Using the concept of distributed medical CPS models, we study the semantics relation of communication Quality of Service (QoS) with clinical messages, criticality of clinical data, and an ambulance's undertaken route all in a disease-aware manner. In this study, we define the notion of distributed emergency care, and propose a novel adaptive physiology-aware communication framework which is aware of the patient condition, the underlying network bandwidth, and the criticality of clinical data in the context of the specific diseases. This unreliable and limited communication bandwidth together with the produced mass of clinical data and the many information exchanges pose a major challenge in real-time supervision of patients. However, the communication along the roads in rural areas can range irregularly from 4G to low speed 2G links, including some parts of routes with cellular network communication breakage. There are critical times during transport when physician experts can provide vital assistance to the ambulance Emergency Medical Technicians (EMT) to associate best treatments. It enables remote monitoring of patients by the physician experts at the tertiary center.
Use of telecommunication technologies can enhance effectiveness and safety of emergency ambulance transport of patients from rural areas to a regional center hospital. While the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, it is only 5 to 10,000 in most rural areas and the highest death rates are often found in the most rural counties. Our practice suggests that there are in fact additional potential domains beyond medicine where our middleware can provide needed utility.įor emergency medical cyber-physical systems, enhancing the safety and effectiveness of patient care, especially in remote rural areas, is essential. Our work is intended to assist clinicians, EMT, and medical staff to prevent unintended deviations from medical best practices, and overcome connectivity and coordination challenges that exist in a distributed hospital network. We evaluated the performance of ModelSink on distributed sets of medical models that we have developed to assess how ModelSink performs in various loads. Through ModelSink, we achieve an efficient communication architecture, open-loop-safe protocol, and queuing and mapping mechanisms compliant with the semantics of statechart-based model-driven development. Being motivated by the synchronization requirements during emergency ambulance transport, we use medical best-practice models as a case study to illustrate the notion of distributed models. In this paper, we describe ModelSink, a middleware to address the problem of communication and synchronization of heterogeneous distributed models.
This makes it necessary to offer methods for model-driven communication and synchronization in a distributed environment. Unfortunately, these medical models require continuous and real-time communication across individual medical models in physically distributed treatment locations which provides vital assistance to the clinicians and physicians.
Taking medicine for example, models of best-practice guidelines during rural ambulance transport are distributed across hospital settings from a rural hospital, to an ambulance, to a central tertiary hospital. Advances in the science of distributed systems has led to the development of large scale statechart models which are distributed among multiple locations. Model-based development is a widely-used method to describe complex systems that enables the rapid prototyping.