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Introduction While computerized provider order entry (CPOE) has become standard for medication, laboratory, referral, and imaging ordering, use in surgical case requests is not well described. Our many surgical clinics used varying workflows for case requests, leading to data duplication and data storage outside of the electronic health record (EHR). We hypothesized that a provider-entered order-based case request (OBCR) tool would improve data entry efficiency and provide a more comprehensive [...]

Registered nurses increasingly work in remote care and digital interaction roles, offering flexibility and expansion of their scope of practice. These roles may expose nurses to digital compassion fatigue, a phenomenon proposed to be characterized by the negative psychological and emotional impact of caring for patients remotely through the use technology.

To quantify differences between (1) stratifying patients by predicted disease onset risk alone and (2) stratifying by predicted disease onset risk and severity of downstream outcomes. We perform a case study of predicting sepsis.

screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract screening.

To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data.

The objective of this work is to demonstrate the value of simulation testing for rapidly evaluating artificial intelligence (AI) products.

This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language models to create secure, Health Insurance Portability and Accountability Act-compliant synthetic patient notes for global biomedical research.

Extended reality (XR) applications are gaining support as a method of reducing anxieties about medical treatments and conditions; however, their impacts on health service inequalities remain underresearched. We therefore undertook a synthesis of evidence relating to the equity implications of these types of interventions.

Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs.

While many aspects of nursing documentation are considered an essential part of clinical communication and care coordination, other types of nursing documentation have been implemented to meet compliance and other secondary use needs. Adding required documentation without carefully assessing its association with patient outcomes adds excessive documentation burden on nurses. There is a gap in the evidence of the association between additional required nursing documentation and improvements in patient outcomes.