Search AMIA.org

Large language models (LLMs) offer significant potential for automating clinical trial classification by eligibility criteria. However, the optimal input data remain unclear: while abstracts provide a condensed signal, full-text articles contain substantially more information. Whether this additional signal improves performance or whether accompanying noise negatively affects the model's reasoning capabilities remains unclear.

To extend the PyCox to accommodate time-varying covariates using counting process data, addressing immortal time bias in survival analysis.

We provide an overview of the transformation of REACHnet's research-quality electronic health records (EHR) dataset into the Multi-State EHR-Based Network for Disease Surveillance (MENDS) dataset to support chronic disease surveillance.

Teamwork influences patient outcomes, but electronic health records (EHRs) do not reliably capture patient care team members, limiting its study. EHR audit logs provide insights into healthcare workers' workflows and may help identify patients' care team members.

Data, information, and knowledge in health care has expanded exponentially over the past 50 years, leading to significant challenges with information overload and complex, fragmented care plans. Generative artificial intelligence (AI) has the potential to facilitate summarization and integration of knowledge to enable efficient care planning and reduce cognitive load of clinicians.

Gender disparities in academic medicine have been previously reported, but prior analyses have relied on either manual labor or fixed databases of name-gender pairs that fail to generalize across different populations and cultures. The objective of this work is to evaluate the utility of large language models (LLMs) as a potential tool to facilitate systematic bibliometric analysis of academic research trends.

This integrative review aims to analyze the REDCap-based patient-facing technologies (PFTs) and propose a tailored maturity model to characterize their development stages, challenges, and future evolution toward intelligent, proactive care.

Prospective studies examining electronic health record (EHR) implementation in multi-professional healthcare provider samples remain scarce, particularly with respect to impacts on providers' workflows and outcomes in real-world clinical settings.

The Hospital Authority in Hong Kong developed an application, known as 'HA Go', to enhance patients' healthcare journeys and optimize the efficiency of healthcare resource allocation. This study aimed to explore the perceptions of HA Go among the key stakeholders and identify barriers and facilitators to its adoption.

Despite the recognized need to leverage health information technology (IT) to facilitate error detection, there are few health IT safety measures in use and limited resources available to guide the conceptualization, development, and testing of new measures. Audit log data provide a rich source of clinician behaviors within the electronic health record (EHR), which can be used to develop health IT safety measures.