The Financial Impact of COVID on Radiology Health Systems.

The coronavirus pandemic of 2019 (COVID-19) was arguably the most pivotal global event that current generations have witnessed, with unprecedented global challenges, and colossal effects on health systems. The financial consequences, in particular, were profound and far-reaching. Staggering estimates of up to $50.7 billion dollars per month in lost revenue for the US health system were reported by the American Hospital Association (Kaye et al., 2021). The pandemic caused significant increases in cost of drugs, disruptions to medical supply chains, day-to-day workflow, and operations in all areas of medicine and various healthcare systems. Radiology experienced a significant burden of the damage, finding itself at the forefront of the pandemic's economic fallout (American Hospital Association).

An assessment of radiation safety practices for transgender and gender non-binary patients in Irish radiology departments.

INTRODUCTION: Studies indicate there may be inadequate care given to transgender and non-binary (TGNB) patients in healthcare environments, with radiology departments not being equipped to cater for this group. There is currently a deficit in research concerning the use of radiation safety measures for TGNB patients. The purpose of this research was to examine opinions of Irish Radiation Safety Experts (RSE) on current status of radiation safety protocols and techniques in place for TGNB patients and consider any changes necessary. METHODOLOGY: Ten semi-structured interviews were conducted with RSEs from eight Irish hospitals, including five radiation protection officers (RPO) and five medical physicists. Question included: current radiation safety protocols for TGNB patients, potential issues and challenges with current practice, and recommendations of new measures. Coding was used to facilitate content analysis for interpretation of findings. RESULTS: No reference to TGNB patients in local policies or guidelines was evident. Interviews established key radiation safety risks including inadvertent exposure of the foetus and insensitive patient care. Prominent categories identified included additional education, gender identification at patient registration and consideration of current policies and guidelines. The extent to which RSEs promoted the implementation of further measures to radiology departments varied. CONCLUSIONS: A clear lack of guidance and instruction for radiation safety for TGNB patients is evident. Whilst there are few TGNB patients in Irish hospitals, participants believed that inclusive changes should be made concurrent with Ireland's evolving culture and in the interest of equality of patient safety. IMPLICATIONS FOR PRACTICE: Inclusive changes should be made to radiology departments concurrent with Ireland's evolving culture. However, barriers to implementing such measures include a lack of available resources, investment, and instruction from authoritative bodies.

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup.

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of the lack of pediatric artificial intelligence (AI), improves the understanding of relevant pediatric AI issues, and offers solutions to address the inadequacies in pediatric AI development. In short, the design, training, validation, and safe implementation of AI in children require careful and specific approaches that can be distinct from those used for adults. On the eve of widespread use of AI in imaging practice, the group invites the radiology community to align and join Image IntelliGently (www.imageintelligently.org) to ensure that the use of AI is safe, reliable, and effective for children.

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI.

Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.

Climate Change and Radiology: Impetus for Change and a Toolkit for Action.

This special report discusses the importance of climate change for health care and radiology. The impact of climate change on human health and health equity, the contribution of health care and medical imaging to the climate crisis, and the impetus for change within radiology to create a more sustainable future are covered. The authors focus on actions and opportunities to address climate change in our role as radiologists. A toolkit highlights actions we can take toward a more sustainable future, linking each action with the expected impact and outcome. This toolkit includes a hierarchy of actions from first steps to advocating for system-level change. This includes actions we can take in our daily lives, in radiology departments and professional organizations, and in our relationships with vendors and industry partners. As radiologists, we are adept at managing rapid technological change, which makes us ideally suited to lead these initiatives. Alignment of incentives and synergies with health systems are highlighted given that many of the proposed strategies also result in cost savings.

The role of Artificial intelligence in the assessment of the spine and spinal cord.

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.

Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment.

BACKGROUND: The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice as an AI-replace tool, replacing the radiologist completely. OBJECTIVE: The aim of this study was to investigate how the tool is used in clinical practice. Are radiologists more inclined to use BoneXpert to assist rather than replace themselves, and how much time is saved? MATERIALS AND METHODS: We sent a survey consisting of eight multiple-choice questions to 282 radiologists in departments in Europe already using the software. RESULTS: The 97 (34%) respondents came from 18 countries. Their answers revealed that before installing the automated method, 83 (86%) of the respondents took more than 2 min per bone age rating; this fell to 20 (21%) respondents after installation. Only 17/97 (18%) respondents used BoneXpert to completely replace the radiologist; the rest used it to assist radiologists to varying degrees. For instance, 39/97 (40%) never overruled the automated reading, while 9/97 (9%) overruled more than 5% of the automated ratings. The majority 58/97 (60%) of respondents checked the radiographs themselves to exclude features of underlying disease. CONCLUSION: BoneXpert significantly reduces reporting times for bone age determination. However, radiographic analysis involves more than just determining bone age. It also involves identification of abnormalities, and for this reason, radiologists cannot be completely replaced. AI systems originally developed to replace the radiologist might be more suitable as AI assist tools, particularly if they have not been validated to work autonomously, including the ability to omit ratings when the image is outside the range of validity.

Synchronous Virtual Patient Consultations in Radiology.

We describe our experience in synchronous virtual radiologist consultations, whereby a radiologist at the PACS uses a conferencing platform to join a primary care visit between a patient at home and a referring provider, at home or at clinic, to directly explain imaging results and partner with the referrer in forming management recommendations. We explore the model's significance in the context of patient-centered care. Implementation, obstacles, and potential impact on health care disparities are also discussed.

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS: In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS: The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). INTERPRETATION: Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. FUNDING: Deutsche Forschungsgemeinschaft.

Radiology in the Era of Value-Based Healthcare: A Multi-Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Basics of time-driven activity-based costing (TDABC) and applications in breast imaging.

Time-drive activity-based costing (TDABC) is a practical way of calculating costs, decreasing waste, and improving efficiency. Although TDABC has been utilized in other service industries for years, it has only recently gained attention in healthcare. In this review article, we define the basic concepts and steps of TDABC and provide examples for applications in breast imaging.

From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value.

The radiology practice has access to a wealth of data in the radiologist information system, dictation reports, and electronic health records. Although many artificial intelligence applications in radiology have focused on computer vision and the interpretive use cases, many opportunities exist to enhance the radiologist's value proposition through business analytics. This article explores how AI lends an analytical lens to the radiology practice to create value.

Patients' views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire.

OBJECTIVES: The patients' view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology. METHODS: Six domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach's alpha and composite reliability. RESULTS: The exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5). CONCLUSIONS: This study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale. KEY POINTS: • Although AI systems are increasingly developed, not much is known about patients' views on AI in radiology. • Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients' views on AI in radiology, revealing five factors. • Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.

Xpektrin, un simulador de espectros fácil de usar y ampliamente distribuible para radiología general / Xpektrin, an easy to use and highly distributable X-Ray spectra simulator in general radiography

Resumen: Se presenta una aplicación basada en Microsoft Excel llamada Xpektrin para el cálculo de dosis en radiología general. La aplicación permite simular espectros de rayos X en radiología general utilizando el modelo TASMICS a partir de mediciones del kerma en aire (Kair) y de la capa Hemirreductora (HVL). Tiene implementado el cálculo de magnitudes radiométricas y dosimétricas, como el kerma en aire en la superficie de entrada (Ke) y la dosis en piel (Dskin), en función de la elección arbitraria de los factores de exposición, el tipo y grosor de filtro, la distancia foco-piel y el tamaño de campo. Xpektrin fue validado con la herramienta computacional SPEKTR 3.0, utilizando mediciones de dosis y de HVL de tubos de rayos X de tres recintos hospitalarios. Se encontró buena correlación en ambas aplicaciones entre las mediciones experimentales y los valores calculados de HVL y con coeficientes de Pearson R² ≥ 0.99 en todos los casos. Sin embargo, se obtuvo mejor concordancia con los valores experimentales de HVL con Xpektrin (mediana de diferencias -0.43%, -0.04% y 0.01%) que con SPEKTR 3.0 (mediana de diferencias -3.31%, 0.10% y -7.85%), en particular para el tubo con mayor filtración. Xpektrin está optimizada para ser utilizada en los departamentos de radiología para la determinación de dosis de pacientes individuales en función de los parámetros utilizados durante la exposición, por lo que puede ser utilizada como parte de un sistema de registro dosimétrico o como apoyo para el establecimiento de niveles de referencia para diagnóstico (NRD), siendo particularmente útil en servicios con equipos sin registros automáticos de dosis. Además, debido a sus características de simulador, puede ser útil como herramienta pedagógica. El uso de Excel permite que sea altamente distribuible y fácil de usar, sin necesidad de conocimientos de programación.

Abstract: Xpektrin, an easy to use and highly distributable X-Ray Spectra Simulator in General Radiography. An application based on Microsoft Excel called Xpektrin is presented for dose calculation in general radiology. The application was developed to simulate X-ray spectra in general radiography using the TASMICS model. Using as inputs air kerma (Kair) and Half-value layer (HVL) measurements, Xpektrin allows the calculation of several radiometric and dosimetric quantities, such as the entrance surface air kerma (Ke) and the skin dose (Dskin), depending on the exposure factors, filter material type, filter thickness, focus-skin distance and field size. Xpektrin was validated against the Matlab toolkit SPEKTR 3.0, using dose and HVL measurements of X-ray tubes from three different hospitals. It was found good correlation in both applications between the experimental measurements and the calculated HVL and Kair values with Pearson coefficients R² ≥ 0.99 in all cases. However, experimental and calculated HVL have better agreement with Xpektrin (median percent difference -0.43%, -0.04% and 0.01%) than SPEKTR 3.0 (median percent difference -3.31%, 0.10% and -7.85%), particularly for the tube with greater filtration thickness. Xpektrin is optimized to be used in radiology departments for patient dose determination depending on the exposure parameters and may be used as part of a dosimetric record system or as a support for the determination of Diagnostic Reference Levels, which may be useful when no automatic dose records are available. In addition, due to its simulator characteristics, it can be useful as a pedagogical tool. Using Excel allows Xpektrin to be highly distributable and easy to use, without the need for programming skills.

Evaluation of the Patient Request Process for Radiology Imaging in U.S. Hospitals.

Background In the United States, patients have the right to access their protected health information. However, to the knowledge of the authors, no study has evaluated the patient request process and the barriers to patient access of their radiology images. Purpose To assess U.S. hospital compliance with federal regulations and patient ease of access to imaging studies. Materials and Methods In this cross-sectional study conducted from June 6 to December 3, 2018, 80 U.S. hospitals were contacted by telephone to determine their patient request process for imaging studies. A scripted interview was used to simulate the patient experience in requesting imaging studies. Hospitals were compared in terms of formats of release (compact disc [CD] via pick up, CD via mail, e-mail, online patient portal, or other online access), departments from which cine files can be requested, fees, and processing times. Results All 80 hospitals stated that they could provide imaging studies on CDs. Only six (8%) hospitals provided imaging studies via e-mail and three (4%) via an online patient portal. Requests for cine files were fulfilled by a department separate from diagnostic radiology in 47 of 80 (59%) hospitals. Patient charges ranged from $0 to $75 for a single CD, no charge to $6 via e-mail, and no charge via an online patient portal. Fifty-nine (74%) hospitals stated that they could release copies within 24 hours, 10 (13%) within 2-5 days, eight (10%) within 5-10 days, and three (4%) within 10-30 days from request date. Imaging studies from outside of the diagnostic radiology department may need to be requested through the departments that performed the study. Conclusion This study demonstrated that although fees and processing times are compliant with federal regulations, patient access to imaging studies is limited primarily to compact disc format. The request process is also complicated for patients because of dispersion of imaging studies across departments. © RSNA, 2019 Online supplemental material is available for this article.

Assessment of Explicitly Stated Interval Change on Noncontrast Head CT Radiology Reports.

BACKGROUND AND PURPOSE: Consistent and standardized reporting of interval change for certain diagnoses may improve the clinical utility of radiology reports. The purpose of this study was to assess explicitly stated interval change of various findings in noncontrast head CT reports. MATERIALS AND METHODS: A retrospective review was performed on successive noncontrast head CT radiology reports from the first 2 weeks of January 2014. Reports with at least 1 prior comparison CT scan were included. Reports with normal examination findings and those that made comparison with only other types of examinations (eg, MR imaging) were excluded. Descriptive and subgroup statistical analyses were performed. RESULTS: In total, 200 patients with 230 reports and 979 radiographic findings were identified. The average interval between reports was 344.9 ± 695.9 days (range, 0-3556 days). Interval change was mentioned 67.3% (n = 659) of the time for all findings (n = 979). Explicitly stated interval change was significantly associated with nonremote findings (P < .001) and generalized statements of interval change (P < .001). The proportion of interval change reported ranged from 95.3% of the time for hemorrhagic to 36.4% for soft-tissue/osseous categorizations. CONCLUSIONS: Interval change reporting was variable, mentioned for 67.3% of noncontrast head CT report findings with a prior comparison CT scan. Structured radiology reports may improve the consistent and clear reporting of interval change for certain findings.

Role of Radiology in the Assessment of Skeletons from Archeological Sites.

Radiology is an indispensable investigative tool for physical anthropologists and paleopathologists. Since its birth in 1895, X-ray has been useful in studying archeobiological finds. As a nondestructive technique of investigations, radiology allows for analysis of archeological finds without damaging them. Radiological investigations in anthropology are very important to assist: (1) reconstruction of biological profile (age at death, sex, stature, and ethnicity; (2) diagnosis pathological conditions, and life style (diet, physical stress, etc.); (3) interpretation of postdepositional process (diagenetic or taphonomic factors). We are sure that the importance of radiology in anthropology will continue to increase, and we confident that these disciplines will ultimately fuse and lead to the birth of a new professional branch of research: "Archeoradiology" or "Anthroradiology."

Cost-Effectiveness Analysis: An Overview of Key Concepts, Recommendations, Controversies, and Pitfalls.

The field of radiology has witnessed a burst of technological advances that improve diagnostic quality, reduce harm to patients, support clinical needs, and better serve larger more diverse patient populations. One of the critical challenges with these advances is proving that value outweighs the cost. The use of cutting-edge technology is often expensive, and the reality is that our society cannot afford all the screening and diagnostic tests that are being developed. At the societal level, we need tools to help us decide which health programs should be funded. Therefore, decision makers are increasingly looking toward scientific methods to compare health technologies in order to improve allocation of resources. One of such methods is cost-effectiveness analysis. In this article, we review key features of cost-effectiveness analysis and its specific issues as they relate to radiology.