Tony Norris

Mobile technologies and the holistic management of chronic diseases

Ageing populations and unhealthy lifestyles have led to some chronic conditions such as diabetes and heart disease reaching epidemic proportions in many developed nations. This paper explores the potential of mobile technologies to improve this situation. The pervasive nature of these technologies can contribute holistically across the whole spectrum of chronic care ranging from public information access and awareness, through monitoring and treatment of chronic disease, to support for patient carers. A related study to determine the perceptions of healthcare providers to m-health confirmed the view that attitudes were likely to be more important barriers to progress than technology. A key finding concerned the importance of seamless and integrated m-health processes across the spectrum of chronic disease management.

Improving health care data quality: A practitioner's perspective

Health care is increasingly an information-driven activity, and therefore, the quality of the underlying data assumes critical importance. This paper identifies the origins and characteristics of health data, and surveys their acquisition and usage in the delivery and planning of modern health care. The factors that influence data quality are discussed, together with an assessment of the main quality issues and a rigorous account of strategies for improving data quality, including a review of various national strategies. The study exposes the theoretical foundations and practicalities of health data quality enhancement and demonstrates the significant benefits that a data quality improvement strategy can bring across the whole spectrum of health care.

Measuring Shared and Team Situation Awareness of Emergency Decision Makers

Large scale emergencies are usually responded to by a team of emergency managers or a number of sub teams for safety and efficiency. Team coordination has attracted considerable research interest, especially from the cognitive, human factors, and ergonomic aspects because shared situation awareness (SSA) and team situation awareness (TSA) of team members are critical for optimal decision making. This paper describes the development of an information system (SAVER) based on SSA and TSA oriented systems design. Validation and evaluation of the implemented design show that decision performance is improved by the SAVER system.

Prototyping and Evaluation of a Computerized Emergency Management System Based on an Ontological Inference Design

In this paper we discuss the design of a computerized emergency management system that uses ontology-based inference to improve situation awareness and decision making. The proposed system architecture is embodied in a prototype called SAVER (Situation Aware Vigilant Emergency Reasoner) which is successfully tested and evaluated. The results validate the concept and show that an ontology-based system can be used to transform incoming data into a meaningful and useful form and predict future states of situations using inference and rules.

Disaster EHealth-Sustainability in the Extreme

Disaster eHealth is a new area of research and endeavor. In order to make a practical contribution disaster eHealth approaches should consider the role of a disaster eHealth appliance. Both disaster management and disaster medicine may find that such approaches allow critical information to be gathered and situational awareness improved. Self-care of chronic disease and caregiving by others as well as the injuries and disease caused by the disaster may be also supported by this approach. Re-using existing devices may offer a relatively low-cost and sustainable approach to providing such devices, and infrastructure to use them.

Futuristic Smart Architecture for a Rapid Disaster Response

The ability to control and contain an unexpected disaster event such as a bushfire or flooding in real-time is fraught with logistic and planning challenges. Information is difficult to assimilate both from structured and unstructured data that may be collected in real-time. Unreliability of mostly unstructured data from social media and mobile devices, though extremely helpful, can make it difficult to deploy needed help/assistance in time. Ad hoc planning specifically targeted at saving lives may be severely hampered when part of the infrastructure is destroyed that is normally relied on for data collection. Also when part of the infrastructure is destroyed that normally is relied on for collecting structured data, the situation can even make it harder for ad hoc planning specifically targeted at saving lives first. In such situations, a combination of unstructured data that carries uncertainties and limited structured data from infrastructure that might still be working after/during a disaster event can be used to the best of advantages and still enhance the control process to better achieve desired outcomes: i.e. real-time event monitoring through real-time limited and high uncertainty data; filtering unstructured data through crowd sourcing, not only for reliability but sometimes for language translation as well; short-term predictions of anticipated changes from already existing interoperable simulation models, using the limited structured data from infrastructure that might be still standing following a disaster; and all this with the aim of appropriate, timely responses to saving lives in a rapidly evolving environment. A generic management framework designed to be used during a “phase transition” between pre- and post events, and characterised by the interoperability of distributed simulation models, and the collection and sharing of structured and unstructured data via cloud services and “connected devices”, is essential for the consistent provision of highly effective responses. We explore this framework from a science and innovations perspective, advocating “antifragility” for emergency response system designs. For antifragility systems, failures do not stand for a breakdown or malfunctioning of normal system functions, but rather represent the adaptations necessary to cope with the real world complexity through the management of “robustness trade-offs” as it occurs in dynamic and real-world contexts.