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Related Experiment Videos

Modeling risk in distributed healthcare information systems.

Ilias Maglogiannis1, Elias Zafiropoulos

  • 1University of Aegean, Department of Information & Communication Systems Engineering, Samos, Greece. imaglo@aegean.gr

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study introduces a new risk analysis model for networked healthcare information systems. It identifies critical events and vulnerabilities to enhance data security in healthcare networks.

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Area of Science:

  • Health Informatics
  • Information Security
  • Risk Management

Background:

  • Networked healthcare information systems are critical for modern healthcare delivery.
  • These systems face significant risks from various threats and vulnerabilities.
  • Effective risk analysis is essential for ensuring patient data security and system integrity.

Purpose of the Study:

  • To present a novel modeling approach for risk analysis of networked healthcare information systems.
  • To integrate the Context-Risk Analysis and Management Method (CRAMM) with Bayesian networks for comprehensive risk assessment.
  • To identify and prioritize critical events within healthcare information networks.

Main Methods:

  • Utilizing the Context-Risk Analysis and Management Method (CRAMM) to identify assets, threats, and vulnerabilities.
  • Employing Bayesian networks to model the interrelationships between identified risk factors.
  • Conducting "what-if" scenario analyses to determine critical operational events.

Main Results:

  • The developed framework effectively models complex interrelationships in healthcare IT systems.
  • Critical events and their impact on system operation were identified and prioritized.
  • The approach provides a structured method for assessing and managing risks in healthcare networks.

Conclusions:

  • The proposed risk analysis framework is a valuable tool for securing networked healthcare information systems.
  • The integration of CRAMM and Bayesian networks offers a robust methodology for risk assessment.
  • The study demonstrates the practical applicability of the framework in a real-world healthcare setting.