A network model of activities in primary care consultations

Ahmet Kocaballi, Enrico Coiera, Huong Ly Tong, Sarah J. White, Juan Quiroz, Fahimeh Rezazadegan, Simon Willcock, Liliana Laranjo da Silva

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
22 Downloads (Pure)


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.
Original languageEnglish
Article numberocz046
Pages (from-to)1074-1082
Number of pages9
JournalJournal of the American Medical Informatics Association : JAMIA
Issue number10
Early online date22 Apr 2019
Publication statusPublished - 1 Oct 2019

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • digital scribe
  • electronic health record
  • general practitioners
  • medical informatics
  • primary health care
  • speech-based summarization


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