Dessislava Pachamanova1, Wiljeana Glover2, Zhi Li2, Michael Docktor3,4, Nitin Gujral3. 1. Mathematics and Science Division, Babson College, Wellesley, Massachusetts, USA. 2. Operations and Information Management Division, Babson College, Wellesley, Massachusetts, USA. 3. Dock Health, Boston, Massachusetts, USA. 4. Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Lawrence P Casalino; Sean Nicholson; David N Gans; Terry Hammons; Dante Morra; Theodore Karrison; Wendy Levinson Journal: Health Aff (Millwood) Date: 2009-05-14 Impact factor: 6.301
Authors: Julie Ann Sakowski; James G Kahn; Richard G Kronick; Jeffrey M Newman; Harold S Luft Journal: Health Aff (Millwood) Date: 2009-05-14 Impact factor: 6.301
Authors: Jonathan D Hron; Chase R Parsons; Lee Ann Williams; Marvin B Harper; Fabienne C Bourgeois Journal: Appl Clin Inform Date: 2020-07-01 Impact factor: 2.342
Authors: Kelly J Thomas Craig; Van C Willis; David Gruen; Kyu Rhee; Gretchen P Jackson Journal: J Am Med Inform Assoc Date: 2021-04-23 Impact factor: 4.497