Proceedings / AMIA ... Annual Symposium. AMIA Symposium
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Even in the information-rich environment of hospitals, health-care providers face challenges in addressing their various information needs. Through a study of a patient-care team in a tertiary care Surgical Intensive Care Unit (SICU), we expanded our understanding of health-care providers' information needs in two important ways. ⋯ We found that organizational information was extremely important to SICU team members. Furthermore, the first resource that team members utilized was not electronic or paper but rather human: another team member.
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Comparative Study
Neural network modeling to predict the hypnotic effect of propofol bolus induction.
Dose requirements of propofol to achieve loss of consciousness depend on the interindividual variability. Until now when propofol was administered by a single bolus, how to define the optimal individual dose and to assess its hypnotic effect have not been clearly studied. The goal of this study is to develop an artificial neural network model to predict the hypnotic effect of propofol on the basis of common clinical parameters. ⋯ The bispectral index of EEG was used to record the consciousness level of patients and served as the output factor. The predictive results of neural net models were superior to that of clinician. This model could potentially help determine the optimal dose of propofol and thus reduce the anesthetic cost.
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Collaboration is an important part of healthcare delivery. However, in home care, collaboration is difficult due to the mobility and schedule variability of the workers. ⋯ We present recommendations for incorporating support for each of these areas into point-of-care clinical information systems that provide access to shared patient records. Finally, we discuss general design approaches for incorporating this type of support, including the need for workers to maintain awareness of the activities of others, and the need to integrate communication with the presentation of the health record.
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We present a prototype of a decision support system for anesthesia that applies set covering theory. The system is designed to generate dynamically configured check-lists for intra-operative problems. These lists have the potential to help anesthesiologists detect and manage problems in a timely manner. ⋯ A set covering algorithm that accommodates multiple problem sets was used to implement the prototype. A simulated case and the system behavior are presented. The ultimate goals of a system such as the one presented are to function as an intelligent alarm module for electronic monitors and to facilitate the task of correcting intra-operative problems.
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Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. ⋯ During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility.