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NASA NTRSDate not supplied

Exploration Medical Integrated Product Team Clinical Decision Support Market Survey

NASA’s Exploration Medical Integrated Product Team (XMIPT) has identified Clinical Decision Support (CDS) technology as a critical need for future human space exploration. Such technology will support real time diagnosis, monitoring, and treatment of spaceflight medical conditions. The need for such tools and support systems is critical for in-mission clinical decision-making, especially when Earth-based support is unavailable due to communication delays or blackouts. Supporting technologies may or may not involve Artificial Intelligence (AI), and would support astronaut crew with minimal clinical training, or even those with advanced training if they are, for example, in need of a refresher, experiencing multiple stressors, or temporarily overloaded with tasking. This need traces to the Development of Earth Independent Operations Technologies for NASA’s Mars Campaign Office. The CDS Market Survey purpose, methods, outcomes thus far, and near-term steps will be discussed.

Decision support↗

NASA NTRS1998

Errors in Aviation Decision Making: Bad Decisions or Bad Luck?

Despite efforts to design systems and procedures to support 'correct' and safe operations in aviation, errors in human judgment still occur and contribute to accidents. In this paper we examine how an NDM (naturalistic decision making) approach might help us to understand the role of decision processes in negative outcomes. Our strategy was to examine a collection of identified decision errors through the lens of an aviation decision process model and to search for common patterns. The second, and more difficult, task was to determine what might account for those patterns. The corpus we analyzed consisted of tactical decision errors identified by the NTSB (National Transportation Safety Board) from a set of accidents in which crew behavior contributed to the accident. A common pattern emerged: about three quarters of the errors represented plan-continuation errors, that is, a decision to continue with the original plan despite cues that suggested changing the course of action. Features in the context that might contribute to these errors were identified: (a) ambiguous dynamic conditions and (b) organizational and socially-induced goal conflicts. We hypothesize that 'errors' are mediated by underestimation of risk and failure to analyze the potential consequences of continuing with the initial plan. Stressors may further contribute to these effects. Suggestions for improving performance in these error-inducing contexts are discussed.

Orasanu, Judith↗

NASA NTRS2013

Common Web Mapping and Mobile Device Framework for Display of NASA Real-time Data

Scientists have strategic goals to deliver their unique datasets and research to both collaborative partners and more broadly to the public. These datasets can have a significant impact locally and globally as has been shown by the success of the NASA Short‐term Prediction Research and Transition (SPoRT) Center and SERVIR programs at Marshall Space Flight Center. Each of these respective organizations provides near real‐time data at the best resolution possible to address concerns of the operational weather forecasting community (SPoRT) and to support environmental monitoring and disaster assessment (SERVIR). However, one of the biggest struggles to delivering the data to these and other Earth science community partners is formatting the product to fit into an end user's Decision Support System (DSS). The problem of delivering the data to the end‐user's DSS can be a significant impediment to transitioning research to operational environments especially for disaster response where the deliver time is critical. The decision makers, in addition to the DSS, need seamless access to these same datasets from a web browser or a mobile phone for support when they are away from their DSS or for personnel out in the field. A framework has been developed for MSFC Earth Science program that can be used to easily enable seamless delivery of scientific data to end users in multiple formats. The first format is an open geospatial format, Web Mapping Service (WMS), which is easily integrated into most DSSs. The second format is a web browser display, which can be embedded within any MSFC Science web page with just a few lines of web page coding. The third format is accessible in the form of iOS and Android native mobile applications that could be downloaded from an "app store". The framework developed has reduced the level of effort needed to bring new and existing NASA datasets to each of these end user platforms and help extend the reach of science data.

Burks, Jason E.↗

NASA NTRS2013

Common Web Mapping and Mobile Device Framework for Display of NASA Real-time Data

Burks, Jason↗

NASA NTRS2004

A Decision Support Framework for Feasibility Analysis of International Space Station (ISS) Research Capability Enhancing Options

The assembly and operation of the ISS has generated significant challenges that have ultimately impacted resources available to the program's primary mission: research. To address this, program personnel routinely perform trade-off studies on alternative options to enhance research. The approach, content level of analysis and resulting outputs of these studies vary due to many factors, however, complicating the Program Manager's job of selecting the best option. To address this, the program requested a framework be developed to evaluate multiple research-enhancing options in a thorough, disciplined and repeatable manner, and to identify the best option on the basis of cost, benefit and risk. The resulting framework consisted of a systematic methodology and a decision-support toolset. The framework provides quantifiable and repeatable means for ranking research-enhancing options for the complex and multiple-constraint domain of the space research laboratory. This paper describes the development, verification and validation of this framework and provides observations on its operational use.

Ortiz, James N.↗

NASA NTRS2013

Innovations for ISS Plug-In Plan (IPiP) Operations

Limited resources and increasing requirements will continue to influence decisions on ISS. The ISS Plug-In Plan (IPiP) supports power and data for utilization, systems, and daily operations through the Electrical Power System (EPS) Secondary Power/Data Subsystem. Given the fluid launch schedule, the focus of the Plug-In Plan has evolved to anticipate future requirements by judicious development and delivery of power supplies, power strips, Alternating Current (AC) power inverters, along with innovative deployment strategies. A partnership of ISS Program Office, Engineering Directorate, Mission Operations, and International Partners poses unique solutions with existing on-board equipment and resources.

Moore, Kevin D.↗

NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗

NASA NTRS1980

Decision making and problem solving with computer assistance

In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.

Kraiss, F.↗

NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗

NASA NTRS1989

Designers as users - Design supports based on crew system design practices

The behavioral processes in aviation crew system design are theoretically discussed. Interviews with designers concerning actual design experiences were conducted in a structured manner, focusing on the nature of the design problem, the designers's tasks, how decisions governing the human operator's role are made, and how this decision making can be supported. Two taxonomies emerge, one of which summarizes the nature of the design problem and the second of which summarizes the tasks that designers perform toward solving the problem. Combining the taxonomies yields a design problem space that is used to classify existing forms of support. The results of this classification and the implications for needed supports are discussed.

Cody, William J.↗

NASA NTRS2023

Distributed Sensing and Reasoning for Advanced Air Mobility Health Management and Mission Assurance

As envisioned, Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) will introduce new vehicles and operations within the national airspace, moving people and cargo safely and efficiently at a much larger scale than today. Driven by transformative technology and revolutionary aircraft, this movement must still manage technical, regulatory, operational, and policy challenges. NASA’s work in support of AAM and UAM includes, but is not limited to tools, technologies, and architectures for distributed sensing of aircraft, data & reasoning services exchange, Human-Autonomy Teaming (HAT), contingency management, and vehicle health management. This paper builds upon these concepts and evaluates the use of distributed sensing and infrastructure assistance towards health management and mission assurance of UAM vehicles in specific operational scenarios. Through analysis of these example missions, aided by the data produced by the conceptual distributed sensing and reasoning infrastructure, we define opportunities for state estimation, diagnosis, and key decision points affecting the health state of the vehicle and the airspace volume. As a result, we define a number of measurable health state parameters providing relevant information to drive decision-making in contingency situations or feed automation tools in support of operators and managers.

Safety↗

NASA NTRS2016

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

The National Aeronautics and Space Administration's (NASA's) first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature ATM technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise time-based scheduling in the Terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools to enable precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind another aircraft. Recent simulations and IM algorithm development at NASA have focused on trajectory-based IM operations where aircraft equipped with IM avionics are expected to achieve a spacing goal, assigned by air traffic controllers, at the final approach fix. The recently published IM Minimum Operational Performance Standards describe five types of IM operations. This paper discusses the results and conclusions of a human-in-the-loop simulation that investigated three of those IM operations. The results presented in this paper focus on system performance and integration metrics. Overall, the IM operations conducted in this simulation integrated well with ground-based decisions support tools and certain types of IM operational were able to provide improved spacing precision at the final approach fix; however, some issues were identified that should be addressed prior to implementing IM procedures into real-world operations.

Swieringa, Kurt A.↗

NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗

NASA NTRS2000

Human Factors Report: TMA Operational Evaluations 1996 and 1998

The Traffic Management Advisor (TMA) is a component of the Center-TRACON Automation System (CTAS), a suite of decision-support tools for the air traffic control (ATC) environment which is being developed at NASA Ames Research Center. TMA has been operational at the ATC facilities in Dallas/Fort Worth, Texas, since an operational field evaluation in 1996. The Operational Evaluation demonstrated significant benefits, including an approximately 5 percent increase in airport capacity. This report describes the human factors results from the 1996 Operational Evaluation and an investigation of TMA usage performed two years later, during the 1998 TMA Daily Use Field Survey. The results described are instructive for CTAS focused development, and provide valuable lessons for future research in ATC decision-support tools where it is critical to merge a well-defined, complex work environment with advanced automation.

Lee, Katharine K.↗

NASA NTRS2004

Pilot in Command: An Illustration of Autonomous Flight Management

Several years of NASA research have produced the concept for air traffic management called "Distributed Air/Ground Traffic Management," a major operational advancement that should significantly increase the capacity of the National Airspace System. A key component, "Autonomous Flight Management," introduces a new class of aircraft operations in which pilots are authorized to freely maneuver and execute optimal trajectories independent from air traffic controllers. These aircraft operators would benefit from significant increases in flexibility to optimize all flight operations and from avoiding most of the delays associated with ground-controlled operations. Responsibilities for aircraft separation and arrival flow conformance are transferred to the flight deck, and the pilots use computerized decision-support tools to accomplish these tasks. A research prototype of these tools called the "Autonomous Operations Planner" is being developed at the NASA Langley Research Center. This 14-minute video illustrates Autonomous Flight Management from the airline pilot's perspective.

Wing, David J.↗

NASA NTRS2019

Field Evaluation of the Baseline Integrated Arrival, Departure, and Surface (IADS) Capabilities at Charlotte Douglas International Airport (CLT)

NASA is currently developing a suite of decision support capabilities for integrated arrival, departure, and surface (IADS) operations in a metroplex environment. The effort is being made in three phases, under NASA's Airspace Technology Demonstration 2 (ATD-2) sub-project, through a strong partnership with the Federal Aviation Administration (FAA), air carriers, airport, and general aviation community. The Phase 1 Baseline IADS capabilities provide enhanced operational efficiency and predictability of flight operations through data exchange and integration, tactical surface metering, and automated coordination of release time of controlled flights for overhead stream insertion. The users of the IADS system include the personnel at the Charlotte Douglas International Airport (CLT) air traffic control tower, American Airlines ramp tower, CLT terminal radar approach control (TRACON), and Washington Center. This paper describes the Phase 1 Baseline IADS capabilities and field evaluation conducted at CLT from September 2017 for a year. From the analysis of operations data, it is estimated that 538,915 kilograms of fuel savings, and 1,659 metric tons of CO2 emission reduction were achieved during the period with a total of 944 hours of engine run time reduction. The amount of CO2 savings is estimated as equivalent to planting 42,560 urban trees. The results have also shown that the surface metering had no negative impact on on-time arrival performance of both outbound and inbound flights. The technology transfer of Phase 1 Baseline IADS capabilities has been made to the FAA and aviation industry, and the development of additional capabilities for the subsequent phases is underway.

and Surface (IADS)↗

NASA NTRS2023

Exploration Medical Capability Clinical Decision Support Use Cases for CDSS Test Bed

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data, and crew time), limited options for evacuation, and those associated with delayed or constrained communications. Each of these challenges necessitates greater degrees of crew autonomy as our distance from Earth increases. Specifically, as communication delays intensify - and evacuation capability diminishes the further we explore space - the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will become key to mission continuation and success. This need will be especially true should a crewmember become ill or injured wherein treatment and disposition “in-situ” ultimately falls to the crew itself to determine. To augment the requisite knowledge, skills, and abilities (KSAs) of a time-constrained exploration mission crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution. A CDSS would facilitate, guide, and inform Earth-independent medical operations while assisting crewmembers through various clinical presentations. The CDSS would allow crewmembers to take advantage of pre-mission training tied to the in-flight/in-mission use of pre-planned protocols that offer both a range of diagnostic options and “just-in-time” (refamiliarization) training and assistance. CDSS will expand such capabilities by improving the utility and effectiveness of various available diagnostic, treatment, and health maintenance tools, techniques, and measures.

ExMC↗

NASA NTRS2016

How Do Lessons Learned on the International Space Station (ISS) Help Plan Life Support for Mars?

How can our experience in developing and operating the International Space Station (ISS) guide the design, development, and operation of life support for the journey to Mars? The Mars deep space Environmental Control and Life Support System (ECLSS) must incorporate the knowledge and experience gained in developing ECLSS for low Earth orbit, but it must also meet the challenging new requirements of operation in deep space where there is no possibility of emergency resupply or quick crew return. The understanding gained by developing ISS flight hardware and successfully supporting a crew in orbit for many years is uniquely instructive. Different requirements for Mars life support suggest that different decisions may be made in design, testing, and operations planning, but the lessons learned developing the ECLSS for ISS provide valuable guidance.

Lessons learned↗