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Electronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use.

A comprehensive and machine-understandable cancer drug-side effect (drug-SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk-benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug-SE pairs [...]

To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities.

Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.

Electronic prescribing systems have often been promoted as a tool for reducing medication errors and adverse drug events. Recent evidence has revealed that adoption of electronic prescribing systems can lead to unintended consequences such as the introduction of new errors. The purpose of this study is to identify and characterize the unrealized potential and residual consequences of electronic prescribing on pharmacy workflow in an outpatient pharmacy.

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it [...]

Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical 'phenotypes' accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one 'gold standard' chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across [...]

In the era of Big Data, omic-scale technologies, and increasing calls for data sharing, it is generally agreed that the use of community-developed, open data standards is critical. Far less agreed upon is exactly which data standards should be used, the criteria by which one should choose a standard, or even what constitutes a data standard. It is impossible simply to choose a domain and have it naturally follow which [...]

To understand the factors that influence success in scrubbing personal names from narrative text.

Annotations to physical workspaces such as signs and notes are ubiquitous. When densely annotated, work areas become communication spaces. This study aims to characterize the types and purpose of such annotations.