Special Issue on CDS Failures: Unintended Delays in Pediatric Post-Operative Antibiotic Administration from Overly Complex CDS Instructions.

Background: The timely administration of post-operative antibiotics is crucial for preventing surgical site infections. Despite surgical ordering workflows designed to facilitate care across settings, delays in antibiotic administration post-transfer to the Pediatric Intensive Care Unit (PICU) were identified. We aimed to develop a clinical decision support (CDS) system to enhance timely order activation in a large pediatric health system.

Early clinical evaluation of a vendor developed pediatric artificial intelligence sepsis model in the emergency department.

Objective: To conduct an independent external validation of an implemented vendor-developed emergency department (ED) pediatric sepsis predictive model.

Materials And Methods: We performed a retrospective cross-sectional study within 2 ED sites of a large pediatric health system between January 1, 2021 and April 1, 2024. A nurse-facing interruptive alert appeared when the model score exceeded the threshold, triggering clinicians to call a sepsis huddle.

Human performance evaluation of a pediatric artificial intelligence sepsis model.

Objective: To assess the influence of an implemented artificial intelligence model predicting pediatric sepsis (defined by IPSO-Improving Pediatric Sepsis Outcomes collaborative) in the emergency department (ED) on human performance measures.

Materials And Methods: Two ED sites within a large pediatric health system in the Southeastern United States between January 1, 2021 and April 1, 2024. We interviewed ED providers and nurses within 72 hours of caring for a patient identified as potentially having sepsis by the predictive model.

Standardization of Blood Product Orders Improves Patient Safety in Pediatric Transfusion Medicine: A Collaborative Project.

Context.—: Complexity of ordering and transfusing blood is particularly evident in the pediatric population. Simplification, clarification, and standardization of blood orders can decrease complexity and improve patient safety.

Clinical Decision Support Enhanced by User Acceptance Testing to Promote Situational Awareness for Pediatric Patients with a Difficult Airway.

Children with a difficult airway are at high risk of decompensation in the setting of respiratory distress. Situational awareness among all team members, and a shared plan in case of an emergency, can reduce the chance of catastrophic outcomes.This study aimed to improve difficult airway situational awareness while minimizing alert burden in a quaternary care pediatric healthcare system through the application of clinical decision support (CDS).

Special Issue on CDS Failures: Performance Degradation between Development and Deployment of a Predictive Model for Central-Line Associated Blood Stream Infections in Hospitalized Children.

Background: Central line-associated bloodstream infections (CLABSIs) are associated with substantial pediatric morbidity and mortality. The capacity to predict which children with central lines are at greatest risk of CLABSI could inform surveillance and prevention efforts. Our team previously published in silico predictive models for CLABSI.

Disparities in Pediatric Emergency Department Revisits Within 7 Days by Disease Process.

Objective: To identify disease processes in which patients experienced the greatest rates of emergency department (ED) revisit disparities by race, ethnicity, language, insurance, and Childhood Opportunity Index.

Methods: We performed a retrospective, cross-sectional study of ED visits at a comprehensive pediatric health system encompassing three EDs. We included all pediatric (0 to 18 y) ED encounters that led to a discharge home (2018 to 2022).

Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for Critical Ill Children with Sepsis in the Pediatric ED with Pediatric ICU Disposition.

Importance: Pediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to promptly identify and treat sepsis. This study demonstrates the feasibility of a multi-tiered artificial intelligence (AI) to enhance sepsis clinical decision-making within a complex emergency department (ED) workflow.

Evaluation of Influenza Vaccine Clinical Decision Support Systems Bundle for Hospitalized Children.

Background: Hospitalized children are at increased risk of influenza-related complications; however, influenza vaccine coverage remains low among this group. We aim to evaluate the effectiveness of a suite of clinical decision support (CDS) strategies to improve inpatient influenza vaccination in children.

Methods: We conducted this retrospective cohort study among hospitalized children eligible for the seasonal influenza vaccine.

Incidence of Leading Causes of Pediatric CKD Using Electronic Health Record-Driven Computable Phenotype.

Key Points: We created computable phenotypes to accurately identify cases of pediatric CKD by underlying diagnosis. Combined annual incidence of five leading causes of pediatric CKD was high, 47.07 (95% confidence interval, 45.

Clinical Decision Support to Reduce Hospital Length-of-Stay for Cancer Patients with Fever and Neutropenia.

Pediatric cancer patients with fever and neutropenia are at risk for bacterial sepsis, traditionally requiring extended hospital stays on antibiotics until neutrophil counts recover. According to a newly validated scoring system, a subset of these patients is at lower risk and eligible for early discharge and reduced intravenous (IV) antibiotic exposure.Reduce length-of-stay (LOS) for febrile neutropenic patients using clinical decision support (CDS) to identify low-risk patients.

Alert design in the real world: a cross-sectional analysis of interruptive alerting at 9 academic pediatric health systems.

Objective: To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Materials And Methods: We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification.

Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.

To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE, and Web of Science with controlled vocabulary. Inclusion criteria: AI intervention in a pediatric clinical setting that learns from data (i.

Associations between Child Opportunity Index and Pediatric Cardiac Surgical Outcomes.

Objective: To assess the relationship between the Child Opportunity Index (COI), a comprehensive measurement of social determinants of health, and specific COI domains on patient-specific outcomes following congenital cardiac surgery in the metropolitan region of Atlanta, Georgia.

Study Design: In this retrospective chart review, we included patients who underwent an index operation for congenital heart disease between 2010 and 2020 in a single pediatric health care system. Patients' addresses were geocoded and mapped to census tracts.

Pharmacist Metrics in the Pediatric Intensive Care Unit: an Exploration of the Medication Regimen Complexity-Intensive Care Unit (MRC-ICU) Score.

Introduction: The medication regimen complexity-intensive care unit (MRC-ICU) score has been developed and validated as an objective predictive metric for patient outcomes and pharmacist workload in the adult critically ill population. The purpose of this study was to explore the MRC-ICU and other workload metrics in the pediatric ICU (PICU).

Methods: This study was a retrospective cohort of pediatric ICU patients admitted to a single institution -between February 2, 2022 - August 2, 2022.

Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis.

Background: Asthma is a common cause of morbidity and mortality in children. Predictive models may help providers tailor asthma therapies to an individual's exacerbation risk. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown.

User-Centered Design and Evaluation of Clinical Decision Support to Improve Early Peanut Introduction: Formative Study.

Background: Peanut allergy has recently become more prevalent. Peanut introduction recommendations have evolved from suggesting peanut avoidance until the age of 3 years to more recent guidelines encouraging early peanut introduction after the Learning Early about Peanut Allergy (LEAP) study in 2015. Guideline adherence is poor, leading to missed care opportunities.

Quasi-experimental, Nonrandomized Initiative to Minimize Sleep Disruptions among Hospitalized Children.

Unlabelled: Hospitalized children experience frequent sleep disruptions. We aimed to reduce caregiver-reported sleep disruptions of children hospitalized on the pediatric hospital medicine service by 10% over 12 months.

Methods: In family surveys, caregivers cited overnight vital signs (VS) as a primary contributor to sleep disruption.