Partnering with Engineers to Identify and Empirically Evaluate Delays in Magnetic Resonance Imaging: Laying the Foundations for Quality Improvement and System-based Practice in Radiology

ArticleinAcademic radiology 19(1):109-15 · November 2011with9 Reads
Impact Factor: 1.75 · DOI: 10.1016/j.acra.2011.09.006 · Source: PubMed

The aim of this study was to evaluate the feasibility of partnering with engineering students and critically examining the merit of the problem identification and analyses students generated in identifying sources impeding effective turnaround in a large university department of diagnostic radiology. Turnaround involves the time and activities beginning when a patient enters the magnetic resonance scanner room until the patient leaves, minus the time the scanner is conducting the protocol. A prospective observational study was conducted, in which four senior undergraduate industrial and operations engineering students interviewed magnetic resonance staff members and observed all shifts. On the basis of 150 hours of observation, the engineering students identified 11 process steps (eg, changing coils). They charted machine use for all shifts, providing a breakdown of turnaround time between appropriate process and non-value-added time. To evaluate the processes occurring in the scanning room, the students used a work-sampling schedule in which a beeper sounded 2.5 times per hour, signaling the technologist to identify which of 11 process steps was occurring. This generated 2147 random observations over a 3-week period. The breakdown of machine use over 105 individual studies showed that non-value-added time accounted for 62% of turnaround time. Analysis of 2147 random samples of work showed that scanners were empty and waiting for patients 15% of the total time. Analyses showed that poor communication delayed the arrival of patients and that no one had responsibility for communicating when scanning was done. Engineering students used rigorous study design and sampling methods to conduct interviews and observations. This led to data-driven definition of problems and potential solutions to guide systems-based improvement.