Reading Room Interruptions are Less Disruptive When Using Asynchronous Communication Methods

Radiologist interruptions, though often necessary, can be disruptive. Prior literature has shown interruptions to be frequent, occurring during cases, and predominantly through synchronous communication methods such as phone or in person causing significant disengagement from the study being read. Asynchronous communication methods are now more widely available in hospital systems such as ours. Considering the increasing use of asynchronous communication methods, we conducted an observational study to understand the evolving nature of radiology interruptions. We hypothesize that compared to interruptions occurring through synchronous methods, interruptions via asynchronous methods reduce the disruptive nature of interruptions by occurring between cases, being shorter, and less severe. During standard weekday hours, 30 radiologists (14 attendings, 12 residents, and 4 fellows) were directly observed for approximately 90-min sessions across three different reading rooms (body, neuroradiology, general). The frequency of interruptions was documented including characteristics such as timing, severity, method, and length. Two hundred twenty-five interruptions (43 Teams, 47 phone, 89 in-person, 46 other) occurred, averaging 2 min and 5 s with 5.2 interruptions per hour. Microsoft Teams interruptions averaged 1 min 12 s with only 60.5% during cases. In-person interruptions averaged 2 min 12 s with 82% during cases. Phone interruptions averaged 2 min and 48 s with 97.9% during cases. A substantial portion of reading room interruptions occur via predominantly asynchronous communication tools, a new development compared to prior literature. Interruptions via predominantly asynchronous communications tools are shorter and less likely to occur during cases. In our practice, we are developing tools and mechanisms to promote asynchronous communication to harness these benefits.

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

OBJECTIVES

Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools.

METHODS

Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports.

RESULTS

Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001).

CONCLUSION

The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future.

CRITICAL RELEVANCE STATEMENT

With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest.

KEY POINTS

• A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.

Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience

BACKGROUND

To evaluate the implementation process of structured reporting (SR) in a tertiary care institution over a period of 7 years.

METHODS

We analysed the content of our image database from January 2016 to December 2022 and compared the numbers of structured reports and free-text reports. For the ten most common SR templates, usage proportions were calculated on a quarterly basis. Annual modality-specific SR usage was calculated for ultrasound, CT, and MRI. During the implementation process, we surveyed radiologists and clinical referring physicians concerning their views on reporting in radiology.

RESULTS

As of December 2022, our reporting platform contained more than 22,000 structured reports. Use of the ten most common SR templates increased markedly since their implementation, leading to a mean SR usage of 77% in Q4 2022. The highest percentages of SR usage were shown for trauma CT, focussed assessment with ultrasound for trauma (FAST), and prostate MRI: 97%, 95%, and 92%, respectively, in 2022. Overall modality-specific SR usage was 17% for ultrasound, 13% for CT, and 6% for MRI in 2022. Both radiologists and referring physicians were more satisfied with structured reports and rated SR better than free-text reporting (FTR) on various attributes.

CONCLUSIONS

The increasing SR usage during the period under review and the positive attitude towards SR among both radiologists and clinical referrers show that SR can be successfully implemented. We therefore encourage others to take this step in order to benefit from the advantages of SR.

KEY POINTS

1. Structured reporting usage increased markedly since its implementation at our institution in 2016. 2. Mean usage for the ten most popular structured reporting templates was 77% in 2022. 3. Both radiologists and referring physicians preferred structured reports over free-text reports. 4. Our data shows that structured reporting can be successfully implemented. 5. We strongly encourage others to implement structured reporting at their institutions.

Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment

The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.

Artificial intelligence: Advances and new frontiers in medical imaging

Artificial intelligence (AI) has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago. These algorithms, ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks, have continuously sought to revolutionize medical imaging. With the number of imaging studies outpacing the amount of trained of readers, AI has been implemented to streamline workflow efficiency and provide quantitative, standardized interpretation. AI relies on massive amounts of data for its algorithms to function, and with the wide-spread adoption of Picture Archiving and Communication Systems (PACS), imaging data is accumulating rapidly. Current AI algorithms using machine-learning technology, or computer aided-detection, have been able to successfully pool this data for clinical use, although the scope of these algorithms remains narrow. Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage, however interpretation has generally remained a human responsibility for now. In this review article, we will summarize the current successes and limitations of AI in radiology, and explore the exciting prospects that deep-learning technology offers for the future.

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

Multi-institutional Experience with Patient Image Access Through Electronic Health Record Patient Portals

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.

AI Integration in the Clinical Workflow

Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist's workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.

Prospective Analysis of Radiology Resource Utilization and Outcomes for Participation in Oncology Multidisciplinary Conferences

RATIONALE AND OBJECTIVES

Radiology participation is necessary in oncology multidisciplinary conferences (MDCs), but the resources required to do so are often unaccounted for. In this prospective study we provide an analysis of resource utilization as a function of outcomes for all MDCs covered by an entire radiology section and provide a time-based cost estimate.

MATERIALS AND METHODS

Following institutional review board approval, prospective data on all MDCs covered by abdominal radiologists at a single tertiary care academic center were obtained over nine weeks. A predefined questionnaire was used by a single observer who attended every imaging review and recorded the total time spent by the radiologists and several outcome measures. The total time recorded was used to provide a time-based cost estimate using a national salary survey.

RESULTS

Six radiologists participated in a total of 57 MDCs, with 577 cases reviewed and discussed. 181 (31%) cases were performed at outside facilities requiring full reinterpretation. Clinically significant revisions to original reports were recorded in 107 (18.5%) cases. Radiologist input directly resulted in alteration of cancer staging in 65 (11%) patients and specific recommendations for follow-up diagnostic workup in 280 (48%) of cases. The mean total time devoted by the staff radiologist per week to MDCs was 18.7 hours/week, nearly a half of full-time effort, or 8% of total effort per radiologist. The total annual projected cost of radiology coverage for each weekly MDC was $26,920.

CONCLUSION

Section-wide radiologist participation in MDCs directly resulted in change in clinical management in nearly half of reviewed cases. This was achieved at a notable time cost, highlighting the need for efficient integration of radiology MDC participation into radiologist workflow and compensation models.

Evaluation of Audiovisual Reports to Enhance Traditional Emergency Musculoskeletal Radiology Reports

Traditional radiology reports are narrative texts that include a description of imaging findings. Recent implementation of advanced reporting software allows for incorporation of annotated key images and hyperlinks directly into text reports, but these tools usually do not substitute in-person consultations with radiologists, especially in challenging cases. Use of on-demand audio/visual reports with screen capture software is an emerging technology, providing a more engaged imaging service. Our study evaluates a video reporting tool that utilizes PACS integrated screen capture software for musculoskeletal imaging studies in the emergency department. Our hypothesis is that referring orthopedic surgeons would find that recorded audio/video reports add value to conventional reports, may increase engagement with radiology staff, and also facilitate understanding of imaging findings from urgent musculoskeletal cases. Seven radiologists prepared a total of 47 audiovisual reports for 9 attending orthopedic surgeons from the emergency department. We applied two surveys to evaluate the experience of the referring physicians using audio/visual reports as a complementary material from the conventional text report. Positive responses were statistically significant in most questions including: if the clinical suspicion was answered in the video; willingness to use such technology in other cases; if the audiovisual report made the imaging findings more understandable than the traditional report; and if the audiovisual report is faster to understand than the traditional text report. Use of audiovisual reports in emergency musculoskeletal cases is a new approach to evaluate potentially challenging cases. These results support the potential of this technology to re-establish the radiologist's role as an essential member of patient care and also provide more engaging, precise, and personalized reports. Further studies could streamline these methods in order to minimize work redundancy with traditional text reporting or even evaluate acceptance of using only audiovisual radiology reports. Additionally, widespread adoption would require integration with the entire radiology workflow including non-urgent cases and other medical specialties.

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.

Limitations in and Solutions for Improving the Functionality of Picture Archiving and Communication System: an Exploratory Study of PACS Professionals' Perspectives

Picture Archiving and Communication System (PACS) technology is evolving leading to improvements in the PACS functionality. However, the needs and expectations of PACS users are increasing to cope with the rising demands for improving the workflow and enhancing efficiency in healthcare. The aim was to study the limitations in the current generation of PACS and solutions for improving PACS functionality. This was a longitudinal online observational study of the perspectives of PACS professionals accessed through four online discussion groups on PACS using the LinkedIn network. In this exploratory study, the methodology involved a thematic analysis of qualitative data comprising 250 online posts/comments made by 124 unique PACS professionals collected between January 2014 and December 2015. Participants were mostly male (n = 119, 96%) from the North America (n = 88, 71%). Key themes on limitations in the current generation of PACS were image transmission problems, network and hardware issues, difficulties in changing specific settings, issues in hardcoded Digital Imaging and Communication in Medicine attributes, and problems in implementing open source PACS. Main themes on solutions for improving PACS functionality were the integration of multisite PACS, multimedia for PACS, web-based PACS, medical image viewer, open source PACS, PACS on mobile phones, vendor neutral archives for PACS, speech recognition and integration in PACS, PACS backup and recovery, and connecting PACS with other hospital systems. Despite ongoing technological developments, the current generation of PACS has limitations that affect PACS functionality leading to unmet needs and requirements of PACS users, which could impact workflow and efficiency in healthcare.

A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection

A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location. A preprocessing module performed image quality and dimension normalization, and a post-processing module found the PICC tip accurately by pruning false positives. Our best model, trained on 400 training cases and selectively tuned on 50 validation cases, obtained absolute distances from ground truth with a mean of 3.10 mm, a standard deviation of 2.03 mm, and a root mean squares error (RMSE) of 3.71 mm on 150 held-out test cases. This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices.

Who You Gonna Call? Automatically Connecting Radiologists to the Right Clinician

Contacting clinicians to convey critical results is a critical part of radiology workflow, but many obstacles prevent easy and timely communication. Integration of radiology applications and workflow with an EHR-based patient coverage database demonstrated subjective and objective improvement in radiologist workflow and satisfaction.

PathBot: A Radiology-Pathology Correlation Dashboard

Pathology is considered the "gold standard" of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as "tumor boards" and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

Value-Based Assessment of Radiology Reporting Using Radiologist-Referring Physician Two-Way Feedback System-a Design Thinking-Based Approach

In the era of value-based healthcare, many aspects of medical care are being measured and assessed to improve quality and reduce costs. Radiology adds enormously to health care costs and is under pressure to adopt a more efficient system that incorporates essential metrics to assess its value and impact on outcomes. Most current systems tie radiologists' incentives and evaluations to RVU-based productivity metrics and peer-review-based quality metrics. In a new potential model, a radiologist's performance will have to increasingly depend on a number of parameters that define "value," beginning with peer review metrics that include referrer satisfaction and feedback from radiologists to the referring physician that evaluates the potency and validity of clinical information provided for a given study. These new dimensions of value measurement will directly impact the cascade of further medical management. We share our continued experience with this project that had two components: RESP (Referrer Evaluation System Pilot) and FRACI (Feedback from Radiologist Addressing Confounding Issues), which were introduced to the clinical radiology workflow in order to capture referrer-based and radiologist-based feedback on radiology reporting. We also share our insight into the principles of design thinking as applied in its planning and execution.

Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images

Volumetric analysis of the kidney parenchyma provides additional information for the detection and monitoring of various renal diseases. Therefore the purposes of the study were to develop and evaluate a semi-automated segmentation tool and a modified ellipsoid formula for volumetric analysis of the kidney in non-contrast T2-weighted magnetic resonance (MR)-images. Three readers performed semi-automated segmentation of the total kidney volume (TKV) in axial, non-contrast-enhanced T2-weighted MR-images of 24 healthy volunteers (48 kidneys) twice. A semi-automated threshold-based segmentation tool was developed to segment the kidney parenchyma. Furthermore, the three readers measured renal dimensions (length, width, depth) and applied different formulas to calculate the TKV. Manual segmentation served as a reference volume. Volumes of the different methods were compared and time required was recorded. There was no significant difference between the semi-automatically and manually segmented TKV (p = 0.31). The difference in mean volumes was 0.3 ml (95% confidence interval (CI), -10.1 to 10.7 ml). Semi-automated segmentation was significantly faster than manual segmentation, with a mean difference = 188 s (220 vs. 408 s); p < 0.05. Volumes did not differ significantly comparing the results of different readers. Calculation of TKV with a modified ellipsoid formula (ellipsoid volume × 0.85) did not differ significantly from the reference volume; however, the mean error was three times higher (difference of mean volumes -0.1 ml; CI -31.1 to 30.9 ml; p = 0.95). Applying the modified ellipsoid formula was the fastest way to get an estimation of the renal volume (41 s). Semi-automated segmentation and volumetric analysis of the kidney in native T2-weighted MR data delivers accurate and reproducible results and was significantly faster than manual segmentation. Applying a modified ellipsoid formula quickly provides an accurate kidney volume.

Radiologists' Variation of Time to Read Across Different Procedure Types

The workload of US radiologists has increased over the past two decades as measured through total annual relative value units (RVUs). This increase in RVUs generated suggests that radiologists' productivity has increased. However, true productivity (output unit per input unit; RVU per time) is at large unknown since actual time required to interpret and report a case is rarely recorded. In this study, we analyzed how the time to read a case varies between radiologists over a set of different procedure types by retrospectively extracting reading times from PACS usage logs. Specifically, we tested two hypotheses that; i) relative variation in time to read per procedure type increases as the median time to read a procedure type increases, and ii) relative rankings in terms of median reading speed for individual radiologists are consistent across different procedure types. The results that, i) a correlation of -0.25 between the coefficient of variation and median time to read and ii) that only 12 out of 46 radiologists had consistent rankings in terms of time to read across different procedure types, show both hypotheses to be without support. The results show that workload distribution will not follow any general rule for a radiologist across all procedures or a general rule for a specific procedure across many readers. Rather the findings suggest that improved overall practice efficiency can be achieved only by taking into account radiologists' individual productivity per procedure type when distributing unread cases.

CT and MR Protocol Standardization Across a Large Health System: Providing a Consistent Radiologist, Patient, and Referring Provider Experience

Building and maintaining a comprehensive yet simple set of standardized protocols for a cross-sectional image can be a daunting task. A single department may have difficulty preventing "protocol creep," which almost inevitably occurs when an organized "playbook" of protocols does not exist and individual radiologists and technologists alter protocols at will and on a case-by-case basis. When multiple departments or groups function in a large health system, the lack of uniformity of protocols can increase exponentially. In 2012, the University of Colorado Hospital formed a large health system (UCHealth) and became a 5-hospital provider network. CT and MR imaging studies are conducted at multiple locations by different radiology groups. To facilitate consistency in ordering, acquisition, and appearance of a given study, regardless of location, we minimized the number of protocols across all scanners and sites of practice with a clinical indication-driven protocol selection and standardization process. Here we review the steps utilized to perform this process improvement task and insure its stability over time. Actions included creation of a standardized protocol template, which allowed for changes in electronic storage and management of protocols, designing a change request form, and formation of a governance structure. We utilized rapid improvement events (1 day for CT, 2 days for MR) and reduced 248 CT protocols into 97 standardized protocols and 168 MR protocols to 66. Additional steps are underway to further standardize output and reporting of imaging interpretation. This will result in an improved, consistent radiologist, patient, and provider experience across the system.

Comparison-Bot: an Automated Preliminary-Final Report Comparison System

Regular comparison of preliminary to final reports is a critical part of radiology resident and fellow education as prior research has documented substantial preliminary to final discrepancies. Unfortunately, there are many barriers to this comparison: high study volume; overnight rotations without an attending; the ability to finalize reports remotely; the subtle nature of many changes; and lack of easy access to the preliminary report after finalization. We developed a system that automatically compiles and emails a weekly summary of report differences for all residents and fellows. Trainees can also create a custom report using a date range of their choice and can view this data on a resident dashboard. Differences between preliminary and final reports are clearly highlighted with links to the associated study in Picture Archiving and Communication Systems (PACS) for efficient review and learning. Reports with more changes, particularly changes made in the impression, are highlighted to focus attention on those exams with substantive edits. Our system provides an easy way for trainees to review changes to preliminary reports with immediate access to the associated images, thereby improving their educational experience. Departmental surveys showed that our report difference summary is easy to understand and improves the educational experience of our trainees. Additionally, interesting descriptive statistics help us understand how reports are changed by trainee level, by attending, and by exam type. Finally, this system can be easily ported to other departments who have access to their Health Level 7 (HL7) data.

Resources Required for Semi-Automatic Volumetric Measurements in Metastatic Chordoma: Is Potentially Improved Tumor Burden Assessment Worth the Time Burden?

The Response Evaluation Criteria in Solid Tumors (RECIST) is the current standard for assessing therapy response in patients with malignant solid tumors; however, volumetric assessments are thought to be more representative of actual tumor size and hence superior in predicting patient outcomes. We segmented all primary and metastatic lesions in 21 chordoma patients for comparison to RECIST. Primary tumors were segmented on MR and validated by a neuroradiologist. Metastatic lesions were segmented on CT and validated by a general radiologist. We estimated times for a research assistant to segment all primary and metastatic chordoma lesions using semi-automated volumetric segmentation tools available within our PACS (v12.0, Carestream, Rochester, NY), as well as time required for radiologists to validate the segmentations. We also report success rates of semi-automatic segmentation in metastatic lesions on CT and time required to export data. Furthermore, we discuss the feasibility of volumetric segmentation workflow in research and clinical settings. The research assistant spent approximately 65 h segmenting 435 lesions in 21 patients. This resulted in 1349 total segmentations (average 2.89 min per lesion) and over 13,000 data points. Combined time for the neuroradiologist and general radiologist to validate segmentations was 45.7 min per patient. Exportation time for all patients totaled only 6 h, providing time-saving opportunities for data managers and oncologists. Perhaps cost-neutral resource reallocation can help acquire volumes paralleling our example workflow. Our results will provide researchers with benchmark resources required for volumetric assessments within PACS and help prepare institutions for future volumetric assessment criteria.

What is the relation between number of sessions worked and productivity of radiologists-a pilot study?

Increasing workloads and the current austerity measures are putting UK radiology departments under considerable stress. We need to look at the most efficient ways to manage radiology departments in order to cope with increasing demand. Consequently, a system is needed that can compare productivity between radiologists with different jobs. We measured workload in a UK radiology department and compared the productivities of consultants working different numbers of sessions, which are called programmed activities (PAs), to identify the optimal job plan structure for reporting productivity. Reporting data was gathered from electronic records for 14 consultants working different numbers of PA during the period April 2010-March 2011. These were converted into relative value unit (RVU) scores using a modified RCSI RVU system. Crude and net workloads were calculated for each consultant by dividing their total RVU score by the number of PAs they were contracted for and how many they spent reporting. The consultants reported 118,001 imaging studies. There was statistically significant variation in productivity between consultants working different numbers of PAs on χ (2) analysis (p < 0.05). Consultants working 12 PAs were more productive than consultants working 11 PAs, with net workloads of 7636 RVU/PA/year versus net 6146 RVU/PA/year, p < 0.05. Although UK consultants working 12 PAs per week are more productive than their colleagues, the reasons why are unclear. We have identified a method that can be developed further to identify efficient working practices in UK radiology departments. However, a UK-specific RVU system would make this productivity analysis more accurate.

An Audio/Video Reporting Workflow to Supplement Standardized Radiology Reports

Radiology studies are inherently visual and the information contained within is best conveyed by visual methodology. Advanced reporting software allows the incorporation of annotated key images into text reports, but such features may be less effective compared with in-person consultations. The use of web technology and screen capture software to create retrievable on-demand audio/visual reports has not yet been investigated. This approach may preempt potential curbside consultations while providing referring clinicians with a more engaged imaging service. In this work, we develop and evaluate a video reporting tool that utilizes modern screen capture software and web technology. We hypothesize that referring clinicians would find that recorded on-demand video reports add value to clinical practice, education, and that such technology would be welcome in future practice. A total of 45 case videos were prepared by radiologists for 14 attending and 15 trainee physicians from emergency and internal medicine specialties. Positive survey feedback from referring clinicians about the video reporting system was statistically significant in all areas measured, including video quality, clinical helpfulness, and willingness to use such technology in the future. Trainees unanimously found educational value in video reporting. These results suggest the potential for video technology to re-establish the radiologist's role as a pivotal member of patient care and integral clinical educator. Future work is needed to streamline these methods in order to minimize work redundancy with traditional text reporting. Additionally, integration with an existing PACS and dictation system will be essential to ensuring ease of use and widespread adoption.