<p> In the U.S. medical sector, software failures in safety-critical systems in healthcare have led to serious adverse health problems, including patient deaths and recalls of medical systems. Despite the efforts in developing techniques to build resilient systems, there is a lack of consensus regarding the definition of resilience metrics and a limited number of quantitative analysis approaches. In addition, there is insufficient guidance on evaluating resilience design patterns and the value they can bring to safety-critical systems. </p><p> This research employed the interdependency analysis framework to evaluate the static resilience of safety-critical systems used in the healthcare field and identified software subsystems that are vulnerable to failures. Resilience design patterns were first implemented to these subsystems to improve their ability to withstand failures. This implementation was followed by an evaluation to determine the overall impacts on system’s static resilience. </p><p> The methodology used a common medical system structure that collects common attributes from various medical devices and reflects major functionalities offered by multiple medical systems. Fault tree analysis and Bayesian analysis were used to evaluate the static resilience aspects of medical safety-critical systems, and two design patterns were evaluated within the praxis context: <i> Monitoring</i> and <i>N-modular redundancy</i> resilience patterns. </p><p> The results ultimately showed that resilience design patterns improve the static resilience of safety-critical systems significantly. While this research suggests the importance of resilience design patterns, this study was limited to explore the impact of structural resilience patterns on static resilience. Thus, to evaluate the overall resilience of the system, more research is needed to evaluate dynamic resilience in addition to studying the impact of different types of resilience design patterns. </p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10981524 |
| Date | 01 December 2018 |
| Creators | Farag, Mohamed S. |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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