Analytics Methodology to Quantify MRI Exam Utilization
Quantifying the utilization of clinical MRI exams is challenging. Using analytics tools to characterize MR exam utilization, improvement opportunities can be identified, and such tools are essential to improve the value of MRI. The purpose of this work is to develop and validate an analytics methodology that quantifies MRI exam utilization. An analytics methodology that quantifies MRI exam utilization was developed. To demonstrate the utility of this methodology, we quantified the impact of three types of interventions: 1. comparing similar protocols with the comprehensive protocol and focused surveillance protocol, 2. evaluating workflow changes with rectal cancer staging, and 3. targeted individual protocol modification with MR enterography. We successfully developed an analytics methodology to quantify MRI exam utilization. The three protocol interventions resulted in a reduction in exam time. The surveillance protocol reduced average exam time both when compared with screening protocols in the same year and when compared to patients' prior screening exam. For rectal cancer staging, the change in methodology of communication led to a 4:33 min decrease in average exam time. MR enterography protocol modification led to a 9:55 min exam time reduction. All interventions were shown to improve MRI workflow and significantly reduce exam time (P < 0.05). We successfully developed an analytics methodology for quantifying MRI utilization and demonstrated its value with three example use cases. We conclude that the application of analytics tools for assessment of MRI utilization provides an objective strategy that permits data-driven decisions to modify protocols and improve value.
Authors
- John W. Garrett (ORCID: https://orcid.org/0000-0002-8152-736X)
- Ali Pirasteh (ORCID: https://orcid.org/0000-0003-0797-4636)
- Sheena Chu (ORCID: https://orcid.org/0000-0001-7874-0955)
- Scott B. Reeder (ORCID: https://orcid.org/0000-0003-4728-8171)
Institutions
- University of Wisconsin–Madison (US)
Publication Details
- Journal
- Journal of Imaging Informatics in Medicine
- Published
- 2026-06-09
- DOI
- https://doi.org/10.1007/s10278-026-02031-w
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00