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Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

The demographic and practice-related characteristics of physicians who use social networking websites, portable devices to access the internet, email to communicate with patients, podcasts, widgets, RSS feeds, and blogging were investigated. Logistic regression was used to analyze a survey of US primary care physicians, pediatricians, obstetrician/gynecologists, and dermatologists (N=1750). Reported technology use during the last 6 months ranged from 80.6% using a portable device to access the internet to 12.9% [...]

Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EHR documentation styles: dictation, structured documentation, and free text.

Many public health agencies monitor population health using syndromic surveillance, generally employing information from emergency department (ED) visit records. When combined with other information, objective evidence of fever may enhance the accuracy with which surveillance systems detect syndromes of interest, such as influenza-like illness. This study found that patient chief complaint of self-reported fever was more readily available in ED records than measured temperature and that the majority of patients [...]

Coreference resolution of concepts, although a very active area in the natural language processing community, has not yet been widely applied to clinical documents. Accordingly, the 2011 i2b2 competition focusing on this area is a timely and useful challenge. The objective of this research was to collate coreferent chains of concepts from a corpus of clinical documents. These concepts are in the categories of person, problems, treatments, and tests.

We describe a novel, crowdsourcing method for generating a knowledge base of problem-medication pairs that takes advantage of manually asserted links between medications and problems.

Patient discharge summaries provide detailed medical information about hospitalized patients and are a rich resource of data for clinical record text mining. The textual expressions of this information are highly variable. In order to acquire a precise understanding of the patient, it is important to uncover the relationship between all instances in the text. In natural language processing (NLP), this task falls under the category of coreference resolution.

To develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs).

This paper describes a natural language processing system for the task of pneumonia identification. Based on the information extracted from the narrative reports associated with a patient, the task is to identify whether or not the patient is positive for pneumonia.