Computers, informatics, nursing : CIN
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Timely detection of deterioration in status for intensive care unit patients can be problematic due to variation in data availability and the necessity of integrating data from multiple sources. This can lead to opaqueness of clinical trends and failure to rescue. Automated deterioration detection using electronic medical record data can reduce the risk of failure to rescue. ⋯ Positive and negative predictive values and sensitivity and specificity measures varied across studies. Three systems generated clinician alerts. Automated deterioration detection using electronic medical record data may be an important aid in caring for intensive care unit patients, but its usefulness is limited by variable electronic medical record detection approaches and performance.
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Timely detection of deterioration in status for intensive care unit patients can be problematic due to variation in data availability and the necessity of integrating data from multiple sources. This can lead to opaqueness of clinical trends and failure to rescue. Automated deterioration detection using electronic medical record data can reduce the risk of failure to rescue. ⋯ Positive and negative predictive values and sensitivity and specificity measures varied across studies. Three systems generated clinician alerts. Automated deterioration detection using electronic medical record data may be an important aid in caring for intensive care unit patients, but its usefulness is limited by variable electronic medical record detection approaches and performance.
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An anesthesia information management system is a dynamic electronic documentation system that generates the legal records of patient care while the patient is receiving anesthesia. The generated documentation can be used to guide patient care, facilitate billing for services, and be used for clinical research. The purpose of this article was to synthesize the previous empirical and theoretical literature pertaining to the concept of accuracy in documentation in a wide range of disciplines in order to refine the concept and more effectively guide future research, clinical practice, and policy development in anesthesia informatics. ⋯ In nursing, accuracy can be defined as the presence of correct data that provide a complete, comprehensive, and precise representation of patient care. In anesthesia, accuracy is often defined in terms of correctness and completeness of data. Correctness, completeness, comprehensiveness, and precision are the primary constituents of accuracy with each discipline emphasizing different aspects.
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This article describes the development and implementation of a nursing clinical decision support system that prompts nurses to place mechanically ventilated patients in a semirecumbent position in the absence of contraindications. A literature review is provided regarding (1) ventilator-associated pneumonia and recommendations from expert panels to maintain mechanically ventilated patients in a semirecumbent position (unless contraindicated) and (2) clinical decision support systems. ⋯ Despite recommendations from organizations such as the Center for Disease Control and Prevention to maintain mechanically ventilated patients in a head-elevated position, there is evidence that the practice is not adequately adhered to. Therefore, a nursing clinical decision support system (in the form of a reminder) that prompts nurses to adhere to this recommendation was designed and implemented.
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A working framework is presented for interdisciplinary professionals for designing, building, and evaluating clinical decision support rules (expert rules) within the electronic health record. The working framework outlines the key workflow processes for eight health system organizations for selecting, designing, building, activating, and evaluating rules. In preparation, an interdisciplinary team selected expert rules for their organizations. ⋯ These steps offered direction to subsequent clinic and hospital organizations in a similar situation. This case study identified four key considerations when implementing and evaluating the clinical decision support expert rules within care delivery. In summary, the processes for decision support expert rules required rigorous development and change control processes to support operation.