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Providers have been reluctant to disclose patient data for public-health purposes. Even if patient privacy is ensured, the desire to protect provider confidentiality has been an important driver of this reluctance.

We evaluated the effects of integrating order subsets for the most common medical diagnoses into a general medical admission order set of our electronic medical records (EMR) in order to improve order set integration by clinicians.

The goals of this study were to explore the information needs of case managers who provide services to persons living with HIV (PLWH) and to assess the applicability of the Information Needs Event Taxonomy in a new population.

Informatics for Integrating Biology and the Bedside (i2b2) is an initiative funded by the NIH that aims at building an informatics infrastructure to support biomedical research. The University of Pavia has recently integrated i2b2 infrastructure with a registry of inherited arrhythmogenic diseases. Within this project, the authors created a novel i2b2 cell, named R Engine Cell, which allows the communication between i2b2 and the R statistical software. As survival analyses [...]

The authors investigated use of the internet-based patient portal, kp.org, among a well-characterized population of adults with diabetes in Northern California. Among 14,102 diverse patients, 5671 (40%) requested a password for the patient portal. Of these, 4311 (76%) activated their accounts, and 3922 (69%), logged on to the patient portal one or more times; 2990 (53%) participants viewed laboratory results, 2132 (38%) requested medication refills, 2093 (37%) sent email messages [...]

To foster informed decision-making about health social networking (SN) by patients and clinicians, the authors evaluated the quality/safety of SN sites' policies and practices.

To evaluate the incidence of duplicate medication orders before and after computerized provider order entry (CPOE) with clinical decision support (CDS) implementation and identify contributing factors.

Information extraction and classification of clinical data are current challenges in natural language processing. This paper presents a cascaded method to deal with three different extractions and classifications in clinical data: concept annotation, assertion classification and relation classification.

The BT-Nurse system uses data-to-text technology to automatically generate a natural language nursing shift summary in a neonatal intensive care unit (NICU). The summary is solely based on data held in an electronic patient record system, no additional data-entry is required. BT-Nurse was tested for two months in the Royal Infirmary of Edinburgh NICU. Nurses were asked to rate the understandability, accuracy, and helpfulness of the computer-generated summaries; they were [...]

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.