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The use of real-time clinical decision support (CDS), such as Our Practice Advisory (OPAs), augments clinical decisions while helping to reduce errors and ensuring compliance with organizational best practices.1 In complex large health systems, processes for standardization and adherence to emergency department (ED)-based suicide screening practices are challenging and may benefit from the use of CDS-based tools adhering to the five rights of CDS.2 To improve suicide screening compliance for [...]

The timely administration of postoperative antibiotics is crucial for preventing surgical site infections. Despite surgical ordering workflows designed to facilitate care across settings, delays in antibiotic administration posttransfer 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. We hypothesized that the time to release signed and held orders by [...]

Missed diagnosis of genetic conditions is a persistent challenge in clinical care, particularly for familial hypercholesterolemia (FH), hereditary breast and ovarian cancer (HBOC), and Lynch syndrome-conditions designated by the U.S. Centers for Disease Control and Prevention (CDC) as Tier 1 genomic applications. This scoping review summarizes evidence on the use of electronic health record (EHR)-based algorithms to identify individuals with these conditions.

Drug interaction checking software is ubiquitous in clinical decision support systems (CDSS-DI) but patient relevance and accuracy are variable and the impact on patient outcomes is unproven. We compared the effectiveness of CDSS-DI with similar care without CDSS-DI.

To optimize in-context learning in biomedical natural language processing by improving example selection.

Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient's clinical notes to generate text-based explanations for risk prediction alerts.

In real-world data (RWD), defining the observation period-the time during which a patient is considered observable-is critical for estimating incidence rates (IRs) and other outcomes. Yet, in the absence of explicit enrollment information, this period must often be inferred, introducing potential bias.

To explore the performance of 4 large language model (LLM) chatbots for the analysis of 2 of the most commonly used tools for the advanced analysis of systematic reviews (SRs) and meta-analyses.

Real-world data (RWD) analyses primarily rely on structured clinical documentation collected through routine clinical care or driven by medical billing requirements. Patient-reported outcome measures (PROMs), integrated into electronic health records (EHRs), are an additional data source that could offer valuable insights into a patient's perspective and contribute to a more comprehensive understanding of health outcomes in RWD studies. This study aims to characterize agreement between PROMs symptoms and structured clinical [...]

Recommendation systems have emerged as prevalent and effective tools. Investigating the impact of recommendation algorithms on users' health information adoption behavior can aid in optimizing health information services and advancing the construction and development of online health community platforms.