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The aim of this study was to measure the effect of an electronic heparin-induced thrombocytopenia (HIT) alert on provider ordering behaviors and on patient outcomes.

Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. It is a critical component in automated text processing. We investigate the performance of machine learning taggers for clinical concept extraction, particularly the portability of taggers across documents from multiple data sources.

To evaluate existing automatic speech-recognition (ASR) systems to measure their performance in interpreting spoken clinical questions and to adapt one ASR system to improve its performance on this task.

To assess intensive care unit (ICU) nurses' acceptance of electronic health records (EHR) technology and examine the relationship between EHR design, implementation factors, and nurse acceptance.

Clinical Queries filters were developed to improve the retrieval of high-quality studies in searches on clinical matters. The study objective was to determine the yield of relevant citations and physician satisfaction while searching for diagnostic and treatment studies using the Clinical Queries page of PubMed compared with searching PubMed without these filters.

To assess whether a low level of decision support within a hospital computerized provider order entry system has an observable influence on the medication ordering process on ward-rounds and to assess prescribers' views of the decision support features.

Electronic personal health record systems (PHRs) support patient centered healthcare by making medical records and other relevant information accessible to patients, thus assisting patients in health self-management. We reviewed the literature on PHRs including design, functionality, implementation, applications, outcomes, and benefits. We found that, because primary care physicians play a key role in patient health, PHRs are likely to be linked to physician electronic medical record systems, so PHR adoption [...]

To understand how the source of information affects different adverse event (AE) surveillance methods.

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.