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

Abstract

Introduction: Predictive analytics may be a useful adjunct to identify training needs for exploration class medical officers onboard deep space vehicles.

Methods: This study used a preliminary version of NASA's newest medical predictive analytics tool, the Medical Extensible Database Probabilistic Risk Assessment Tool (MEDPRAT), to test the application of predictive analytics to exploration crew medical officer curriculum design for 5 distinct design reference mission (DRM) profiles. Partial and fully treated paradigms were explored. Curriculum elements were identified using a leave-one-out analysis and a threshold of 5% risk increase over the fully treated baseline.

Results: For the partial treatment scenario, among the 5 DRM profiles 4-32 curriculum elements met the 5% RRI increase. For the absolute treatment scenario, among the 5 DRM profiles, 13-126 curriculum elements met the 5% RRI increase. For the partial treatment paradigm, 13 capabilities are present in at least 3 of the 5 DRM profiles, and these elements may constitute a common baseline curriculum. This covers 41% of the skillsets needed for an ISS-like profile, 100% of a late Artemis-like profile, 41% of a Mars mission-like profile, 100% of a Starship orbital-like profile, and 68% of a Starship lunar flyby-like profile.

Conclusions: This proof-of-concept study demonstrated that predictive analytics can rapidly generate generic and mission profile-specific exploration CMO curricula using an evidence-based process driven by optimizing mission risk reduction. This technique may serve as part of a human-machine team approach to medical curriculum planning for future space missions. It has significant potential to improve astronaut health and save time and effort for planners, trainers, and trainees.