Search AMIA.org

A discussion and debate on the American Medical Informatics Association's (AMIA) Ethical, Legal, and Social Issues (ELSI) Working Group listserv in 2021 raised important issues related to a forthcoming conference in Texas. Texas had recently enacted a restrictive abortion law and restricted voting rights. Several AMIA members advocated for a boycott of the state and the scheduled conference. The discussion led the AMIA Board of Directors to request that the [...]

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

Recent technological development along with the constraints imposed by the coronavirus disease 2019 (COVID-19) pandemic have led to increased availability of patient-generated health data. However, it is not well understood how to effectively integrate this new technology into large health systems. This article seeks to identify interventions to increase utilization of electronic blood glucose monitoring for patients with diabetes.

Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias.

We developed a comprehensive, medication-related clinical decision support (CDS) software prototype for use in the operating room. The purpose of this study was to compare the usability of the CDS software to the current standard electronic health record (EHR) medication administration and documentation workflow.

The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models.

To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.

This study sought both to support evidence-based patient identity policy development by illustrating an approach for formally evaluating operational matching methods, and also to characterize the performance of both referential and probabilistic patient matching algorithms using real-world demographic data.

This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses.

Tobacco use/smoking for epidemiologic studies is often derived from electronic health record (EHR) data, which may be inaccurate. We previously compared smoking from the United States Veterans Health Administration (VHA) EHR clinical reminder data with survey data and found excellent agreement. However, the smoking clinical reminder items changed October 1, 2018. We sought to use the biomarker salivary cotinine (cotinine ≥30) to validate current smoking from multiple sources.