Search AMIA.org

The 21st Century Cures Act Final Rule's information blocking provisions, which prohibited practices likely to interfere with, prevent, or materially discourage access, exchange, or use of electronic health information (EHI), began to apply to a limited set of data elements in April 2021 and expanded to all EHI in October 2022. We sought to describe hospital leaders' perceptions of the prevalence of practices that may constitute information blocking, by actor [...]

Hospital acquired infections (HAIs) are one of the top 10 leading causes of death within the United States. While current standard of HAI risk prediction utilizes only a narrow set of predefined clinical variables, we propose a graph convolutional neural network (GNN)-based model which incorporates a wide variety of clinical features.

Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) [...]

Transgender people have a right to be called by their chosen names. However, the current electronic health record (EHR)-even with dramatic changes in recent years-does not allow for proper documentation to accurately and sensitively capture the experiences of transgender patients. This article suggests that EHRs should be modified to allow for distinctions in legal and chosen name fields, recognize the wide-ranging experiences and needs of transgender patients, and promote inclusive [...]

To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.

We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.

We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs).

The objective was to develop a dataset definition, information model, and FHIR® specification for key data elements contained in a German molecular genomics (MolGen) report to facilitate genomic and phenotype integration in electronic health records.