Using multiple cognitive task analysis methods for supervisory control interface design in high-throughput biological screening processes

Cognitive task analysis (CTA) approaches are currently needed in many domains to provide explicit guidance on redesigning existing systems. This study used goal-directed task analysis (GDTA) along with abstraction hierarchy (AH) modeling to characterize the knowledge structure of biopharmacologists in planning, executing and analyzing the results of high-throughput organic compound screening operations, as well as the lab automation and equipment used in these operations. It was hypothesized that combining the results of the GDTA and AH models would provide a better understanding of complex system operator needs and how they may be addressed by existing technologies, as well as facilitate identification of automation and system interface design limitations. We used comparisons of the GDTA and AH models along with taxonomies of usability heuristics and types of automation in order to formulate interface design and automation functionality recommendations for existing software applications used in biological screening experiments. The proposed methodology yielded useful recommendations for improving custom supervisory control applications that led to prototypes of interface redesigns. The approach was validated through an expert usability evaluation of the redesigns and was shown to be applicable to the life sciences domain.