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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analytics Methodology to Quantify MRI Exam Utilization

John W. Garrett, Ali Pirasteh, Sheena Chu, Scott B. Reeder
Journal of Imaging Informatics in Medicine
Radiomics and Machine Learning in Medical Imaging
article

Analytics Methodology to Quantify MRI Exam Utilization

John W. Garrett, Ali Pirasteh, Sheena Chu, Scott B. Reeder
article en

Abstract

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.

Journal of Imaging Informatics in Medicine
University of Wisconsin–Madison (US)
Openalex Percentile: Top 6%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analytics Methodology to Quantify MRI Exam Utilization — John W. Garrett, Ali Pirasteh, et al. · Journal of Imaging Informatics in Medicine (2026) | TGRS Research Map | TGRS