Consumable Material Waste and Workflow Efficiency Comparison Between Multi-use Syringeless and Single-use Syringe-Based Injectors in Computed Tomography.

RATIONALE AND OBJECTIVES: Syringeless power injectors obviate the need for reloading iodinated contrast media (ICM) and plastic consumable pistons between exams. This study evaluates the potential time and material waste (ICM, plastic, saline, and total) saved using a multi-use syringeless injector (MUSI) compared to a single-use syringe-based injector (SUSI). MATERIALS AND METHODS: Two observers recorded technologist time spent using a SUSI and a MUSI over three clinical workdays. CT technologists (n = 15) were polled on their experience between the systems using a 5-point Likert scale survey. ICM, plastic, and saline waste data from each system were collected. A mathematical model was created to estimate total and categorical waste from each injector system over a 16-week period. RESULTS: On average, CT technologists spent 40.5 seconds less per exam with MUSI compared to SUSI (p < .001). Technologists rated MUSI work efficiency, user-friendliness, and overall satisfaction (strongly or somewhat improved) higher relative to SUSI (p < .05). Iodine waste was 31.3 L and 0.0 L for SUSI and MUSI, respectively. Plastic waste was 467.7 kg and 71.9 kg for SUSI and MUSI, respectively. Saline waste was 43.3 L and 52.5 L for SUSI and MUSI, respectively. Total waste was 555.0 kg and 124.4 kg for SUSI and MUSI respectively. CONCLUSION: Switching from SUSI to MUSI resulted in a 100%, 84.6%, and 77.6% reduction in ICM, plastic, and total waste. This system may fortify institutional endeavors toward green radiology initiatives. The potential time saved administering contrast using MUSI may improve CT technologist efficiency.

Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions.

PURPOSES: The aims of the study are to identify factors contributing to computed tomography (CT) trauma scan turnaround time variation and to evaluate the effects of an automated intervention on time metrics. METHODS: Throughput metrics were captured via picture archiving and communication system from January 1, 2018, to December 16, 2019, and included 17,709 CT trauma scans from our institution. Initial data showed that imaging technologist variation played a significant role in trauma imaging turnaround time. In December 2019, we implemented a 2-pronged intervention: (1) educational intervention to techs and (2) modified trauma CT abdomen/pelvis to autogenerate and autosend reformats to picture archiving and communication system. A total of 13,169 trauma CT scans were evaluated from the postintervention period taking place from January 2020 to March 2021. Throughput metrics such as last image to first report interval and emergency department length of stay were captured and compared with performing technologist, time of day, and weekday versus weekend scans. RESULTS: Substantial variability among trauma CT scans was observed. For CT trauma abdomen/pelvis, the interval from last image to initial report decreased from 26.4 to 24.0 minutes ( P = 0.001) while the interval between first and last image time decreased from 11.4 to 4.2 minutes ( P < 0.001). Emergency department length of stay also decreased from 3.9 to 3.7 hours ( P < 0.0001) in the postintervention period. Variation among imaging technologist was statistically significant and became less significant after intervention ( P = 0.09, P = 0.54). CONCLUSIONS: Factors such as imaging technologist variability, time of day, and day of the week of trauma scans played a significant role in CT trauma turnaround time variability. Automation interventions can help with efficiency in image turnaround time.

Applying a New CT Quality Metric in Radiology: How CT Pulmonary Angiography Repeat Rates Compare Across Institutions.

OBJECTIVES: To quantify overall CT repeat and reject rates at five institutions and investigate repeat and reject rates for CT pulmonary angiography (CTPA). METHODS: In this retrospective study, we apply an automated repeat rate analysis algorithm to 103,752 patient examinations performed at five institutions from July 2017 to August 2019. The algorithm identifies repeated scans for specific scanner and protocol combinations. For each institution, we compared repeat rates for CTPA to all other CT protocols. We used logistic regression and analysis of deviance to compare CTPA repeat rates across institutions and size-based protocols. RESULTS: Of 103,752 examinations, 1,447 contained repeated helical scans (1.4%). Overall repeat rates differed across institutions (P < .001) ranging from 0.8% to 1.8%. Large-patient CTPA repeat rates ranged from 3.0% to 11.2% with the odds (95% confidence intervals) of a repeat being 4.8 (3.5-6.6) times higher for large- relative to medium-patient CTPA protocols. CTPA repeat rates were elevated relative to all other CT protocols at four of five institutions, with strong evidence of an effect at two institutions (P < .001 for each; odds ratios: 2.0 [1.6-2.6] and 6.2 [4.4-8.9]) and somewhat weaker evidence at the others (P = .005 and P = 0.011; odds ratios: 2.2 [1.3-3.8] and 3.7 [1.5-9.1], respectively). Accounting for size-based protocols, CTPA repeat rates differed across institutions (P < .001). DISCUSSION: The results indicate low overall repeat rates (<2%) with CTPA rates elevated relative to other protocols. Large-patient CTPA rates were highest (eg, 11.2% at one institution). Differences in repeat rates across institutions suggest the potential for quality improvement.

A Multiinstitutional Study on Wasted CT Scans for Over 60,000 Patients.

OBJECTIVE. Repeated imaging is an unnecessary source of patient radiation exposure, a detriment to patient satisfaction, and a waste of time and money. Although analysis of rates of repeated and rejected images is mandated in mammography and recommended in radiography, the available data on these rates for CT are limited. MATERIALS AND METHODS. In this retrospective study, an automated repeat-reject rate analysis algorithm was used to quantify repeat rates from 61,102 patient examinations obtained between 2015 and 2018. The algorithm used DICOM metadata to identify repeat acquisitions. We quantified rates for one academic site and one rural site. The method allows scanner-, technologist-, protocol-, and indication-specific rates to be determined. Positive predictive values and sensitivity were estimated for correctly identifying and classifying repeat acquisitions. Repeat rates were compared between sites to identify areas for targeted technologist training. RESULTS. Of 61,102 examinations, 4676 instances of repeat scanning contributed excess radiation dose to patients. Estimated helical overlap repeat rates were 1.4% (95% CI, 1.2-1.6%) for the rural site and 1.1% (95% CI, 1.0-1.2%) for the academic site. Significant differences in rates of repeat imaging required because of bolus tracking (11.6% vs 4.3%; p < 0.001) and helical extension (3.3% vs 1.8%; p < 0.001) were observed between sites. Positive predictive values ranged from 91% to 99% depending on the reason for repeat imaging and site location. Sensitivity of the algorithm was 92% (95% CI, 87-96%). Rates tended to be highest for emergent imaging procedures and exceeded 9% for certain protocols. CONCLUSION. Our multiinstitutional automated quantification of repeat rates for CT provided a useful metric for unnecessary radiation exposure and identification of technologists in need of training.

Inter- and intra-observer repeatability of aortic annulus measurements on screening CT for transcatheter aortic valve replacement (TAVR): Implications for appropriate device sizing.

OBJECTIVES: To investigate intra- and inter-observer repeatability of aortic annulus CT measurements for transcatheter aortic valve replacement (TAVR) by readers with different levels of experience and evaluate the impact of different multi-reader paradigms to improve prosthesis sizing. METHODS: 82 TAVR screening CTAs were evaluated twice by three raters with six (R1 = radiologist), three (R2 = 3D-laboratory technician) or zero (R3 = medical student) years of experience. Results were translated into hypothetical TAVR size recommendations. Intra- and inter-observer repeatability between single readers and three different multi-reader paradigms ([A]: two readers, [B]: three readers, or [C]: two readers + an optional third reader) were evaluated. RESULTS: Intra-observer variability did not differ significantly (range: 50.1-67.8mm2). However, we found significant differences in mean inter-observer variance (p = 0.001). Multi-reader paradigms led to significantly increased precision (lower variability) for scenarios [B] and [C] (p = 0.03, p < 0.05). Compared to single readers, all multi-reader strategies clearly lowered the rate of discrepant device size categorization between repeated measurements (22-26% to 5-10%). CONCLUSIONS: Aortic annulus CT measurements for TAVR are highly reproducible. Multi-reader strategies provide higher precision than evaluations from single readers with different levels of experience and could effectively be implemented with two readers and an optional third reader (Paradigm C) in a clinical setting.

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging.

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.