State of the art in clinical decision support applications in pediatric perioperative medicine.

Abstract

The 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. Clinical 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. With 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.