Related Experiment Video
Updated: Dec 1, 2025
03:07
Single-Port Robotic-assisted Transaxillary Breast-conserving Surgery: A Prospective, Single-arm, Non-randomized Phase IIa Clinical Trial
Published on: August 19, 2025
524
Using an Agent-based Model to Examine Deimplementation of Breast Cancer Screening
Sarah A Nowak1, Andrew M Parker2, Archana Radhakrishnan3
1Larner College of Medicine, University of Vermont, Burlington, VT.
Medical Care
|November 9, 2020
Summary
Provider social networks and patient experiences significantly influence breast cancer screening deimplementation. Peer effects among providers can entrench current practices, impacting population screening rates.
Area of Science:
- Public Health
- Health Services Research
- Social Network Analysis
Background:
- Breast cancer screening deimplementation is a growing concern.
- Provider social networks and patient interactions may influence screening decisions.
- Understanding these dynamics is crucial for optimizing screening guidelines.
Purpose of the Study:
- To examine how provider social networks and patient experiences affect breast cancer screening deimplementation.
- To model the impact of social influences on screening behaviors over time.
Main Methods:
- Construction of the Breast Cancer-Social network Agent-based Model (BC-SAM) for 10,000 women (40+) over 30 years.
- Incorporation of patient and provider modules with social network influences.
- Calibration of provider decisions using data from the CanSNET national survey.
Main Results:
- Decreased provider recommendations for younger/older women led to reduced screening rates in the 50-74 age group via spillover.
- Patient screening rates responded slowly to changes in provider recommendations.
- Provider experiences with patients/family modestly increased screening recommendations.
- Provider peer effects substantially impacted population screening rates and entrenched existing practices.
Conclusions:
- Modeling cancer screening as a complex social system reveals potential effects on deimplementation.
- Findings can inform targeted interventions to reduce overscreening.
- Social network analysis provides insights into screening behavior dynamics.
