Browsing by Author "Regina Stoll, Committee Member"

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    Cognitive Task Analyses for Life Science Automation Training Program Design.
    (2008-08-11) Green, Rebecca; Regina Stoll, Committee Member; Robert St. Amant, Committee Member; Christopher Mayhorn, Committee Co-Chair; David Kaber, Committee Chair
    The purpose of this study was to develop a systematic approach to the translation of Cognitive Task Analyses (CTAs), including Goal Directed Task Analysis (GDTA) and Abstraction Hierarchy (AH) models, into a Situation Awareness (SA) based training program for operators of High-throughput (biological) screening (HTS) systems. Traditional on-the-job (OTJ) training of new HTS operators usually consists of several weeks of assisting a lead biochemist to become familiar with methods and automated systems. Unfortunately, this approach to training is typically unstructured and learning results may be highly variable. In order to design instruction to support learning of cognitive processes as part of HTS, the information demands engendered by the task need to be identified. This can be achieved using CTAs as the basis for training program design. Various CTA methods, including the Critical Decision Method (CDM) and Precursor-Action-Results-Interpretation, have been used to develop training. However, no standardized methods exist for relating the outcomes of the integration of multiple CTA methods to support training program design. This study, therefore, combined information requirements from a GDTA and system resource requirements identified through AH models to establish content on HTS processes for delivery through an electronic training program. The goals and sequences of task steps within the training program were identified by the GDTA. The use of AH models of the HTS system provided a method for determining the purpose and function of the software and devices relative to different operator functional requirements. This combination of information from the CTAs provided a systematic approach for specifying training strategies and parameters. The training program presented learners with content for development of the three levels of operator SA (perception, comprehension, and projection) and knowledge structures pertaining to HTS system operations. Following development of the prototype electronic training program and the comparison traditional training program, an evaluation occurred through a three-part survey with comparison to the traditional lab training provided to expert operators of an HTS system. The evaluation incorporated two knowledge assessment tests, a usability survey, and a survey of the effectiveness of the SA elements of the training program. Results provided preliminary evidence that a CTA-based training program can improve operators' knowledge structures beyond OTJ training. Furthermore, operator performance on SA questions indicated improvements in knowledge structures associated with perceptual elements, comprehension of those elements, and projection of the future states of HTS systems. Additionally, since experience can lead to differences in operator mental models pertaining to HTS systems, the effect of two types of overall experience and individual task experience were measured. Results indicated that the CTA-based training program was effective in providing improved SA knowledge and general knowledge structures for HTS operators beyond their initial knowledge of the system (i.e., considering work experience and education). A heuristic-based evaluation of both training programs identified few unique usability problems, suggesting the usability of the training programs did not interfere with the development of learner knowledge structures. Finally, on the basis of these results, a set of general guidelines for the design of the CTA-based training programs was developed. These guidelines included methods for structuring the components of the training program to support the three levels of SA and the amount of text that should be shown for each task.
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    Design and Prototyping of a Cognitive Model-based Decision Support Tool for Anesthesia Provider Management of Crisis Situations
    (2006-08-21) Segall, Noa; Regina Stoll, Committee Member; Gary Mirka, Committee Member; Christopher Mayhorn, Committee Member; Melanie Wright, Committee Member; Robert St. Amant, Committee Member; David B. Kaber, Committee Chair
    This research involved the prototyping of a decision support tool (expert system) for use by anesthetists in crisis situations, in order to promote prompt and accurate patient diagnosis, care, and safety. The tool alerts anesthetists to a developing crisis, manifested by changes in certain patient physiological variables, and provides them with a list of potential causes and preventive measures for dealing with the crisis. The tool provides advice in an unobtrusive manner. Information is presented in a format requiring minimal interaction with the system interface. Decision support tools for managing patient crisis situations may be useful in large hospitals where an attending anesthesiologist supervises multiple nurse anesthetists or anesthesiology residents that are delivering drugs to patients across operating rooms. Such a tool can provide support to nurses and residents when the attending physician is not present, and can warn of potential crisis situations that would prompt the anesthesia provider to contact an attending physician. The attending physician may also use the tool as a quick method of learning patient status when entering an OR. In addition, the tool could be used by practitioners working alone to deliver anesthesia. A novel approach was applied to the development of the decision support tool to support anesthesiology decision-making. First, a hierarchical task analysis was conducted to identify the procedures of the anesthetist in detecting, diagnosing, and treating a critical incident, specifically, myocardial infarction. Second, a cognitive task analysis was carried out to elicit the necessary goals, decisions, and information requirements of anesthetists during crisis management procedures. The results of these analyses were then used as bases for coding a cognitive model using GOMS (goals, operators, methods, selection rules), a high-level cognitive modeling language. EGLEAN (error-extended GOMS language evaluation and analysis tool), an integrated modeling environment, was used as a platform for developing and compiling the GOMS model and applying it to a Java-based simulation of a patient status display. After the anesthetist's decision-making process was captured in GOMS, a basic interface for the decision support tool was prototyped (extending traditional OR displays) to present output from the computational cognitive model by using ecological interface design principles. Finally, a preliminary validation of the tool and interface (patient state and cognitive model output displays) was performed with samples of expert anesthesiologists and human factors professionals in order to assess the usability and applicability of the decision support tool. The anesthesiologists indicated that they would use the decision support tool in crisis situations and would recommend its use by junior anesthesia providers. The human factors experts provided comments on the interface's compliance with usability principles, such as providing prompt feedback and preventing errors. This research has provided insight into anesthetist decision-making processes in crisis management. It resulted in a prototype of a cognitive model-based decision support tool to augment anesthetist decision-making abilities in these situations.