoeks, Sanne E.; van Lier, Felix; de Graaff, Jurgen C.Current Opinion in Anesthesiology: December 2018 - Volume 31 - Issue 6 - p 723–731 doi: 10.1097/ACO.0000000000000659 TECHNOLOGY, EDUCATION AND SAFETY: Edited by Stephan A. Loer Buy Abstract Author InformationAuthors Article MetricsMetrics Purpose of review This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed. Recent findings The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients. Additionally, they are useful in finding patterns such as similar outcome in subtypes of beta-blockers and lower incidence of pneumonia in preoperative influenza vaccinations in the elderly. Summary Big Data is becoming increasingly popular with the collaborative collection of registries offering anesthesia a way to explore rare perioperative complications and outcome to encourage further hypotheses testing. Although Big Data has its flaws in security, lack of expertise and methodological concerns, the future potential of analytics combined with genomics, machine learning and real-time decision support looks promising. Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands Correspondence to Jurgen C. de Graaff, MD, Department of Anesthesiology, Erasmus Medical Center, SB-3646, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. Tel: +31 10 704 34964; e-mail: firstname.lastname@example.org Copyright © 2018 YEAR Wolters Kluwer Health, Inc. All rights reserved.