A network model of activities in primary care consultations
Open accessAbstract
OBJECTIVE: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. MATERIALS AND METHODS: This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. RESULTS: Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient's present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. DISCUSSION: Primary care consultations do not appear to follow a classic linear model of defined information seeking activities; rather, they are fragmented, highly interdependent, and can be reactively triggered. CONCLUSION: The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries.
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Clinical encounters do not necessarily follow a linear order of their components, which exacerbates summarization or information extraction.
“However, clinical encounters do not necessarily follow a linear order of their components,27,28 which exacerbates summarization or information extraction.22”
From Challenges of developing a digital scribe to reduce clinical documentation burden (2019) · cites this paper as [28]
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Challenges of developing a digital scribe to reduce clinical documentation burden
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- Authors
- Kocaballi, A. B., Coiera, E., Tong, H. L., White, S. J., Quiroz, J. C., Rezazadegan, F., Willcock, S., & Laranjo, L.
- Affiliations
- Macquarie University (AU)
- Corresponding author
- Coiera, Enrico
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-22
- Volume
- 26
- Issue
- 10
- Pages
- 1074-1082
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC6748800
- OpenAlex
- W2942340548
- Cited by
- 23
- References
- 17
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 6 · 2021: 2 · 2020: 5 · 2019: 1
- FWCI
- 3.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- UTS ePRESS (University of Technology Sydney) (submittedVersion, other-oa)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- National Health and Medical Research Council (APP1134919)
- Centre for Research Excellence in Digital Health
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Health Sciences › Health Professions › General Health Professions › Primary Care and Health Outcomes
- Keywords
- primary care visits, clinical documentation, verbal cues, consultation structure, medical history
- MeSH terms
- Automation, Documentation, Family Practice, Humans, Medical History Taking, Natural Language Processing, Physical Examination, Primary Health Care, Electronic Health Records
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