Studies in health technology and informatics
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Stud Health Technol Inform · Jan 2013
International priorities for research in nursing informatics for patient care.
The Nursing Informatics International Research Network (NIIRN) is a group of experts who are collaborating on the development of internationally relevant research programs for nursing informatics. In this paper we outline key findings of a survey exploring international research priorities for nursing informatics. The survey was available online during May-August 2012. ⋯ The two most highly ranked areas of importance for research were development of systems to provide real time feedback to nurses and assessment of the impact of HIT on nursing care and patient outcomes. The lowest ranked research topics were theory development and integrating genomic data into clinical information systems. The identification of these priorities provides a basis for future international collaborative research in the field of nursing informatics.
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Stud Health Technol Inform · Jan 2013
Traumatic brain injury rehabilitation: analysis of common data elements.
Physical medicine rehabilitation interventions for post-acute traumatic brain injury (TBI) are heterogeneous and subject to differences based on multi-disciplinary treatment plans [1]. There is no universal knowledge representation (KR) model for TBI rehabilitation which impedes data collection, aggregation, computation, and sharing. This paper describes results of an analysis of the National Institute for Neurological Disorders and Stroke (NINDS) TBI "Common Data Elements" (CDE) clinical data standardization set. ⋯ A content coverage study was performed on the "Treatment/Intervention" sub-set of CDEs. Results show that coverage of the CDEs is broad but lacks depth to represent the context of data collection in the TBI rehabilitation process. Next steps will be development of a KR model and identification and validation of domain concepts for a foundational ontology.
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This paper is concerned with the development of an Emergency Medical Services (EMS) system which interfaces with a Holistic Emergency Care Record (HECR) that aims at managing emergency care holistically by supporting EMS processes and is accessible by Android-enabled mobile devices.
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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
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%.