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Monitoring vital signs and locations of certain classes of ambulatory patients can be useful in overcrowded emergency departments and at disaster scenes, both on-site and during transportation. To be useful, such monitoring needs to be portable and low cost, and have minimal adverse impact on emergency personnel, e.g., by not raising an excessive number of alarms. The SMART (Scalable Medical Alert Response Technology) system integrates wireless patient monitoring (ECG, SpO(2)) [...]

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.

We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using [...]

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 [...]

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 [...]

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.

There has been major progress both in description logics and ontology design since SNOMED was originally developed. The emergence of the standard Web Ontology language in its latest revision, OWL 1.1 is leading to a rapid proliferation of tools. Combined with the increase in computing power in the past two decades, these developments mean that many of the restrictions that limited SNOMED's original formulation no longer need apply. We argue [...]

This paper presents a model designed to enable rapid detection and assessment of biological threats that may require swift intervention by the international public health community.

A significant portion of patients already known to be colonized or infected with Methicillin-Resistant Staphylococcus aureus (MRSA) may not be identified at admission by neighboring hospitals.