Guideline-based decision support for the mobile patient incorporating data streams from a body sensor network

We present a mobile decision support system (mDSS) which helps patients adhere to best clinical practice by providing pervasive and evidence-based health guidance via their smartphones. Similar to some existing clinical DSSs, the mDSS is designed to execute clinical guidelines, but it operates on streaming data from, e.g., body sensor networks instead of persistent data from clinical databases. Therefore, we adapt the typical guideline-based architecture by basing the mDSS design on existing data stream management systems (DSMSs); during operation, the mDSS instantiates from the guideline knowledge a network of concurrent streaming processes, avoiding the resource implications of traditional database approaches for processing patient data which may arrive at high frequencies via multiple channels. However, unlike typical DSMSs, we distinguish four types of streaming processes to reflect the full disease management process: Monitoring, Analysis, Decision and Effectuation. A prototype of the mDSS has been developed and demonstrated on an Android smartphone

Requirements for a Nutrition Education Demonstrator

[Context and Motivation] Development of innovative ICT-based applications is a complex process involving collaboration of all relevant disciplines. This complexity arises due to differences in terminology, knowledge and often also the ways of working between developers in the disciplines involved. [Question/problem] Advances in each discipline bring a rich design environment of theories, models, methods and techniques. Making a selection from these makes the development of distributed applications very challenging, often requiring a holistic approach to address the needs of the disciplines involved. This paper describes early stage requirements acquisition of a mobile nutrition education demonstrator which supports overweight persons in adopting healthier dietary behaviour. [Principal idea/results] We present a novel way to combine and use known requirements acquisition methods involving a two stage user needs analysis based on scenarios which apply a theory-based model of behavioural change and are onstructed in two phases. The first phase scenarios specify an indicative description reflecting the use of the transtheoretical model of behavioural change. In the second phase, a handshake protocol adds elements of optative system-oriented descriptions to the scenarios such that the intended system can support the indicative description. [Contribution] The holistic and phased approach separates design concerns to which each of the disciplines contributes with their own expertise and domain principles. It preserves the applied domain principles in the design and it bridges gaps in terminology, knowledge and ways of working

MobiHealth: Ambulant Patient Monitoring Over Next Generation Public Wireless Networks

The wide availability of high bandwidth public wireless networks as well as the miniaturisation of medical sensors and network access hardware allows the development of advanced ambulant patient monitoring systems. The MobiHealth project developed a complete system and service that allows the continuous monitoring of vital signals and their transmission to the health care institutes in real time using GPRS and UMTS networks. The MobiHealth system is based on the concept of a Body Area Network (BAN) allowing high personalization of the monitored signals and thus adaptation to different classes of patients. The system and service has been trialed in four European countries and for different patient cases. First results confirm the usefulness of the system and the advantages it offers to patients and medical personnel

Mobile monitoring application to support sustainable behavioural change towards healthy lifestyle

We describe the development of body area networks (BANs) incorporating sensors and other devices to provide intelligent mobile services in healthcare and well-being. The first BAN applications were designed to simply transmit biosignals and display them remotely. Further developments include analysis and interpretation of biosignals in the light of context data. By including feedback loops, BAN telemonitoring was also augmented with teletreatment services. Recent developments include incorporation of clinical decision support by applying techniques from artificial intelligence. These developments represent a movement towards smart healthcare, making health BAN applications more intelligent by incorporating feedback, context awareness, personalization, and decision support.\ud The element of decision support was first introduced into the BAN health and well-being applications in the Food Valley Eating Advisor (FOVEA) project. Obesity and overweight represent a growing threat to health and well-being in modern society. Physical inactivity has been shown to contribute significantly to morbidity and mortality rates, and this is now a global trend bringing huge costs in terms of human suffering and reduction in life expectancy as well as uncontrolled growth in demand on healthcare services. Part of the solution is to foster healthier lifestyle. A major challenge however is that exercise and dietary programs may work for the individual in the short term, but adherence in the medium and long term is difficult to sustain, making weight management a continuing struggle for individuals and a growing problem for society, governments, and health services. Using ICT to support sustainable behavioral change in relation to healthy exercise and diet is the goal of the FOVEA monitoring and feedback application. We strive to design and develop intelligent BAN-based applications that support motivation and adherence in the long term. We present this healthy lifestyle application and report results of an evaluation conducted by surveying professionals in related disciplines

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 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

Mobile Patient Monitoring: The Mobihealth System

The forthcoming wide availability of high bandwidth public wireless networks will give rise to new mobile healthcare services. To this end, the MobiHealth project has developed and trialed a highly customisable vital signs monitoring system based on a body area network (BAN) and a mobile-health (m-health) service platform utilising next generation public wireless networks. The developed system allows the incorporation of diverse medical sensors via wireless connections, and the live transmission of the measured vital signs over public wireless networks to healthcare providers. Nine trials with different healthcare scenarios and patient groups in four different European countries have been conducted. These have been performed to test the service and the network infrastructure including its suitability for mobile healthcare applications. Preliminary results have documented the feasibility of using the system, but also demonstrated logistical problems with use of the BANs and the infrastructure for transmitting mobile healthcare data

Interpreting streaming biosignals:in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support

We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge sources. We illustrate with two prototype applications: the first is designed to detect epileptic seizures and support appropriate intervention. The second is a decision support application aiding weight management; the goal is to promote health and prevent chronic illnesses associated with overweight/obesity. We begin to explore extending these and other m-health applications with generic AI-based decision support and machine learning. Monitoring success of different behavioural change strategies could provide a basis for machine learning, enabling adaptive clinical decision support by personalising and adapting strategies to individuals and their changing needs. Data mining applied to BAN data aggregated from large numbers of patients opens up possibilities for discovery of new clinical knowledge