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ExMC Ground-Based Space Radiation Analog Pilot Drug Stability Studay: Final Data Review

Uncertainty regarding space radiation effects on medication degradation and potency remains high, though early data suggest that space radiation may affect the potency and quality of some pharmaceuticals. In the absence of empirical spaceflight measurements to characterize this risk area for long-duration planetary missions, high-fidelity ground-based targeted radiation analogs could provide valuable insights. The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element conducted a pilot study to characterize the chemical stability of four selected medications exposed to rapid switching, mixed-species simulated galactic cosmic radiation (GCR) beams at the NASA Space Radiation Laboratory (NSRL), at Brookhaven National Laboratory (BNL). The research objective was to compare the effects of simulated GCR beam exposures on drug stability to those previously observed following spaceflight.

Vernie R Daniels↗

Artificial Intelligence (AI) Methods for Automating the Impact Tool Evidence Library

INTRODUCTION: The development of the Evidence Library for use with the IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) probability risk assessment tool involved a multilayered, time intensive process of data collection and analysis by subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team to produce clinical findings forms (CliFFs) for 120 medical conditions. Artificial Intelligence Large Language Models (LLMs) can be leveraged to facilitate this process, thus reducing labor and time. TOPIC: CliFFs contain information about medical conditions as they pertain to spaceflight. This includes condition definitions, incidence data, crew task impairment estimates caused by conditions, treatment protocols and references to literature used for gathering condition evidence. Guided by the Evidence Library Methods document and the CliFF development instructions, a team has leveraged Microsoft Azure AI services and open-source documentation to construct an AI-assisted automated pipeline for CliFF development. This process is designed to search, retrieve, and evaluate the applicable data, and ultimately generate a completed CliFF. The LLM evaluates the relevance of each of the source materials to spaceflight, either as direct evidence or as an analog. The model extracts keywords and generates brief summaries to enhance search and retrieval in later stages of CliFF development. For instance, it can calculate epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. APPLICATION: Large Language Models (LLMs) can efficiently summarize large amounts of text. Leveraging this technology will automate data retrieval and evidence gathering for medical databases, like the IMPACT tool, by aiding in the labor-intensive process of analyzing large bodies of literature and organizing it into a formatted document like a CliFF. This added efficiency will enable expeditious expansion of the Evidence Library with additional medical conditions and update previous CLiFFs as new technology becomes available.

Ali Al↗

NASA Crew Health & Performance Capability Development for Exploration: 2022 to 2023 Overview

Radiation, reduced gravity, distance from earth, isolation and confinement, and habitation within artificially created and controlled life support environments are hazards that present risk to human space explorers. These hazards necessitate development of new technologies to protect crew health and performance during future long-duration missions to the moon and Mars. NASA’s System Capability Leads coordinate with agency experts, programs, and exploration architecture teams to identify and prioritize technology investments in support of future missions. This paper describes progress over the past year in CHP technology development, ground testbed development, ground-based testing, and in-flight technology demonstrations. Technology maturation progress and future plans are described in the following capability areas: crew health countermeasures; spacesuit physiology and performance; food and nutrition; radiation protection; and exploration medical capabilities.

Andrew F.J. Abercromby↗

Crew Medical Training to Progressively Enable EIMO

Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.

Jay Lemery↗

Investigating Risks Due to Artemis EVA Tempo Via Probabilistic Risk Assessment

Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.

Modeling↗

The Value of a Spaceflight Clinical Decision Support System for Earth-Independent Medical Operations

As NASA prepares for crewed lunar missions over the next several years, plans are also underway to journey farther into deep space. Deep space exploration will require a paradigm shift in astronaut medical support toward progressively earth-independent medical operations (EIMO). The Exploration Medical Capability (ExMC) element of NASA’s Human Research Program (HRP) is investigating the feasibility and value of advanced capabilities to promote and enhance EIMO. Currently, astronauts rely on real-time communication with ground-based medical providers. However, as the distance from Earth increases, so do communication delays and disruptions. Moreover, resupply and evacuation will become increasingly complex, if not impossible, on deep space missions. In contrast to today’s missions in low earth orbit (LEO), where most medical expertise and decision-making are ground-based, an exploration crew will need to autonomously detect, diagnose, treat, and prevent medical events. Due to the sheer amount of pre-mission training required to execute a human spaceflight mission, there is often little time to devote exclusively to medical training. One potential solution is to augment the long duration exploration crew’s knowledge, skills, and abilities with a clinical decision support system (CDSS). An analysis of preliminary data indicates the potential benefits of a CDSS to mission outcomes when augmenting cognitive and procedural performance of an autonomous crew performing medical operations, and we provide an illustrative scenario of how such a CDSS might function.

Brian K. Russell↗

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗

The Evolution of the Impact Evidence Library Methods – What We Learned

During fiscal years 2020-2022 NASA’s Human Research Program, Exploration Medical Capability Element has worked to source this evidence for the model. During the process several lessons learned have been identified and drive recommendations for future efforts. The ICL 1.0: Spaceflight Medical Operational expertise should be consulted regarding the addition of future conditions. A clinician experienced with the nuances of spaceflight is recommended to have a minimum of 20 hours per condition to adequately source the evidence. Condition definitions should be carefully crafted to maintain mutual exclusivity. Common side effects from medications, common and consequential incidental findings in laboratory studies, and consequential side effects of diagnostic and procedural capabilities should be considered for inclusion as unique conditions in future iterations of the ICL 1.0. Incidence: Spaceflight incidence, although based on small population sizes, is the gold standard. Terrestrial incidence should be applied cautiously. Probability of minor vs. severe cases of the condition: Data informing severity probability of the medical condition are nearly always terrestrial. Special attention should be paid to this metric as it drives outcomes in the model and terrestrial data often overestimate severity of expected cases in spaceflight. Condition duration: Three clinical phases currently exist in the model. They represent the time necessary for diagnosis, treatment/convalescence/recurrence, and the remaining duration of the mission after maximal recovery, respectively. Further subdivision of acute and convalescent treatment/recurrence is recommended. Probability of need for evacuation: Surrogates for “Return to Definitive Care” were chosen prior to data collection. In most cases these surrogates were “need for hospitalization” or “need for surgery.” For many conditions these may not be realistic drivers of “Return to Definitive Care” and should be re-examined. Probability of Crew Mortality: Terrestrial mortality data is difficult to apply to astronauts who, invariably, have less comorbidities than terrestrial subjects. It must be applied judiciously. The proportion of mission tasks impaired by the condition: “Task Impairment” was assigned to each condition prior to the condition duration data becoming available. Future efforts should assign Task Impairment as the final step in sourcing the evidence.

A.J. Kreykes↗

ExMC Systems Engineering Developments

The Exploration Medical Capability (ExMC) Element within the Human Research Program (HRP) applies systems engineering (SE) principles along with the use of Model-Based Systems Engineering (MBSE) tools to identify and communicate the requirements for medical and crew health and performance (CHP) systems. In the past fiscal year, the MBSE approach sought to advance the digital engineering toolset for medical and CHP system representation. These digital artifacts provide enhanced views of the relationships among requirements, standards, functions, and capabilities, to name a few, that is best suited for a user’s objectives. The MBSE tools and SE practices were applied to the development of the revised Earth Independent Medical Operations (EIMO) medical system and the Artemis III and IV CHP System models. In addition, a System of Systems concept was integrated into the EIMO model to facilitate the identification of system interfaces that interact with the medical system. Finally, the ExMC SE team has initiated several efforts to bring operationally relevant digital engineering practices to the Human Health and Performance Directorate (HHPD). This included the development of a pilot program within the directorate to help foster utilization of tools such as MagicDraw for system modeling and Power BI for visualizing extracted data in an easily accessible dashboard format. Additionally, with the increase in complexity of the integration effort for Artemis missions, ExMC has endeavored to bring digital engineering strategies to potentially increase efficiency in review processes. This talk will provide a high-level overview of the ExMC SE team accomplishments since the last Investigators’ Workshop, an introduction to upcoming SE talks, and the ongoing systems engineering work.

systems engineering↗

Flight Surgeon Survey Results Review: Long-Duration Artemis Medical System ConOps

Background: The Artemis Mission Functional Medical Concept of Operation (ConOp) is an operational document that describes the functional medical capabilities and concept of operations for early Artemis missions of around 30 days duration. It is currently under revision for longer missions (180+ days), aiming to build a foundational diagnostic and treatment capabilities list considering mission parameters, crew selection and training, and mass/volume constraints. An interview of NASA operational flight surgeons was completed to start the revision of the medical ConOp. Methods: In the survey, 20 experienced NASA flight surgeons responded to 57 standardized questions covering 17 categories, 22 questions of which were covered in this analysis including 4 categories: Cardiovascular, Catastrophic Situations, Pulmonary, and Respiratory/O2 Support. These results were categorized into recommendations and observations. The recommendations were used to objectively quantify the responses to look for consensus, differences and uncertainty among responses. The observations remain important but were not included in this review. Analysis: Regarding questions related to changes in medical conditions treated for long-duration missions, percentage of “No Change” responses varied from 20% to 75%. In the “Change” treatment plan responses, a variety of recommendations were made, which were further made into graphics for better illustration. Additionally, recommendations on capability questions in multiple systems were visualized altogether in one product. Discussion: This work demonstrates the importance of collaboration and thorough analysis amongst experienced NASA flight surgeons for helping establish medical systems and contributing to the likelihood of mission success during Artemis long-duration missions. Through this review, we were able to identify some positive attributes as well as areas for improvement, which will facilitate further discussions.

Andrew Bushong↗

Performance Support Tools for Space Medical Operations

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to ISS, medical equipment will be located on ISS, and carried into CEV in the event of an emergency. Flight Surgeons (FS) on the ground in Mission Control will be expected to direct the Crew Medical Officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. Human factors personnel at Johnson Space Center have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground. This area of research involved the feasibility of Just-in-time (JIT) training techniques and concepts for real-time medical procedures. In Phase 1, preliminary feasibility data was gathered for two types of prototype display technologies: a hand-held PDA, and a Head Mounted Display (HMD). The PDA and HMD were compared while performing a simulated medical procedure using ISS flight-like medical equipment. Based on the outcome of Phase 1, including data on user preferences, further testing was completed using the PDA only. Phase 2 explored a wrist-mounted PDA, and compared it to a paper cue card. For each phase, time to complete procedures, errors, and user satisfaction were captured. Information needed by the FS during ISS mission support, especially for an emergency situation (e.g. fire onboard ISS), may be located in many different places around the FS s console. A performance support tool prototype is being developed to address this issue by bringing all of the relevant information together in one place. The tool is designed to include procedures and other information needed by a FS during an emergency, as well as procedures and information to be used after the emergency is resolved. Several walkthroughs of the prototype with FSs have been completed within a mockup of an ISS FS console. Feedback on the current tool design as well as recommendations for existing ISS FS displays were captured.

Byrne, Vicky E.↗

Human Factors in Training - Space Medicine Proficiency Training

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to ISS, medical equipment will be located on ISS, and carried into CEV in the event of an emergency. Flight Surgeons (FS) on the ground in Mission Control will be expected to direct the Crew Medical Officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. Work on medical training has been conducted in collaboration with the Medical Training Group at the Space Life Sciences Directorate and with Wyle Lab which provides medical training to crew members, Biomedical Engineers (BMEs), and to flight surgeons under the JSC Space Life Sciences Directorate s Bioastronautics contract. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). Human factors researchers at Johnson Space Center have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research. This abstract focuses on two areas of work involving Performance Support Tools for Space Medical Operations. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. In Phase 1, preliminary feasibility data was gathered for two types of prototype display technologies: a hand-held PDA, and a Head Mounted Display (HMD). The PDA and HMD were compared while performing a simulated medical procedure using ISS flight-like medical equipment. Based on the outcome of Phase 1, including data on user preferences, further testing was completed using the PDA only. Phase 2 explored a wrist-mounted PDA, and compared it to a paper cue card. For each phase, time to complete procedures, errors, and user satisfaction ratings were captured.

Connell, Erin↗

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗

ExMC Systems Engineering Status

The Exploration Medical Capability (ExMC) Element within the Human Research Program (HRP) applies systems engineering principles along with the use of Model-Based Systems Engineering (MBSE) tools to identify and communicate the requirements for medical and crew health and performance (CHP) systems. The MBSE approach to medical system design offers a paradigm shift toward greater integration between the vehicle and a human health and performance system. In addition, the MBSE tools provide a means in which systems engineers can develop different views of the relationships between and among requirements, standards, functions, and capabilities, to name a few, that is best suited for a user’s objectives. Applying these tools, ExMC Systems Engineering (SE) developed three MBSE models in support of multiple projects in fiscal year (FY) 2023. These included the Long-Duration Lunar Orbit and Lunar Surface (LDLOLS) Medical System Foundation, Earth-Independent Medical Operations (EIMO) medical system ConOps, and the 2023 Artemis CHP System model. This talk will provide a high-level overview of what the ExMC SE team has accomplished since the last Investigators’ Workshop, an introduction to upcoming SE talks, and the ongoing systems engineering work.

Systems engineering↗

ExMC Systems Engineering Status

The Exploration Medical Capability (ExMC) Element within the Human Research Program (HRP) applies systems engineering principles along with the use of Model-Based Systems Engineering (MBSE) tools to identify and communicate the requirements for medical and crew health and performance (CHP) systems. The MBSE approach to medical system design offers a paradigm shift toward greater integration between the vehicle and a human health and performance system. In addition, the MBSE tools provide a means in which systems engineers can develop different views of the relationships between and among requirements, standards, functions, and capabilities, to name a few, that is best suited for a user’s objectives. Applying these tools, ExMC Systems Engineering (SE) developed three MBSE models in support of multiple projects in fiscal year (FY) 2023. These included the Long-Duration Lunar Orbit and Lunar Surface (LDLOLS) Medical System Foundation, Earth-Independent Medical Operations (EIMO) medical system ConOps, and the 2023 Artemis CHP System model. This talk will provide a high-level overview of what the ExMC SE team has accomplished since the last Investigators’ Workshop, an introduction to upcoming SE talks, and the ongoing systems engineering work.

Systems engineering↗

Human Factors in Training: Space Medical Proficiency Training

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and the International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to the ISS, medical equipment will be located on the ISS, and carried into CEV in the event of an emergency. Flight surgeons (FS) on the ground in Mission Control will be expected to direct the crew medical officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). This is a joint project consisting of human factors team from the Ames Research Center (ARC) with Immanuel Barshi as Principal Investigator and the Johnson Space Center (JSC). Human factors researchers at JSC have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. Work on medical training has been conducted in collaboration with the Medical Training Group at the Johnson Space Center (JSC) and with Wyle Laboratories that provides medical training to crew members, biomedical engineers (BMEs), and to flight surgeons under the Bioastronautics contract. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. A second area of research involves FS performance support tools. Information needed by the FS during the ISS mission support, especially for an emergency situation (e.g., fire onboard ISS), may be located in many different places around the FS s console. A performance support tool prototype is being developed to address this issue by bringing all of the relevant information together in one place. The tool is designed to include procedures and other information needed by a FS during an emergency, as well as procedures and information to be used after the emergency is resolved. Several walkthroughs of the prototype with FSs have been completed within a mockup of an ISS FS console. Feedback on the current tool design as well as recommendations for existing ISS FS displays were captured. The tool could have different uses depending on the situation and the skill of the user. An experienced flight surgeon could use it during an emergency situation as a decision and performance support tool, whereas a new flight surgeon could use it as JITT, or part of his/her regular training. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research.

Byrne, Vicky E.↗

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

Does the Health Maintenance Facility Provide Speciality Capabilities?

The Health Maintenance Facility (HMF) is capable of handling all minor illnesses, most moderate illnesses, and some major illnesses on board a space station. Its primary purpose should be to treat problems that are mission threatening, not life threatening. The HMF will have greater medical capabilities than those currently on Navy submarines. Much of the discussion in this document focuses on the possibilities of treating specific medical conditions on board a space station. The HMF will be limited to caring for critically ill patients for a few days, so a crew return vehicle will be important.

Boyce, Joey↗