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Use of Emergency Departments by Frail Elderly Patients : Temporal Patterns and Case Complexity
(2019)
Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients’ case complexity. This study examines differences in arrival rates for frail vs. non-frail patients in detail and compares case complexity in frail patients within vs. outside of regular GP working hours. Arrival times and case variables (admission rate, ED length of stay [LOS], triage level and comorbidities) were extracted from the EHR of an ED in an urban German teaching hospital. We employed Poisson time series regression to determine patterns in hourly arrival rates over the week. Frail elderly patients presented more likely to the ED during already high frequented hours, especially at midday and in the afternoon. Case complexity for frail patients was significantly higher compared to non-frail patients, but varied marginally in time only with respect to triage level and ED LOS. The results suggest that frailty-attuned emergency care should be available in EDs during the busiest hours. Based on EHR data, hospitals thus can tailor their staff needs.
The Internet of Things (IoT) relies on sensor devices to measure real-world phenomena in order to provide IoT services. The sensor readings are shared with multiple entities, such as IoT services, other IoT devices or other third parties. The collected data may be sensitive and include personal information. To protect the privacy of the users, the data needs to be protected through an encryption algorithm. For sharing cryptographic cipher-texts with a group of users Attribute-Based Encryption (ABE) is well suited, as it does not require to create group keys. However, the creation of ABE cipher-texts is slow when executed on resource constraint devices, such as IoT sensors. In this paper, we present a modification of an ABE scheme, which not only allows to encrypt data efficiently using ABE, but also reduces the size of the cipher-text, that must be transmitted by the sensor. We also show how our modification can be used to realise an instantaneous key revocation mechanism.
Objectives
To develop a time-efficient motor control (MC) test battery while maximising diagnostic accuracy of both a two-level and three-level classification system for patients with non-specific low back pain (LBP).
Design
Case–control study.
Setting
Four private physiotherapy practices in northern Germany.
Participants
Consecutive males and females presenting to a physiotherapy clinic with non-specific LBP (n=65) were compared with 66 healthy-matched controls.
Primary outcome measures
Accuracy (sensitivity, specificity, Youden index, positive/negative likelihood ratio, area under the curve (AUC)) of a clinically driven consensus-based test battery including the ideal number of test items as well as threshold values and most accurate items.
Results
For both the two and three-level categorisation system, the ideal number of test items was 10. With increasing number of failed tests, the probability of having LBP increases. The overall discrimination potential for the two-level categorisation system of the test is good (AUC=0.85) with an optimal cut-off of three failed tests. The overall discrimination potential of the three-level categorisation system is fair (volume under the surface=0.52). The optimal cut-off for the 10-item test battery for categorisation into none, mild/moderate and severe MC impairment is three and six failed tests, respectively.
Conclusion
A 10-item test battery is recommended for both the two-level (impairment or not) and three-level (none, mild, moderate/severe) categorisation of patients with non-specific LBP.
Reduzierung im Außer-Haus-Verzehr zu erleichtern, analysiert unsere Studie die Auswirkungen von zwei gängigen Interventionsstrategien zur Reduzierung von Speiseresten in einem ganzheitlichen Verhaltensmodell. Auf der Grundlage eines quasi-experimentellen Baseline-Interventions-Designs haben wir in einem Strukturgleichungsmodell untersucht, wie sich das Aushängen von Informationspostern und die Reduzierung von Portionsgrößen auf persönliche, soziale und umweltbezogene Faktoren auswirken. Anhand von Daten aus Online-Befragungen und Beobachtungen von 880 Gästen (503 Baseline, 377 Intervention) während zwei Wochen in einer Universitätsmensa erlaubt das vorgeschlagene Modell, die Effekte der beiden Interventionen auf die Tellerreste spezifischen Veränderungen von Verhaltensdeterminanten zuzuordnen. Es hat sich gezeigt, dass die Verringerung der Portionsgröße bei den Zielgerichten mit einer geringeren Menge an Tellerabfällen zusammenhängt, die auf bewusster Wahrnehmung, die sich in einer geringeren Bewertung der Portionsgröße ausdrückt. Die Auswirkungen des Sehens von Informationsplakaten auf veränderten persönlichen Einstellungen, subjektiven Normen und wahrgenommener Verhaltenskontrolle beruhen. Je nachdem, wie eine Person auf die Information reagiert (indem sie sich nur bemüht, alle Speisen aufzuessen, oder indem sie sich bemüht und zusätzlich ein anderes Gericht in der Kantine wählt), ergeben sich jedoch entgegengesetzte Auswirkungen auf diese Determinanten und folglich auch auf die Tellerreste. Insgesamt sprechen die differenzierten Ergebnisse zu den Interventionseffekten dafür, dass ganzheitlichere und tiefer gehende Analysen von Interventionen zur Reduktion von Essensresten und damit zu einem nachhaltigeren Lebensmittelkonsum in der Außer-Haus-Verpflegung beitragen können.
This paper describes the development and test of a novel LiDAR based combine harvester steering system using a harvest scenario and sensor point cloud simulation together with an established simulation toolchain for embedded software development. For a realistic sensor behavior simulation, considering the harvesting environment and the sensor mounting position, a phenomenological approach was chosen to build a multilayer LiDAR model at system level in Gazebo and ROS. A software-in-the-loop simulation of the mechatronic steering system was assembled by interfacing the commercial AppBase framework for point cloud processing and feature detection algorithms together with a machine model and control functions implemented in MATLAB/ Simulink. A test of ECUs in a hardware-in-the-loop simulation and as well as HMI elements in a driver-in-the-loop simulation was achieved by using CAN hardware interfaces and a CANoe based restbus simulation.