MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers.

Aims Heart failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress. This disease often causes decompensations, which may lead to hospital admissions, deteriorating patients' quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF. Methods and resultsWe used two different sets of data (January 2012 to August 2017): one related to the number of hospital admissions and the other one related to the environmental factors (weather and air quality). Admissions related data were grouped in weeks, and then two different studies were performed: (i) a univariate regression to determine whether the admissions may influence future hospitalizations prediction and (ii) a multivariate regression to determine the impact of environmental factors on admission rates. A total number of 8338 hospitalizations of 5343 different patients are available in this dataset, with a mean of 4.02 admissions per day. In European warm period (from June to October), there are significant less admissions than that in the cold period (from December to March), with a clear seasonality of admissions, because there is a similar pattern every year. Air temperature is the most significant environmental factor (r = À0.3794, P < 0.001) related to HF hospital admissions, showing an inversed correlation. Some other attributes, such as precipitation (r = 0.0795, P = 0.05), along with SO 2 (precursor of acid rain) (r = 0.2692, P < 0.001) and NOX air (major air pollutant formed by combustion systems and motor vehicles) (r = 0.2196, P < 0.001) quality parameters, are also relevant. Humidity and PM10 parameters do not have significant correlations in this study (r = 0.0469 and r = À0.0485 respectively), neither relevant P-values (P = 0.238 and P = 0.324, respectively). Conclusions Several environmental factors, such as weather temperature and precipitation, and major air pollutants, such as SO 2 and NOX air, have an impact on the HF-related hospital admissions rate and, hence, on HF decompensations and patient's quality of life.

Rapid advances in ICT and collection of large amount of mobile health data are giving room to new ways of treating patients. Studies suggest that telemonitoring systems and predictive models for clinical support and patient empowerment may improve several pathologies, such as heart failure, which admissions rate is high. In the current medical practice, clinicians make use of simple rules that generate large number of false alerts. In order to reduce the false alerts, in this study, the predictive models to prevent decompensations that may lead into admissions are presented. They are based on mobile clinical data of 242 heart failure (HF) patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza). The best predictive model obtained is a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier using Bernoulli distribution. This predictive model performs with an AUC = 67% and reduces the false alerts per patient per year from 28.64 to 7.8. This way, the system predicts the risk of admission of ambulatory patients with higher reliability than current alerts.

Ubiquity of Information and CommunicationTechnology enables innovative telemedicine treatment applications for disease management of ambulant patients. Development of new treatment applications must comply with medical protocols and 'way of working' to obtain safety and efficacy evidence before acceptance and use by medical practitioners. Usually, medical researchers design new treatment applications and engineers elicit application requirements in collaboration with these researchers to bridge the knowledge and 'way of working' gaps between them.This paper presents an elicitation method for new telemedicine applications in a collaborative setting of time-constraint medical practitioners and requirements engineers if the medical researcher is absent. Engineers compensate this lack of resources through cross-disciplinary studies and use of pathophysiological models in the absence of medical evidence. The paper discusses the application of a mixed elicitation method presented in earlier work in the addressed setting. The method applies a scenario based user needs analysis augmented by domain activity and user-system interaction analysis. The elicitation is conducted in a separation of concerns fashion combined with collaboration handshake protocols to align domain activities and user-system interactions. Later phase elicitation of user-system interaction requirements may apply known methods and is not addressed.

Clinical decision-support functions of telemedicine systems use patient's monitored clinical data to support treatment of outpatients. However, the quality of monitored clinical data may vary due to performance variations of technological resources inside a deployed telemedicine system. This paper discusses models to compute quality of clinical data affected by quality of service provided by technological resources along the data processing and delivery chain between the point of monitoring and point of decision. We discuss prospective effects of quality of clinical data degradation on outpatient treatment with medical practitioners, and implement these effects in the clinical decision-making process during design time. Consequently, the designed telemedicine system is technological context and quality-aware and preserves patient's safety and treatment efficacy.

Telemedicine depends on Information and Communication Technology (ICT) to support remote treatment of patients. This dependency requires the telemedicine system design to be resilient for ICT performance degradation or subsystem failures. Nevertheless, using telemedicine systems create a dependency between medical and technological concerns. We propose a layering technique that links medical and technological concerns by using a two-staged scenario based requirements elicitation method. This layering technique provides functional relations between technological variables (e.g. raw ECG signal) and their technological context (e.g. measurements conditions), clinical variables (e.g. heart rate), and clinical abstractions (e.g. physical exercise target heart rate) and the non-functional quality of data relations between the layers. We use a hierarchical ontology to specify these functional and non-functional relations, which enables the development of technological context and quality-aware telemedicine systems that are able to cope with technological disruptions whilst preserving patient safety.

Clinical data are crucial for any medical case to study and understand a patient’s condition and to give the patient the best possible treatment. Pervasive healthcare systems apply information and communication technology to enable the usage of ubiquitous clinical data by authorized medical persons. However, quality of clinical data in these applications is, to a large extent, determined by the technological context of the patient. A technological context is characterized by potential technological disruptions that affect optimal functioning of technological resources. The clinical data based on input from these technological resources can therefore have quality degradations. If these degradations are not noticed, the use of this clinical data can lead to wrong treatment decisions, which potentially puts the patient’s safety at risk. This paper presents an ontology that specifies the relation among technological context, quality of clinical data, and patient treatment. The presented ontology provides a formal way to represent the knowledge to specify the effect of technological context variations in the clinical data quality and the impact of the clinical data quality on a patient’s treatment. Accordingly, this ontology is the foundation for a quality of data framework that enables the development of telemedicine systems that are capable of adapting the treatment when the quality of the clinical data degrades, and thus guaranteeing patients’ safety even when technological context varies.

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