Journal of biomedical informatics
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More research is needed to understand the effects of health information technology (HIT) and health information exchange (HIE) on quality, safety, efficiency, finances, consumers and providers in community-based settings. New York State is investing heavily in HIT and HIE adoption through the HEAL NY program. ⋯ HITEC (The Health Information Technology Evaluation Collaborative) was established to measure systematically the effects of HIT and HIE on consumers, providers, health care quality, patient safety, public health, and financial return on investment in New York State, as no individual grantee is able to conduct cross-cutting evaluations. The results of these evaluations should inform decisions made by leaders in HIT and HIE in New York State and across the nation.
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Review Meta Analysis
Conceptual knowledge acquisition in biomedicine: A methodological review.
The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.
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The intensive care unit (ICU) is an instance of a very dynamic health care setting where critically ill patients are being managed. To provide good care, an extensive and coordinated communication amongst the role players, use of numerous information systems and operation of devices for monitoring and treatment purposes are required. The purpose of this research is to study error evolution and management within this environment. ⋯ These clinicians and nurses were interviewed to complement the observation data and to delineate their individual workflows. These pieces of the ICU workflow were used to develop a generalize-able cognitive model to represent the intricate workflow applicable to other health care settings. The proposed model can be used to identify and characterize medical errors and for error prediction in practice.
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Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration. ⋯ Validation of AUC values across all models suggests that certain risk factors have remained important over the last decade. However, the lack of calibration suggests that small changes in patient populations and data collection methods quickly reduce the accuracy of patient level estimations over time. Possible solutions to this problem involve either recalibration of models using local data or development of new local models.