Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology:
Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance.
Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks.
Provides a standard model upon which to build an empirical study to evaluate task complexity.
Humans can contribute to error at all stages of the medical device product life-cycle. Use error associated with medical devices can result in catastrophic consequences for end users and inefficient use of healthcare system resources. Industry-wide statistics about medical device use error has the potential to aid in identifying opportunities for human factors intervention, however publicly available statistics are sparse. The Food and Drug Administration (FDA) requires medical device manufactures, importers, and device user facilities to track and report adverse events for post-market surveillance through medical device reports (MDRs). This data is available in an online database: Manufacturer and User Facility Experience (MAUDE). This study provides a comprehensive evaluation of use error adverse events in MAUDE (2010-2018) based on device class, device operator, and event outcome, to address the lack of industry-wide statistics on medical device use error. Results indicate that use error is significantly represented in adverse event reporting, constituting 28.1% of reports labeled with device problem codes. Events associated with patient device operators were predominately associated with diabetes-related medical devices, while provider operators were associated with a wider array of devices. Additionally, it was found that most use error reports were attributed to issues with device output; using the device in accordance with manufacturer expectations; and physically activating, positioning, or separating device components. This work demonstrates the viability of using MAUDE to attain industry wide statistics on medical device use error for later integration in industry-wide or device-specific risk mitigation strategies.
Human beings are physically and cognitively variable, leading to a wide array of potential system use cases. To design safe and effective systems for highly heterogeneous populations, engineers must cater to this variability to minimize the chance of error and system failure. This can be a challenge because of the increasing costs associated with providing additional product variety. Most guidance for navigating these trade-offs is intended for late-stage design, when significant resources have been expended, thus risking expensive redesign or exclusion of users when new human concerns become apparent. Despite the critical need to evaluate accommodation-cost trade-offs in early stages of design, there is currently a lack of structured guidance. In this work, an approach to function modeling is proposed that allows the simultaneous consideration of human and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost. The model was demonstrated on a design case study. 16 senior mechanical engineering students were recruited and tasked with performing the allocation task manually. The results were compared to the output of the optimization model. Results indicated that participants were unable to produce concepts with the same accommodation-cost efficiency as the optimization model. Further, the optimization model successfully produced a wide range of potential product concepts, demonstrating its utility as a decision-aid.
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