Predicting slot lengths of MRI exams to decrease observed discrepancies between planning and execution

This retrospective study aimed to reveal discrepancies between planned (Tplan) and actual (Tact) slot lengths of abdomen MRI exams, and to improve Tplan by predicting slot lengths via a machine learning algorithm. Tplan and Tact were retrieved from RIS and modality logfiles, respectively, covering 3038 MRI exams of 17 protocols performed at an abdomen department. Comparisons showed that 30% of exams exceeded planned slot lengths. On the other hand, exams completed within planning failed to manifest good adherence to schedule, as many of them were assigned with an unnecessarily long slot. While adjusting the planned exam duration by a fixed amount of time for each protocol could move Tplan closer to the mean or median Tact, the large spread of Tact would still be unaffected. This is why this study goes one step further, introducing a method to predict the required slot length not only per protocol, but for each individual exam. A Random Forest Regression model was trained on historic data to predict individual slot lengths (Tpred) based on patient and exam context. The correlation between Tpred and Tact was found to be better than that of Tplan and Tact, with Pearson correlation factors of 0.66 and 0.50, respectively. The overall adherence to schedule was also improved by the prediction, as seen by a reduction of both the root mean squared error (-28%) and the standard deviation (-16%) of the differences between planned/predicted slot times and Tact. To provide further insights into the discrepancies between planning and execution of MRI exams, nineteen exams from the Liver protocol with verified clinical information were selected. This case study showed that patient conditions, diagnostic purposes and the selection of sequences during exams could explain some variations of exam durations, but the potential for improving the exam time prediction by including this additional context is limited.

Analysis and predictability of technologists' perception of MR exam complexity

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

IMPLICATIONS FOR PRACTICE

Results and observations of this study could be utilized to assist operational scheduling in the radiology department and reduce MR technologists' stress levels.