• Ellen Wang, B Randall Brenn, and Clyde T Matava.
• Department of Anesthesiology, Pain and Perioperative Medicine, Lucile Packard Children's Hospital, Stanford University, Palo Alto, California.
• Curr Opin Anaesthesiol. 2020 Jun 1; 33 (3): 388-394.

Purpose Of ReviewThe goal of this review is to describe the recent improvements in clinical decision tools applied to the increasingly large and complex datasets in the pediatric ambulatory and inpatient setting.Recent FindingsClinical decision support has evolved beyond simple static alerts to complex dynamic alerts for: diagnosis, medical decision-making, monitoring of physiological, laboratory, and pharmacologic inputs, and adherence to institutional and national guidelines for both the patient and the healthcare team. Artificial intelligence and machine learning have enabled advances in predicting outcomes, such as sepsis and early deterioration, and assisting in procedural technique.SummaryWith more than a decade of electronic medical data generation, clinical decision support tools have begun to evolve into more sophisticated and complex algorithms capable of transforming large datasets into succinct, timely, and pertinent summaries for treatment and management of pediatric patients. Future developments will need to leverage patient-generated health data, integrated device data, and provider-entered data to complete the continuum of patient care and will likely demonstrate improvements in patient outcomes.

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