Software-Based Evaluation of Optimization Potential for Clinical MRI Scanners in Radiology

Abstract

Objective The aim of the study was to use a software application to analyze the examination times and changeover times of two clinically highly applied MRI scanners at a university hospital for radiology and to evaluate whether this could result in optimization potential for examination planning in the daily clinical routine of MRI diagnostics.

Materials and Methods Based on the newly developed software application “Teamplay Usage” (Siemens Healthineers, Germany), the examinations carried out on two MRI scanners (1.5 T and 3 T) were investigated within an analysis period of 12 months with regard to the type of examination and its duration. In addition, compliance with the previously defined planning time (30, 45, 60 min.) was checked and deviations were analyzed. In addition, the changeover times between the examinations were determined and a possible influence due to the exchange of MRI coils was investigated for a selection of change combinations.

Results For the total of 7184 (1.5 T: 3740; 3 T: 3444) examinations included in the study, the median examination time was 43:02 minutes (1.5 T: 43:17 min.; 3 T: 42:45 min.). The ten most frequent types of examinations per MRI scanner were completed within the predefined plan time of 54.5 % (1.5 T) and 51.9 % (3 T), taking into account a previously defined preparation and post-processing time of 9 minutes per examination. Overall, more time was spent on examinations with a planned time of 30 minutes, whereas the majority of the examinations planned with 45 minutes were also completed within this time. Examinations with a planned time of 60 minutes usually took less time. A comparison between the planned time and the determined examination duration of the most common types of examinations showed overall a slight potential for optimization. Coil exchanges between two examinations had a small, but statistically not significant effect on the median changeover time (p = 0.062).

Conclusion Utilizing a software-based analysis, a detailed overview of the type of examination, examination duration, and changeover times of frequently used clinical MRI scanners could be obtained. In the clinic examined, there was little potential for optimization of examination planning. An exchange of MRI coils necessary for different types of examination only had a small effect on the changeover times.

Key Points: 

• The use of the “Teamplay Usage” software application enables a comprehensive overview of the type of examination, examination duration, and changeover times for MRI scanners.

• Adjustments to examination planning for MRI diagnostics show optimization potential, which, however, is to be assessed as low in the clinic examined.

• Necessary replacements of MRI coils only have a small effect on the changeover times.

Citation Format

• Meyl TP, Berghöfer A, Blatter T et al. Software-Based Evaluation of Optimization Potential for Clinical MRI Scanners in Radiology. Fortschr Röntgenstr 2022; 194: 391 – 399

Key words

health policy and practice - scheduling - optimization - MR imaging

Publication History

Received: 07 February 2021

Accepted: 30 September 2021

Article published online:
22 October 2021

• References

• 1 Swensen SJ, Johnson CD. Radiologic quality and safety: mapping value into radiology. Journal of the American College of Radiology: JACR 2005; 2: 992-1000
• 2 Rubin GD. Costing in Radiology and Health Care: Rationale, Relativity, Rudiments, and Realities. Radiology 2017; 282: 333-347
• 3 Rawson JV, Kannan A, Furman M. Use of Process Improvement Tools in Radiology. Current problems in diagnostic radiology 2016; 45: 94-100
• 4 Qiu H, Wang D, Wang Y. et al. MRI appointment scheduling with uncertain examination time. Journal of Combinatorial Optimization 2019; 37: 62-82
• 5 Maurer MH, Hamm B, Teichgräber U. ServiceBlueprinting as a service management tool in radiology. European journal of radiology 2011; 79: 333-336
• 6 Wessman BV, Moriarity AK, Ametlli V. et al. Reducing barriers to timely MR imaging scheduling. Radiographics: a review publication of the Radiological Society of North America, Inc 2014; 34: 2064-2070
• 7 Roth CJ, Boll DT, Wall LK. et al. Evaluation of MRI acquisition workflow with lean six sigma method: case study of liver and knee examinations. Am J Roentgenol American journal of roentgenology 2010; 195: W150-W156
• 8 Talati IA, Krishnan P, Filice RW. Developing Deeper Radiology Exam Insight to Optimize MRI Workflow and Patient Experience. Journal of digital imaging 2019; 32: 865-869
• 9 Tokur S, Lederle K, Terris DD. et al. Process analysis to reduce MRI access time at a German University Hospital. International journal for quality in health care: journal of the International Society for Quality in Health Care 2012; 24: 95-99
• 10 Siemens Healthineers: Teamplay performance management applications. Erlangen, Germany, 2020.
• 11 Jones S, Cournane S, Sheehy N. et al. A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance. Journal of digital imaging 2016; 29: 645-653
• 12 Revesz G, Shea FJ, Ziskin MC. Patient flow and utilization of resources in a diagnostic radiology department. Radiology 1972; 104: 21-26
• 13 Janower ML. Radiology department management system: technologists’ costs. Radiology 1986; 159: 566
• 14 Recht M, Macari M, Lawson K. et al. Impacting key performance indicators in an academic MR imaging department through process improvement. Journal of the American College of Radiology: JACR 2013; 10: 202-206
• 15 Rosenkrantz AB, Lawson K, Ally R. et al. Focused process improvement events: sustainability of impact on process and performance in an academic radiology department. Journal of the American College of Radiology: JACR 2015; 12: 75-81
• 16 Beker K, Garces-Descovich A, Mangosing J. et al. Optimizing MRI Logistics: Prospective Analysis of Performance, Efficiency, and Patient Throughput. Am J Roentgenol American journal of roentgenology 2017; 209: 836-844
• 17 Recht MP, Block KT, Chandarana H. et al. Optimization of MRI Turnaround Times Through the Use of Dockable Tables and Innovative Architectural Design Strategies. Am J Roentgenol American journal of roentgenology 2019; 212: 855-858
• 18 Kruskal JB, Reedy A, Pascal L. et al. Quality initiatives: lean approach to improving performance and efficiency in a radiology department. Radiographics: a review publication of the Radiological Society of North America, Inc 2012; 32: 573-587
• 19 O’Brien JJ, Stormann J, Roche K. et al. Optimizing MRI Logistics: Focused Process Improvements Can Increase Throughput in an Academic Radiology Department. Am J Roentgenol American journal of roentgenology 2017; 208: W38-W44
• 20 Avey GD, Belden DS, Zea RD. et al. Factors predicting the time-length variability of identically protocoled MRI exams. Journal of magnetic resonance imaging: JMRI 2019; 49: e265-e270