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With the advent of 4G and other long-term evolution (LTE) wireless networks, the traditional boundaries of patient record propagation are diminishing as networking technologies extend the reach of hospital infrastructure and provide on-demand mobile access to medical multimedia data. However, due to legacy and proprietary software, storage and decommissioning costs, and the price of centralization and redevelopment, it remains complex, expensive, and often unfeasible for hospitals to deploy their infrastructure for online and mobile use. This paper proposes the SparkMed data integration framework for mobile healthcare (m-Health), which significantly benefits from the enhanced network capabilities of LTE wireless technologies, by enabling a wide range of heterogeneous medical software and database systems (such as the picture archiving and communication systems, hospital information system, and reporting systems) to be dynamically integrated into a cloud-like peer-to-peer multimedia data store. Our framework allows medical data applications to share data with mobile hosts over a wireless network (such as WiFi and 3G), by binding to existing software systems and deploying them as m-Health applications. SparkMed integrates techniques from multimedia streaming, rich Internet applications (RIA), and remote procedure call (RPC) frameworks to construct a Self-managing, Pervasive Automated netwoRK for Medical Enterprise Data (SparkMed). Further, it is resilient to failure, and able to use mobile and handheld devices to maintain its network, even in the absence of dedicated server devices. We have developed a prototype of the SparkMed framework for evaluation on a radiological workflow simulation, which uses SparkMed to deploy a radiological image viewer as an m-Health application for telemedical use by radiologists and stakeholders. We have evaluated our prototype using ten devices over WiFi and 3G, verifying that our framework meets its two main objectives: 1) interactive- delivery of medical multimedia data to mobile devices; and 2) attaching to non-networked medical software processes without significantly impacting their performance. Consistent response times of under 500 ms and graphical frame rates of over 5 frames per second were observed under intended usage conditions. Further, overhead measurements displayed linear scalability and low resource requirements.
Information Technology in Biomedicine, IEEE Transactions on (Volume:16 , Issue: 1 )
Date of Publication: Jan. 2012