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The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous [...]

To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs).

On July 1, 2012 Australia launched a personally controlled electronic health record (PCEHR) designed around the needs of consumers. Using a distributed model and leveraging key component national eHealth infrastructure, the PCEHR is designed to enable sharing of any health information about a patient with them and any other health practitioner involved in their care to whom the patient allows access. This paper discusses the consumer-facing part of the program.

We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible.

This study describes the implementation and impact of an electronic test result acknowledgement (RA) system in the Mater Mothers' Hospital in Brisbane, Australia. The Verdi application electronically records clinicians' acknowledgement of the review of results. Hospital data (August 2011-August 2012) were extracted to measure clinicians' acknowledgement practices. There were 27,354 inpatient test results for 6855 patients. All test results were acknowledged. 60% (95% CI 59% to 61%) of laboratory and [...]

To examine the impact of a personal health record (PHR) on medication-use safety among older adults.

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training [...]

Adverse drug events, the unintended and harmful effects of medications, are important outcome measures in health services research. Yet no universally accepted set of International Classification of Diseases (ICD) revision 10 codes or coding algorithms exists to ensure their consistent identification in administrative data. Our objective was to synthesize a comprehensive set of ICD-10 codes used to identify adverse drug events.

To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype.

Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures [...]