Search AMIA.org

This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest.

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways.

The BT-Nurse system uses data-to-text technology to automatically generate a natural language nursing shift summary in a neonatal intensive care unit (NICU). The summary is solely based on data held in an electronic patient record system, no additional data-entry is required. BT-Nurse was tested for two months in the Royal Infirmary of Edinburgh NICU. Nurses were asked to rate the understandability, accuracy, and helpfulness of the computer-generated summaries; they were [...]

BACKGROUND: The ability to electronically exchange health information among healthcare providers holds enormous promise to improve care coordination and reduce costs. Provider-to-provider data exchange is an explicit goal of the American Recovery and Reinvestment Act of 2009 and may be essential for the long-term success of the Affordable Care Act of 2010. However, little is known about what factors affect clinicians' usage of health information exchange (HIE) functionality. OBJECTIVE: To [...]

To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design.

A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records.

The aim of this study was to measure the effect of an electronic heparin-induced thrombocytopenia (HIT) alert on provider ordering behaviors and on patient outcomes.