Nekane Larburu

Making medical treatments resilient to technological disruptions in telemedicine systems

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 nonfunctional 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.

Quality of data computational models and telemedicine treatment effects

Clinical decision-support functions of telemedicine systems use patients 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 patients safety and treatment efficacy.

Application of a conceptual framework for the modelling and execution of clinical guidelines as networks of concurrent processes

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guideline 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 individual patients 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.

A Quality-of-Data Aware Mobile Decision Support System for Patients with Chronic Illnesses

We present a mobile decision support system mDSS which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data QoD awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines.

Quality-of-Data Management for Telemedicine Systems

This paper describes techniques to manage the quality-of-data (QoD), particularly in telemedicine systems. Hence, clinical data users, such as clinical decision support systems that support ‘real-time’ guidance of ambulatory patients, can process the data together with QoD in order to make the ‘best’ treatment decisions. Current information and communication technology (ICT) applied in telemedicine systems enables remote clinical data collection and delivery to the point of decision, thereby giving opportunities to develop new, pervasive, healthcare applications. However, the QoD in such distributed and decentralized environments is not always guaranteed. We propose QoD management techniques that take into account the multidimensionality of QoD and its ‘fitness for use’. Additionally, we present a technique that handles the quality of ‘real-time’ streaming data. We developed a telemedicine system prototype to investigate the applicability and the usefulness of the proposed techniques.

The Conceptual MADE Framework for Pervasive and Knowledge-Based Decision Support in Telemedicine

Telemedicine systems are inherently distributed, but, especially in the context of the Internet-of-Things, their complete physical configuration may only be determined after design time by considering, for example, the individual patients needs. Therefore, to enable pervasive and knowledge-based decision support to be provided in telemedicine, a conceptual framework was developed for modelling and executing clinical knowledge as networks of four types of concurrent processes: Monitoring M, Analysis A, Decision D and Effectuation E. In this way, the required decision support functionality can, as presented in this article, be distributed at run-time by mapping different portions of the knowledge across the devices constituting the system. This MADE framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype knowledge-based system that executes the resulting MADE network. Thus it is shown to support the full development trajectory of a telemedicine system, including analysis, design and implementation.