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To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.

The Patient-Centered Outcomes Research Institute (PCORI) has launched PCORnet, a major initiative to support an effective, sustainable national research infrastructure that will advance the use of electronic health data in comparative effectiveness research (CER) and other types of research. In December 2013, PCORI's board of governors funded 11 clinical data research networks (CDRNs) and 18 patient-powered research networks (PPRNs) for a period of 18 months. CDRNs are based on the electronic [...]

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants' experience, expertise, and history of collaboration. The technical design will employ an information model [...]

The state of Louisiana, like the nation as a whole, is facing the salient challenge of improving population health and efficiency of healthcare delivery. Research to inform innovations in healthcare will best enhance this effort if it is timely, efficient, and patient-centered. The Louisiana Clinical Data Research Network (LACDRN) will increase the capacity to conduct robust comparative effectiveness research by building a health information technology infrastructure that provides access to [...]

We developed a novel computer application called Glyph that automatically converts text to sets of illustrations using natural language processing and computer graphics techniques to provide high quality pictographs for health communication. In this study, we evaluated the ability of the Glyph system to illustrate a set of actual patient instructions, and tested patient recall of the original and Glyph illustrated instructions.

Antibiotics are commonly recognized as non-indicated for acute bronchitis and upper respiratory tract infection (URI), yet their widespread use persists. Clinical decision support in the form of electronic warnings is hypothesized to prevent non-indicated prescriptions. The purpose of this study was to identify the effect of clinical decision support on a common type of non-indicated prescription.

Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and [...]

To reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries.

To assess the perceived readiness of Medicaid and Children's Health Insurance Program (CHIP) enrollees to use information technologies (IT) in order to facilitate improvements in the application processes for these public insurance programs.

Delivering useful clinical decision support to providers who are ordering high risk drugs for high risk patients is imperative for safe pharmacotherapy. This paper presents a focused electronic clinical decision support intervention designed to decrease the risk of corrected QT interval (QTc) related adverse drug events in a high risk patient population. Results showed that a customized alert can both decrease the number of alerts sent to providers while still [...]