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Sanchez Tijmes F, Kandel S, Kavanagh J, Carey S, May M, Chen S, Mattsson J, Rogalla P. Chest CT at X-Ray Dose Using a Noise-Mitigating Weighted Projection: The Thoracic Tomogram. Diagnostic Performance for Pneumonia Detection in Hemato-Oncology Patients. Can Assoc Radiol J 2024; 75:621-630. [PMID: 38240217 DOI: 10.1177/08465371231215670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024] Open
Abstract
PURPOSE To compare the diagnostic performance of a thick-slab reconstruction obtained from an ultra-low-dose CT (termed thoracic tomogram) with standard-of-care low-dose CT (SOC-CT) for rapid interpretation and detection of pneumonia in hemato-oncology patients. METHODS Hemato-oncology patients with a working diagnosis of pneumonia underwent an SOC-CT followed by an ultra-low-dose CT, from which the thoracic tomogram (TT) was reconstructed. Three radiologists evaluated the TT and SOC-CT in the following categories: (I) infectious/inflammatory opacities, (II) small airways infectious/inflammatory changes, (III) atelectasis, (IV) pleural effusions, and (V) interstitial abnormalities. The TT interpretation time and radiation dose were recorded. Sensitivity, specificity, diagnostic accuracy, ROC, and AUC were calculated with the corresponding power analyses. The agreement between TT and SOC-CT was calculated by Correlation Coefficient for Repeated Measures (CCRM), and the Shrout-Fleiss intra-class correlations test was used to calculate interrater agreement. RESULTS Forty-seven patients (mean age 58.7 ± 14.9 years; 29 male) were prospectively enrolled. Sensitivity, specificity, accuracy, AUC, and Power for categories I/II/III/IV/V were: 94.9/99/97.9/0.971/100, 78/91.2/86.5/0.906/100, 88.6/100/97.2/0.941/100, 100/99.2/99.3/0.995/100, and 47.6/100/92.2/0.746/87.3. CCRM between TT and SOC-CT for the same categories were .97/.81/.92/.96/.62 with an interobserver agreement of .93/.88/.82/.96/.61. Mean interpretation time was 18.6 ± 5.4 seconds. The average effective radiation dose of TT was similar to a frontal and lateral chest X-ray (0.27 ± 0.08 vs 1.46 ± 0.64 mSv for SOC-CT; P < .01). CONCLUSION Thoracic tomograms provide comparable diagnostic information to SOC-CT for the detection of pneumonia in immunocompromised patients at one-fifth of the radiation dose with high interobserver agreement.
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Affiliation(s)
- Felipe Sanchez Tijmes
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - Sonja Kandel
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - John Kavanagh
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - Sean Carey
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - Mary May
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - Shiyi Chen
- Biostatistics Department, University Health Network, Prince Margaret Hospital, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
| | - Jonas Mattsson
- Division of Medical Oncology and Hematology, University Health Network, Princess Margaret Hospital, Toronto, ON, Canada
| | - Patrik Rogalla
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, Toronto, ON, Canada
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Welch AC, Gorden JA, Mooney SJ, Wilshire CL, Zeliadt SB. Understanding Washington State's Low Uptake of Lung Cancer Screening in Two Steps: A Geospatial Analysis of Patient Travel Time and Health Care Availability of Imaging Sites. Chest 2024:S0012-3692(24)00657-3. [PMID: 38815622 DOI: 10.1016/j.chest.2024.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Early detection of lung cancer reduces cancer mortality; yet uptake for lung cancer screening (LCS) has been limited in Washington State. Geographic disparities contribute to low uptake, but do not wholly explain gaps in access for underserved populations. Other factors, such as an adequate workforce to meet population demand and the capacity of accredited screening facility sites, must also be considered. RESEARCH QUESTION What proportion of the eligible population for LCS has access to LCS facilities in Washington State? STUDY DESIGN AND METHODS We used the enhanced two-step floating catchment area (E2SFCA) model to evaluate how geographic accessibility in addition to availability of LCS imaging centers contribute to disparities. We used available data on radiologic technologist volume at each American College of Radiology (ACR)-accredited screening facility site to estimate the capacity of each site to meet potential population demand. Spearman rank correlation coefficients of the spatial access ratios were compared with the 2010 Rural-Urban Commuting Area (RUCA) codes and area deprivation index quintiles to identify characteristics of populations at risk for lung cancer with greater and lesser levels of access. RESULTS A total of 549 radiologic technologists were identified across the 95 ACR-accredited screening facilities. We observed that 95% of the eligible population had proximate geographic access to any ACR facility. However, when we incorporated the E2SFCA method, we found significant variation of access for eligible populations. The inclusion of the availability measure attenuated access for most of the eligible population. Furthermore, we observed that rural areas were substantially correlated, and areas with greater socioeconomic disadvantage were modestly correlated, with lower access. INTERPRETATION Rural and socioeconomically disadvantaged areas face significant disparities. The E2SFCA models demonstrated that capacity is an important component and how geographic access and availability jointly contribute to disparities in access to LCS.
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Affiliation(s)
- Allison C Welch
- Thoracic Surgery and Interventional Pulmonology Clinic, Swedish Medical Center and Cancer Institute, Seattle, WA; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA
| | - Jed A Gorden
- Thoracic Surgery and Interventional Pulmonology Clinic, Swedish Medical Center and Cancer Institute, Seattle, WA
| | - Stephen J Mooney
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA
| | - Candice L Wilshire
- Thoracic Surgery and Interventional Pulmonology Clinic, Swedish Medical Center and Cancer Institute, Seattle, WA
| | - Steven B Zeliadt
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA.
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Ko CH, Chien LN, Chiu YT, Hsu HH, Wong HF, Chan WP. Demands for medical imaging and workforce Size: A nationwide population-based Study, 2000-2020. Eur J Radiol 2024; 172:111330. [PMID: 38290203 DOI: 10.1016/j.ejrad.2024.111330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/22/2023] [Accepted: 01/18/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE The aim of this study was to investigate associations between workforce and workload among radiologists in Taiwan. MATERIALS AND METHODS Data for the period 2000-2020 describing the demand for imaging services and radiologists have been obtained from databases and statistical reports of the Ministry of Health and Welfare. The future demand for radiologists was based on Taiwanese people aged 40 and over. RESULTS The workforce of Taiwan's radiologists has increased by 6 % annually over the past 20 years (from 450 to 993), performing 2125, 3202 and 3620 monthly examinations (mainly conventional radiography and CT) in medical centers, regional hospitals and district hospitals. Between 2000 and 2020, the use of CT and MRI increased by more than 3.5 times. Demand for interventional radiology also increased by 1.77 times, 2.25 times, and 5 times, respectively. To maintain this volume of services in 2040, at least 1168 radiologists are needed, about 1.18 times more in 2020. CONCLUSION Taiwan has 2.4 to 2.9 times fewer radiologists than the United States and 3 times fewer than Europe, while the annual workload is approximately 2 to 3.4 times greater than that of the United States and 1.4 to 2.5 times greater than that of the United Kingdom. This report may serve as a reference for policy makers who address the challenges of the growing workload among radiologists in countries of similar situations.
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Affiliation(s)
- Chih-Hsiang Ko
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Li-Nien Chien
- Institute of Health and Welfare Policy, National Yang Ming Chiao Tung University, Taipei City 11221, Taiwan
| | - Yu-Ting Chiu
- School of Health Care Administration, College of Management, Taipei Medical University, New Taipei City 235, Taiwan
| | - Hsian-He Hsu
- Department of Radiology, Tri-Service General Hospital and National Defense Medical Center, Taipei 11490, Taiwan
| | - Ho-Fai Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333423, Taiwan
| | - Wing P Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
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Wood EJ, Stabo N, Garret JW, Rose S, Bartels C, Szczykutowicz TP, Avey G, Mao L, Lubner MG. Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions. J Comput Assist Tomogr 2023:00004728-990000000-00155. [PMID: 36944097 DOI: 10.1097/rct.0000000000001449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
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.
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Affiliation(s)
- Erika J Wood
- From the University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Nicholas Stabo
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - John W Garret
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Sean Rose
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX
| | - Carrie Bartels
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Timothy P Szczykutowicz
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Greg Avey
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Lu Mao
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX
| | - Meghan G Lubner
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
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Lee L, Kazmer A, Colman MW, Gitelis S, Batus M, Blank AT. PET-CT staging affects time to treatment in sarcoma. Surg Oncol 2022; 41:101732. [DOI: 10.1016/j.suronc.2022.101732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/10/2022] [Accepted: 02/22/2022] [Indexed: 10/19/2022]
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Geissler F, Heiß R, Kopp M, Wiesmüller M, Saake M, Wuest W, Wimmer A, Prell V, Uder M, May MS. Personalized computed tomography - Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. ROFO-FORTSCHR RONTG 2020; 193:437-445. [PMID: 33142337 DOI: 10.1055/a-1253-8558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
PURPOSE The aim of this study was to develop an algorithm for automated estimation of patient height and weight during computed tomography (CT) and to evaluate its accuracy in everyday clinical practice. MATERIALS AND METHODS Depth images of 200 patients were recorded with a 3D camera mounted above the patient table of a CT scanner. Reference values were obtained using a calibrated scale and a measuring tape to train a machine learning algorithm that fits a patient avatar into the recorded patient surface data. The resulting algorithm was prospectively used on 101 patients in clinical practice and the results were compared to the reference values and to estimates by the patient himself, the radiographer and the radiologist. The body mass index was calculated from the collected values for each patient using the WHO formula. A tolerance level of 5 kg was defined in order to evaluate the impact on weight-dependent contrast agent dosage in abdominal CT. RESULTS Differences between values for height, weight and BMI were non-significant over all assessments (p > 0.83). The most accurate values for weight were obtained from the patient information (R² = 0.99) followed by the automated estimation via 3D camera (R² = 0.89). Estimates by medical staff were considerably less precise (radiologist: R² = 0.78, radiographer: R² = 0.77). A body-weight dependent dosage of contrast agent using the automated estimations matched the dosage using the reference measurements in 65 % of the cases. The dosage based on the medical staff estimates would have matched in 49 % of the cases. CONCLUSION Automated estimation of height and weight using a digital twin model from 3D camera acquisitions provide a high precision for protocol design in computer tomography. KEY POINTS · Machine learning can calculate patient-avatars from 3D camera acquisitions.. · Height and weight of the digital twins are comparable to real measurements of the patients.. · Estimations by medical staff are less precise.. · The values can be used for calculation of contrast agent dosage.. CITATION FORMAT · Geissler F, Heiß R, Kopp M et al. Personalized computed tomography - Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. Fortschr Röntgenstr 2021; 193: 437 - 445.
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Affiliation(s)
- Frederik Geissler
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Rafael Heiß
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Markus Kopp
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Marco Wiesmüller
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Marc Saake
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Wolfgang Wuest
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Radiology, University Hospital Erlangen, Erlangen, Germany.,Imaging Science Institute, Erlangen, Germany
| | | | | | - Michael Uder
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Radiology, University Hospital Erlangen, Erlangen, Germany.,Imaging Science Institute, Erlangen, Germany
| | - Matthias Stefan May
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Department of Radiology, University Hospital Erlangen, Erlangen, Germany.,Imaging Science Institute, Erlangen, Germany
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Sheth S, Mudge B, Fishman EK. The pre-CT checklist: A simple tool to improve workflow and patient safety in an outpatient CT setting. Clin Imaging 2020; 66:101-105. [DOI: 10.1016/j.clinimag.2020.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 04/30/2020] [Accepted: 05/14/2020] [Indexed: 11/16/2022]
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8
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Szczykutowicz TP, Brunnquell CL, Avey GD, Bartels C, Belden DS, Bruce RJ, Field AS, Peppler WW, Wasmund P, Wendt G. A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging. J Digit Imaging 2019; 31:201-209. [PMID: 29404851 DOI: 10.1007/s10278-018-0055-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Timothy P Szczykutowicz
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
- 1005 Wisconsin Institutes for Medical Research, 1111 Highland Ave, Madison, WI, 53705, USA.
| | - Christina L Brunnquell
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Gregory D Avey
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Carrie Bartels
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Daryn S Belden
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard J Bruce
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Walter W Peppler
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Peter Wasmund
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Gary Wendt
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
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Wetzl M, Weller M, Heiss R, Schrüfer E, Wuest W, Thierfelder C, Lerch D, Cavallaro A, Amarteifio P, Uder M, May MS. Mobile Workflow in Computed Tomography of the Chest. J Med Syst 2018; 43:14. [PMID: 30535865 PMCID: PMC6290687 DOI: 10.1007/s10916-018-1131-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/28/2018] [Indexed: 01/04/2023]
Abstract
A CT system with a tablet as mobile user interface and a wireless remote control for positioning and radiation release has recently been presented. Our aim was to evaluate the effects of a mobile CT examination workflow on the radiographers’ performance compared to conventional examinations. A prototype of a radiation protection cabin was installed besides the gantry of a CT system. The CT system was equipped with a simplified user interface on a portable tablet and a mobile remote control. 98 patients with an indication for CT of the chest were randomly assigned to examination using the mobile devices (study group, n = 47) or using the conventional stationary workflow on the console (reference group, n = 51). Three ceiling mounted fisheye cameras were used for motion tracking of the radiographers, two in the examination room and one in the control room. Relative density of detection heat-maps and area counts were assessed using a dedicated software tool to quantify radiographers’ movements. Duration of each task of the examination was manually recorded using a stopwatch. In the reference group 25% of the area counts were located inside of the examination room, while it was 48% in the study group. The time spent in the same room with the patient increased from 3:06 min (29%) to 6:01 min (57%) using the mobile workflow (p < 0.05), thereof 0:59 min (9%) were spent in moderate separation with maintained voice and visual contact in the radiation protection cabin. Heat-maps showed an increase of the radiographer’s working area, indicating a higher freedom of movement. Total duration of the examination was slightly less in the study group without statistical significance (median time: study 10:36, reference 10:50 min; p = 0.29). A mobile CT examination transfers the radiographers’ interaction with the scanner from the control room into the examination room. There, radiographers’ freedom of movement is higher, without any tradeoffs regarding the examination duration.
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Affiliation(s)
- Matthias Wetzl
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Melanie Weller
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - Rafael Heiss
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Eleni Schrüfer
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Wolfgang Wuest
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | | | - Daniel Lerch
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - Alexander Cavallaro
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
- Imaging Science Institute, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Patrick Amarteifio
- Imaging Science Institute, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Siemens Healthcare GmbH, Henkestr. 127, 91052, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
- Imaging Science Institute, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Matthias Stefan May
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany.
- Imaging Science Institute, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
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A Patient-Centered Radiology Quality Process Map: Opportunities and Solutions. AJR Am J Roentgenol 2016; 207:940-946. [DOI: 10.2214/ajr.16.16803] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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McDonald RJ, Schwartz KM, Eckel LJ, Diehn FE, Hunt CH, Bartholmai BJ, Erickson BJ, Kallmes DF. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 2015. [PMID: 26210525 DOI: 10.1016/j.acra.2015.05.007] [Citation(s) in RCA: 224] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES To examine the effect of changes in utilization and advances in cross-sectional imaging on radiologists' workload. MATERIALS AND METHODS All computed tomography (CT) and magnetic resonance imaging (MRI) examinations performed at a single institution between 1999 and 2010 were identified and associated with the total number of images for each examination. Annual trends in institutional numbers of interpreted examinations and images were translated to changes in daily workload for the individual radiologist by normalizing to the number of dedicated daily CT and MRI work assignments, assuming a 255-day/8-hour work day schedule. Temporal changes in institutional and individual workload were assessed by Sen's slope analysis (Q = median slope) and Mann-Kendall test (Z = Z statistic). RESULTS From 1999 to 2010, a total of 1,517,149 cross-sectional imaging studies (CT = 994,471; MRI = 522,678) comprising 539,210,581 images (CT = 339,830,947; MRI = 199,379,634) were evaluated at our institution. Total annual cross-sectional studies steadily increased from 84,409 in 1999 to 147,336 in 2010, representing a twofold increase in workload (Q = 6465/year, Z = 4.2, P < .0001). Concomitantly, the number of annual departmental cross-sectional images interpreted increased from 9,294,140 in 1990 to 94,271,551 in 2010, representing a 10-fold increase (Q = 8707876/year, Z = 4.5, P < .0001). Adjusting for staffing changes, the number of images requiring interpretation per minute of every workday per staff radiologist increased from 2.9 in 1999 to 16.1 in 2010 (Q = 1.7/year, Z = 4.3, P < .0001). CONCLUSIONS Imaging volumes have grown at a disproportionate rate to imaging utilization increases at our institution. The average radiologist interpreting CT or MRI examinations must now interpret one image every 3-4 seconds in an 8-hour workday to meet workload demands.
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Zhang L, Domröse S, Mahnken A. Reconciling quality and cost: A case study in interventional radiology. Eur Radiol 2015; 25:2898-904. [PMID: 26002125 DOI: 10.1007/s00330-015-3702-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 01/16/2015] [Accepted: 03/03/2015] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To provide a method to calculate delay cost and examine the relationship between quality and total cost. METHODS The total cost including capacity, supply and delay cost for running an interventional radiology suite was calculated. The capacity cost, consisting of labour, lease and overhead costs, was derived based on expenses per unit time. The supply cost was calculated according to actual procedural material use. The delay cost and marginal delay cost derived from queueing models was calculated based on waiting times of inpatients for their procedures. RESULTS Quality improvement increased patient safety and maintained the outcome. The average daily delay costs were reduced from 1275 € to 294 €, and marginal delay costs from approximately 2000 € to 500 €, respectively. The one-time annual cost saved from the transfer of surgical to radiological procedures was approximately 130,500 €. The yearly delay cost saved was approximately 150,000 €. With increased revenue of 10,000 € in project phase 2, the yearly total cost saved was approximately 290,000 €. Optimal daily capacity of 4.2 procedures was determined. CONCLUSIONS An approach for calculating delay cost toward optimal capacity allocation was presented. An overall quality improvement was achieved at reduced costs. KEY POINTS • Improving quality in terms of safety, outcome, efficiency and timeliness reduces cost. • Mismatch of demand and capacity is detrimental to quality and cost. • Full system utilization with random demand results in long waiting periods and increased cost.
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Affiliation(s)
- Li Zhang
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen and Marburg, Philipps University of Marburg, Baldinger Strasse, 35033, Marburg, Germany,
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Delivery of Appropriateness, Quality, Safety, Efficiency and Patient Satisfaction. J Am Coll Radiol 2014; 11:7-11. [DOI: 10.1016/j.jacr.2013.07.016] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2013] [Accepted: 07/15/2013] [Indexed: 11/20/2022]
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Zhang L, Hefke A, Figiel J, Schwarz U, Rominger M, Klose KJ. Identifying radiological needs of referring clinicians. J Digit Imaging 2013; 26:393-401. [PMID: 23179407 PMCID: PMC3649054 DOI: 10.1007/s10278-012-9551-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Abstract
To provide prospective information about quality- and satisfaction-related product features in radiology, a customer-centered approach for acquiring clinicians' requirements and their prioritizations is essential. We introduced the Kano model for the first time in radiology to obtain such information. A Kano questionnaire, consisting of pairs of questions regarding 13 clinician requirements related to computed tomography (CT), magnetic resonance imaging (MRI) access and report turnaround time (RTT), was developed and administered. Each requirement was assigned a Kano category, and its satisfaction and dissatisfaction coefficients were calculated and presented in a Kano diagram. The data were stratified based on different clinics and on staff and resident clinicians. The time interval was evaluated between the completion of an examination and the first attempt to access the report by a clinician. Consultation for modality selection and scheduling and access to CT within 24 h and RTT within 8 to 24 h were considered as must-be requirements. Access to CT within 4 h and within 8 h, access to MRI within 8 h and within 24 h, and access to RTT within 4 h were one-dimensional requirements. The extension of operation time for CT or MRI, as well as MRI access within 4 h, was considered attractive. Eight out of nine clinics considered RTT within 8 h as a must-be requirement. There were differences in responses both among different clinics and between staff and resident clinicians. Access attempts to reports by clinicians in the first 4 h after the examination completion accounted for 65 % of CTs and 49 % of MRIs.
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Affiliation(s)
- Li Zhang
- Department of Diagnostic Radiology, Philipps University Marburg, University Hospital Giessen and Marburg, Baldinger Strasse, 35033, Marburg, Germany.
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Grant L, Appleby J, Griffin N, Adam A, Gishen P. Facing the future: the effects of the impending financial drought on NHS finances and how UK radiology services can contribute to expected efficiency savings. Br J Radiol 2011; 85:784-91. [PMID: 22167516 DOI: 10.1259/bjr/20359557] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The recent turmoil within the banking sector has led to the development of the most significant recession since the "great depression" of the 1930s. Although the coalition government has promised to "guarantee that health spending increases in real terms in each year of Parliament", this may still not be enough to meet future needs over the coming years due to increasing demand and cost pressures. The expected mismatch between actual National Health Service (NHS) funding post-2011 and that required to satisfy increasing demand has been estimated by the Department of Health to require efficiency savings representing up to one-fifth of the overall NHS budget. This paper explains the reasons behind the anticipated slowdown in the growth of real NHS funding, and how, as a discipline, radiology can increase the efficiency of the services it provides in anticipation of future financial austerity within the NHS.
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Affiliation(s)
- L Grant
- Department of Radiology, The Royal Free Hospital, London, UK.
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Boland GWL, Guimaraes AS, Mueller PR. The radiologist's conundrum: benefits and costs of increasing CT capacity and utilization. Eur Radiol 2008; 19:9-11; discussion 12. [PMID: 18766347 DOI: 10.1007/s00330-008-1159-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2008] [Revised: 05/02/2008] [Accepted: 05/24/2008] [Indexed: 12/29/2022]
Affiliation(s)
- Giles W L Boland
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Hospital-Owned and Operated Outpatient Imaging Centers: Strategies for Success. J Am Coll Radiol 2008; 5:900-6. [DOI: 10.1016/j.jacr.2008.04.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2007] [Indexed: 11/21/2022]
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Abstract
OBJECTIVE This article will identify strategies and tactics that can be used to enhance CT capacity. The potential financial benefits to the organization and the impact on market share will be discussed. CONCLUSION Many organizations are challenged to meet stakeholder demands of providing additional CT capacity and reduction of patient waiting lists. However, much can be achieved through workflow redesign, the addition of key personnel, and implementation of information system platforms and databases.
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