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Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing
Yannan Lin1, Anne C Hoyt2, Vladimir G Manuel3,4
1Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Summary
This study introduces an AI-aided diagnostic imaging workup to reduce mammogram recalls and patient anxiety. Simulation shows minimal workflow disruption, suggesting feasibility for faster cancer diagnosis and treatment.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Healthcare
Background:
- Clinical integration of artificial intelligence (AI) in diagnostic imaging workflows is not well-established.
- Abnormal screening mammograms often lead to patient anxiety due to delays in diagnostic examinations and potential recall rates.
Purpose of the Study:
- To evaluate the feasibility and workflow impact of an AI-aided, same-day diagnostic imaging workup.
- To reduce patient anxiety and recall rates following abnormal screening mammograms.
Main Methods:
- Discrete simulation modeling was employed to assess workflow changes.
- The simulation considered specific operational parameters: 9 am to 12 pm operation, and radiologists managing all patient types (screenings, diagnostics, biopsies).
Main Results:
- The AI-aided same-day workup demonstrated minimal workflow disruption, with a 4% reduction in daily patient volume or a 2% increase in operating time.
- Identified costs include AI software expenses and potential losses from unused same-day diagnostic slots.
Conclusions:
- AI-aided same-day diagnostic imaging workups are potentially feasible with minimal workflow impact.
- Further research should explore benefits like improved patient satisfaction, reduced anxiety, lower recall rates, and expedited cancer diagnosis and treatment.

