Integrating AI into Clinical Workflows: A Simulation Study on Implementing AI-aided Same-day Diagnostic Testing Following an Abnormal Screening Mammogram.
Authors
Affiliations (9)
Affiliations (9)
- Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Family Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- UCLA Clinical and Translational Science Institute, Los Angeles, CA, USA.
- Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
- Naveen Jindal School of Management at the University of Texas at Dallas, Richardson, TX, USA.
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
- Department of Biomedical and Translational Sciences, Carle Illinois School of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
Abstract
Artificial intelligence (AI) shows promise in clinical tasks, yet its integration into workflows remains underexplored. This study proposes an AI-aided same-day diagnostic imaging workup to reduce recall rates following abnormal screening mammograms and alleviate patient anxiety while waiting for the diagnostic examinations. Using discrete simulation, we found minimal disruption to the workflow (a 4% reduction in daily patient volume or a 2% increase in operating time) under specific conditions: operation from 9 am to 12 pm with all radiologists managing all patient types (screenings, diagnostics, and biopsies). Costs specific to the AI-aided same-day diagnostic workup include AI software expenses and potential losses from unused pre-reserved slots for same-day diagnostic workups. These simulation findings can inform the implementation of an AI-aided same-day diagnostic workup, with future research focusing on its potential benefits, including improved patient satisfaction, reduced anxiety, lower recall rates, and shorter time to cancer diagnoses and treatment.