Search AMIA.org

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data.

Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET).

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 healthcare professionals; 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 GPT-generated message responses versus real doctor responses across the spectrum of message types within a patient portal.

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.

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 [...]

To assess what practice-, provider-, and patient population-level predictors predict adoption of an ADHD ehealth technology in community pediatric settings, pediatric providers nationwide were recruited and offered free use of an evidence-based mental-health-focused ehealth quality improvement intervention (mehealth for ADHD). Practice-, provider-, and patient population-level factors predicting provider's adoption of the intervention were studied. We hypothesized that providers who were younger, nearing re-credentialing, having more patients with ADHD, working at [...]

Electronic health record (EHR) systems are essential for modern healthcare but contribute to significant documentation burden, affecting physician workflow and well-being. While previous studies have identified differences in EHR usage across demographics, systematic methods for identifying high-burden physician groups remain limited. This study applies cluster analysis to uncover distinct EHR usage profiles and provide a framework to inform the development of targeted interventions.

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.

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.