Effect of Implementation of Picture Archiving and Communication System on Radiologist Reporting Time and Utilization of Radiology Services: A Case Study in Iran.
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SUBMITTER: Hasani N
PROVIDER: S-EPMC7256163 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Medical education in China is in a transitional period, from passive learning models to experiential education. We modified an experiential education method for radiology education. The aim of this study is to evaluate the effect of this method on undergraduate radiology education. METHOD:With the help of the picture archiving and communication system (PACS) and RadiAnt DICOM Viewer, we modified an experiential education method that simulates similar working conditions for undergraduate medical students to formulate radiology diagnosis similar to clinical radiologists. A total of 101 students were allocated into either the experiential education group or the control group. The final examination scores and a 5-point Likert scale self-assessment questionnaire of radiologic skills were collected from all the students as an objective assessment and a subjective assessment respectively. A questionnaire was also used to assess the satisfaction with the experiential model in the experiential education group. Mann-Whitney U test was used to compare the ranked data, and t-tests were used to compare the numeric data. RESULTS:The experiential education group demonstrated significantly higher scores (7.4?±?1.3) compared to the control group (6.7?±?1.5, p?<?0.05) in the question type "description and diagnosis". The self-assessment questionnaire indicated that the experiential education was related to increased familiarity with the diagnosis thinking principle and the sequences and reconstruction methods of computer tomography (CT) imaging, which also strengthen participants' self-confidence to perform future clinical work (p?<?0.05). The self-assessment questionnaire in the experiential education group showed that the majority of students were satisfied with the organization (82.5%), interactivity (85%) and quality (85%) of the learning activity. Most students found this model of learning to be helpful for studying radiology (85%) and for understanding anatomy (90%). CONCLUSION:Compared with the traditional radiology education approach, the experiential education method showed greater efficacy in improving students' analysis and diagnostic skills and their self-confidence.
| S-EPMC6805614 | biostudies-literature
Project description:With the increasing prevalence of Picture Archiving and Communication Systems (PACS) in healthcare institutions, there is a growing need to measure their success. However, there is a lack of published literature emphasizing the technical and social factors underlying a successful PACS.An updated Information Systems Success Model was utilized by radiology technologists (RTs) to evaluate the success of PACS at a large medical center in Taiwan. A survey, consisting of 109 questionnaires, was analyzed by Structural Equation Modeling.Socio-technical factors (including system quality, information quality, service quality, perceived usefulness, user satisfaction, and PACS dependence) were proven to be effective measures of PACS success. Although the relationship between service quality and perceived usefulness was not significant, other proposed relationships amongst the six measurement parameters of success were all confirmed.Managers have an obligation to improve the attributes of PACS. At the onset of its deployment, RTs will have formed their own subjective opinions with regards to its quality (system quality, information quality, and service quality). As these personal concepts are either refuted or reinforced based on personal experiences, RTs will become either satisfied or dissatisfied with PACS, based on their perception of its usefulness or lack of usefulness. A satisfied RT may play a pivotal role in the implementation of PACS in the future.
| S-EPMC3849362 | biostudies-literature
Project description:ObjectivesTo study the impact of COVID-19 on chest CT practice during the different waves using Dose Archiving and Communication System (DACS).MethodsRetrospective study including data from 86,136 chest CT acquisitions from 27 radiology centers (15 private; 12 public) between January 1, 2020, and October 13, 2021, using a centralized DACS. Daily chest CT activity and dosimetry information such as dose length product (DLP), computed tomography dose index (CTDI), and acquisition parameters were collected. Pandemic indicators (daily tests performed, incidence, and hospital admissions) and vaccination rates were collected from a governmental open-data platform. Descriptive statistics and correlation analysis were performed.ResultsFor the first two waves, strong positive and significant correlations were found between all pandemic indicators and total chest CT activity, as high as R = 0.7984 between daily chest CT activity and hospital admissions during the second wave (p < 0.0001). We found differences between public hospitals and private imaging centers during the first wave, with private centers demonstrating a negative correlation between daily chest CT activity and hospital admissions (-0.2819, p = 0.0019). Throughout the third wave, simultaneously with the rise of vaccination rates, total chest CT activity decreased with significant negative correlations with pandemic indicators, such as R = -0.7939 between daily chest CTs and daily incidence (p < 0.0001). Finally, less than 5% of all analyzed chest CTs could be considered as low dose.ConclusionsDuring the first waves, COVID-19 had a strong impact on chest CT practice which was lost with the arrival of vaccination. Low-dose protocols remained marginal.Key points• There was a significant correlation between the number of daily chest CTs and pandemic indicators throughout the first two waves. It was lost during the third wave due to vaccination arrival. • Differences were observed between public and private centers, especially during the first wave, less during the second, and were lost during the third. • During the first three waves of COVID-19 pandemic, few CT helical acquisitions could be considered as low dose with only 3.8% of the acquisitions according to CTDIvol and 4.3% according to DLP.
| S-EPMC9908501 | biostudies-literature
Project description:This study describes an automatic technique to accurately determine the maximum head circumference (MHC) measurement from MRI studies within the Picture Archiving and Communications System, and can automatically add this measurement to the final radiology report. Participants were selected through a retrospective chart review of patients referred to the neurosurgery clinic. Forty-nine pediatric patients with ages ranging from 5 months to 11 years were included in the study. We created 14 printed ring structures to mirror the head circumference values at various ages along the x-axis of the Nellhaus chart. The 3D-printed structures were used to create MRI phantoms. Analytical obtainment of circumference values from the 3D objects and phantom images allowed for a fair estimation and correction of errors on the image-based-measuring instrument. Then, standard manual MHC measurements were performed and compared to values obtained from the patients' MRI T1 images using the tuned instrument proposed in this document. A T-test revealed no statistical difference between the manual assessments and the ones obtained by the automation p = 0.357, α = 0.05. This automatic application augments the more error-prone manual MHC measurement, and can add a numerical value to the final radiology report as a standard application.
| S-EPMC8326831 | biostudies-literature
Project description:To compare the content, clarity, and clinical usefulness of conventional (ie, free-form) and structured radiology reports of body computed tomographic (CT) scans, as evaluated by referring physicians, attending radiologists, and radiology fellows at a tertiary care cancer center.The institutional review board approved the study as a quality improvement initiative; no written consent was required. Three radiologists, three radiology fellows, three surgeons, and two medical oncologists evaluated 330 randomly selected conventional and structured radiology reports of body CT scans. For nonradiologists, reports were randomly selected from patients with diagnoses relevant to the physician's area of specialization. Each physician read 15 reports in each format and rated both the content and clarity of each report from 1 (very dissatisfied or very confusing) to 10 (very satisfied or very clear). By using a previously published radiology report grading scale, physicians graded each report's effectiveness in advancing the patient's position on the clinical spectrum. Mixed-effects models were used to test differences between report types.Mean content satisfaction ratings were 7.61 (95% confidence interval [CI]: 7.12, 8.16) for conventional reports and 8.33 (95% CI: 7.82, 8.86) for structured reports, and the difference was significant (P < .0001). Mean clarity satisfaction ratings were 7.45 (95% CI: 6.89, 8.02) for conventional reports and 8.25 (95% CI: 7.68, 8.82) for structured reports, and the difference was significant (P < .0001). Grade ratings did not differ significantly between conventional and structured reports.Referring clinicians and radiologists found that structured reports had better content and greater clarity than conventional reports.
| S-EPMC3121011 | biostudies-other
Project description:BackgroundThe advancement of computer information technology would maximize its potential in operating rooms with touchless input devices. A picture archiving and communication system (PACS) was compared with a touchless input device (LMC-GW), relaying to another person to control a mouse through verbal guidance, and directly controlling a mouse.MethodsParticipants (n = 34; mean age, 29.6 years) were prospectively enrolled and given nine scenarios to compare the three methods. Each scenario consisted of eight tasks, which required 6 essential functions of PACS. Time elapsed and measurement values were recorded for objective evaluation, while subjective evaluation was conducted with a questionnaire.ResultsIn all 8 tasks, manipulation using the mouse took significantly less time than the other methods (all p < 0.05). Study selection, panning, zooming, scrolling, distance measuring, and leg length measurement took significantly less time when LMC-GW was used compared to relaying to another person (all p < 0.01), whereas there were no significant differences in time required for measuring the angles and windowing. Although the touchless input device provided higher accessibility and lower contamination risk, it was more difficult to handle than the other input methods (all p < 0.01).ConclusionsThe touchless input device provided superior or equal performance to the method of verbal instruction in the environment of operating room. Surgeons agreed that the device would be helpful for manipulating PACS in operating rooms with less contamination risk and disturbance of workflow. The touchless input device can be an alternative option for direct manipulation of a mouse in operation rooms in the future.
| S-EPMC8380534 | biostudies-literature
Project description:PurposePersonalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction.Materials and methodsAn algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations.ResultsUNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study.ConclusionIntegration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.
| S-EPMC9606757 | biostudies-literature
Project description:BackgroundStructured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists' opinions and expectations concerning the radiologist's report to identify general needs in daily practice and ways to improve interdisciplinary communication.MethodsA 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation.ResultsA total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1-3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists' service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists' service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist.ConclusionStructured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.
| S-EPMC8620527 | biostudies-literature
Project description:ObjectivesTo present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists.MethodsThe AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test.ResultsThere was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful.ConclusionDesigning an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization.Critical relevance statementThe framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care.Key points• AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.
| S-EPMC10792147 | biostudies-literature
Project description:ObjectiveUnder the Hospital Value-Based Purchasing Program of the Centers for Medicare & Medicaid Services, patient satisfaction accounts for 30% of the measures of and payments for quality of care. Understanding what drives patient satisfaction data and how the data are obtained, converted into scores, and formulated into rankings is increasingly critical for imaging departments. The objectives of this article are to describe the potential impact of patient satisfaction ratings on institutions and individuals, explain how patient satisfaction is rated and ranked, identify drivers that affect the ratings and rankings, and probe the resulting challenges unique to radiology departments.ConclusionResearch results indicate that training providers to make simple modifications in their language and behavior during patient care can significantly impact patient satisfaction, which, in turn, can impact both quality-of-care ratings and the bottom line of hospitals. Training providers is a simple and cost-effective way to potentiate the clinical expression of compassion into improvement of patient satisfaction and financial reward, a national trend that no one in the game can afford to ignore.
| S-EPMC4427188 | biostudies-literature