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Increasing use of electronic health records (EHRs) provides new opportunities for public health surveillance. During the 2009 influenza A (H1N1) virus pandemic, we developed a new EHR-based influenza-like illness (ILI) surveillance system designed to be resource sparing, rapidly scalable, and flexible. 4 weeks after the first pandemic case, ILI data from Indian Health Service (IHS) facilities were being analyzed.

This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions.

The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the [...]

To study the relation between electronic health record (EHR) variables and healthcare process events.

Celiac disease (CD) is a lifelong immune-mediated disease with excess mortality. Early diagnosis is important to minimize disease symptoms, complications, and consumption of healthcare resources. Most patients remain undiagnosed. We developed two electronic medical record (EMR)-based algorithms to identify patients at high risk of CD and in need of CD screening.

To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR).

Many cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled.

In a growing interdisciplinary field like biomedical informatics, information dissemination and citation trends are changing rapidly due to many factors. To understand these factors better, we analyzed the evolution of the number of articles per major biomedical informatics topic, download/online view frequencies, and citation patterns (using Web of Science) for articles published from 2009 to 2012 in JAMIA. The number of articles published in JAMIA increased significantly from 2009 to [...]

Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia.