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Predicting Exploration Crew Medical Officer Training Needs: Applying Evidence-Based Predictive Analytics to Space

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Predictive analytics can identify essential medical training for space missions. This tool helps create tailored curricula, improving astronaut health and saving planning time.

Keywords:
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Area of Science:

  • Space Medicine
  • Medical Training
  • Predictive Analytics

Background:

  • Identifying medical training needs for deep space missions is crucial.
  • Exploration-class medical officers require specialized skillsets.
  • Predictive analytics offers a novel approach to curriculum design.

Purpose of the Study:

  • To evaluate the utility of predictive analytics in designing medical curricula for space exploration missions.
  • To assess the Medical Extensible Database Probabilistic Risk Assessment Tool (MEDPRAT) for curriculum development.
  • To identify common and mission-specific medical training requirements.

Main Methods:

  • Utilized a preliminary version of NASA's MEDPRAT tool.
  • Applied predictive analytics to 5 distinct Design Reference Mission (DRM) profiles.
  • Employed a leave-one-out analysis with a 5% risk increase threshold to identify curriculum elements.

Main Results:

  • Between 4-32 curriculum elements (partial treatment) and 13-126 (full treatment) met the risk threshold across DRM profiles.
  • Identified 13 core medical capabilities applicable to at least 3 of the 5 DRM profiles.
  • Demonstrated varying coverage of skillsets for different mission types (e.g., 100% for Starship orbital, 41% for Mars).

Conclusions:

  • Predictive analytics can efficiently generate evidence-based, mission-specific medical curricula for space exploration.
  • This approach supports a human-machine team strategy for optimizing medical training.
  • The technique has the potential to enhance astronaut health outcomes and streamline training development.