Long queues and wait times often occur at hospitals and affect smooth delivery of health services. To improve hospital operations, prior studies have developed scheduling techniques to minimize patient wait times. However, these studies lack in demonstrating how such techniques respond to real-time information needs of hospitals and efficiently manage wait times. This article presents a multi-method study on the positive impact of providing real-time scheduling information to patients using the RFID technology. Using a simulation methodology, we present a generic scenario, which can be mapped to real-life situations, where patients can select the order of laboratory services. The study shows that information visibility offered by RFID technology results in decreased wait times and improves resource utilization. We also discuss the applicability of the results based on field interviews granted by hospital clinicians and administrators on the perceived barriers and benefits of an RFID system.
The volume of Web robot traffic seen by Web servers and clouds continue to increase with the popularity of Internet of Things (IoT) devices. Such traffic exhibits decidedly different statistical and resource request patterns compared to humans. However, the optimizations ensuring high levels of Web systems and cloud performance requires traffic to exhibit the statistical and behavioral patterns of humans, not robots. This necessitates the design of novel Web system optimizations to handle Web robot traffic effectively. Caches are a basic component of high performing Web systems, but their effectiveness relies on accurate resource request prediction. In this paper, we explore a suite of classifiers for the resource request type prediction problem for robot traffic. Our analysis reveals: (i) a striking difference in the request patterns of robots across multiple servers from the same domain; and (ii) that Elman neural networks hold promise to predict request types despite these differences.
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