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We explore relationships between health information seeking activities and engagement with healthcare professionals via a privacy-sensitive analysis of geo-tagged data from mobile devices.

To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter.

To model the financial effects of implementing a hospital-wide electronic medical record (EMR) system in a tertiary facility in Malawi.

Implementation of Computerized Provider Order Entry (CPOE) has many potential advantages. Despite the potential benefits of CPOE, several attempts to implement CPOE systems have failed or met with high levels of user resistance. Implementation of CPOE can fail or meet high levels of user resistance for a variety of reasons, including lack of attention to users' needs and the significant workflow changes required by CPOE. User satisfaction is a critical [...]

Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations.

Point-of-care information needs are common and frequently unmet. One solution to this problem is the use of Infobuttons, which are context-sensitive links from electronic health records (EHR) to knowledge resources, sometimes involving an intermediate broker known as an Infobutton Manager. Health Level Seven (HL7) has developed the Context-Aware Knowledge Retrieval (Infobutton) standard to standardize the integration between EHR systems and knowledge resources. While the standard specifies a set of context [...]

Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.

Ascertainment of potential subjects has been a longstanding problem in clinical research. Various methods have been proposed, including using data in electronic health records. However, these methods typically suffer from scaling effects-some methods work well for large cohorts; others work for small cohorts only.

To build an effective co-reference resolution system tailored to the biomedical domain.