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In an effort to understand better the federal electronic health record (EHR) incentive programme's challenges, this study compared hospitals that did and did not receive meaningful use (MU) payments in the programme's first year based on the challenges they anticipated a year before.

Securing protected health information is a critical responsibility of every healthcare organization. We explore information security practices and identify practice patterns that are associated with improved regulatory compliance.

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 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.

Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem.

To model the financial effects of implementing a hospital-wide electronic medical record (EMR) system in a tertiary facility in Malawi.

Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.

To observe the effect of electronic medical record (EMR) system sophistication on preventive women's healthcare.

To examine how clinicians on the receiving end of admission handoffs use electronic health records (EHRs) in preparation for those handoffs and to identify the kinds of impacts such usage may have.