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Development of Requirements Analysis Tool for Medical Autonomy in Long-Duration Space Exploration Missions

Authors: Lara Dutil-Fafard, Caroline Rhéaume, Patrick Archambault, Daniel Lafond, Neal W. Pollock


Improving resources for medical autonomy of astronauts in prolonged space missions, such as a Mars mission, requires not only technology development, but also decision-making support systems. The Advanced Crew Medical System - Medical Condition Requirements study, funded by the Canadian Space Agency, aimed to create knowledge content and a scenario-based query capability to support medical autonomy of astronauts. The key objective of this study was to create a prototype tool for identifying medical infrastructure requirements in terms of medical knowledge, skills and materials. A multicriteria decision-making method was used to prioritize the highest risk medical events anticipated in a long-term space mission. Starting with those medical conditions, event sequence diagrams (ESDs) were created in the form of decision trees where the entry point is the diagnosis and the end points are the predicted outcomes (full recovery, partial recovery, or death/severe incapacitation). The ESD formalism was adapted to characterize and compare possible outcomes of medical conditions as a function of available medical knowledge, skills, and supplies in a given mission scenario. An extensive literature review was performed and summarized in a medical condition database. A PostgreSQL relational database was created to allow query-based evaluation of health outcome metrics with different medical infrastructure scenarios. Critical decision points, skill and medical supply requirements, and probable health outcomes were compared across chosen scenarios. The three medical conditions with the highest risk rank were acute coronary syndrome, sepsis, and stroke. Our efforts demonstrate the utility of this approach and provide insight into the effort required to develop appropriate content for the range of medical conditions that may arise.

Keywords: Decision Support System, space medicine, exploration mission, medical autonomy, scenario-based queries, event sequence diagram

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[1] D. Hamilton, K. Smart, S. Melton, J. D. Polk, and K. Johnson-Throop, “Autonomous medical care for exploration class space missions,” J. Trauma, vol 64, pp. S354–S363, April 2008.
[2] A. Martin, P. Sullivan, C. Beaudry, R. Kuyumjian, and J-M. Comtois, “Space medicine innovation and telehealth concept implementation for medical care during exploration-class missions,” Acta Astronautica, vol. 81, pp. 30–33, Jan. 2012.
[3] E. Antonsen, T. Bayuse, R. Blue, V. Daniel, M. Hailey, S. Hussey, E. Kerstman, M. Krihak, K. Latorella, J. Mindock, J. Myers, R. Mulcahy, R. Reed, D. Reyes, M. Urbina, and M. Walton, Risk of adverse health outcomes and decrements in performance due to in-flight medical conditions. Human Research Program Exploration Medical Capabilities Element Progress Report, National Aeronautics and Space Administration, Lyndon B. Johnson Space Center, Houston, TX, Jan. 2017.
[4] L. Bridge, and S. Watkins, Impact of Medical Training Level on Medical Autonomy for Long-Duration Space Flight, National Aeronautics and Space Administration, Jan. 2012.
[5] R. L. Summer, S. Johnston, T. H. Marshburn, and D. R. Williams, “Emergencies in Space,” Ann. Emerg. Med, vol. 46, pp.177–184, Aug. 2005.
[6] L. A. Boley, L. Saile, E. Kerstman, Y. Garcia, J. Myers, and K. Gilkey, Integrated Medical Model Medical Conditions List, IMM-GEN-309 Rev1 report to NASA. Wyle Science, Technology and Engineering, 2017.
[7] D. B. Gillis, and D. R. Hamilton, “Estimating outcomes of astronauts with myocardial infarction in exploration class space missions,” Aviat. Space Environ. Med, 83, 79–91, Feb 2012.
[8] D. F. Andersen, J. A. M. Vennix, G. P. Richardson, and E. A. J. A. Rouwette, “Group model building: problem structuring, policy simulation and decision support,” J. Operational Res. Soc, vol. 58, pp. 691–694, May 2007.
[9] R. J. Scott, R. Y. Cavana, and D. Cameron, “Recent evidence on the effectiveness of group model building,” Eur. J. Operational Res, vol. 249, pp. 908–918, July 2016.
[10] J. A. M. Vennix, “Group model building: tackling messy problems,” System Dynamics Rev, vol. 15, pp. 365–401, Sep. 1999.
[11] J. D. Sterman, Business dynamics: systems thinking and modeling for a complex world, New York: Irwin-McGraw-Hill, Feb. 2000.
[12] M. Grabisch, and C. Labreuche, “A decade of application of the Choquet (and Sugeno integrals in multi-criteria decision aid,” Ann. Operations Res., vol. 175, pp. 247–286, 2010.
[13] G. Choquet. “Theory of capacities,” Annales de l'Institut Fourier, vol. 5 pp. 131–295, 1953.
[14] P. Lucas, Bayesian networks in medicine: a model-based approach to medical decision making, In EUNITE workshop on Intelligent Systems in patient Care, K-P Adlassnig, Ed. Vienna, Austria: Austrian Computer Society, pp. 73–97 Dec. 2001.
[15] F. Taroni, A. Biedermann, S. Bozza, P. Garbolino, and C. Atiken, Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, Chichester, UK: Wiley, pp.45–84, 2014, ch. 2.
[16] S. Few. “Uses and misuses of color,” DM Rev, vol. 15, pp. 62–64, Nov. 2005.
[17] S. Silva, B. Sousa, and J. Madeira, “Using color in visualization: a survey,” Computers & Graphics, vol. 35, pp. 320–333, April 2011.
[18] G. Thonier, and M. Stephanides, “Virtual reality based surgical assistance and training system for long duration space missions,” Medicine Meets Virtual Reality 2001: Outer Space, Inner Space, Virtual Space, vol. 81, pp. 315–321, 2001.