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,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.
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Design and Prototyping of a Cognitive Model-based Decision Support Tool for Anesthesia Provider Management of Crisis Situations
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