ArticleJournal of the American Medical Informatics Association : JAMIA2021
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.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study.JMIR formative research · 2025Article
- Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.Yearbook of medical informatics · 2022Article
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Authors and funding
5 authors.
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Abstract
objectiveThis 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
methodsStructural 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.
resultsTen 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.
conclusionsStructural 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.
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