Search AMIA.org

Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models.

Uncontrolled hypertension is common and frequently related to inadequate adherence to prescribed medications, resulting in suboptimal blood pressure control and increased healthcare utilization. Although healthcare providers have the opportunity to improve medication adherence, they may lack the tools to address adherence at the point of care. This study aims to assess the usability of a digital tool designed to improve medication adherence and blood pressure control among patients with hypertension [...]

Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the performance of LLMs in classifying radiology reports for the presence of pulmonary embolism (PE) under various conditions, including different prompt designs and data perturbations.

Copy-paste (CP) and copy-forward (CF) are common electronic health record (EHR) documentation tools that purportedly improve provider efficiency, but they can also contribute to documentation burden while increasing note bloat and errors. Our understanding of provider perceptions of these tools remains limited.This study aimed to increase understanding of provider perceptions and self-reported usage patterns of CP and CF across different clinical environments and provider roles, including the impact of these [...]

Venous thromboembolism (VTE) prophylaxis in hospitalized patients must balance risks of bleeding and thrombosis. Clinical changes such as bleeding or renal injury can also trigger changes or delays in thromboprophylaxis. Electronic health record alerts (EHRAs) can allow for targeted notification to providers to improve venous thromboembolism prophylaxis and improve patient outcomes at the risk of alert fatigue if not carefully designed and implemented.This study aimed to develop and refine an [...]

With extended life expectancy, the number of people in need of care has been growing. To optimally support them, it is important to know the patterns and conditions of their daily life that influence the need for support, and thus, the classification of the care need. In this study, we aim to utilize a large corpus consisting of care benefits applications to do an explorative analysis of factors affecting care [...]

To compare clinical summaries generated from simulated patient primary care electronic health records (EHRs) by GPT-4, to summaries generated by clinicians on multiple domains of quality including utility, concision, accuracy, and bias.

To compare Observational Medical Outcomes Partnership (OMOP) Logical Observation Identifiers Names and Codes (LOINC) and Veterans Aging Cohort Study (VACS) methods for extracting laboratory chemistry data from Veterans Health Administration (VA) electronic health records (EHR).

Digital dashboards are used to monitor patients and improve inpatient outcomes in hospital settings. A systematic review assessed the impact of dashboards across five outcomes of hospital mortality, hospital length of stay (LOS), economic impacts, harms, and patient and carer satisfaction.

To develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery, with a focus on the benefits of temporal modeling and model interpretability.