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Patient portals bridge patient and provider communications but exacerbate physician and nursing burnout. Large language models (LLMs) can generate message responses that are viewed favorably by health care professionals/providers (HCPs); however, these studies have not included diverse message types or new prompt-engineering strategies.Our goal is to investigate and compare the quality and precision of GPT-generated message responses versus real doctor responses across the spectrum of message types within a patient [...]

While computerized provider order entry (CPOE) has become standard for medication, laboratory, referral, and imaging ordering, use in surgical case requests has not been well-described. At a large county hospital, many surgical clinics used a variety of 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 [...]

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.

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.

Although 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 secondary uses. 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.This study aimed [...]

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.

This pilot study aimed to evaluate the impact of an ambient listening AI tool, DAX CoPilot (DAX), on clinical documentation efficiency among primary care providers in a community-based setting.We conducted a randomized controlled trial among volunteer clinicians (physicians, nurse practitioners, and physician assistants in family medicine, internal medicine, pediatrics, and urgent care), who were asked to use DAX with a standardized note template (n = 25) or to continue with traditional documentation [...]

To validate a phenotyping algorithm for gradations of diverticular disease severity and investigate relationships between unmet social needs and disease severity.

Electronic health records (EHRs) have significantly impacted healthcare improving access to patient information and enhancing communication among the health care team. However, the lack of usability and increased documentation burden has greatly contributed to clinician burnout. Improvements in EHR design that include physician input are critical to developing specific changes that make EHRs more intuitive and less cumbersome to use; however, it can be challenging to gather input from physicians [...]

The goal of this study was to investigate the association of perceived discrimination with health outcomes and disparities.