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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.

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 [...]

This study investigates the effects of digital image compression on automatic quantification of immunohistochemical nuclear markers. We examined 188 images with a previously validated computer-assisted analysis system. A first group was composed of 47 images captured in TIFF format, and other three contained the same images converted from TIFF to JPEG format with 3x, 23x and 46x compression. Counts of TIFF format images were compared with the other three groups [...]

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 [...]

To externally validate EPICON, a computerized system for grouping diagnoses from EMRs in general practice into episodes of care. These episodes can be used for estimating morbidity rates.

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 [...]

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 [...]

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.