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 Swipe
Dessislava A. Pachamanova
,
Wiljeana Jackson Glover
,
Zhi Li
,
Michael Docktor
,
Nitin Gujral
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocab185
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocab185
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.
Related Topics
Concepts
Task (project management)
Context (archaeology)
Pandemic
Workflow
Mental health
Health Insurance Portability and Accountability Act
Software portability
Computer science
Health care
Task analysis
Applied psychology
Psychology
Medicine
Coronavirus disease 2019 (COVID-19)
Disease
Computer security
Confidentiality
Psychiatry
Political science
Geography
Database
Infectious disease (medical specialty)
Pathology
Law
Archaeology
Programming language
Economics
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocab185
- https://academic.oup.com/jamia/article-pdf/28/12/2707/41325424/ocab185.pdf
- OA Status
- bronze
- Cited By
- 9
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3195151010
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3195151010Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/jamia/ocab185Digital Object Identifier
- Title
-
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 pandemicWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-12Full publication date if available
- Authors
-
Dessislava A. Pachamanova, Wiljeana Jackson Glover, Zhi Li, Michael Docktor, Nitin GujralList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocab185Publisher landing page
- PDF URL
-
https://academic.oup.com/jamia/article-pdf/28/12/2707/41325424/ocab185.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/jamia/article-pdf/28/12/2707/41325424/ocab185.pdfDirect OA link when available
- Concepts
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Task (project management), Context (archaeology), Pandemic, Workflow, Mental health, Health Insurance Portability and Accountability Act, Software portability, Computer science, Health care, Task analysis, Applied psychology, Psychology, Medicine, Coronavirus disease 2019 (COVID-19), Disease, Computer security, Confidentiality, Psychiatry, Political science, Geography, Database, Infectious disease (medical specialty), Pathology, Law, Archaeology, Programming language, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 1, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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