Analysis and predictability of technologists' perception of MR exam complexity.

As MRI becomes a routine clinical diagnostic method, its complexity of techniques, protocols and scanning is growing. On the other hand, aggravated by the ubiquitous shortage of workforce, technologists' stress level and burnout rates are increasing. In this context, our study aims to shed light on technologists' perceived complexity of MR exams, by analyzing a multidimensional dataset composed of workflow, patient, and operational details, and further predicting perceived exam complexity. In this IRB-approved study, data about imaging workflow, exam context, and patient characteristics were collected over one year from MR modality logfiles and from technologist questionnaires, including the perceived exam complexity. The association of individual factors with complexity was analyzed via Fisher's exact tests and Cramér's V values. Predictability of exam complexity was further evaluated via ROC analysis of three different multivariate classifiers. Retakes, delays, and extended exam duration are associated with perceived complexity (V ≥ 0.2). From the set of possible predictors, patient mobility and communication ability have the most influence on perceived complexity (V > 0.2), followed by special equipment needs (pulse oximetry, intubation, or ECG), protocol details and other patient characteristics. Feasibility of predicting the perceived exam complexity from a multivariate set of exam and patient details known at the time of scheduling has been demonstrated (AUC = 0.73), and suitable classification algorithms have been identified. Perceived exam complexity is associated with various factors. Our results suggest that it can be predicted sufficiently well to support early operational decision making. Some factors, however, may not be readily available in hospital IT systems and must be obtained before scheduling. Results and observations of this study could be utilized to assist operational scheduling in the radiology department and reduce MR technologists' stress levels.

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Copyright © 2023 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of interest statement Xinyu Wang, Falk Uhlemann, Jörn Borgert, Siva Chaitanya Chaduvula, Ranjith Tellis, and Thomas Amthor are employees of Philips.