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Updated: Aug 5, 2026
14:32
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
Published on: February 16, 2011
Visualizing dermatology clinic routines using narrative networks
Julie Ryan Wolf1, Amanda Rodriguez Orengo2, Gayin Lee1
1Department of Dermatology, University of Rochester Medical Center, Rochester, New York, USA.
Summary
Understanding clinic routines requires mapping interdependent events. Narrative event networks revealed physicians had the highest awareness, while other staff roles showed limited understanding of the complete patient visit process.
Area of Science:
- Health Services Research
- Organizational Behavior
- Information Science
Background:
- Healthcare routines involve interdependent events crucial for patient care.
- Understanding individual roles in these routines is vital for process efficiency.
- Disparities in understanding organizational routines among staff complicate empirical research.
Purpose of the Study:
- To investigate and visualize the complex processes within a dermatology clinic.
- To identify and analyze the varying levels of role-specific awareness in clinic routines.
- To explore the utility of narrative event networks in mapping interdependent healthcare activities.
Main Methods:
- Qualitative structured interviews were conducted with 36 dermatology clinic staff members.
- Role-specific clinic visit activities were captured through these interviews.
- ThreadNet software was used to create narrative event networks for visualizing clinic processes.
Main Results:
- Physicians demonstrated the highest cross-role awareness, understanding 54% of clinic activities.
- Outpatient access specialists (25%) and residents (27%) had the least understanding of the complete visit process.
- Licensed practical nurses understood 37% of the complete visit process.
Conclusions:
- Narrative event networks effectively visualize fragmented patterns in clinic routines.
- Enhancing cross-role awareness is critical for improving healthcare process efficiency.
- Applying narrative networks to sequence data can aggregate interdependent patterns for better process understanding.
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