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This study sought to explore the relationship of workarounds related to the implementation of an electronic medication administration record and medication safety practices in five Midwestern nursing homes.

The authors organized a Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records. This challenge was issued as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, to survey, facilitate, and examine studies in medical language understanding for clinical narratives. This article describes the smoking challenge, details the data and the annotation process, explains the [...]

This article describes our system entry for the 2006 I2B2 contest "Challenges in Natural Language Processing for Clinical Data" for the task of identifying the smoking status of patients. Our system makes the simplifying assumption that patient-level smoking status determination can be achieved by accurately classifying individual sentences from a patient's record. We created our system with reusable text analysis components built on the Unstructured Information Management Architecture and Weka [...]

As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naïve Bayes classifier to determine smoking status from the same records after all [...]

The Clinical Language Understanding group at Nuance Communications has developed a medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references to patient smoking in clinical reports. The extraction engine identifies smoking references; documents that contain no smoking references are classified as UNKNOWN. For the remaining documents, the extraction engine uses linguistic analysis to associate features such as status and time [...]

TimeText is a temporal reasoning system designed to represent, extract, and reason about temporal information in clinical text.

Advances in information technology (IT) enable a fundamental redesign of health care processes based on the use and integration of electronic communication at all levels. New communication technologies can support a transition from institution centric to patient-centric applications. This white paper defines key principles and challenges for designers, policy makers, and evaluators of patient-centered technologies for disease management and prevention. It reviews current and emerging trends; highlights challenges related to [...]

Evaluations of individual terminology systems should be driven in part by the intended usages of such systems. Clinical interface terminologies support interactions between healthcare providers and computer-based applications. They aid practitioners in converting clinical "free text" thoughts into the structured, formal data representations used internally by application programs. Interface terminologies also serve the important role of presenting existing stored, encoded data to end users in human-understandable and actionable formats. The [...]

Over the past year, several payers, employers, and commercial vendors have announced personal health record projects. Few of these are widely deployed and few are fully integrated into ambulatory or hospital-based electronic record systems. The earliest adopters of personal health records have many lessons learned that can inform these new initiatives. We present three case studies--MyChart at Palo Alto Medical Foundation, PatientSite at Beth Israel Deaconess Medical Center, and Indivo [...]

Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors.