Communication and Synchronization of Distributed Medical Models: Design, Development, and Performance Analysis

TL;DR

This paper introduces ModelSink, a middleware solution for real-time communication and synchronization of distributed medical models, demonstrated through emergency ambulance transport scenarios, enhancing coordination in distributed healthcare systems.

Contribution

The paper presents ModelSink, a novel middleware that enables efficient, safe, and semantics-compliant communication and synchronization of heterogeneous distributed models in healthcare.

Findings

ModelSink improves communication efficiency in distributed medical models.

It maintains synchronization safety during emergency scenarios.

Performance evaluations show robustness under various loads.

Abstract

Model-based development is a widely-used method to describe complex systems that enables the rapid prototyping. Advances in the science of distributed systems has led to the development of large scale statechart models which are distributed among multiple locations. 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. 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. This makes it necessary to offer methods for model-driven communication and synchronization in a distributed environment. In this paper, we describe ModelSink, a middleware to address the problem of communication and synchronization of heterogeneous distributed models. 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. 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. We evaluated the performance of ModelSink on distributed sets of medical models that we have developed to assess how ModelSink performs in various loads. 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. Our practice suggests that there are in fact additional potential domains beyond medicine where our middleware can provide needed utility.