Identifying patterns in administrative tasks through structural topic modeling: A study of task definitions, prevalence, and shifts in a mental health practice's operations during the COVID-19 pandemic.

Abstract

This case study illustrates the use of natural language processing for identifying administrative task categories, prevalence, and shifts necessitated by a major event (the COVID-19 [coronavirus disease 2019] pandemic) from user-generated data stored as free text in a task management system for a multisite mental health practice with 40 clinicians and 13 administrative staff members. Structural topic modeling was applied on 7079 task sequences from 13 administrative users of a Health Insurance Portability and Accountability Act-compliant task management platform. Context was obtained through interviews with an expert panel. Ten task definitions spanning 3 major categories were identified, and their prevalence estimated. Significant shifts in task prevalence due to the pandemic were detected for tasks like billing inquiries to insurers, appointment cancellations, patient balances, and new patient follow-up. Structural topic modeling effectively detects task categories, prevalence, and shifts, providing opportunities for healthcare providers to reconsider staff roles and to optimize workflows and resource allocation.