A quality improvement approach to improving care of febrile infants.

Background And Objectives: Significant practice variation exists in managing young infants with fever. Quality improvement strategies can aid in risk stratification and standardization of best care practices, along with a reduction of unnecessary interventions. The aim of this initiative was to safely reduce unnecessary admissions, antibiotics, and lumbar punctures (LPs) by 10% in low-risk, febrile infants aged 29 to 90 days presenting to the emergency department (ED) over a 12-month period.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

Background: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.

Objective: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients.

A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital.

Objective: To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR).

Materials And Methods: Statistical classifiers were trained on feature representations derived from unstructured text in patient EHRs. We used a proxy dataset of patients COVID-19 polymerase chain reaction (PCR) tests for training.

A computable phenotype for patients with SARS-CoV2 testing that occurred outside the hospital.

Objective: To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR).

Materials And Methods: Statistical classifiers were trained on feature representations derived from unstructured text in patient electronic health records (EHRs). We used a proxy dataset of patients COVID-19 polymerase chain reaction (PCR) tests for training.