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.

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guide- line to be partitioned and distributed at run-time across a knowledge-based telemedicine system, which is distributed by definition but whose exact physical configuration can only be determined after design-time by considering, amongst other factors, the indi- vidual patient's needs. The framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype that executes the guideline on a smartphone. The framework is shown to support the full development trajectory of a decision support system, including analysis, design and implementation.

Ubiquity of Information and Communication Technology 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.

Mobile devices with ever-increasing functionality and the ubiquitous availability of wireless communication networks are driving forces behind innovative mobile applications enriching our daily life. One of the performance measures for a successful application deployment is the ability to support application-data flows by heterogeneous networks within certain delay boundaries. However, the quantitative impact of this measure is unknown and practically infeasible to determine at real time due to the mobile device resource constraints. We research practical methods for measurement-based performance evaluation of heterogeneous data communication networks that support mobile application-data flows. We apply the lightweight Comparative Output-Input Analysis (COIA) method estimating an additional delay based on an observation interval of interest (e.g., 1 second) induced on the flow. An additional delay is the amount of delay that exceeds nonavoidable, minimal end-to-end delay caused by the networks propagation, serialization, and transmission. We propose five COIA methods to estimate additional delay, and we validate their accuracy with measurements obtained from the existing healthcare and multimedia streaming applications. Despite their simplicity, our methods prove to be accurate in relation to an observation interval of interest, and robust under a variety of network conditions. The methods offer novel insights into application-data delays with regards to the performance of heterogeneous data communication networks.

Mobile devices with ever increasing functionality are an important driving force behind innovative mobile applications that enrich our daily life. The ubiquitous availability of wireless data communication networks is an additional driving force. Their ability to support application data flows is one of the performance criteria for successful mobile application deployment. Nevertheless, the quantitative impact of this performance is unknown and practically infeasible to determine at real-time at the application-level due to mobile device resource constraints. We research practical methods for measurement-based application-level performance evaluation of data communication networks that support mobile application data flows. In this paper, we apply the lightweight Comparative Output-Input Analysis (COIA) method that estimates additional delay at observation time scale of interest (e.g. 1 s) induced on the application data flow. The additional delay is the amount of delay exceeding non-avoidable, minimal end-to-end data delay caused by data communication network propagation and transmission delays. We propose five COIA methods to estimate the additional delay. We validate their accuracy with measurements obtained from an m-health application, namely health telemonitoring provided by the MobiHealth system. Despite their simplicity, our methods prove to be accurate in relation to the observation time scale of interest, and robust under a variety of network conditions. The methods offer novel insights into the impact of data communication network performance on the application data flow delay behaviour.

This paper presents the early phase requirements elicitation for a work-related neck-shoulder pain teletreatment trial and the assessment of those requirements in respect of their importance to the trial and the feasibility of the needed software adaptations of the telemedicine system within the constraints of the project organizing the trial. The elicitation adopts specific solutions and constraints that are typical for working practices in medicine. In particular, the contribution of this paper is its holistic approach in addressing the intertwining of the requirements elicitation and the trial design. Trial design defines the trial settings and the treatment protocols in a standardized way such that treatment efficacy can be evaluated based on evidence. A cyclic approach which applies a set of elicitation techniques and informal specification styles (e.g. tables and mock-ups) is adopted in order to converge on a set of requirements understood and agreed upon also by the trial designers, who are medical professionals, often unaware of formal methods, and who co-shape the telemedicine system.

Emerging healthcare applications rely on personal mobile devices to monitor and transmit patient vital signs to hospital-backend servers for further analysis. However, these devices have limited resources that must be used optimally in order to meet the application user requirements (e.g. safety, usability, reliability, performance). This paper reports on a case study of a Chronic Obstructive Pulmonary Disease telemonitoring application delivered by the mobihealth system. This system relies on a commercial mobile device with multiple (wireless) Network Interfaces (NI). Our study focuses on how NI activation strategies affect the application end-to-end data delay (important in case of an emergency situation) and the energy consumption of the device (important for device sustainability while a patient is mobile). Our results show the trade-off between end-to-end delay and battery life-time achieved by various NI activation strategies, in combination with application-data flow adaptation for realtime and near real-time data transmission. For a given mobile device, our study shows an increase in battery life-time of 40- 90 %, traded against higher end-to-end data delay. The insights of our studies can be used for application-data flow adaptation aiming to increase battery life-time and device sustainability for mobile patients; which effectively increases the healthcare application usability.

We are investigating the use of body area networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patientspsila biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device.