Integrating secondary prevention of rheumatic heart disease into the primary healthcare system in Northern Uganda.

Introduction: Over 46 million people are living with rheumatic heart disease (RHD) globally, resulting in 380 000 premature deaths each year. Effective RHD prevention strategies are known but their implementation in low-resource settings has lagged. This study evaluated the feasibility and effectiveness of integrating secondary antibiotic prophylaxis into primary health centres to improve access and adherence to RHD care.

Heterogeneous Effect of Automated Alerts on Mortality.

Unlabelled: We analyzed data from 13,483 hospitalized patients with acute kidney injury (AKI) from three randomized controlled trials to assess the heterogeneous effects of automated electronic alerts on 14-day mortality. We modeled and predicted individualized alert effects on a subset of the ELAIA-1 patients and validated it internally on ELAIA-1 holdout patients and externally on ELAIA-2 and UPenn trial patients. Patients predicted to benefit from alerts had significantly lower mortality compared to those predicted to be harmed (p-interaction<0.

Precision risk assessment for pediatric hospitalization using address-level data in Cincinnati, Ohio.

Objective: This study leveraged residence- and neighborhood-specific socio-environmental data linked to population-wide healthcare data to characterize risk for pediatric hospitalization for every residential address in Cincinnati, Ohio.

Materials And Methods: We linked hospitalization data (07/01/2016-06/30/2022) to parcel-level housing data from the Hamilton County Auditor and Cincinnati Department of Buildings & Inspections and block-level crime data from the Cincinnati Police Department. Addresses were localized to 2010 census tracts to join variables from the U.

Forecasting school violence risk with incomplete interview data: an automated assessment approach.

Objectives: School violence risk prevention in the United States relies on manual assessments that are time-consuming and subjective. We developed a machine learning algorithm named Automated RIsk Assessment (ARIA), using natural language processing (NLP) to find linguistic patterns in standardized interview questions that can predict risk of aggression. Our goal was to evaluate the incremental change in performance with the addition of each question to simulate situations where interviews cannot be completed.

Multisite Implementation of a Sexual Health Survey and Clinical Decision Support to Promote Adolescent Sexually Transmitted Infection Screening.

Background:  Adolescents are at high risk for sexually transmitted infections (STIs) and frequently present to emergency departments (EDs) for care. Screening for STIs using confidential patient-reported outcomes represents an ideal use of electronic screening methodology.

Objectives:  The objectives of this study were to implement a patient-facing, confidential electronic survey to assess adolescent risk for STIs and consent for testing with integrated provider-facing electronic clinical decision support (CDS) across six geographically dispersed pediatric EDs and evaluate implementation based on survey and CDS usage metrics.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

Background And Objectives: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation.

Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents.

Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability.

Development and User Testing of a Dynamic Tool for Rheumatic Heart Disease Management.

Objective: Most rheumatic heart disease (RHD) registries are static and centralized, collecting epidemiological and clinical data without providing tools to improve care. We developed a dynamic cloud-based RHD case management application with the goal of improving care for patients with RHD in Uganda.

Methods: The Active Community Case Management Tool (ACT) was designed to improve community-based case management for chronic disease, with RHD as the first test case.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

Objective: To determine whether automated, electronic alerts increased referrals for epilepsy surgery.

Methods: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit.

Built environment factors predictive of early rapid lung function decline in cystic fibrosis.

Background: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined.

Objective: To identify built environment characteristics predictive of rapid CF lung function decline.

Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012-2017).

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

Background: Artificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation.

Objective: We aimed to describe the key components for successful development and integration of two AI technology-based research pipelines for clinical practice.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

Background: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing.

The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning.

Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware.

Aligning Provider Prescribing With Guidelines for Soft Tissue Infections.

Objective: Despite evidence-based guidelines, antibiotics prescribed for uncomplicated skin and soft tissue infections can involve inappropriate microbial coverage. Our aim was to evaluate the appropriateness of antibiotic prescribing practices for mild nonpurulent cellulitis in a pediatric tertiary academic medical center over a 1-year period.

Methods: Eligible patients treated in the emergency department or urgent care settings for mild nonpurulent cellulitis from January 2017 to December 2017 were identified by an International Classification of Diseases, Tenth Revision, code for cellulitis.

An ontology-based review of transgender literature: Revealing a history of medicalization and pathologization.

Objectives: To evaluate the linguistic changes of transgender-related resources prior to 1999 to create a comprehensive dataset of resources using an ontology-derived search system, laying a framework for ontology-based reviews to be used in informatics.

Methods: We analyzed 77 bibliographies and 11 databases for transgender resources published prior to 31 December 1999. We used 858 variants of the term "transgender" to identify resources.

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Objectives: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery.

Development of the Gender, Sex, and Sexual Orientation ontology: Evaluation and workflow.

Objective: The study sought to create an integrated vocabulary system that addresses the lack of standardized health terminology in gender and sexual orientation.

Materials And Methods: We evaluated computational efficiency, coverage, query-based term tagging, randomly selected term tagging, and mappings to existing terminology systems (including ICD (International Classification of Diseases), DSM (Diagnostic and Statistical Manual of Mental Disorders ), SNOMED (Systematized Nomenclature of Medicine), MeSH (Medical Subject Headings), and National Cancer Institute Thesaurus).

Results: We published version 2 of the Gender, Sex, and Sexual Orientation (GSSO) ontology with over 10 000 entries with definitions, a readable hierarchy system, and over 14 000 database mappings.

An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time.

Objective: The study sought to create an online resource that informs the public of coronavirus disease 2019 (COVID-19) outbreaks in their area.

Materials And Methods: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard.

Results: The Web resource, called the COVID-19 Watcher, can be accessed online (https://covid19watcher.

Information Technology-Assisted Screening for Gonorrhea and Chlamydia in a Pediatric Emergency Department.

Purpose: The aim of the study was to design and implement a novel, universally offered, computerized clinical decision support (CDS) gonorrhea and chlamydia (GC/CT) screening tool embedded in the emergency department (ED) clinical workflow and triggered by patient-entered data.

Methods: The study consisted of the design and implementation of a tablet-based screening tool based on qualitative data of adolescent and parent/guardian acceptability of GC/CT screening in the ED and an advisory committee of ED leaders and end users. The tablet was offered to adolescents aged 14-21 years and informed patients of Centers for Disease Control and Prevention GC/CT screening recommendations, described the testing process, and assessed whether patients agreed to testing.