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A real-time location system (RTLS) can wirelessly identify and track the location of people or assets. An RTLS is a well-known type of Internet of Things (IoT) application, where the position of connected, smart devices inside a building is monitored on a continuous basis. This monitoring often involves radio-frequency identification (RFID) technologies (Bolic et al., 2010; Sundaresan et al., 2015). Hospitals have started using RTLS technology for tracking smart and expensive medical devices such as mobile intravenous pumps and cardiac monitors. A recent market study (MarketsAndMarkets.com, 2016) reports that the RTLS market for healthcare will grow from half a billion USD in 2015 to an estimated 3.1 billion USD by 2022. RTLS technology enables hospitals to be “smarter”, but this often comes at a high cost. Hospitals are hence looking for supplemental RTLS applications, beyond asset tracking, in order to justify and amortize their investment.
This paper explores the feasibility of reusing RTLS technology for tracking patients and their assigned medical devices and healthcare providers in real time, and of detecting when they are connecting or disconnecting. We use the terms association and disassociation to explain the transitions between the existence and the non-existence of connectivity between a patient, a device, and the healthcare provider legally and clinically responsible for the patient connected to that device. Specifically, by receiving the identification of a patient and a device, and by linking them, a patient-device (also called patient-to-device – P2D) association happens (He et al., 2011). Such process is triggered by a healthcare provider (typically a nurse) next to the patient. Similarly, a P2D disassociation represents the termination of an association between a patient, a device, and possibly the healthcare provider responsible for the association. A disassociation may be caused by a variety of reasons such as being explicitly requested by a healthcare provider or by having a patient moving away (by mistake or on purpose) too far from the associated device (Herbst et al., 2010). To distinguish these two cases, the terms manual disassociation and automatic disassociation are respectively used thereon.
With emerging, IoT-enabled mobile equipment that communicates patient status information to hospital information systems, correct patient-device associations are becoming essential to ensure that the right medication is provided to the right patient, that information about the right patient is transmitted to electronic health records, and that the right healthcare provider is alerted when a device is unexpectedly disconnected. Currently, there are some issues regarding device-patient associations in hospitals. As patient safety is a primary objective for healthcare providers, considering the accuracy of patient identification is essential, especially during the device association process. Manual association by healthcare providers is one of the current methods for assigning the patient to the device according to their identifier (ID), but it is time consuming and may lead to errors (Byunggil & Howon, 2007).
Another motivation for our work is rooted in a common method for managing associations between patients and devices: the use of barcoding systems. Such approach is also time consuming and not always practical, despite relatively low percentages of errors reported by health providers. Bar codes can also be damaged or switched, they have low storage capacity, and they require a line of sight by the nurse to be used (ECRI Institute, 2008). The third motivation is the location of required mobile devices (intravenous pumps, cardiac monitors, etc.), often unknown to care providers. Tracking equipment is crucial in asset management, especially in a centralized distribution model (Shirehjini, 2012). Yet, a recent study by Horblyuk et al. (2012) illustrates that next to 60% of mobile devices remain idle at any given time while nurses spend over 20 minutes per shift looking for mobile equipment. With better real-time tracking, hospitals can often reduce the amount of equipment inventory, which consequently reduces maintenance, replacement, and service costs.