Journal of biomedical informatics
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The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. ⋯ Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.
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Over the past two decades, high false alarm (FA) rates have remained an important yet unresolved concern in the Intensive Care Unit (ICU). High FA rates lead to desensitization of the attending staff to such warnings, with associated slowing in response times and detrimental decreases in the quality of care for the patient. False arrhythmia alarms are commonly due to single channel ECG artifacts and low voltage signals, and therefore it is likely that the FA rates may be reduced if information from other independent signals is used to form a more robust hypothesis of the alarm's etiology. ⋯ The FA suppression algorithm reduced the incidence of false critical ECG arrhythmia alarms from 42.7% to 17.2%, where simultaneous ECG and ABP data were available. The present algorithm demonstrated the potential of data fusion to reduce false ECG arrhythmia alarms in a clinical setting, but the non-zero TA reduction rate for ventricular tachycardia indicates the need for further refinement of the suppression strategy. To avoid suppressing any true alarms, the algorithm could be implemented for all alarms except ventricular tachycardia. Under these conditions the FA rate would be reduced from 42.7% to 22.7%. This implementation of the algorithm should be considered for prospective clinical evaluation. The public availability of a real-world ICU database of multi-parameter physiologic waveforms, together with their associated annotated alarms is a new and valuable research resource for algorithm developers.
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The dynamic and distributed work environment in critical care requires a high level of collaboration among clinical team members and a sophisticated task coordination system to deliver safe, timely and effective care. A complex cognitive system underlies the decision-making process in such cooperative workplaces. This methodological review paper addresses the issues of translating cognitive research to clinical practice with a specific focus on decision-making in critical care, and the role of information and communication technology to aid in such decisions. ⋯ Critical care, in this paper, includes both intensive (inpatient) and emergency (outpatient) care. We define translational cognition as the research on basic and applied cognitive issues that contribute to our understanding of how information is stored, retrieved and used for problem-solving and decision-making. The methods and findings are discussed in the context of constraints on decision-making in real-world complex environments and implications for supporting the design and evaluation of decision support tools for critical care health providers.
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Predicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. ⋯ We compared our models with ones that were developed on the same patient subpopulations but which did not use the episodes. The new models show improved performance on each of the five days. They also provide insight in the effect of the various selected episodes on mortality.