Studies in health technology and informatics
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Stud Health Technol Inform · Jan 2013
Using the intubating laryngeal tube in a manikin - user evaluation of a new airway device.
This work describes the use of a new intubation device, the intubating laryngeal tube (iLTA) as developed by Boedeker. Emergency Department residents and staff from the University of Nebraska Medical Center performed intubations using the Laerdal Difficult Airway Trainer Manikin(TM). The participants' perceived value of the intubating laryngeal tube as well as its efficacy in intubation performance were measured and found to be highly favorable.
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Stud Health Technol Inform · Jan 2013
Moving mobile: using an open-sourced framework to enable a web-based health application on touch devices.
Computer devices using touch-enabled technology are becoming more prevalent today. The application of a touch screen high definition surgical monitor could allow not only high definition video from an endoscopic camera to be displayed, but also the display and interaction with relevant patient and health related data. ⋯ This paper describes an approach taken to overcome these problems. A real case study was used to demonstrate the novelty and efficiency of the proposed method.
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Stud Health Technol Inform · Jan 2013
Nursing critical patient severity classification system predicts outcomes in patients admitted to surgical intensive care units: use of data from clinical data repository.
To examine the Critical Patient Severity Classification System (CPSCS) recorded by nurses to predict ICU and hospital lengths of stay and mortality, data were drawn from patients admitted to 2 surgical intensive care units (SICUs) at a university hospital in Seoul, South Korea in 2010. This retrospective study used a large data set retrieved from the Clinical Data Repository System. ⋯ The CPSCS was a statistically significant predictor of ICU and hospital LOS and mortality when patients' demographic characteristics were adjusted. In the era of emphasis on using big data, analysis of nursing assessment data should be evaluated to show importance of nursing contribution to predict patients' clinical outcomes.
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Stud Health Technol Inform · Jan 2013
Utilizing electronic health record data to determine the health of the medication process after the relocation of a children's hospital.
Hospital relocation is a highly complex undertaking, which has the potential to interrupt operations and poses risks for patients, staff, and providers. Little is known how hospital relocation impacts on workflow and communication. ⋯ Overall performance of the medication process has declined slightly. We identified regional (unit) differences with the pediatric intensive care unit, which had the most significant changes to its workflow, experiencing a more than doubling of the time from ordering to medication administration. Overall, there was no significant difference in time-sensitive medication administration times. Evaluating the medication ordering-dispensing-administration process through readily available EHR data demonstrated that the impact of a hospital' s relocation on workflow and communication can be successfully monitored.
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Stud Health Technol Inform · Jan 2013
Engineering natural language processing solutions for structured information from clinical text: extracting sentinel events from palliative care consult letters.
Despite a trend to formalize and codify medical information, natural language communications still play a prominent role in health care workflows, in particular when it comes to hand-overs between providers. Natural language processing (NLP) attempts to bridge the gap between informal, natural language information and coded, machine-interpretable data. This paper reports on a study that applies an advanced NLP method for the extraction of sentinel events in palliative care consult letters. ⋯ A random selection of 215 anonymized consult letters was used for the study. The results of the NLP extraction were evaluated by comparison with coded sentinel event data captured independently by clinicians. The average accuracy of the automated extraction was 73.6%.