Analysis of scheduling efficiency by harmonizing RIS-HL7 and MR-DICOM data

Purpose: The typical process of scheduling radiology exams is based on rules that assign a fixed slot size to all studies, or at best modify the slot size in 15 or 30 min increments based on ordered protocol. This process is crude as it does not consider clinical, technical, or demographic factors that factor into real exam duration. While the information for scheduled slot size exists in the RIS (via HL7), the details of the performed exams and their true duration exist in DICOM metadata. These data siloes make it difficult to optimize scheduled slot size using data-driven strategies. We sought to quantify the current state of radiology scheduling in a large academic pediatric hospital by comparing harmonized RIS-HL7 and MRI-DICOM information for the most common exam types.

Methods/Materials: We used a commercially available software platform, Quantivly (Somerville, MA), that harmonizes radiology metadata (RIS-HL7 and DICOM) to provide a unified data layer describing imaging operations. The distribution of slot size per protocol was calculated from RIS-HL7. Performed exam duration was calculated as the time from the start of the first acquisition to the end of the last acquisition, and adding a 5 min buffer to the exam start/end to account for patient setup. Slot utilization was quantified by taking the ratio of performed exam duration to the slot size. Exams were ranked by slot utilization over a one-month period.

Results: The scheduled and performed exam duration were displayed together in a single daily view to quickly visualize delays and device utilization (Figure 1). The distribution of scheduled slot size and slot utilization was displayed for each exam type with the mean ranging from 39% (MR-Arthrogram Upper Extremity) to 100% (MR-Brain/Vent Check).

Conclusions: A fixed slot size based on ordered protocol leads to inefficient and highly variable slot utilization (range: 39% - 100%). Instead, scheduled slot size should account for clinical, demographic, and technical factors to increase scanner utilization without incurring new delays, thereby increasing patient access and satisfaction. For future work, we aim to generate a predicted scheduled slot size based on historical data of performed exam duration.