User & situation models for medical information delivery

Abstract

The human exploration of Mars today represents a ographics. challenging and interesting venture for humanity, right at the cutting edge of attainability, both technologically and socially. It is unique in the sense that for the first time in history, humans are leaving the Earth with a very limited return capability. Also, communication with Earth is limited, with a small communication window and significant time lag. Due to these and other intrinsic characteristics of the enterprise, the crew must be able to resolve any possible incident with little or no help from Earth. In a medical emergency, it is impossible for an Earth-based medical team to provide appropriate remote support. Therefore any support requiring real-time use of a knowledge-base, must be provided locally. The present work explores the design of a Medical Assistant System for such conditions. It investigates the issues regarding the kind of interaction, the system's degree of intrusion, the system's context sensitivity, and the support level that the medical assistant must provide. The purpose of this thesis is to identify design tradeoffs based on scenarios of use. The design of the system contemplates the situated action approach, the cooperative problem solving approach, the utilization of user and situation models, the separation of the presentation from the information itself, and the system's role of an assistant. The system architecture is based on a client-server model using Object Oriented Programming. The system is divided into modules providing a very flexible environment for interoperability and interchange of software components, whether working in a distributed or local environment. Scenarios of use, consisting of walkthroughs through the supported tasks, are utilized to present the dynamics of the system, interaction between user and system, and variation of behavior according to the situation and user characteristics. This prototype has helped to explore the design space, allowing the identification of system limitations, tradeoffs, new requirements, technological issues, implications for other areas, and future research implementation.