Identifying diagnostic errors in the emergency department using trigger-based strategies.

Importance: Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety.

Objective: To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors.

Early prediction of shock in intensive care unit patients by machine learning using discrete electronic health record data.

Purpose: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record.

Methods And Results: We included 11,305 adult cardiac, medical, neurological, and surgical ICU patients who did not have shock within 4 h of ICU admission. We used routine vital sign data collected from the first 4 h of the ICU stay to predict new-onset shock within the subsequent 4 h.

Emergency department risk model: timely identification of patients for outpatient care coordination.

Objective: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes.

Aims: To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type.

Methods: We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A (HbA) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory.