Influence of Information System on Emergency Department Shift Leaders’ Mobility

Emergency departments are hospital units for patients, who need emergency and acute care. Due to the state of patients, shift leader nurses and physicians need to do important decisions concerning patients’ treatment and care, quickly and efficiently. To be able to do that, they have to get needed information easily and efficiently. That is called knowledge-based leadership. When knowledge-based leadership is successful, the organisations’ information and knowledge are processed in a way, that they may be exploited in a best possible way. Unfortunately, nowadays the situation is not ideal in emergency departments, even though a lot of money has been invested in new information systems. Staff dissatisfaction with existing information systems have been reported and can be seen on staff satisfaction. On the other hand, there is not much research on how the information systems impact on shift leader nurses’ and physicians’ work. A quasi-experimental study was done in the years 2015 – 2016 in emergency departments in three central hospitals in Finland exploring the movement patterns of the shift leader professionals before and after implementation of a new information system in on unit, called intervention unit. Data was collected with Bluetooth beacons installed in different hospital rooms and corridors and with smartphones carried by shift leader nurses and physicians. Improving the knowledge management of leaders with new information systems, it is expected, that it decreases the effort needed to seek information to support decision-making. However, the result was not so unambiguous. The movement patterns changed, but they changed also in control units. A new information system can influence on hospital leader shift personnel mobility. Unfortunately, the lack of data made it impossible to count and compare actual walked distances. There should be more data – that is – from every day, every shift and every shift leader professional around the clock. Only that way, the comparison would be reliable. The mobility data from hospitals was already collected, when this thesis was written, so collecting the data was not part of this thesis. Keywords: information system evaluation, indoor positioning, Bluetooth beaco

Monitoring Workers on Construction Sites using Data Fusion of Real-Time Worker’s Location, Body Orientation, and Productivity State

Traditionally, on-site construction production monitoring depends primarily on manual processes that are time-consuming and error-prone. State-of-the-art technologies have been utilized lately to improve these processes to support timely decisions pertinent to the productivity and safety of onsite operations. This research introduces a novel construction site monitoring system to track workers' location, body orientation, and productivity state. The developed system uses Bluetooth Low Energy (BLE) based reference transmitting beacons fixed on job sites and a set of receiving beacons mounted on workers’ hardhats, chests, and wrists. The system works via three modules, i.e. (i) RTLS (Real-Time Location System) module; (ii) body orientation detection module; and (iii) productivity state detection module. The RTLS module is developed to continuously track the location of the workers and subsequently extract the actual labor workspaces. The RTLS is explicitly designed for construction by satisfying requirements for widespread on-site adoption, including cost efficiency, deployability, scalability, adjustability to the construction site dynamism, and the expected accuracy. The main features of the developed RTLS are (i) substituting commonly used BLE receivers with BLE receiving beacons; (ii) proposing a modular infrastructure placement strategy; (iii) deploying Trilateration and Min-Max as localization techniques; (iv) post-processing the worker’s estimated locations. As per the body orientation detection module, it identifies workers' body orientation on the job sites, using the impacts of signal blockage by a human body to identify an approximate worker's body orientation. It works based on geometrical relationships and Received Signal Strength Indicator (RSSI) values between the chest-mounted receiving beacon and the reference transmitting beacons. Last but not least, the productivity state detection module determines workers' productivity state (i.e., direct work, support work, delay) and travel state, using the accelerometer sensor embedded in the body-mounted receiving beacons. Consequently, the collected data of the system modules are fused to augment real-time knowledge of workers' status on job sites

Study of Activity Tracking through Bluetooth Low Energy-Based Network

This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization