# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 1 Introduction  
Healthcare is one of the primary sectors in modern society, accounting for a substantial portion of global investments and remaining a top priority on the agendas of various countries. Healthcare institutions and managers have faced several challenges, including the increasing pressure to enhance productivity, reduce costs, and improve resource management [1]. In this context, methodologies such as project management techniques, lean healthcare, Six Sigma, and implementing new technologies have emerged as potential solutions in scientific research and practice [2, 3, 4]. These approaches are directly associated with hospital operational excellence and are frequently combined with emerging technologies [5]. Furthermore, lean is a facilitator for implementing Industry 4.0 technologies, acting as a foundational step before technological adoption [6]. However, healthcare processes areinherently complex, involving multiple stakeholders and technical specificities that make their study challenging [7; 8], making hospital operations a critical area of focus. Despite the growing adoption of technology in healthcare, in some fields, it remains an underdeveloped sector compared to other industries [9]. For example, real-time location system (RTLS) development in hospitals is often hindered by high implementation costs, the need for customisation to diverse organisational structures, and the absence of a universally optimal sensor. Deployment also faces significant barriers, including limited infrastructure, staff resistance, patient privacy concerns, and low perceived usefulness by some healthcare providers, despite documented gains in operational efficiency and safety [10]. Various authors have highlighted the vast amounts of data generated in hospitals, often fraught with errors, gaps, lack of integration, and low reliability, hindering the use of advanced approaches like big data analytics and process mining [11]. Furthermore, when implementing improvement approaches such as lean healthcare, the lack of reliable data is a recurrent challenge [12]. In this context, by automatically collecting workflow data, RTLS can improve the quality of records while reducing the need for manual data entry and freeing up resources [13]. The emergence and popularisation of new technologies have opened doors for innovative data collection methods with higher accuracy, accessibility, and real-time data exchange. Real-time Location Systems (RTLSs) are a combination of sensors for the identification and tracking of the location of people or assets in real or almost real time [14]. The degree of real-time tracking depends on technological limitations and cost-effectiveness, as not all resources require data collection at millisecond intervals. For instance, tracking assets that move only within hourly intervals would result in unnecessary resource consumption [14; 15]. This automatic data collection, combined with the ability to process and interpret it, enables detailed studies of patient, staff, and product flows, facilitating data-driven improvements with proven benefits to operations [10]. For instance, these technologies have been used to enhance the understanding of emergency departments by quantifying physicians' time allocation and movements [16]. Furthermore, integrating RTLS with artificial intelligence, simulation techniques, cloud computing, and other Industry 4.0 technologies facilitates the creation of digital twins. These are virtual models that evolve in real time based on data from their real counterparts, enabling simulation and feedback to support decision making [17], which can significantly enhance hospital operations management and more sustainable systems [17; 18; 19], by optimising resource utilisation, minimising redundancies, and supporting lean process improvements [20; 21]. One of the challenges of digital twins is collecting diverse and unstructured data, which can be mitigated through the use of RTLS [18].