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This novel hybrid approach can accurately locate negated concepts in clinical radiology reports not only when in close proximity to, but also at a distance from, negation signals.

The implementation of health information technology (HIT) is accelerating, driven in part by a growing interest in computerized physician order entry (CPOE) as a tool for improving the quality and safety of patient care. Computerized physician order entry could have a substantial impact on patients in intensive care, where the potential for medical error is high, and the clinical workflow is complex. In 2009, only 17% of hospitals had functional CPOE systems in place. In intensive care unit (ICU) settings, CPOE has been shown to reduce the occurrence of some medication errors, but evidence of a beneficial effect on clinical outcomes remains limited. In some cases, new error types have arisen with the use of CPOE. Intensive care unit workflow and staff relationships have been affected by CPOE, often in unanticipated ways. The design of CPOE software has a strong impact on user acceptance. Intensive care unit-specific order sets lessen the cognitive workload associated with the use of CPOE and improve user acceptance. The diffusion of new technological innovations in the ICU can have unintended consequences, including changes in workflow, staff roles, and patient outcomes. When implementing CPOE in critical care areas, both organizational and technical factors should be considered. Further research is needed to inform the design and management of CPOE systems in the ICU and to better assess their impact on clinical end points, cost-effectiveness, and user satisfaction.

A b s t r a c t Objective: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLSÒ Specialist Lexicon to improve noun phrase identification within clinical radiology documents.Design: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)Ò Clinical Document Architecture (CDA)-compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance.Results: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. Conclusion:The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.