The application of model-based systems engineering to the practice of clinical medicine

Inas S. Khayal, Amro M. Farid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Humanity is currently facing an unprecedented chronic disease burden. Healthcare needs have significantly shifted from treating acute to treating chronic conditions. Chronic diseases tend to involve multiple factors with complex interactions between them evidenced by the continually growing medical knowledge base. The health profession requires the ability to manage this rapidly deepening knowledge base to assimilate the lessons from research and clinical care experience by systematically capturing, assessing and translating it into the highest level of reliable care. A more systems approach to practicing medicine exists and is referred to as functional medicine. It takes into account the many subsystems in the human body and their many interactions. Although the science behind treating the patient as a system exists, the application of systems tools and techniques have not been utilized. It is only natural to begin to formalize the systems thinking using the established tools from the systems engineering field. Specifically, this paper is the first to contribute to the need for systems tools in the practice of clinical medicine and includes an example application of model-based systems engineering to clinical medicine.

Original languageEnglish
Title of host publication11th Annual IEEE International Systems Conference, SysCon 2017 - Proceedings
ISBN (Electronic)9781509046225
DOIs
StatePublished - 26 May 2017
Event11th Annual IEEE International Systems Conference, SysCon 2017 - Montreal, Canada
Duration: 24 Apr 201727 Apr 2017

Publication series

Name11th Annual IEEE International Systems Conference, SysCon 2017 - Proceedings

Conference

Conference11th Annual IEEE International Systems Conference, SysCon 2017
Country/TerritoryCanada
CityMontreal
Period24/04/1727/04/17

Keywords

  • engineering systems
  • improving health outcomes
  • managing knowledge complexity
  • model-based systems engineering

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