Mitigating the Impact of Intergenerational Risk Factors on Stunting: Insights From Seven of the Most Food Insecure Districts in South Africa.

A large body of research investigates the determinants of stunting in young children, but few studies have considered which factors are the most important predictors of stunting. We examined the relative importance of predictors of height-for-age z-scores (HAZ) and stunting among children under 5 years of age in seven of the most food-insecure districts in South Africa using data from the Grow Great Community Stunting Survey of 2022. We used dominance analysis and variable importance measures from conditional random forest models to assess the relative importance of predictors.

Prompt engineering with a large language model to assist providers in responding to patient inquiries: a real-time implementation in the electronic health record.

Background: Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering.

Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

Background:  Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring.

Objectives:  In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes.

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.

Background: The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery.