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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Published on: May 15, 2020

The cloud paradigm applied to e-Health.

Jordi Vilaplana1, Francesc Solsona, Abella

  • 1Computer Science Department, University of Lleida, Jaume II 69, 25001 Lleida, Spain.

BMC Medical Informatics and Decision Making
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a scalable cloud e-health system designed for high Quality of Service (QoS). Increasing the number of servers significantly improves system performance for diverse medical applications.

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Area of Science:

  • Computer Science
  • Health Informatics
  • Queueing Theory

Background:

  • Cloud computing offers scalable, maintainable, and efficient solutions for enterprise software.
  • Organizations benefit from reduced infrastructure needs and on-demand resource allocation.
  • Cloud architectures facilitate massive data processing and energy savings.

Purpose of the Study:

  • To design a cloud-based e-health system architecture.
  • To model the system using queueing theory with Quality of Service (QoS) constraints.
  • To ensure platform scalability for growing patient numbers and diverse diseases.

Main Methods:

  • An M/M/m queue model was employed to represent the e-health cloud system.
  • Quality of Service (QoS) was incorporated, focusing on maximum request waiting times.
  • A Jackson network of two M/M/m queues was utilized for detailed analysis.

Main Results:

  • Queueing theory analysis demonstrated significant performance enhancements.
  • Increased server capacity directly correlated with improved system performance.
  • The model provides a foundation for evaluating system throughput and response times.

Conclusions:

  • Platform scalability is crucial for maintaining high Quality of Service (QoS) in e-health systems.
  • The proposed architecture is adaptable to various diseases and patient loads.
  • This cloud platform can enhance therapies for psychological components in chronic diseases and addiction treatment.