Health data privacy is essential for the acceptance of digital health applications. Hence, privacy is a precondition for future healthcare delivery. This study compares the perception of the current state of health data privacy in officially registered and therefore regulated health applications (medical devices) according to the medical product act as well as non-regulated health applications (devices with medical functionality) in Germany. To this end, an empirical study based on a questionnaire is conducted (n=53). The results show that there are significant differences between the analysed health applications with respect to perceived data privacy. In particular, there is a significant difference of the levels of perceived security between both types of devices. Low privacy for one type of device may hamper trust in digital health applications in general as there are spill-over effects regarding the perception of data privacy. Thus, the study suggests that legal regulations for devices with medical functionality should be adapted to protect health data adequately.
Underwater activities are an essential part of different industrial fields, and if in some cases autonomous and remotely controlled solutions could be used, often intervention of human divers is pivotal. In order to operate at depths of tens to hundreds of meters, the main technique that allows safe and efficient operation is called saturation diving, which foresees gradual adaptation of divers to harsh underwater conditions by means of hyperbaric chambers. This type of facility requires highly qualified personnel for management; however, nowadays, most training is done on empty industrial plants, which is costly and limits the possibility to take into account vital parameters of personnel inside them as well as making it practically impossible to reproduce emergency situations. This paper proposes an innovative approach in M&S for the hyperbaric plants devoted to support training and certification of life support supervisors (LSS) as well as other operators involved in diving activities.
Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.
Depth of anesthesia (DoA) is determined by assessment of relevant clinical signs, interpretation of hemodynamic measurements, and EEG measurements. The induction and proper dosing of anesthetic agents is an essential task of the anesthesiologist during a diagnostic procedure or surgery under general anesthesia. Therefore, DoA control seems to be a suitable problem to tackle with a closed loop control approach. One must be able to acquire the relevant signals online and in real time, but patient monitors are intentionally not able to connect to an external device during a procedure for safety reasons. The article introduces a universal image-based system for signal acquisition from a patient monitor that operates in the Matlab-Simulink environment for convenient integration into DoA modelling, simulation, and control. In addition, it provides the anesthesiologist with a simple dashboard that displays key acquired signal values and trends. The system has been tested on a Masimo Root with SedLine patient monitor. The results show that the PSi signal can be reliably acquired.
This article aims at developing a new ontology for healthcare systems (HS) simulation. The ontology includes various classes that represent major components of HS simulation and their relationships as an integrated whole. It is formally expressed using system entity structure language with links to basic models developed in various formalisms and stored in a model base repository. Entities are mapped into web ontology language (OWL) classes and can be visualized in Protégée and queried with SPARQL. Classes are built based on agreed-upon concepts in HS simulation domain and serve to document and formalize knowledge while providing notable benefits such as common representation of healthcare models from different simulation platforms, model reuse, querying simulation models, and browsing. The paper also presents an illustrative case study to showcase the use of the ontology while capturing successfully within its scope an outbreak of cholera disease and its mitigation plan.
