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Arian Anderson

Publications and source records attributed to Arian Anderson.

36 records · Page 2
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Improving Quantification of Medical Evacuation Risk for Human Spaceflight in the IMPACT Model

Traditionally, mission planners have used a heuristic and qualitative approach to design medical systems based on prior experience however this approach may result in high variability and implicit bias to design and could put missions and crewmembers at risk. The Informing Mission Planning through Analysis of Complex Tradespaces (IMPACT) tool is a probabilistic risk assessment tool designed by NASA to model medical risk in long duration missions outside of low earth orbit (LEO). This tool can quantify risk metrics such as risk of crewmember death, loss of crew task time, and the need for medical evacuation to better inform and augment the more traditional approach to medical system design. The risk metric of Return to Definitive Care (RTDC) represents the likelihood of requiring medical evacuation and has been difficult to quantify in a reliable and standardized manner. Initial development of the RTDC metric was constrained due to the complexity of a medical evacuation decision and the lack of prior spaceflight data and this presentation will discuss the review and improvement process used to increase model accuracy and fidelity.

Prashant Parmar↗
NASA NTRSDate not supplied

Overlapping Conditions in IMPACT

Introduction The IMPACT 1.0 model assumes independence of 119 different medical conditions. That is, it is assumed that one medical event maps to a single medical condition. However, it is often the case that a single medical event may lead to several different concurrent conditions (e.g., a single accident resulting in multiple fractures, chest/abdominal trauma, and sepsis). There is a significant overlap between many of the conditions in ICL 1.0. For example, depression and anxiety are two separate conditions but often co-exist. This overlap may lead to an overestimation in the incidence calculation. In future iterations of the model, we plan to address progression of medical conditions. Methods There are two possible methods to approach progression of a medical condition. The first is to take any medical condition in ICL, calculate its incidence and determine its outcomes no matter what sequelae occur. For example, calculate the incidence of prostatitis and determine its metrics (e.g., loss of crew life, risk of medical evacuation or task time affected) no matter what the sequelae might be (sepsis, hydronephrosis, renal failure, etc.). The second approach is to take any medical condition in the ICL and calculate the outcomes based on transitions to any other condition. For example, calculate the incidence of nephrolithiasis and determine the incidence of each possible transition (e.g., hydronephrosis, renal failure, UTI, pyelonephritis, sepsis) and then determine its metrics. With perfectly informative evidence, the answers should be the same using either method. The question is which approach best reduces the opportunity for overlapping. Results The team identified 31 conditions that were the most likely to transition to a secondary condition such as sepsis and respiratory failure. It was felt that the first approach, as described above, would be the easiest to implement and would have the greatest reduction in overlapping conditions. The identified conditions were primarily infectious conditions, like UTI and pneumonia (which could lead to both respiratory failure and sepsis), toxic inhalations, cardiac arrest, seizures, and trauma. Once these conditions were identified, the conditions were divided between the four clinicians on the team to identify those that were not consistent with the others in way they were approached. There were 13 conditions that were found to be problematic. Some condition definitions needed to be changed to eliminate references to transitions or to update them in cases where the definitions were out of date. Some conditions required new evidence to correct the loss of crew life, and one condition was felt to have so much overlap that it was recommended for elimination. A second effort required modification of the sepsis incidence calculation to prevent double counting of the conditions, such as prostatitis, that could progress to sepsis. Conclusion The team made significant progress in clarifying the method for dealing with transitions from one medical condition to another. In addition, errors were found and corrected in definitions, incidence, and loss of crew life as well as a condition in which reference was made to a CLiFF that was no longer included. These modifications will provide greater fidelity in the IMPACT model and reduce overlap.

Arian Anderson↗
NASA NTRSDate not supplied

Progressively Enabling Earth Independent Medical Operations (EIMO)

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.

John Lemery↗
NASA NTRSDate not supplied

Supply and Resource Management to Progressively Enable EIMO

BACKGROUND: Current medical operations in Low Earth Orbit (LEO) allow for real-time audio-video communication with a flight surgeon at mission control, resupply, and medical evacuation to earth on the order of hours. As mission profiles change from LEO to the Moon, Mars, and beyond, medical risk as a contribution to overall mission risk is anticipated to rise substantially. Concurrently, due to the distance, the medical systems on board vehicles proposed for these mission types are expected to have reduced mass and volume allocation. Together, astronaut crews will be at a higher risk of major medical events, be required to perform a broader set of tasks, and have substantially reduced resources and support to do so. Earth Independent Medical Operations (EIMO) aims to identify and fill the gaps present in this progressively changing paradigm. OVERVIEW: Medical supplies, resources, and skills are central to spaceflight medical systems. Vehicles used for non-LEO missions are anticipated to be smaller and thus the medical system will also need to have reduced mass, volume, and power. Medical resources are another form of consumable and may need resupply or pre-deployment to meet crew needs. One particular concern is the degradation of medications which become less efficacious and potentially toxic with time, particularly given environmental conditions such as temperature, humidity, oxygen, and radiation which have not yet been fully studied. Supply and resource management in LEO is dependent on resupply, however the supply chain of transporting equipment does not yet have a clear infrastructure for missions beyond LEO. DISCUSSION: EIMO is intended to systemically identify and fill these gaps with forward-looking solutions. In mission monitoring of resources with technology like RFID, improving medical resource longevity, targeted resupply and careful pre-mission planning will be central to facilitate crew health and performance. One approach to optimizing resources is the Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool, which uses probabilistic risk assessment (PRA) to quantitatively predict medical risk and identify resources and skills that mitigate this risk. This evidence based, quantitative analysis prediction tool and several other approaches to meeting the challenge of supply and resource management are discussed.

Arian Anderson↗
NASA NTRSDate not supplied

Task Load Management in Earth Independent Medical Operations

BACKGROUND: Medical care in spaceflight carries a high task load and can easily overwhelm a small crew. Present day operations in low Earth orbit (LEO) offload most medical tasks to ground teams in mission control. This team includes dozens of flight surgeons, specialists, and engineers and supports the on-orbit crew in monitoring environmental systems, tracking medications, guiding procedures, providing expert advice, and many other tasks. However, the physical limitations of the speed of light and technical limitations of bandwidth, channel capacity, and signal processing mean that missions beyond LEO cannot rely on this level of telemedical support. The further we travel from Earth the more these tasks will fall on the shoulders of the crew and the greater the risk of task saturation to the wellbeing of the crew and the success of the mission. Exploration class space crews will need progressively more robust systems for managing task load as they progress further out in space. OVERVIEW: Medical task management systems will need to assist with two broad categories of tasks; cognitively intensive tasks and procedure execution tasks. In both cases the goal is for the systems to operate in the background with minimal human-in-the-loop intervention. To accomplish this such systems will need to be designed with careful consideration for human factors and human systems integration to maximize efficiency, minimize alarm fatigue, and avoid inadvertently increasing task loads. Finally, the key domains of space medicine tasking can be used to map present day and near future technologies to the areas where they are best suited to support and identify gaps which can be targeted for research and development. DISCUSSION: Task load is a major challenge for Earth Independent Medical Operations to overcome. It will require careful coordination between experts in a variety of fields paying attention to human factors and human systems integration as well as technical and medical expertise. If done well medical task management systems can handle many of the tasks currently run by humans in mission control and enable human crews to maintain terrestrial standards of care in the extraterrestrial environment.

Dana Levin↗
NASA NTRSDate not supplied

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↗
NASA NTRSDate not supplied

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗
NASA NTRSDate not supplied

Quantifying Medical Risk on a Long Duration Lunar Mission: A Demonstration of NASA’s IMPACT Tradespace Analysis Tool

Background NASA’s human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. The distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and constraints on the medical evacuation of astronauts. Mass, volume, power, and data will be limited while higher demands will be placed on the crew to manage medical care. NASA’s Moon to Mars exploration strategy lays out increasingly complex Artemis missions both in terms of duration and operations. In these more challenging deep space missions, it is important to quantitatively estimate the human medical risk to inform a traditional heuristic approach to medical risk. Prior tools have been developed for missions in low Earth orbit, but a new tool is required to plan for future exploration missions. Methods IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments; an expanded list of 119 medical conditions; the addition of medical resources; and the ability for rapid and iterative analysis. Medical system risk estimates include loss of crew life, consideration of the need for return to definitive care (medical evacuation), and an estimate of crew time affected due to medical conditions. A notional long duration lunar orbit and lunar surface design reference mission (DRM) was chosen with a 4-astronaut crew to represent a sustained exploration Artemis mission. Results/Discussion Overall, IMPACT successfully quantified medical risk and derived an optimized medical system to support crew on a long duration lunar mission. In this DRM, the calculated loss of crew life from a medical event was 0.008 events per mission, risk of potential need for evacuation was 0.30 events per mission, and cumulative crew time affected by medical conditions was 103 days. The medical conditions that most contributed to overall medical risk were decompression sickness, trauma conditions, and respiratory failure. The conditions that had the largest effects on crew performance included musculoskeletal injuries and lunar dust exposure. The IMPACT-generated medical system included resources that target the most common and highest risk conditions. This systematic analysis demonstrates the value of the IMPACT tool in medical system design for human exploration spaceflight missions.

Missions to Mars↗
NASA NTRSDate not supplied

Developing Mars-Based Clinical Scenarios for an Earth Independent Medical Operations (EIMO) – Based Decision Support Service

As crewed missions move beyond Low-Earth Orbit, pre-mission planning cannot fully buy down the medical risks of exploration-class missions. Martian missions, where increased hazards exist, (such as long-duration spaceflight, surface-level EVA operations, and communications delays) will require a paradigm shift in the structure of a medical system. An Earth-Independent Medical Operations-based Medical System (EIMO-MS) will need to optimize four critical domains to help provide medical care: utilization of Pre-Mission Planning, augmentation of Acute and Prolonged Medical Decision Making, automated tracking of Resource Management, and assistance in Task Load Balance. The ideal EIMO-MS will be able to accomplish this goal by having an interactive, adaptable interface that will be able to provide real-time medical services. It must respond based on the level of crewmember training, medical situation, and available medical and non-medical resources. To showcase the capabilities and requirements of such a sophisticated automated MS, a series of clinical scenarios of escalating complexity were developed with clinical and systems engineering input. These scenarios describe in clinical detail what a theoretical future medical system, enhanced with multiple information streams (such as a medical database, an AI-based Decision Support System, real-time monitoring, enhanced in-situ laboratory imaging, etc.) can achieve in conjunction with a trained and experienced crew. Scenarios are comprised of: a context section including objectives and applicable spaceflight environment, a highlighted assumptions section, a clinical narrative section, and a systems engineering activity diagram demonstrating the integrated Medical System (MS). The “swim lanes” of the activity diagram act as the logistical core of each scenario and show how the MS will interact with the crew, ground support, and other in-flight systems. The Design Reference Mission that is used for the scenarios is based on existing reference mission profiles [1] with a projected 30-sol stay on the Martian surface. Scenarios span the spectrum from planned evaluations, minor medical care, urgent care, surgical guidance, critical and expectant management, and behavioral health care. Mission complexity will exponentially increase during deep space and Mars exploration-class missions, and medical support for these missions will likewise need to increase in autonomy and adaptability. The integrated system that will support these missions will need to provide assistance in a variety of anticipated and unforeseen scenarios. These medical scenarios, guided by clinician input, are initial steps in crafting the requirements for an EIMO-based medical system. By working in a systems engineering framework, requirements and capabilities can be extracted and mapped while maintaining a clinical core.

Prashant Parmar↗
NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

In contrast to the current crew health paradigm for low-Earth orbit and Lunar missions, which depends on real-time communication with Mission Control, deep-space exploration missions will require a significant shift in medical operations. This shift is driven by the constraints of operating at a considerable distance from Earth, such as resource limitations—lack of resupply, restricted mass, power, volume, and data—as well as communication delays and the inability to evacuate back to Earth during emergencies. To move toward a more self-reliant medical model, a strategy is needed to gradually increase space-based crew autonomy and reduce risks to mission success in the challenging environment of deep space. This transformative change, known as "Earth-Independent Medical Operations" (EIMO), explores the gradual transfer of medical care and decision-making from Earth-based support to space-based systems. The goal of this transition is to enhance astronaut health and performance while minimizing mission risks. EIMO requires the development of a medical system that integrates seamlessly with mission planning, vehicle and spacesuit design, and data architecture. This integration is crucial for building a robust medical infrastructure that not only safeguards astronaut well-being but also ensures overall mission success. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has revised the EIMO model-based Concept of Operations (ConOps) which outlines an initial vision for EIMO. The ConOps, which is built on the stakeholders’ need, system goals, and objectives (NGOs), presents an array of in-mission scenarios that span a wide range of medical conditions demonstrating the system’s capabilities from basic to complex events. Developed by a multidisciplinary team of systems engineers, scientists, and clinicians within ExMC, the ConOps revision includes two new scenarios(Barotrauma and Self-Medical Management and Behavioral Health and Chronic Medical Care), and implementation of findings from EIMO technical interchange meetings that focused on data and training. The envisioned EIMO Medical System (MS) operates as a system of systems, gathering data from various sources such as reference databases, real-time wearable sensors, point-of-care diagnostics, and environmental controls. The MS also incorporates advanced training tools to support autonomous medical care, assisting the Crew Medical Officer (CMO) during medical events where Ground Support is either unavailable or communication-delayed beyond practicality. Furthermore, MS functions and capabilities were decomposed from the scenarios to establish foundational requirements for EIMO and traced to the NASA Spaceflight Human-System Standard(NASA-STD-3001, Volumes 1 and 2). These traces were performed to gain insights on the alignment of EIMO requirements with the NASA standard. This work serves as an initial recommendation to increase crew autonomy gradually and safely for Mars missions and future deep-space exploration.

medical system↗
NASA NTRSDate not supplied

Assessing the Added Value of Miniature X-Ray in the Setting of US in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in lowEarth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

Jon Steller↗