Search AMIA.org

Extract infant breast milk feeding status from electronic health record clinical notes to support observational drug-safety studies.

We sought to evaluate the accuracy and suitability of a transformer-based model (TM) for indexing clinical study designs, in comparison to National Library of Medicine (NLM) indexing.

[This corrects the article DOI: 10.1093/jamiaopen/ooag056.].

We describe a new point of care randomization (POCR) engine developed at UCSF and implemented at UCLA that supports randomization of patients, encounters, clinics, or any other "object" in Epic, with programmable ratios, strata, and randomly varying block sizes.

To harmonize pregnancy medication records across heterogeneous real-world data sources and assess the comparability of recorded medication utilization.

To examine clinicians' experiences using ambient documentation technology (ADT) in ambulatory practice, with a focus on impacts on documentation workflows, perceived cognitive burden, and clinical reasoning.

Large language model (LLM) chatbots show promise for pre-visit planning, but their English-centric development raises equity concerns for multilingual deployment. We evaluated the multilingual capabilities of the Primary Care Pre-visit Chatbot (PCP-Bot), a GPT-4o-based pre-visit chatbot, across English, Mandarin Chinese, Spanish, and Hindi.

To determine whether numerical and large language model embedding-based representations of the same structured electronic health record data fail on the same patients or on distinct patient groups.

Obesity is a public health crisis with few effective preventative treatments limited by challenges in identifying who is at high risk of weight gain. To address this gap, our group has developed machine learning weight gain prediction models with modest predictive power. In this study, we aimed to build upon our prior work by incorporating a multi-ancestry polygenic risk score (MAPRS) into a machine learning model and hypothesized that its [...]

To characterize patients' perspectives on privacy on the disclosure, documentation, and visibility of social risk information collected during screening in healthcare settings, and to identify ways to design patient-centered, privacy-protecting screening and documentation systems.