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Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS).

To assess the effect of a clinical decision support system (CDSS) integrated into an electronic health record (EHR) on antibiotic prescribing for acute respiratory infections (ARIs) in primary care.

The aim of this paper is to summarize concerns with the de-identification standard and methodologies established under the Health Insurance Portability and Accountability Act (HIPAA) regulations, and report some potential policies to address those concerns that were discussed at a recent workshop attended by industry, consumer, academic and research stakeholders.

We explore relationships between health information seeking activities and engagement with healthcare professionals via a privacy-sensitive analysis of geo-tagged data from mobile devices.

De-identification allows faster and more collaborative clinical research while protecting patient confidentiality. Clinical narrative de-identification is a tedious process that can be alleviated by automated natural language processing methods. The goal of this research is the development of an automated text de-identification system for Veterans Health Administration (VHA) clinical documents.

To review the literature on clinical decision support (CDS) for genetically guided personalized medicine (GPM).

Current image sharing is carried out by manual transportation of CDs by patients or organization-coordinated sharing networks. The former places a significant burden on patients and providers. The latter faces challenges to patient privacy.

To provide a set of high-quality time-series physiologic and event data from anesthetic cases formatted in an easy-to-use structure.

(1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents.

Electronic health records (EHR) are becoming more common because of the federal EHR incentive programme, which is also promoting electronic health information exchange (HIE). To determine whether consumers' attitudes toward EHR and HIE are associated with experience with doctors using EHR, a nationwide random-digit-dial survey was conducted in December 2011. Of 1603 eligible people contacted, 1000 (63%) participated. Most believed EHR and HIE would improve healthcare quality (66% and 79% [...]