Identifying step-level complexity in procedures: Integration of natural language processing into the Complexity Index for Procedures—Step level (CIPS)

Abstract

Abstract Task complexity plays an important role in performance and procedure adherence. While studies have attempted to assess the contribution of different aspects of task complexity and their relationship to workers’ performance and procedure adherence, only a few have focused on application-specific measurement of task complexity. Further, generalizable methods of operationalizing task complexity that are used to both write and evaluate a wide range of routine or non-routine procedures is largely absent. This paper introduces a novel framework to quantify the step-level complexity of written procedures based on attributes such as decision complexity, need for judgment, interdependency of instructions, multiplicity of instructions, and excess information. This framework was incorporated with natural language processing and artificial intelligence to create a tool for procedure writers for identifying complex elements in procedures steps. The proposed technique has been illustrated through examples as well as an application to a tool for procedure writers. This method can be used both to support writers when constructing procedures as well as to examine the complexity of existing procedures. Further, the complexity index is applicable across several high-risk industries in which written procedures are prevalent, improving the linguistic complexity of the procedures and thus reducing the likelihood of human errors with procedures associated with complexity.