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As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Background Reliance on examination reporting of unexpected imaging findings does not ensure receipt of findings or appropriate follow-up. A closed-loop communication system should include provider and patient notifications and be auditable through the electronic health record (EHR). Purpose To report the initial design of and results from using an EHR-integrated unexpected findings navigator (UFN) program that ensures closed-loop communication of unexpected nonemergent findings. Materials and Methods An EHR-integrated UFN program was designed to enable identification and communication of unexpected findings and aid in next steps in findings management. Three navigators (with prior training as radiologic technologists and sonographers) facilitated communication and documentation of results to providers and patients. Twelve months (October 2019 to October 2020) of results were retrospectively reviewed to evaluate patient demographics and program metrics. Descriptive statistics and correlation analysis were performed by using commercially available software. Results A total of 3542 examinations were reported within 12 months, representing 0.5% of all examinations performed (total of 749 649); the median patient age was 62 years (range, 1 day to 98 years; interquartile range, 23 years). Most patients were female (2029 of 3542 [57%]). Almost half of the examinations submitted were from chest radiography and CT (1618 of 3542 [46%]), followed by MRI and CT of the abdomen and pelvis (1123 of 3542 [32%]). The most common unexpected findings were potential neoplasms (391 of 3542 [11%]). The median time between examination performance and patient notification was 12 days (range, 0-136 days; interquartile range, 13 days). A total of 2127 additional imaging studies were performed, and 1078 patients were referred to primary care providers and specialists. Most radiologists (89%, 63 of 71 respondents) and providers (65%, 28 of 43 respondents) found the system useful and used it most frequently during regular business hours. Conclusion An electronic health record-integrated, navigator-facilitated, closed-loop communication program for unexpected radiologic findings led to near-complete success in notification of providers and patients and facilitated the next steps in findings management. © RSNA, 2021 See also the editorial by Safdar in this issue.

Background As payers transition to value-based care reimbursement models, health care systems are striving to identify ways to improve quality while simultaneously reducing costs. One approach has been to reduce radiology utilization, given rapid increases in imaging services over the past 20 years. We hypothesize that the implementation of a peer-comparison radiology dashboard designed to reduce unwanted clinical variation may also lead to reductions in cost within a large primary care network. Methods Radiology orders placed by primary care providers (n = 159) within a large primary care network were analyzed to evaluate baseline costs and variation. To increase transparency, an imaging utilization rate was included in providers’ monthly quality dashboards, allowing for peer comparison. A retrospective pre-post analysis using reimbursement data from Centers for Medicare and Medicaid services (CMS) estimated cost savings associated with the intervention. Results We observed a 17.3% decrease in median imaging rate (P Conclusions Peer-comparison feedback designed to reduce clinical variation yielded significant reductions in radiology costs in a primary care network. This strategy may serve as an inexpensive opportunity for health care systems to reduce total cost of care.

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user’s ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient’s images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

While in the last decade there has been significant technical infrastructure development to support standards-based image exchange through organizations like Integrating the Healthcare Enterprise, Carequality, DICOM, and HL7 FHIR, the human operationalization of such infrastructure using centralized, intuitive, standards-based applications remains the cornerstone of effective and reliable electronic image exchange. Image libraries managing the highly transactional and often uncertain inflows and outflows of images have a unique perspective on the challenges of image exchange. This manuscript will summarize frequent collaboration and communication, release of information, staffing, technology, information localization, and analytics difficulties for image exchange from the perspective of the image library staff managing the transactions.

The objective is to determine patients’ utilization rate of radiology image viewing through an online patient portal and to understand its impact on radiologists. IRB approval was waived. In this two-part, multi-institutional study, patients’ image viewing rate was retrospectively assessed, and radiologists were anonymously surveyed for the impact of patient imaging access on their workflow. Patient access to web-based image viewing via electronic patient portals was enabled at 3 institutions (all had open radiology reports) within the past 5 years. The number of exams viewed online was compared against the total number of viewable imaging studies. An anonymized survey was distributed to radiologists at the 3 institutions, and responses were collected over 2 months. Patients viewed 14.2% of available exams – monthly open rate varied from 7.3 to 41.0%. A total of 254 radiologists responded to the survey (response rate 32.8%); 204 were aware that patients could view images. The majority (155/204; 76.0%) felt no impact on their role as radiologists; 11.8% felt negative and 9.3% positive. The majority (63.8%) were never approached by patients. Of the 86 who were contacted, 46.5% were contacted once or twice, 46.5% 3–4 times a year, and 4.7% 3–4 times a month. Free text comments included support for healthcare transparency (71), concern for patient confusion and anxiety (45), and need for attention to radiology reports and image annotations (15). A small proportion of patients viewed their radiology images. Overall, patients’ image viewing had minimal impact on radiologists. Radiologists were seldom contacted by patients. While many radiologists feel supportive, some are concerned about causing patient confusion and suggest minor workflow modifications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-021-00565-9.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

A core principle of ethical data sharing is maintaining the security and anonymity of the data, and care must be taken to ensure medical records and images cannot be reidentified to be traced back to patients or misconstrued as a breach in the trust between health care providers and patients. Once those principles have been observed, those seeking to share data must take the appropriate steps to curate the data in a way that organizes the clinically relevant information so as to be useful to the data sharing party, assesses the ensuing value of the data set and its annotations, and informs the data sharing contracts that will govern use of the data. Embarking on a data sharing partnership engenders a host of ethical, practical, technical, legal, and commercial challenges that require a thoughtful, considered approach. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. This is Part 2 of a Report on the workgroup's efforts in exploring these issues.

Radiology is at the forefront of the artificial intelligence transformation of health care across multiple areas, from patient selection to study acquisition to image interpretation. Needing large data sets to develop and train these algorithms, developers enter contractual data sharing agreements involving data derived from health records, usually with postacquisition curation and annotation. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. The workgroup identified five broad domains of activity important to collaboration using patient data: privacy, informed consent, standardization of data elements, vendor contracts, and data valuation. This is Part 1 of a Report on the workgroup's efforts in exploring these issues.

Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019–October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: (a) image delivery, (b) quality control, (c) a results database, (d) results processing, (e) results presentation and delivery, (f) error correction, and (g) a dashboard for performance monitoring. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow. Analyses included the assessment of the number of examinations processed, error rates, and corrections. The AI system processed 1748 lymphoscintigraphy examinations. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. A dashboard allowed monitoring of the AI system performance in real time. All 14 survey respondents “somewhat agreed” or “strongly agreed” that the AI system was well integrated into the clinical workflow. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice. Keywords: PACS, Computer Applications-General (Informatics), Diagnosis © RSNA, 2021

Purpose To determine the value of image-rich radiology reports (IRRR) by evaluating the interest and preferences of referring physicians, potential impact on clinical workflow, and the willingness of radiologists to create them. Methods Referring physicians and radiologists were interviewed in this prospective, HIPAA-compliant study. Subject willingness to participate in the study was determined by an e-mail. A single investigator conducted all interviews using a standard questionnaire. All subjects reviewed a video mockup demonstration of IRRR and three methods for viewing embedded images, as follows: (1) clickable hyperlinks to access a scrollable stack of images, (2) scrollable and enlargeable small-image thumbnails, and (3) scrollable but not enlargeable medium-sized images. Questionnaire responses, free comments, and general impressions were captured and analyzed. Results Seventy-two physicians (36 clinicians, 36 radiologists) were interviewed. Thirty-one clinicians (86%) expressed interest in using IRRR. Seventy-seven percent of subjects believed IRRR would improve communication. Ten clinicians (28%) preferred method 1, 18 (50%) preferred method 2, and 8 (22%) preferred method 3 for embedding images. Thirty clinicians (83%) stated that IRRR would improve efficiency. Twenty-two radiologists (61%) preferred selecting a tool button with a mouse and right-clicking images to embed them, 13 (36%) preferred pressing a function key, and 11 (31%) preferred dictating series and image numbers. The average time radiologists were willing to expend for embedding images was 66.7 seconds. Conclusions Referring physicians and radiologist both believe IRRR would add value by improving communication with the potential to improve the workflow efficiency of referring physicians.

To objectively and subjectively evaluate a large, academic radiology department's transition to electronic health record (EHR) centered workflow.Multiple metrics were compared from before and after the move to EHR-driven workflow. Examination ordering and reading priority data were obtained for 30 days both before and after the transition. Sixteen radiologists were observed opening a computed tomography (CT) examination, and time to open, mouse clicks, and keystrokes were recorded. Information available to the radiologist during interpretation was also compared. Additionally, a 12 question survey was sent out to the residents and faculty both before and after the transition.Implementation of an eight-level reading priority system increased worklist granularity and improved identification of more urgent studies to read. Radiologists opened CT studies in picture archiving and communications system-driven workflow in 52.4 ± 16.9 seconds using 9.5 ± 3.9 clicks and 6.3 ± 2.9 keystrokes, compared to 17.3 ± 9.5 seconds, 4.8 ± 1.5 clicks, and 0.1 ± 0.3 keystrokes in EHR-driven workflow (p0.001 for each measure). More information was available to the radiologist during examination interpretation, and 54.7% of radiologists rated the ease of use of the new system as good or very good (compared to 4.2% for the old system, p0.001).Transitioning to an EHR-driven workflow at a large academic medical center improved efficiency, was favorable to radiologists, and enhanced examination prioritization.

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient’s electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges.

The decision to implement an orders-based versus an encounters-based imaging workflow poses various implications to image capture and storage. The impacts include workflows before and after an imaging procedure, electronic health record build, technical infrastructure, analytics, resulting, and revenue. Orders-based workflows tend to favor some imaging specialties while others require an encounters-based approach. The intent of this HIMSS-SIIM white paper is to offer lessons learned from early adopting institutions to physician champions and informatics leadership developing strategic planning and operational rollouts for specialties capturing clinical multimedia.

Clinical specialties have widely varied needs for diagnostic image interpretation, and clinical image and video image consumption. Enterprise viewers are being deployed as part of electronic health record implementations to present the broad spectrum of clinical imaging and multimedia content created in routine medical practice today. This white paper will describe the enterprise viewer use cases, drivers of recent growth, technical considerations, functionality differences between enterprise and specialty viewers, and likely future states. This white paper is aimed at CMIOs and CIOs interested in optimizing the image-enablement of their electronic health record or those who may be struggling with the many clinical image viewers their enterprises may employ today.

Enterprise imaging governance is an emerging need in health enterprises today. This white paper highlights the decision-making body, framework, and process for optimal enterprise imaging governance inclusive of five areas of focus: program governance, technology governance, information governance, clinical governance, and financial governance. It outlines relevant parallels and differences when forming or optimizing imaging governance as compared with other established broad horizontal governance groups, such as for the electronic health record. It is intended for CMIOs and health informatics leaders looking to grow and govern a program to optimally capture, store, index, distribute, view, exchange, and analyze the images of their enterprise.

Thoracic outlet syndrome is a clinical entity characterized by compression of the neurovascular bundle, and may be associated with additional findings such as venous thrombosis, arterial stenosis, or neurologic symptoms. The goal of imaging is to localize the site of compression, the compressing structure, and the compressed organ or vessel, while excluding common mimics. A literature review is provided of current indications for diagnostic imaging, with discussion of potential limitations and benefits of the respective modalities. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. In this document, we provided guidelines for use of various imaging modalities for assessment of thoracic outlet syndrome.

RADPEER is a product developed by the ACR that aims to assist radiologists with quality assessment and improvement through peer review. The program opened in 2002, was initially offered to physician groups in 2003, developed an electronic version in 2005 (eRADPEER), revised the scoring system in 2009, and first surveyed the RADPEER membership in 2010. In 2012, a survey was sent to 16,000 ACR member radiologists, both users and nonusers of RADPEER, with the goal of understanding how to make RADPEER more relevant to its members. A total of 31 questions were used, some of which were repeated from the 2010 survey. The ACR's RADPEER committee has published 3 papers on the program since its inception. In this report, the authors summarize the survey results and suggest future opportunities for making RADPEER more useful to its membership.

We prospectively compared the ability of neuroradiologists to diagnose medulloblastoma with novice raters using only apparent diffusion coefficient (ADC) values measured on ADC maps. One hundred and three pediatric patients with pre-operative magnetic resonance imaging scans showing a posterior fossa tumor with histological verification were retrospectively identified from a ten-year period at a tertiary care medical center. A single observer measured the lowest ADC values in all tumors to determine the mean minimum ADC (ADCmin) value that provided greatest accuracy in distinguishing medulloblastomas from other tumors, which was determined to be 0.66×10−3 mm2/s. Imaging studies, including ADC maps, from 90 patients were provided to two neuroradiologists, who provided a diagnosis, which was later dichotomized as medulloblastoma or other. Two medical students measured ADCmin within tumors and those with ADCmin < 0.66×10−3 mm2/s were recorded as medulloblastoma; any other value was recorded as other. Diagnostic accuracy was measured. ADCmin values allowed a correct identification of lesions as either medulloblastoma or other in 91% of cases. After diagnoses by the two neuroradiologists were categorized as either medulloblastoma or other, their diagnoses were correct in 90% and 84% of cases, respectively. In 19 cases, at least one neuroradiologist was incorrect; the addition of ADC values to clinical interpretation would have allowed a correct diagnosis in 63% of such cases. Diagnostic accuracy based on ADC values by medical students was comparable to that of sub specialty-trained neuroradiologists. Our findings suggest that the addition of ADC values to standard film interpretation may improve the diagnostic rate for these tumors.

Care providers today routinely obtain valuable clinical multimedia with mobile devices, scope cameras, ultrasound, and many other modalities at the point of care. Image capture and storage workflows may be heterogeneous across an enterprise, and as a result, they often are not well incorporated in the electronic health record. Enterprise Imaging refers to a set of strategies, initiatives, and workflows implemented across a healthcare enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all clinical imaging and multimedia content to enhance the electronic health record. This paper is intended to introduce Enterprise Imaging as an important initiative to clinical and informatics leadership, and outline its key elements of governance, strategy, infrastructure, common multimedia content, acquisition workflows, enterprise image viewers, and image exchange services.

RADPEER™ is a quality assessment and improvement product developed and marketed by the ACR. Although the program has been available since 2002 and the scoring system was revised in 2009, the ACR allows considerable flexibility in its implementation. Although that flexibility supports the local needs of radiology groups using the program, it also may lead to suboptimal implementation of the program and may limit the usefulness of the data obtained. The authors, who are members of the ACR RADPEER Committee, provide 11 specific suggestions to optimize the performance of RADPEER and suggest opportunities for future improvement of the program.

Rationale and Objectives The aim of this study was to assess if graphical user interface screen-capture software applied to a magnetic resonance (MR) hardware console could nonintrusively allow the analysis of discrepancies between expected and experienced MR table time. Materials and Methods Individual MR examination acquisition processes were captured in audio-video interleave streams for 10 patients who underwent comprehensive liver MR imaging. These audio-video streams were dissected into periods of true image data acquisition, scanner activity without direct image data acquisition, and scanner inactivity. Results Total expected time required for standardized liver MR image acquisition was estimated at 15 minutes. Experienced table times varied highly, ranging from 19:00 to 58:08 minutes. Image data acquisition occurred during approximately 58% (range, 43.3%–73.7%) of overall table time. Image data were obtained approximately 77% (range, 65.6%–87.0%) of the time the scanner spent active. Conclusion Graphical user interface screen-capture software installed on an MR console nonintrusively revealed significant periods of table time spent not obtaining true image data and explained discrepancies between expected and experienced MR table times. Table-time calculations using Digital Imaging and Communications in Medicine image headers and scanner-logged time stamps are underestimations of true table time because they do not take into account some scanner activities not directly leading to image formation.

1All authors: Department of Radiology, University of Michigan Health Systems and University of Michigan Hospitals, B1 132F Taubman Center, 1500 E Medical Center Dr., TC 2910, Ann Arbor, MI 48109. Address correspondence to P. Cronin. ontrast-enhanced CT is widely used for assessment of a broad spectrum of thoracic and abdominal conditions. Findings of sudden cardiac arrest during CT examination have been reported [1]. Lack of flow during a fully arrested state causes pooling and layering of contrast material in the major dependent areas of the venous system, such as the inferior vena cava (IVC) and the major tributaries of the hepatic veins. We describe two cases of dependent venous contrast pooling shown on CT as early indicators of imminent circulatory failure and death.

Medical students on the radiology elective in our institution create electronic presentations to present to each other as part of the requirements for the rotation. Access was given to previous students' presentations via the web-based system, Medical Imaging Resource Center (MIRC) project, created and supported by the Radiological Society of North America (RSNA). RadPix Power 2 MIRC (Weadock Software, LLC, Ann Arbor, MI) software converted the Microsoft PowerPoint (Redmond, WA) presentations to a MIRC-compatible format. The textual information on each slide is searchable across the entire MIRC database. Future students will be able to benefit from the work of their predecessors.

This is an updated review of management of vertebral compression fracture for both benign osteoporotic and malignant causes. Vertebral compression fracture radiologic imaging evaluation is discussed. A literature review is provided of current indications for vertebral augmentation with percutaneous vertebroplasty and kyphoplasty as well as medical management. Limitations and potential benefits of these procedures are discussed. Variant tables describing various clinical situations are also provided to assist in determining appropriate use of these treatments for patient care. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to formulate recommendations for imaging or treatment.

Sinonasal imaging is performed in 2 major clinical scenarios: inflammatory rhinosinusitis or suspected mass lesion. Rhinosinusitis affects more than 16% of the US population annually. It poses an immense economic burden, accounting for more than 26 million outpatient visits annually and costing more than $4.3 billion annually in direct medical expenses. Most cases of uncomplicated acute and subacute rhinosinusitis are diagnosed clinically and should not require any imaging procedure. CT of the sinuses without contrast is the imaging method of choice in patients with recurrent acute sinusitis or chronic sinusitis. Sinusitis cannot be diagnosed on the basis of imaging findings alone. CT scan findings should be interpreted in conjunction with clinical and endoscopic findings. MRI is currently used for evaluation of sinus disease as a complementary study in cases of aggressive sinus infection with ocular/intracranial complications, potential invasive fungal sinusitis in immunocompromised patients or in the evaluation of a sinonasal mass. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

The purpose of this investigation was to assess workflow for medical imaging studies, specifically comparing liver and knee MRI examinations by use of the Lean Six Sigma methodologic framework. The hypothesis tested was that the Lean Six Sigma framework can be used to quantify MRI workflow and to identify sources of inefficiency to target for sequence and protocol improvement.Audio-video interleave streams representing individual acquisitions were obtained with graphic user interface screen capture software in the examinations of 10 outpatients undergoing MRI of the liver and 10 outpatients undergoing MRI of the knee. With Lean Six Sigma methods, the audio-video streams were dissected into value-added time (true image data acquisition periods), business value-added time (time spent that provides no direct patient benefit but is requisite in the current system), and non-value-added time (scanner inactivity while awaiting manual input).For overall MRI table time, value-added time was 43.5% (range, 39.7-48.3%) of the time for liver examinations and 89.9% (range, 87.4-93.6%) for knee examinations. Business value-added time was 16.3% of the table time for the liver and 4.3% of the table time for the knee examinations. Non-value-added time was 40.2% of the overall table time for the liver and 5.8% for the knee examinations.Liver MRI examinations consume statistically significantly more non-value-added and business value-added times than do knee examinations, primarily because of respiratory command management and contrast administration. Workflow analyses and accepted inefficiency reduction frameworks can be applied with use of a graphic user interface screen capture program.