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In healthcare, the right information at the right time is a necessity in order to provide the best possible care for a patient. Patient information must also be protected from unauthorized access in order to protect patient privacy. It is also common for patients to visit more than one healthcare provider, which implies the need for crossborder healthcare and a focus on the patient process. Countries work differently with these issues. This paper is focused on three Scandinavian countries, Norway, Sweden and Finland, and their information security problems and needs in healthcare. Data was collected via case studies, and the results were compared to show both similarities and differences between these countries. Similarities include the too wide availability of patient information, an obvious need for risk analysis, and a tendency to focus more on patient safety than on patient privacy. Patients being involved in their own care, and the approach of exchanging patient information are examples of differences. [ABSTRACT FROM AUTHOR]

Identification of continuous-time (CT) systems is a fundamental problem that has applications in virtually all disciplines of science. Examples of mathematical models of CT phenomena appear in such diverse areas as biology, economics, physics, and signal processing. A small selection of references are cited below. Models in the economics of renewable resources, e.g., in biology, is discussed in [9]. Sunspot data modelling by means of CT ARMA models is carried out in [39]. Aspects of economic growth models is the topic of [59]. Models for stock-price fluctuations are discussed in [48] and stochastic volatility models of the short-term interest rate can be found in [2]. The use of Ito's calculus in modern financial theory with applications in financial decision making is presented in [36]. Continuous-time models for the heat dynamics of a building is described in [35]. Modelling of random fatigue crack growth in materials can be found in [50], and models of human head movements appear in [20]. Identification of ship-steering dynamics by means of linear CT models and the maximum likelihood (ML) method is considered in [6]. Numerous other examples of applications of stochastic differential equations (SDEs) can be found in the literature. See, for example, [25, Chapter 7] where various modelling examples, including population dynamics, investment finance, radio-astronomy, biological waste treatment, etc. can be found. [ABSTRACT FROM AUTHOR]