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Decision Support and Error Analysis of Difficult Decisions in Clinical Medicine

  • Alexander M. YemelyanovEmail author
  • J. Andrew Berry
  • Alina A. Yemelyanov
Conference paper
  • 5 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)

Abstract

This work focuses on supporting difficult decisions in clinical medicine by using the self-regulation model of decision-making. Core shaping factors of instrumental and value rationality and their role in the decision process are discussed. The concept of decision-making difficulty is discussed, and a classification of problems by their difficulty level is suggested. The mobile application Express Decision is used as an instrumental tool when healthcare providers/health professionals need to make quick decisions for difficult problems under uncertainty. The application of Express Decision for shared decision-making in advanced heart failure is demonstrated.

Keywords

Difficult decisions under uncertainty Levels of difficulty Self-regulation of decision-making Decision support Error analysis Advanced heart failure 

References

  1. 1.
    Miles, A., Mezzich, J.: The care of the patient and the soul of the clinic: Person-centered medicine as an emergent model of modern clinical practice. Int. J. Person Centered Med. 1(2), 207–222 (2011)Google Scholar
  2. 2.
    Djulbegovic, B., Elqayam, S.: Many faces of rationality: implications of the great rationality debate for clinical decision-making. J. Eval. Clin. Pract. 23(5), 915–922 (2017)CrossRefGoogle Scholar
  3. 3.
    Marewski, J.N., Gigerenzer, G.: Heuristic decision making in medicine. Dialogues Clin. Neurosci. 14(1), 77 (2012)Google Scholar
  4. 4.
    Yemelyanov, A.M.: Self-regulation model of decision-making. In: Ayaz, H. (ed.) Advances in Neuroergonomics and Cognitive Engineering. Advance in Intelligent Systems and Computing, vol. 953. Springer International Publishing, pp. 245–255 (2019)Google Scholar
  5. 5.
    Yemelyanov, A.M., Bedny, I.S.: Instrumental and value rationality of the self-regulation model of decision-making. In: Advance in Neuroergonomics and Cognitive Engineering Advances in Intelligent Systems and Computation. Springer (2020). volume of current issueGoogle Scholar
  6. 6.
    Yemelyanov, A.M.: Modeling and mobile device support of goal-directed decision making under risk and uncertainty, Chapter 4. In: Bedny, G., Bedny, I. (eds.) Study of Human Performance: Applied and Systemic-Structural Activity Theory Approach, pp. 69–102. CRC Press, Taylor & Francis Group, Routledge (2019)CrossRefGoogle Scholar
  7. 7.
    Allen, L.A., et al.: Decision making in advanced heart failure. A scientific statement from the American heart association. Circulation 125(15), 1928–1952 (2012)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Alexander M. Yemelyanov
    • 1
    Email author
  • J. Andrew Berry
    • 1
  • Alina A. Yemelyanov
    • 2
  1. 1.College of Business and ComputingGeorgia Southwestern State UniversityAmericusUSA
  2. 2.School of Public HealthGeorgia State UniversityAtlantaUSA

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