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Purpose: To identify the optimal scheduled time slot for magnetic resonance cholangiopancreatography (MRCP) exams while accounting for the inherent variability in MR exam durations.
Methods: MRCP with contrast exams performed between 10/1/22 and 3/1/22 were selected for analysis. Exam duration was defined as the time between the beginning of the first sequence and the end of the final sequence and was calculated using Quantivly, a platform harmonizing DICOM metadata across vendors to build a new ontology of imaging operations. Exams were considered to fit within one of three time slots (30, 45, and 60-minute) if completed within the nominal time less 10 minutes to allow time to change the room over between patients. The time slot that contained the highest percentage of exams was considered optimal. The percentage of exams that fit in the optimal time slot was compared for all exams grouped by patient status (inpatient, outpatient, emergency) and analyzed for all outpatient exams, and outpatient exams grouped by radiologist protocol (biliary or pancreas) and scanner (N=7).
Results: The 45-minute time slot was optimal for all exam groupings. 72% (N=152) of inpatient exams fit within the 45min. time slot compared to 84% (N=474) of outpatient exams. The 25th, 50th and 75th percentiles of all outpatient exam durations were 23.7min., 27.2min., and 30.7min., respectively. 87% (N=292) of pancreas exams fit in the 45min. time slot compared to 79% of biliary exams (N=182). The percentage of exams that fit in the 45min. time slot ranged from 80-88% for scanners that performed over 10 outpatient exams.
Conclusion: The distribution of exam durations recovered from DICOM headers can be used to identify the optimal time slot for MR exams. Grouping these data by relevant clinical variables can aid in identifying sources of variability that may guide future operational changes that improve clinical efficiency.
Funding Support, Disclosures, and Conflict of Interest: Benoit Scherrer and Robert MacDougall have a financial interest in Quantivly