Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.

Background: Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes.

Using Concept Unique Identifiers to Filter Electronic Health Records for Delirium Cases.

Delirium, an acute mental status change associated with inattention, confusion, hypervigilance, or somnolence due to a medical cause, is considered a medical emergency. Unfortunately, screening and diagnosis of delirium in acute care are often inadequate. It is estimated that 60% of delirium cases are not identified, and in claims data, they are underreported.