Paediatric intensive care units are data-rich environments in which complex clinical decisions are regularly made in children with a wide range of ages, weights, diagnoses, underlying co-morbidities and physiological characteristics. The adoption of technology to effectively manage vast amounts of information is a key priority in paediatric intensive care units (PICUs), and an explosion in machine learning and artificial intelligence promises a revolution in terms of how we use clinical data to improve patient care in the future. In this chapter, we summarise the existing knowledge and evidence regarding information technology use in the PICU setting, focusing on the use of electronic health records for clinical documentation and electronic charting, computerised prescriber order entry (CPOE) systems, clinical decision support systems (CDSS), early warning alerts and telemedicine, and discuss the potential of machine learning and artificial intelligence (AI) systems to augment clinical care by harnessing data that are traditionally ‘lost’ during current decision-making. The information is presented in an easily digestible format with examples, explanatory figures, summary points and take-home messages.