The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload

McDonald, Robert J.; Schwartz, Kara M.; Eckel, Laurence J.; Diehn, Felix E.; Hunt, Christopher H.; Bartholmai, Brian J.; Erickson, Bradley J.; Kallmes, David F.

doi:10.1016/j.acra.2015.05.007

Cited by 327 publications

(174 citation statements)

References 38 publications

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“…Better resource utilization in terms of equipment utilization and completed exams thus can be expected when personnel are at greater ease handling such cases faster, as offered by the training. The alternative would be shortening slot length and imaging time [14, 15], which however would make the operation more vulnerable to disruption by patients needing extra time and placing extra stress on already stressed personnel [16, 17]. Furthermore it may risk poor patient satisfaction ratings.…”

Section: Discussionmentioning

confidence: 99%

Effects of Interpersonal Skills Training on MRI Operations in a Saturated Market: A Randomized Trial

Ajam

Kelly

Ladapo

et al. 2017

Journal of the American College of Radiology

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Purpose Assess effects of team training on operational efficiency during outpatient MRI. Materials and Methods In this IRB-approved HIPAA-compliant study, 6 MRI outpatient sites of a Midwestern hospital system were randomized to serve as controls or have their teams trained in advanced communication skills. The 4th quarter fiscal year 2015 (Q4FY15) was the trial baseline. The trial ended Q3FY16. Equipment utilization (completed scans/available slots), hourly scan rates (total orders completed per machine per hour of operation), and no-show rates stratified by time were analyzed using the Cochran-Mantel-Haenszel method, with individual comparisons performed with Bonferroni correction. Results The study encompassed 27,425 MRI examinations. Overall volume peaked at baseline and then declined over the next quarters. Compared to baseline, untrained sites experienced significant drops in equipment utilization (p<0.01 [Q1FY16]; p<0.0001 [Q2 FY16, Q3FY16]), decreasing from 77% to 65% over the study period, corresponding to a decrease from 1.15 to 0.97 in hourly scan rates. For trained sites, these metrics showed no significant change, with maintenance of hourly scan rates of 1.23 and 1.27 and equipment utilization rates of 83% and 85% between baseline and Q3FY16. No-show rates remained stable at trained sites but increased at untrained sites in the last two quarters (p<0.05). Nationally benchmarked patient satisfaction percentile ranking gradually increased at trained sites from 56th at baseline to 70th and successively decreased at untrained sites from 66th to 44th. Conclusion MRI outpatient facilities trained in advanced communication techniques may have more favorable operational efficiency than untrained sites in a saturated market.

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Section: Discussionmentioning

confidence: 99%

Effects of Interpersonal Skills Training on MRI Operations in a Saturated Market: A Randomized Trial

Ajam

Kelly

Ladapo

et al. 2017

Journal of the American College of Radiology

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“…These factors have contributed to a dramatic increase in radiologists’ workloads. Studies report that, in some cases, an average radiologist must interpret one image every 3–4 seconds in an 8-hour workday to meet workload demands 25 . As radiology involves visual perception as well as decision making under uncertainty 26 , errors are inevitable — especially under such constrained conditions.…”

Section: Ai In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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“…Second, volume burden to radiologists increased in tandem with utilization. In 2015, it was estimated that the average radiologist interprets one CT or MRI image every 3–4 seconds in an 8‐hour workday to satisfy demand . With technological advances in image resolution, radiologists scan more images per study.…”

Section: Challenges In Radiologymentioning

confidence: 99%

“…In 2015, it was estimated that the average radiologist interprets one CT or MRI image every 3-4 seconds in an 8-hour workday to satisfy demand. 5 With technological advances in image resolution, radiologists scan more images per study. These increasing numbers of images requiring interpretation means that turnaround time has greatly increased.…”

Section: Challenges In Radiologymentioning

confidence: 99%