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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.

Personally controlled health records (PCHRs) enable patients to store, manage, and share their own health data, and promise unprecedented consumer access to medical information. To deploy a PCHR in the pediatric population requires crafting of access and security policies, tailored to a record that is not only under patient control, but one that may also be accessed by parents, guardians, and third-party entities. Such hybrid control of health information requires [...]

The advent of electronic medical records and health information exchange raise the possibility of expanding public health reporting to detect a broad range of clinical conditions and of monitoring the health of the public on a broad scale. Expanding public health reporting may require patient anonymity, matching records, re-identifying cases, and recording patient characteristics for localization. The privacy regulations under the Health Insurance Portability and Accountability Act of 1996 (HIPAA) [...]

OBJECTIVE All electronic health (e-health) interventions require validation as health information technologies, ideally in randomized controlled trial settings. However, as with other types of complex interventions involving various active components and multiple targets, health informatics trials often experience problems of design, methodology, or analysis that can influence the results and acceptance of the research. Our objective was to review selected key methodologic issues in conducting and reporting randomized controlled trials [...]

The Pfizer Healthcare Informatics team conducted a series of guided interviews with 35 Pfizer senior leaders to elicit their understanding, desires, and expectations of how Electronic Health Records (EHR) might be used in the pharmaceutical industry today and/or in the future. The interviews yielded fourteen use case categories comprising 42 specific use cases. The highest priority use cases were "Drug Safety & Surveillance," "Clinical Trial Recruitment," and "Support Regulatory Approval." [...]

The objective of this systematic review is to analyse the relative risk reduction on medication error and adverse drug events (ADE) by computerized physician order entry systems (CPOE). We included controlled field studies and pretest-posttest studies, evaluating all types of CPOE systems, drugs and clinical settings. We present the results in evidence tables, calculate the risk ratio with 95% confidence interval and perform subgroup analyses for categorical factors, such as [...]

There is increasing pressure to share health information and even make it publicly available. However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets.

The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and [...]

The aim of this study was to improve naïve Bayes prediction of Medical Subject Headings (MeSH) assignment to documents using optimal training sets found by an active learning inspired method.

All methods of identifying medication prescribing errors are fraught with inaccuracies and systematic bias. A systematic, efficient, and inexpensive way of measuring and quantifying prescribing errors would be a useful step for reducing them. We ask if rapid discontinuations of prescription-orders--where physicians stop their orders within 2 hours--would be an expedient proxy for prescribing errors? To study this we analyzed CPOE-system medication orders entered and then discontinued within 2 hours [...]