Página web de casos clinicorradiológicos para la enseñanza de la radiología / Web site of clinical radiological cases for teaching of Radiology

INTRODUCCIÓN: En el contexto de la implantación del Plan Bolonia en nuestras universidades, la enseñanza tradicional va incorporando nuevas técnicas de formación, muchas de ellas sustentadas en las nuevas tecnologías. MATERIAL Y MÉTODOS: Se ha creado www.radiologiaparaestudiantes.com, página de acceso libre con casos clinicorradiológicos reales, dirigidos a estudiantes de medicina y escogidos por su interés docente. Cada caso consta de una pequeña historia clínica, una imagen problema y una respuesta comentada. Incorpora también una sección de anatomía. RESULTADOS: La página consta en la actualidad de 237 casos distribuidos en diferentes secciones. Ha recibido 240.000 visitas con 1.230.000 páginas visualizadas. España, México y Argentina son los países de donde proceden la mayoría de los usuarios. CONCLUSIÓN: Esta página, combinada con otras formas de docencia, permite el desarrollo de competencias en radiología y acerca a los estudiantes a su actividad médica posterior

INTRODUCTION: In the context of the 'Bolonia Plan' establishment in our universities, traditional education is incorporating new training techniques, many of them underpinned by new technologies. MATERIAL AND METHOD: For this purpose, www.radiologiaparaestudiantes.com has been created. A free access website with clinical-radiological real cases, aimed to medical students and chosen by its teaching interest. Each case consists of a brief medical history, a problem picture and a discussed answer. It also incorporates an anatomical section. RESULTS: The website currently consists of 237 cases divided in different sections. It has received 240.000 visits with 1.230.000 visualized pages. Spain, Mexico and Argentina are the countries where most users come from. CONCLUSION: This website, in combination with other teaching resources, allows the development of radiological skills bringing students closer to its later medical practice

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.

BACKGROUND: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. RESEARCH QUESTION: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis. STUDY DESIGN AND METHODS: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality. RESULTS: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90). INTERPRETATION: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

Improved Utilization Following Conversion of a Fluoroscopy Suite to Hybrid CT/Angiography System.

PURPOSE: To assess changes in operational utilization following conversion of a single IR suite to a hybrid CT/angiography (Angio-CT) system at an academic tertiary care center. MATERIALS AND METHODS: The total number of interventional procedures and diagnostic CT examinations performed in 29 rooms (20 diagnostic radiology, 7 IR, 2 shared between divisions) was calculated in the 24 months before conversion of an IR suite to Angio-CT and 12 months after conversion. The total number of IR procedures (global IR/month) and diagnostic CT scans per month (global CT/month) in both before and after conversion periods was calculated and defined as baseline institutional growth. This was compared against the change in the number of IR procedures performed in the before and after periods in the converted room (Angio-CT/month) as well as the number of diagnostic CT scans performed in the shared rooms (shared CT/month). RESULTS: The percent change in global CT and global IR from the before to the after periods was 39.2% and 3.1%, respectively. Shared CT per month and Angio-CT per month increased by 46.7% and 12.0% across the same time periods, respectively. The ratio of the percent increase in Angio-CT per month to percent increase in global IR per month was 3.87. The ratio of the percent increase in shared CT per month to percent increase in global CT per month was 1.19. CONCLUSIONS: Operational utilization improved in both diagnostic radiology and IR sections following conversion of a conventional fluoroscopic IR suite to an Angio-CT room.

Pediatric Hepatoblastoma, Hepatocellular Carcinoma, and Other Hepatic Neoplasms: Consensus Imaging Recommendations from American College of Radiology Pediatric Liver Reporting and Data System (LI-RADS) Working Group.

Appropriate imaging is imperative in evaluating children with a primary hepatic malignancy such as hepatoblastoma or hepatocellular carcinoma. For use in the adult patient population, the American College of Radiology created the Liver Imaging Reporting and Data System (LI-RADS) to provide consistent terminology and to improve imaging interpretation. At present, no similar consensus exists to guide imaging and interpretation of pediatric patients at risk for developing a liver neoplasm or how best to evaluate a pediatric patient with a known liver neoplasm. Therefore, a new Pediatric Working Group within American College of Radiology LI-RADS was created to provide consensus for imaging recommendations and interpretation of pediatric liver neoplasms. The article was drafted based on the most up-to-date existing information as interpreted by imaging experts comprising the Pediatric LI-RADS Working Group. Guidance is provided regarding appropriate imaging modalities and protocols, as well as imaging interpretation and reporting, with the goals to improve imaging quality, to decrease image interpretation errors, to enhance communication with referrers, and to advance patient care. An expanded version of this document that includes broader background information on pediatric hepatocellular carcinoma and rationale for recommendations can be found in Appendix E1 (online).

Open access image repositories: high-quality data to enable machine learning research.

Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

Interpreting Radiographs with Concurrently Obtained Patient Photographs.

A technology for automatically obtaining patient photographs along with portable radiographs was implemented clinically at a large academic hospital. This article highlights several cases in which image-related clinical context, provided by the patient photographs, provided quality control information regarding patient identification, laterality, or position and assisted the radiologist with the interpretation. The information in the photographs can easily minimize unnecessary calls to the patient's nursing staff for clarifications and can lead to new methods of physically assessing patients. Published under a CC BY 4.0 license.

Automated Billing Code Retrieval from MRI Scanner Log Data.

Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.

10 Steps to Strategically Build and Implement your Enterprise Imaging System: HIMSS-SIIM Collaborative White Paper.

An enterprise imaging (EI) strategy is an organized plan to optimize the electronic health record (EHR) so that healthcare providers have intuitive and immediate access to all patient clinical images and their associated documentation, regardless of source. We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patient experience. Step seven suggests how EI can enhance the work life of caregivers and step eight describes how to develop EI governance. Step nine describes the plan to implement an EI project, and finally, step 10, to understand cybersecurity from a patient safety perspective and to protect images from accidental and malicious intrusion.

ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories.

In the last decades, the amount of medical imaging studies and associated metadata has been rapidly increasing. Despite being mostly used for supporting medical diagnosis and treatment, many recent initiatives claim the use of medical imaging studies in clinical research scenarios but also to improve the business practices of medical institutions. However, the continuous production of medical imaging studies coupled with the tremendous amount of associated data, makes the real-time analysis of medical imaging repositories difficult using conventional tools and methodologies. Those archives contain not only the image data itself but also a wide range of valuable metadata describing all the stakeholders involved in the examination. The exploration of such technologies will increase the efficiency and quality of medical practice. In major centers, it represents a big data scenario where Business Intelligence (BI) and Data Analytics (DA) are rare and implemented through data warehousing approaches. This article proposes an Extract, Transform, Load (ETL) framework for medical imaging repositories able to feed, in real-time, a developed BI (Business Intelligence) application. The solution was designed to provide the necessary environment for leading research on top of live institutional repositories without requesting the creation of a data warehouse. It features an extensible dashboard with customizable charts and reports, with an intuitive web-based interface that empowers the usage of novel data mining techniques, namely, a variety of data cleansing tools, filters, and clustering functions. Therefore, the user is not required to master the programming skills commonly needed for data analysts and scientists, such as Python and R.

Challenges of Implementing Picture Archiving and Communication System in Multiple Hospitals: Perspectives of Involved Staff and Users.

Today, despite the advantages of the PACS system, its implementation in some healthcare organizations faces many challenges. One of the important factors in the successful implementation of a PACS system is identifying and prioritizing the challenges from the perspectives of involved staff and user of this system. Therefore, the aim of this study was to determine and compare the challenges of implementing PACS from perspectives these users in educational hospitals. This study was conducted on all IT and medical equipment staff, and radiology residents (n = 140) in Kerman University of Medical Sciences (KUMS) and Shiraz University of Medical Sciences (SUMS) in 2016. The data were collected through two researcher-made questionnaires. Their validity was approved by radiologists, IT staff, and medical informatics specialists and their reliability through calculation of Cronbach's Alpha (0.969 and 0.795). We used Multivariate Analysis of Variance (MANOVA) to compare the scores given by three groups of participants in the challenges and Univariate Analysis of Variance (ANOVA) to compare the scores in two universities. The participants believed that technical challenges were more important than other challenges (x̄=3.74, SD = 0.7). IT experts (x̄=3.87, SD = 1) and radiology residents (x̄=3.95, SD = 0.9) gave the higher scores to the "shortage of high quality monitors" factor and medical equipment experts (x̄=4.26, SD = 0.87) to the "low speed of communication networks" factor among all technical challenges. The mean scores given to technical (x̄=76.1, SD = 13.5) and managerial (x̄=16, SD = 5.9) challenges in SUMS were more than the scores of the same challenges in KUMS (x̄=69.9, SD = 15.7) and (x̄=11.9, SD = 6.4) (p < 0.05). The technical challenges are the most common challenges to PACS implementation, and different universities experience different levels of technical challenges. Eliminating implementation challenges can reduce the risk of failure in the utilization process. Based on the results of this study, providing necessary infrastructures such as appropriate monitors and upgraded IT equipment can prevent many of the PACS implementation challenges.

Registro Español de Imagen Cardiaca. I Informe Oficial de la Sección de Imagen Cardiaca de la Sociedad Española de Cardiología (2017) / Spanish Cardiovascular Imaging Registry. First Official Report of the Spanish Society of Cardiology Working Group on Cardiovascular Imaging (2017)

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ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.

Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.

A Web-Based Response-Assessment System for Development and Validation of Imaging Biomarkers in Oncology.

Quantitative imaging biomarkers are increasingly used in oncology clinical trials to assist the evaluation of tumor responses to novel therapies. To identify these biomarkers and ensure smooth clinical translation once they have been validated, it is critical to develop a reliable workflow-efficient imaging platform for integration in clinical settings. Here we will present a web-based volumetric response-assessment system that we developed based on an open-source image viewing platform (WEASIS) and a DICOM image archive (DCM4CHEE). Our web-based response-assessment system offers a DICOM imaging archiving function, standard imaging viewing and manipulation functions, efficient tumor segmentation and quantification algorithms, and a reliable database containing tumor segmentation and measurement results. The prototype system is currently used in our research lab to foster the development and validation of new quantitative imaging biomarkers, including the volumetric computed tomography technique, as a more accurate and early assessment method of solid tumor responses to targeted and immunotherapies.

Implementing Shared, Standardized Imaging Protocols to Improve Cross-Enterprise Workflow and Quality.

Value-based imaging requires appropriate utilization and the delivery of consistently high-quality imaging at an acceptable cost. Challenges include developing standardized imaging protocols, ensuring consistent application by technologists, and monitoring quality. These challenges increase as enterprises grow in geographical extent and complexity through mergers or partnerships. Our imaging enterprise includes a university hospital and clinic system, a large county hospital and healthcare system, and a pediatric hospital and health system. Studies across the three systems are interpreted by one large academic radiology group with expertise in various subspecialties. Our goals were as follows: (1) Standardize imaging protocols; (2) adapt the imaging protocols to specific modalities and available equipment; and (3) disseminate this knowledge across all of the sites of care. Our approach involved three components: (1) facilitation of imaging protocol definition across subspecialty radiologist teams; (2) creation of a database which links the clinical imaging protocols to the scanner/machine specific acquisition protocols; and (3) delivery of a protocol library and updates to all users regardless of location. We successfully instituted a process for the development, implementation, and delivery of standardized imaging protocols in a complex, multi-institutional healthcare system. Key elements for success include (1) a Project Champion who is able to articulate the importance of protocol standardization in improving the quality of patient care, (2) strong, effective modality-specific operational committees, (3) a Project Lead to manage the process efficiently, and (4) an electronic publishing of the protocol database to facilitate ease of access and use.

Factors influencing the selection of a picture archiving and communication system: A qualitative study.

BACKGROUND: Picture Archiving and Communication System (PACS) is an evolving technology in health care domains that is used for storage, management, retrieval, transfer, and delivery of medical images. Some medical centers in Iran have installed the PACS in recent years but have not used it appropriately. One of the problems in implementing this system is inability to select appropriate PACS. Several factors are involved in the selection process. The objective of this study was to determine the factors that influence PACS selection. METHODS: This qualitative study aimed to identify factors influencing the PACS selection. Data were collected through semistructured interviews with 10 experts in three educational hospitals and in the position to make decision for the purchase of PACS. Data were analyzed by the conventional qualitative content analysis method proposed by Lundman and Graneheim. RESULTS: Analyses achieved 11 subcategories in two specific and general categories that influence PACS selection. The specific category of this study included six subcategories, and the general category included five subcategories. CONCLUSION: The results of this study determined that usability was the most important factor from the perspective of participants. Since the main users of a system have a critical role in adoption or rejection of a system, ease of use (usability) is significant and must be considered in system selection as a significant factor.

Institutional Implementation of a Structured Reporting System: Our Experience with the Brain Tumor Reporting and Data System.

RATIONALE AND OBJECTIVES: Analyze the impact of implementing a structured reporting system for primary brain tumors, the Brain Tumor Reporting and Data System, on attitudes toward radiology reports at a single institution. MATERIALS AND METHODS: Following Institutional Review Board approval, an initial 22 question, 5 point (1-worst to 5-best), survey was sent to faculty members, house staff members, and nonphysician providers at our institution who participate in the direct care of brain tumor patients. Results were used to develop a structured reporting strategy for brain tumors which was implemented across an entire neuroradiology section in a staged approach. Nine months following structured reporting implementation, a follow-up 27 question survey was sent to the same group of providers. Keyword search of radiology reports was used to assess usage of Brain Tumor Reporting and Data System over time. RESULTS: Fifty-three brain tumor care providers responded to the initial survey and 38 to the follow-up survey. After implementing BT-RADS, respondents reported improved attitudes across multiple areas including: report consistency (4.3 vs. 3.4; p < 0.001), report ambiguity (4.2 vs. 3.2, p < 0.001), radiologist/physician communication (4.5 vs. 3.8; p < 0.001), facilitation of patient management (4.2 vs. 3.6; p = 0.003), and confidence in reports (4.3 vs. 3.5; p < 0.001). Providers were more satisfied with the BT-RADS structured reporting system (4.3 vs. 3.7; p = 0.04). Use of the reporting template progressively increased with 81% of brain tumor reports dictated using the new template by 9 months. CONCLUSION: Implementing a structured template for brain tumor imaging improves perception of radiology reports among radiologists and referring providers involved in the care of brain tumor patients.

Improving Communication of Actionable Findings in Radiology Imaging Studies and Procedures Using an EMR-Independent System.

The primary purpose of this study is to determine if the implementation of an actionable findings communication system (PeerVue) with explicitly defined criteria for the classification of critical results, leads to an increase in the number of actionable findings reported by radiologists. Secondary goals are to 1) analyze the adoption rate of PeerVue and 2) assess the accuracy of the classification of actionable findings within this system. Over a two-year period, 890,204 radiology reports were analyzed retrospectively in order to identify the number of actionable findings communicated before (Year 1) and after the implementation of PeerVue (Year 2) at a tertiary care academic medical center. A sub-sample of 145 actionable findings over a two-month period in Year 2 was further analyzed to assess the degree of concordance with our reporting policy. Before PeerVue, 4623/423,070 (1.09%) of radiology reports contained an actionable finding. After its implementation, this number increased to 6825/467,134 (1.46%) (p < 0.0001). PeerVue was used in 3886/6825 (56.9%) cases with actionable findings. The remaining 2939/6825 (43.1%) were reported using the legacy tagging system. From the sub-sample taken from PeerVue, 104/145 (71.7%) were consistent with the updated reporting policy. A software program (PeerVue) utilized for the communication of actionable findings contributed to a 34% (p < 0.0001) increase in the reporting rate of actionable findings. A sub-analysis within the new system indicated a 56.9% adoption rate and a 71.7% accuracy rate in reporting of actionable findings.

The Role of the Integrated Digital Radiology System in Assessing the Impact of Patient Load on Emergency Computed Tomography (CT) Efficiency.

Time-critical management is of particular significance in the trauma and emergency setting, where intervals from patient arrival to diagnostic imaging and from imaging to radiology report are key determinants of outcome. This study, based in the Trauma and Emergency Unit of a large, tertiary-level African hospital with a fully digital radiology department, assessed the impact of increased workload on computerised tomography (CT) efficiency. Sequential, customised searches of the institutional radiology information system (RIS) were conducted to define two weekends in 2016 with the lowest and highest emergency CT workloads, respectively. The electronic RIS timestamps defining the intervals between key steps in the CT workflow were extracted and analysed for each weekend. With the exception of radiologist reporting time, workflow steps were significantly prolonged by increased workload. This study highlights the potential role of the integrated digital radiology system in enabling a detailed analysis of imaging workflow, thereby facilitating the identification and appropriate management of bottlenecks.

Introduction to the Liver Imaging Reporting and Data System for Hepatocellular Carcinoma.

The Liver Imaging Reporting And Data System (LI-RADS) was created with the support of the American College of Radiology (ACR) to standardize the acquisition, interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). A comprehensive and rigorous system developed by radiologists, hepatologists, pathologists, and surgeons, LI-RADS addresses a wide range of imaging contexts. Currently, 4 algorithms are available publicly on the ACR website: ultrasound for HCC surveillance, computed tomography and magnetic resonance imaging for HCC diagnosis and tumor staging, contrast-enhanced ultrasound for HCC diagnosis, and computed tomography/magnetic resonance imaging for treatment response assessment. Each algorithm is supported by a decision tree, categorization table, lexicon, atlas, technical requirements, and reporting and management guidance. Category codes reflecting the relative probability of HCC and malignancy are assigned to imaging-detected liver observations, with emerging evidence suggesting that LI-RADS accurately stratifies HCC and malignancy probabilities. LI-RADS is an evolving system and has been updated and refined iteratively since 2011 based on scientific evidence, expert opinion, and user feedback, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Concurrent with its most recent update, LI-RADS was integrated into the American Association for the Study of Liver Diseases HCC guidance released in 2018. We anticipate continued refinement of LI-RADS and progressive adoption by radiologists worldwide, with the eventual goal of culminating in a single unified system for international use.

Implementation and Benefits of a Vendor-Neutral Archive and Enterprise-Imaging Management System in an Integrated Delivery Network.

The use of digital imaging has substantially grown in recent decades, in traditional services, new specialties, and departments. The need to share these data among departments and caregivers necessitated central archiving systems that are able to communicate with various viewing applications and electronic medical records. This promoted the development of modern vendor neutral archive (VNA) systems. The need to aggregate and share imaging data from various departments promoted the development of enterprise-imaging (EI) solutions that replace departmental silos of data with central healthcare enterprise databases. To describe the implementation process of a VNA-EI solution in a large health system and its outcomes. We review the background of VNA and EI solutions development and describe the characteristics and advantages of such systems. We then describe our experience in implementation of these solutions in a large integrated healthcare delivery network in northeast Ohio. We then present the process, challenges, costs, advantages, and outcomes of such implementation. The VNA and EI solution was launched in December 2015 and is still ongoing. It currently includes 54 radiology and 26 cardiology sites affiliated with the University Hospitals health system. This process was associated with more than 10% cost savings, 30% reduction in storage costs, superior support for disaster recovery, and 80% decrease in unscheduled outages. All these were achieved despite a 120% increase in archive retrieval needs and a 40% growth in image production. Implementation of a VNA and EI solution was successful and resulted in numerous measurable and qualitative improvements in a large and growing health system.