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A team of physicians, pharmacists, and informatics professionals developed a CDSS added to a commercial electronic medical record system to provide prescribers with patient-specific maximum dosing recommendations based on renal function. We tracked the time spent by team members and used US national averages of relevant hourly wages to estimate costs. The team required 924.5 hours and $48,668.57 in estimated costs to develop 94 alerts for 62 drugs. The most [...]

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 [...]

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

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.

Patients, policymakers, providers, payers, employers, and others have increasing interest in using personal health records (PHRs) to improve healthcare costs, quality, and efficiency. While organizations now invest millions of dollars in PHRs, the best PHR architectures, value propositions, and descriptions are not universally agreed upon. Despite widespread interest and activity, little PHR research has been done to date, and targeted research investment in PHRs appears inadequate. The authors reviewed the [...]

In order to assess the mission and strategic direction in an academic department of biomedical informatics, we used social network analysis to identify patterns of common interest among the department's multidisciplinary faculty. Data representing faculty and their self-identified research methods and expertise were analyzed by applying a network modularity algorithm to detect community structure. Three distinct communities of practice emerged: empirical discovery and prediction; human and organizational factors; and information [...]

Development of public health informatics applications often requires the integration of multiple data sources. This process can be challenging due to issues such as different file formats, schemas, naming systems, and having to scrape the content of web pages. A potential solution to these system development challenges is the use of Web 2.0 technologies. In general, Web 2.0 technologies are new internet services that encourage and value information sharing and [...]

In order to evaluate the accuracy of existing EMR data in predicting follow-up providers, a retrospective analysis was performed on six months of data for inpatient and ED encounters occurring at two hospitals, and on related outpatient data. Sensitivity and Positive Predictive Value (PPV) were calculated for each of eight predictors, to determine their effectiveness in predicting follow-up providers. Our findings indicate that access to longitudinal patient care records can [...]

Family health history is a complex, multifaceted tool for assessing disease risk that can offer insight into the interplay between inherited and social factors relevant to patient care. Family health history tools in electronic health records can enable the user to collect, represent, and interpret structured data that properly supports clinical decisions. If these data can be made interoperable, important health information can be shared with minimal duplication of effort [...]

Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative.