A Pathophysiological Model-Driven Communication for Dynamic Distributed   Medical Best Practice Guidance Systems
  There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport.   In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best-practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice automata using laboratory simulations. Our proof-of-concept experiments shows there is potential for the use of our system in a wide variety of domains. 
Mohammad Hosseini, Yu Jiang, Poliang Wu, Richard B. Berlin Jr.,   Shangping Ren, Lui Sha
cs.CY