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The Massachusetts eHealth Collaborative (MAeHC) was formed to improve patient safety and quality of care by promoting the use of health information technology through community-based implementation of electronic health records (EHRs) and health information exchange. The Collaborative has recently implemented EHRs in a diverse set of competitively selected communities, encompassing nearly 500 physicians serving over 500,000 patients. Targeting both EHR implementation and health information exchange at the community level has [...]

This study sought to assess the value of the Health Level 7/U.S. Food and Drug Administration Structured Product Labeling (SPL) drug knowledge representation standard and its associated terminology sources for drug-intolerance (allergy) decision support in computerized provider order entry (CPOE) systems.

The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification.

This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time [...]

Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference [...]

Spatio and/or temporal surveillance systems are designed to monitor the ongoing appearance of disease cases in space and time, and to detect potential disturbances in either dimension. Patient addresses are sometimes reported at some level of geographic aggregation, for example by ZIP code or census tract. While this aggregation has the advantage of protecting patient privacy, it also risks compromising statistical efficiency. This paper investigated the variation in power to [...]

Time-dependent drug-drug interactions (TDDIs) are drug combinations that result in a decreased drug effect due to coadministration of a second drug. Such interactions can be prevented by separately administering the drugs. This study attempted to reduce drug administration errors due to overridden TDDIs in a care provider order entry (CPOE) system. In four periods divided over two studies, logged TDDIs were investigated by reviewing the time intervals prescribed in the [...]

To predict student performance in an introductory graduate-level biomedical informatics course from application data.

The goal of the study was to develop an in-depth understanding of work practices, workflow, and information flow in chronic disease care, to facilitate development of context-appropriate informatics tools.

Clinical notes, typically written in natural language, often contain substructure that divides them into sections, such as "History of Present Illness" or "Family Medical History." The authors designed and evaluated an algorithm ("SecTag") to identify both labeled and unlabeled (implied) note section headers in "history and physical examination" documents ("H&P notes").