A Pilot of Data-Driven Modeling to Assess Potential for Improved Efficiency in an Academic Breast-Imaging Center

Patient satisfaction and department efficiency are central pillars in defining quality in medicine. Patient satisfaction is often linked to wait times. We describe a novel method to study workflow and simulate solutions to improve efficiency, thereby decreasing wait times and adding value. We implemented a real-time location system (RTLS) in our academic breast-imaging department to study workflow, including measuring patient wait time, quantifying equipment utilization, and identifying bottlenecks. Then, using discrete event simulation (DES), we modeled solutions with changes in staffing and equipment. Nine hundred and ninety-nine patient encounters were tracked over a 10-week period. The RTLS system recorded 551,512 raw staff and patient time stamps, which were analyzed to produce 17,042 staff and/or patient encounter time stamps. Mean patient wait time was 27 min. The digital breast tomosynthesis (DBT) unit had the highest utilization rate and was identified as a bottleneck. DES predicts a 19.2% reduction in patient length of stay with replacement of a full field digital mammogram (FFDM) unit by a DBT unit and the addition of technologists. Through integration of RTLS with discrete event simulation testing, we created a model based on real-time data to accurately assess patient wait times and patient progress through an appointment, evaluate patient staff-interaction, identify system bottlenecks, and quantitate potential solutions. This quality improvement initiative has important implications, potentially allowing data-driven decisions for staff hiring, equipment purchases, and department layout.