The increasing complexity of patient data and the need for reliable, transparent decision-making in healthcare drive the demand for advanced AI systems. This paper introduces a Clinical Decision Support System (CDSS) integrating Explainable AI (XAI) with Retrieval-Augmented Generation (RAG), designed to offer transparent, real-time, and evidence-based recommendations. HIPAA-compliant and grounded in historical case studies, the system facilitates trustworthy decision-making in high-pressure environments for nursing assistants. Although preliminary, simulations indicate the system’s potential to reduce cognitive load and enhance decision accuracy, with future clinical trials planned for comprehensive validation. It ensures HIPAA compliance while utilizing real-time patient data and historical case studies to support transparent and reliable clinical decisions. The system provides real-time, mobile-friendly recommendations based on predictive analytics and case-based reasoning, ensuring that nursing assistants can make informed, transparent decisions even in high-pressure scenarios. Future work will validate the system through simulations and clinical trials to assess its impact on decision accuracy and cognitive load reduction. Designed specifically for nursing assistants, the system provides real-time, explainable recommendations and retrieves relevant historical cases and clinical guidelines to support decision-making. While this work is preliminary and has not been deployed in clinical settings, theoretical analysis suggests that the system has the potential to improve decision accuracy and reduce cognitive load. A roadmap for future validation and clinical trials is outlined to demonstrate how the proposed system can be integrated into healthcare workflows.

As one of the killer applications, instant messaging has become a simple yet efficient tool for peer-to-peer communications in data networks. In telephone networks, short message services are gaining more popularity as well. However, this kind of services typically operates in their own respective networks and is triggered by fairly simple events (such as pushing a "send" button or prescheduling the transmission at later time). A promising direction is to trigger instant messages by environmental information from the physical world. In light of this, this paper proposes to establish an event-driven messaging service over a cellular-and-sensor-integrated network. We have prototyped a system which adopts Global System for Mobile Communication (GSM) as the cellular network, and Bluetooth technology as the sensor network. The latter is to realize a Bluetooth surveillance network with location-sensing capabilities to be deployed within an office building area. While using other technologies is possible, GSM and Bluetooth are two dominating technologies in telephone and data networks. So the proposed technology is immediately feasible, given the fact that many handsets are already Bluetooth-enabled. Through this combination, we demonstrate a visitor system (VS) that offers several attractive features/services for visitors arriving at an office. First, messaging services in VS are driven by preconfigured events which can be collected from the Bluetooth surveillance network. Simple events might be a person entering/leaving a space, while complicated events might be a compound logic statement involving multiple users and multiple locations in VS. Second, we believe that the proposed system justifies the potential of cross-network applications and services. Third, the proposed architecture takes a modular approach by dividing the system into several subsystems according to their functionality. Logically dispatching jobs is the key to future extensions and further value-added services. The system architecture and implementation details are reported. Performance analyses are presented to model the detection latency of a Bluetooth sensor network.

Operating in the unlicensed 2.4-GHz ISM band, a Bluetooth piconet will inevitably encounter the interference problem from other piconets. With a special channel model and packet formats, one research issue is how to predict the packet collision effect in a multipiconet environment. In several earlier works, El-Hoiydi (2001), El-Hoiydi and Decotignie (2001), Lim et al. (2001), this problem is studied, but the results are still very limited in that packets are usually assumed to be uniform in lengths and in that time slots of each piconet are assumed to be fully occupied by packets. These assumptions have been successfully removed in the analytical results proposed in Lin and Tseng (2003). In this paper, we further improve the analytical results in Lin and Tseng (2003) by taking into account the frequency-hopping guard time effect in Bluetooth baseband. The result would offer a way to better estimate the network performance in a multipiconet environment.