Supporting patient care in the emergency department with a computerized whiteboard system.

Efficient information management and communication within the emergency department (ED) is essential to providing timely and high-quality patient care. The ED whiteboard (census board) usually serves as an ED's central access point for operational and patient-related information. This article describes the design, functionality, and experiences with a computerized ED whiteboard, which has the ability to display relevant operational and patient-related information in real time. Embedded functionality, additional whiteboard views, and the integration with ED and institutional information system components, such as the computerized patient record or the provider order entry system, provide rapid access to more detailed information. As an information center, the computerized whiteboard supports our ED environment not only for providing patient care, but also for operational, educational, and research activities.

Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.

Hospital admission delays in the Emergency Department (ED) reduce capacity and contribute to the ED's diversion problem. We evaluated the accuracy of an Artificial Neural Network for the early prediction of hospital admission using data from 43,077 pediatric ED encounters. We used 9 variables commonly available in the ED setting. The area under the receiver operating characteristic curve was 0.897 (95% CI: 0.887-0.896). The instrument demonstrated high accuracy and may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.

Predicting hospital admission for Emergency Department patients using a Bayesian network.

Hospital admission delays in the Emergency Department (ED) reduce volume capacity and contribute to the nation's ED diversion problem. This study evaluated the accuracy of a Bayesian network for the early prediction of hospital admission status using data from 16,900 ED encounters. The final model included nine variables that are commonly available in many ED settings. The area under the receiver operating characteristic curve was 0.894 (95% CI: 0.887-0.902) for the validation set. The system had high accuracy an may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.

Estimating patient's length of stay in the Emergency Department with an artificial neural network.

Predicting a patient's expected length of stay for an Emergency Department encounter is valuable to anticipate impending operational bottlenecks that may lead to diversion. We developed and validated an artificial neural network using data from >16,000 patients using clinical and operational parameters that are commonly available early during an encounter. Performance on the training set predicted length of stay within an average of 2 hours (sigmae2<500), but declined to an average of 7.5 hours (sigmae2<6000) in the validation set. Chief complaint specific trials using the most frequent chief complaints, however, predicted within an average of 3.5 hours (sigmae2 <145), with similar validation.