Identifying Automation Opportunities in Life Science Processes through Operator Task Modeling and Workload Assessment

Abstract

In an effort to automate manual life science processes for high throughput and accuracy, we previously observed that perceived operator workload could be used to identify taxing tasks as targets for robotics. However, we also observed that other factors, including task time and step count, might influence workload. The objective of the present research was to determine whether technician perceptions of workload were driven by process method characteristics, specifically duration, number of steps, and numbers of motor and cognitive operations. Confirmation of influence of these characteristics on perceived workload was expected to provide further direction for automation development for specific methods. A hierarchical task analysis was prepared for a mercury analysis process and revealed various methods for accomplishing goals. Methods included sequences of operations, which were subsequently classified as perceptual, motor or cognitive in nature by using GOMS methodology (Goals, Operators, Methods, and Selection rules). A field study was conducted with three lab technicians completing the mercury analysis process in three replications. Perceived workload for each method was collected using the NASA-Task Load index (TLX). Significant positive correlations were found between method times and operation counts determined based on GOMS models with technician overall TLX ratings. Motor, cognitive and combinations of both operator counts were also correlated with TLX physical, mental demand and effort ratings, accordingly. In general, longer duration methods, including weighing, tuning and pipetting steps, appear to pose high workload for technicians and represent priority targets for automation. Furthermore, a sequence of recollection and planning operations as part of a pipetting task posed the greatest sustained cognitive load for technicians and may represent an opportunity for use of advanced robotic technology with capacity to act as an assistant to technicians.

Keywords: Cognitive Workload, Hierarchical Task Analysis, GOMS, Human-Automation Interaction, Life Science Processes

DOI: 10.54941/ahfe100232