Vista goes online: Decision-analytic systems for real-time decision-making in mission control

The Vista project has centered on the use of decision-theoretic approaches for managing the display of critical information relevant to real-time operations decisions. The Vista-I project originally developed a prototype of these approaches for managing flight control displays in the Space Shuttle Mission Control Center (MCC). The follow-on Vista-II project integrated these approaches in a workstation program which currently is being certified for use in the MCC. To our knowledge, this will be the first application of automated decision-theoretic reasoning techniques for real-time spacecraft operations. We shall describe the development and capabilities of the Vista-II system, and provide an overview of the use of decision-theoretic reasoning techniques to the problems of managing the complexity of flight controller displays. We discuss the relevance of the Vista techniques within the MCC decision-making environment, focusing on the problems of detecting and diagnosing spacecraft electromechanical subsystems component failures with limited information, and the problem of determining what control actions should be taken in high-stakes, time-critical situations in response to a diagnosis performed under uncertainty. Finally, we shall outline our current research directions for follow-on projects

NOVEL INTERACTION TECHNIQUES FOR COLLABORATING ON WALL-SIZED DISPLAYS.

poster abstractPerforming and collaborating on information-intensive tasks - like review-ing and analyzing multiple charts - is an essential, but currently difficult, ac-tivity in desktop environments. The problem is the low resolution of the dis-play that forces users to visualize only few pieces of information concurrent-ly, and to switch focus very frequently. To facilitate productivity and collabo-rative decision-making, teams of users are increasingly adopting wall-sized interactive displays. Yet, to harness the full potential of these devices, it is critical to understand how to best support inter-member cognition and navi-gation in such large information spaces. To navigate information, the wall-display’s overwhelming size (often 18 X 6 feet) make existing desktop-driven interaction and organization techniques (like “point-and-click” and “taskbar”) extremely inefficient. Also, with time, users get exhausted walk-ing to reach different elements spread over the wall-display. Moreover, being aware of the collaborative events happening around the display, while work-ing on it, often exceeds users’ cognitive capacity. To address these limita-tions, we are investigating four novel interaction techniques for wall-display user experiences. “Timeline” allows browsing large collections of elements over time, while or after collaborative work; “Cabinet” supports temporary storage and effortless retrieval of displayed elements; “Magnet” enables us-ers to virtually reach remote objects on the wall display; “In-focus” allows facilitated and non-intrusive awareness of members’ interaction. We are planning to prototype and evaluate these techniques using off-the-shelf in-put modalities such as multi-touch gesture and mid-air gesture, as well as software and wall-sized displays made available by the University Infor-mation Technology Services (UITS) at IUPUI. In our evaluation with users, we hypothesize that, with respect to desktop interaction techniques, the proposed techniques will increase efficiency in navigation and information organization tasks, reduce perceived cognitive load, while at the same time engender better collaboration and decision-making

Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

Cockpit weather information needs

The primary objective is to develop an advanced pilot weather interface for the flight deck and to measure its utilization and effectiveness in pilot reroute decision processes, weather situation awareness, and weather monitoring. Identical graphical weather displays for the dispatcher, air traffic control (ATC), and pilot crew should also enhance the dialogue capabilities for reroute decisions. By utilizing a broadcast data link for surface observations, forecasts, radar summaries, lightning strikes, and weather alerts, onboard weather computing facilities construct graphical displays, historical weather displays, color textual displays, and other tools to assist the pilot crew. Since the weather data is continually being received and stored by the airborne system, the pilot crew has instantaneous access to the latest information. This information is color coded to distinguish degrees of category for surface observations, ceiling and visibilities, and ground radar summaries. Automatic weather monitoring and pilot crew alerting is accomplished by the airborne computing facilities. When a new weather information is received, the displays are instantaneously changed to reflect the new information. Also, when a new surface or special observation for the intended destination is received, the pilot crew is informed so that information can be studied at the pilot's discretion. The pilot crew is also immediately alerted when a severe weather notice, AIRMET or SIGMET, is received. The cockpit weather display shares a multicolor eight inch cathode ray tube and overlaid touch panel with a pilot crew data link interface. Touch sensitive buttons and areas are used for pilot selection of graphical and data link displays. Time critical ATC messages are presented in a small window that overlays other displays so that immediate pilot alerting and action can be taken. Predeparture and reroute clearances are displayed on the graphical weather system so pilot review of weather along the route can be accomplished prior to pilot acceptance of the clearance. An ongoing multiphase test series is planned for testing and modifying the graphical weather system. Preliminary data shows that the nine test subjects considered the graphical presentation to be much better than their current weather information source for situation awareness, flight safety, and reroute decision making

Configural decision support tool for schedule management of multiple unmanned aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 104-108).As unmanned aerial vehicles (UAVs) become increasingly autonomous, current single-UAV operations involving multiple personnel could transition to a single operator simultaneously supervising multiple UAVs in high-level control tasks. These time-critical, single-operator systems will require advance prediction and mitigation of schedule problems to ensure mission success. However, actions taken to address current schedule problems may create more severe future problems. Decision support could help multi-UAV operators evaluate different schedule management options in real-time and understand the consequences of their decisions. This thesis describes two schedule management decision support tools (DSTs) for single-operator supervisory control of four UAVs performing a time-critical targeting mission. A configural display common to both DSTs, called StarVis, graphically highlights schedule problems during the mission, and provides projections of potential new problems based upon different mission management actions. This configural display was implemented into a multi-UAV mission simulation as two different StarVis DST designs, Local and Q-Global. In making schedule management decisions, Local StarVis displayed the consequences of potential options for a single decision, while the Q-Global design showed the combined effects of multiple decisions. An experiment tested the two StarVis DSTs against a no DST control in a multi-UAV mission supervision task. Subjects using the Local StarVis performed better with higher situation awareness and no significant increase in workload over the other two DST conditions. The disparity in performance between the two StarVis designs is likely explained by the Q-Global StarVis projective "what if" mode overloading its subjects with information. This research highlights how decision support designs applied at different abstraction levels can produce different performance results.by Amy S. Brzezinski.S.M

Inter-trial effects in visual pop-out search: factorial comparison of Bayesian updating models

Many previous studies on visual search have reported inter-trial effects, that is, observers respond faster when some target property, such as a defining feature or dimension, or the response associated with the target repeats versus changes across consecutive trial episodes. However, what processes drive these inter-trial effects is still controversial. Here, we investigated this question using a combination of Bayesian modeling of belief updating and evidence accumulation modeling in perceptual decision-making. In three visual singleton (`pop-out') search experiments, we explored how the probability of the response critical states of the search display (e.g., target presence/absence) and the repetition/switch of the target-defining dimension (color/ orientation) affect reaction time distributions. The results replicated the mean reaction time (RT) inter-trial and dimension repetition/switch effects that have been reported in previous studies. Going beyond this, to uncover the underlying mechanisms, we used the Drift-Diffusion Model (DDM) and the Linear Approach to Threshold with Ergodic Rate (LATER) model to explain the RT distributions in terms of decision bias (starting point) and information processing speed (evidence accumulation rate). We further investigated how these different aspects of the decision-making process are affected by different properties of stimulus history, giving rise to dissociable inter-trial effects. We approached this question by (i) combining each perceptual decision making model (DDM or LATER) with different updating models, each specifying a plausible rule for updating of either the starting point or the rate, based on stimulus history, and (ii) comparing every possible combination of trial-wise updating mechanism and perceptual decision model in a factorial model comparison. Consistently across experiments, we found that the (recent) history of the response-critical property influences the initial decision bias, while repetition/ switch of the target-defining dimension affects the accumulation rate, likely reflecting an implicit `top-down' modulation process. This provides strong evidence of a disassociation between response- and dimension-based inter-trial effects

The Contribution of Patient Reported Outcome Measures to Shared Decision-Making in Radiation Oncology at a Midwestern Comprehensive Cancer Center

Background. Chronic diseases, such as lung cancer, require a provider-patient relationship developed over time. This relationship fosters shared decision-making (SDM), a collaborative, dynamic information exchange and analysis between provider and patient regarding treatment and desired outcomes. Established benefits to SDM include an improved quality of life and decreased anxiety and depression. Despite established benefits, recent research suggests radiation oncologists are not engaging in SDM. A decision-aid tool utilizing patient reported outcome measures may increase SDM between radiation oncologists and patients with lung cancer. Patient-reported outcome measures, wherein the patient provides direct assessment of their health and quality of life, can inform and initiate SDM. This study investigated the design and implementation of a collaborative decision-aid tool for patients with lung cancer at a Midwestern cancer center as informed by stakeholders, practice considerations, and the evidence base. Objectives. The primary objective was to develop a collaborative decision-aid tool, using patient-reported outcome measures, that can be implemented in an academic radiation oncology clinic. Secondary objectives then assessed the tool’s impact through surrogates of shared decision making (add-on oncology visits, concomitant medication prescriptions), medical management (adverse events, radiation therapy compliance, chemotherapy compliance) and emergent care and its costs (emergency room visits and estimated costs, inpatient admissions and estimated costs). The hypothesized result was a decision aid designed to increase collaborative communication between radiation oncologists and patients will result in improved shared decision making, yielding better medical management and patient outcomes and reducing emergent care costs. Lastly, an implementation roadmap provided information on experienced barriers, facilitators, and considerations for performance objectives. Materials and Methods. A sequential exploratory mixed methods design was employed. The qualitative strand explored how stakeholders, practice considerations, and the evidence base informed the design and installation of an ideal collaborative decision-aid tool. Semi-structured interviews were completed with both patients who completed radiation therapy for lung cancer and their radiation oncologist. Interviews were coded and evaluated for themes. Interviews were transcribed verbatim, coded using Atlas.ti software, and analyzed thematically and visually. The results of this analysis, combined with information from the literature base and implementation stakeholders, was used to inform design of the collaborative decision-aid tool that was installed employing the principles of clinical implementation using the plan-do-study-act (PDSA) implementation cycle model. Simple descriptive analysis was performed on objective measures. Mixed analysis included data display, comparison, and integration. Results. Six patients and six radiation oncologists participated in the semi-structured interviews. Interviews provided insights that patients did not know what to ask of their radiation oncologists, prioritized survival over reduced side effects, and minimized complaints to their radiation oncologists, often to their detriment. Interviews yielded feedback on commonly used patient reported outcome instruments, identifying context as important and the recall timeframe as difficult. Commonly patient-identified adverse events of concern were fatigue, dyspnea, vomiting, and dysphagia. Radiation oncologists identified a patient’s personality as critical to care and translating responses and symptoms to adverse events of treatment. For this reason, numeric scales were not endorsed as they were seen as ambiguous and lacking context. With this feedback, a collaborative decision-aid tool was designed that focused on adverse events of interest (nausea, vomiting, fatigue, dyspnea, chest pain, weight loss). Rather than numeric scales, responses provided granular context that clued physicians to medical needs (i.e., “I cannot walk to my appointment,” “It hurts when I eat,” “I am not vomiting but I’m not hungry”). This tool was implemented as a quality initiative project for pragmatic impact. Four patients were assigned the tool during the first PDSA implementation cycle. The first follow-up evaluation meeting identified four critical outcomes for the next implementation cycle: how to identify which consults require the decision-aid, how the need for the decision-aid on doctor visits is consistently provided to scheduling, how unplanned visits/special complaints are addressed with regard to the decision-aid, and what actions are necessary if the patient leaves prior to the decision-aid being reviewed. Mixed analysis provided direction for next steps in implementation, tool design, and quantitative data measures. The primary concern, increase in time expended per clinic visit, was not supported by the limited data available from the first implementation cycle. Conclusion. Implementation of collaborative decision-aid within the radiation oncology clinic is feasible without disruption of the on-treatment visit time. Radiation oncologists can use the tool as a guide for routine on-treatment visit review, so that it is harmonized with their routine practice. Care should be taken during implementation to ensure all stakeholders are included in the tool’s implementation and that desired outcomes are appropriately identified to truly capture what impact the tool has, if any, on clinical outcomes. Focusing on the patient with the goal of improving their experience will guide collaborative decision-aid tool adaptation, implementation, and uptake

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care