ISS Technology Demonstrations for Future Spaceflight Medical Systems
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Engineering topics
Publications and source records attributed to Jay Lemery.
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An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
The scope of this report is limited to evaluating drug stability with respect to spaceflight. The effects of spaceflight on drug pharmacokinetics (PK) or drug pharmacodynamics (PD) are not addressed in this report: these topics will be reviewed in future reports. This is the first report of the available evidence pertaining to the chemical and physical stability of drugs in the context of drug impurity, drug repackaging, and exposure to ionizing space radiation. Previous studies are discussed within this report and, where possible, the data are interpreted and contextualized with regards to drug stability.
This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, the gradual transition of medical care and decision making from terrestrial to space-based assets enabling support of astronaut health and performance and reducing overall mission risk is achievable.
On 26th October 2023, ExMC convened a panel of Subject Matter Experts (SMEs) from NASA, broadly representing Headquarters, the Human Research Program, Medical Operations, the Human Health and Performance Directorate at JSC, the Health and Medical Technical Authority, the Flight Operations Directorate, and representation from other Centers including: Ames Research Center (ARC), and Glenn Research Center (GRC). Representatives from the Canadian Space Agency also participated in this TIM. In addition, SMEs from industry included representation from the following entities: Axiom Space, Space Exploration Technologies Corporation, Level Ex, Shiny Box Interactive. Additional SMEs from academia included: University of Houston, Touro University, Weill Cornell Medical College and the Translational Research Institute for Space Health at Baylor College of Medicine. This group of stakeholders discussed the training issues related to facilitating EIMO. Sub-topics for this discussion included the following: - Pre-launch medical curriculum development - Ground-based training on medical hardware - Dental health - Behavioral health - Telemedicine - Just-in-time training - Simulation-based training (extended reality)
BACKGROUND: Features of human space missions beyond low Earth orbit such as increasing distance from Earth, lack of real-time communication, and limited or no evacuation or resupply capability are expected to drive an increase in medical risk and require crews to operate in an increasingly autonomous fashion. A diverse set of stakeholders at NASA are seeking to fund the development of concepts of operations, medical requirements, and medical capabilities for such missions. However, a systematic approach to identification of current medical capability gaps and a strategic framework to gap closure is needed. OVERVIEW: The Exploration Medical Integrated Product Team (XMIPT) has developed a list of nine high-level medical capability gaps and associated activities required for gap closure. The list was derived based on inputs from subject matter experts at NASA including flight surgeons, other clinical providers, as well as operational and research communities regarding medical capabilities required to support human missions to the Moon and the Mars surface. Responses were reviewed and distilled to identify common themes across capabilities. To ensure alignment with established human system risks, the gap list was further refined based on inputs from NASA’s Human System Risk Board. Relevant medical gap closure activities outside of those funded by the XMIPT were identified through solicitation of inputs from Elements of NASA’s Human Research Program (particularly Exploration Medical Capability), the broader medical operations community, and other stakeholders. This medical capability gap list is reviewed and updated regularly as new information becomes available or new stakeholders are identified. DISCUSSION: The medical capability gap list has matured to include a large group of NASA stakeholders and development activities. This has enabled articulation of priorities to funding entities and programmatic stakeholders, while serving as an accessible resource summarizing gap closure activities, relevant programmatic infusion points, and opportunities for collaboration between stakeholders. This presentation will provide an overview of the nine NASA medical capability gaps and their associated gap closure activities.
Foundations to Advance Long Duration Mission Health
Goal: Current Space Medicine operations depend on terrestrial support to manage medical events. As astronauts travel to destinations such as the Moon, Mars, and beyond, distance will substantially limit this support and require increasing medical autonomy from the crew. This paper defines Earth Independent Medical Operations (EIMO) and identifies key elements of a conceptual EIMO system. Methods: The NASA Human Research Program Exploration Medical Capability Element held a 2-day conference at Johnson Space Center in Houston, TX with NASA experts representing all aspects of Space Medicine. Results: EIMO will be a process enabling progressively resilient deep space exploration systems and crews to reduce risk and increase mission success. Terrestrial assets will continue to provide pre-mission screening, planning, health maintenance, and prevention, while onboard medical care will increasingly be the purview of the crew. Conclusions: This paper defines and describes the key components of EIMO.
The mission of the Exploration Medical Capability (ExMC) Element is to advance medical system design and risk-informed decision making for exploration beyond low Earth orbit to promote human health and performance in space. In order to accomplish this mission, the Element takes a progressively Earth-independent approach to three main areas: - Answering key clinical and science research questions that will help to address the challenges of providing medical care in the extreme environment of space - Applying systems engineering processes to medical system design with the goal of developing robust requirements that can be fully integrated into future space exploration vehicle designs - Developing and demonstrating novel medical technologies that will improve future medical capabilities in space This presentation will focus on selected scientific and technical conceptual drivers for the Element, the current and future research risks and gaps, and provide an overview of the Element’s progress in 2022. In particular, it will address the ongoing effort for the IMPACT trade space analysis tool suite, the closeout work on a clinical decision support prototype, potential requirements for a long-duration lunar orbit and lunar surface medical system, and an International Space Station technology demonstration of a point-of-care laboratory analysis capability.
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Exploration medical operations to the Moon and Mars present unprecedented challenges for providing in-mission medical care. The greater distance from Earth is the primary hazard that drives the need for a medical operations paradigm shift from low earth orbit mission. Increasingly complex long duration and long distance missions will have resource constraints (ex: mass, power, volume, data), a paucity of resupply or evacuation opportunities, and disruptions in real-time communications. In order to advance a more autonomous medical approach, a multi-faceted strategy will need to optimize all aspects of human health and performance in space. This strategy will include: increasing onboard medical autonomy through the development of novel crew health and performance systems; decision support capabilities to augment astronauts’ abilities in preventing, diagnosing, and treating medical conditions; creating new procedures and training tools for skill maintenance and just-in-time training; and enabling rapid crew access to data from all on-board systems, leading to better-informed, real-time, autonomous decisions. Collectively, this approach can be referred to as “Earth Independent Medical Operations” (EIMO). The Exploration Medical Capability Element of NASA’s Human Research Program has undertaken a longitudinal planning process to consensus around EIMO. Technical interchange meetings will occur with key constituents in late 2022 and again in early 2023 to define EIMO in practical terms and identify key elements of an EIMO system, with greater conceptual articulation and dissemination planned for the second half of 2023. We will share insights into the strategy for internal and external consensus building around EIMO, with a particular focus on the approach to partner stakeholders within NASA as well as commercial agencies.
For long-duration lunar orbital and lunar surface (LDLOS) exploration missions, the NASA Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a medical system foundation through which clinical considerations may be represented via a systems engineering-based model. Components of the Long Duration Medical System Foundation model include a concept of operations (ConOps), functional decomposition, medical conditions to be addressed, clinical capabilities and resources, technical requirements, and traces of requirements to NASA standards documents and parent-level (Program- and Vehicle habitat system-level) requirements. The Foundation model offers the means to present information in a readily accessible format that is understandable across all clinical, engineering, scientific, and managerial disciplines. Collectively, these components constitute a foundation that serves future programs with similar long duration mission profiles as a starting point for medical system design. The Foundation was developed by a multidisciplinary team of systems engineers, scientists, and clinicians across NASA. The process started with ConOps development, subsequently decomposed into the functionalities needed to diagnose, treat, and prevent medical conditions. The clinical team identified medical conditions most likely needed to be diagnosed and treated during a long-duration lunar exploration mission. Through these approaches, requirements were codified for the LDLOS medical system. These requirements were then traced to NASA standards, medical conditions, medical capabilities, and medical resources, facilitating stakeholders’ use of the Foundation model to analyze traces and to identify medical system interfaces with other vehicle systems or subsystems. In addition, the Foundation may be used as a basis for performing risk trades on medical system mass and volume allocation. This discussion will focus on the processes through which the LDLOS Medical System Foundation was developed, how the Foundation builds a bridge between the medical and engineering domains, and how these processes may be applied more broadly to a crew health and performance system and other system domains.
Background: Probabilistic risk analysis (PRA) is a method for estimating risk in complex engineered systems that, at a basic level, focuses on what can go wrong and the likelihood and consequences of those occurrences. NASA has used PRA as an integral component of medical system risk estimation and design for spaceflight. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool to meet these goals for exploration missions. Overview: IMPACT performs hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. These simulations are based on 120 possible medical conditions (the IMPACT Condition List) selected in a consensus-based process because they are of highest likelihood and/or consequence for exploration spaceflight. The conditions are then tied to clinical capabilities which can be used for management (e.g., inserting an IV) and then to over 600 specific resources needed to deliver a capability (e.g., an angiocath or an ultrasound). Different mission profiles can be simulated with user-specified inputs such as mission duration, destination, number of crew and pre-existing medical conditions, and EVA frequency. While the IMPACT evidence base is designed for exploration environments, these user inputs allow the tool to be used across a broad range of missions. IMPACT’s primary outcome metrics include loss of crew life (LOCL, a measure of in-flight mortality due to medical conditions), need for evacuation (RTDC, return to definitive care), and crew disability (TTL, task time lost based on how medical conditions impact the ability to perform over 1000 specific exploration mission crew tasks). In addition to modeling medical risk, IMPACT also accepts user-specified constraints, such as limitations of mass or volume, and will output a recommended clinical capability set and specific medical resources that meet the mission constraints. Discussion: This abstract will provide an introduction to IMPACT and describe the nature of the underlying medical evidence. It will also detail potential use cases for how this tool can be utilized by NASA or commercial spaceflight providers.