Reject analysis was performed on 288,000 computed radiography (CR) image records collected from a university hospital (UH) and a large community hospital (CH). Each record contains image information, such as body part and view position, exposure level, technologist identifier, and--if the image was rejected--the reason for rejection. Extensive database filtering was required to ensure the integrity of the reject-rate calculations. The reject rate for CR across all departments and across all exam types was 4.4% at UH and 4.9% at CH. The most frequently occurring exam types with reject rates of 8% or greater were found to be common to both institutions (skull/facial bones, shoulder, hip, spines, in-department chest, pelvis). Positioning errors and anatomy cutoff were the most frequently occurring reasons for rejection, accounting for 45% of rejects at CH and 56% at UH. Improper exposure was the next most frequently occurring reject reason (14% of rejects at CH and 13% at UH), followed by patient motion (11% of rejects at CH and 7% at UH). Chest exams were the most frequently performed exam at both institutions (26% at UH and 45% at CH) with half captured in-department and half captured using portable x-ray equipment. A ninefold greater reject rate was found for in-department (9%) versus portable chest exams (1%). Problems identified with the integrity of the data used for reject analysis can be mitigated in the future by objectifying quality assurance (QA) procedures and by standardizing the nomenclature and definitions for QA deficiencies.

No abstract

Despite dramatic innovation in medical imaging and information system technologies, the radiology report has remained stagnant for more than a century. Structured reporting was created in the hopes of addressing well-documented deficiencies in report content and organization but has largely failed in its adoption due to concerns over workflow and productivity. A number of political, economical, and clinical quality-centric initiatives are currently taking place within medicine which will dramatically change the medical landscape including Pay for Performance, Evidence-Based Medicine, and the Physician Quality Reporting Initiative. These will collectively enhance efforts to improve quality in reporting, stimulate new technology development, and counteract the impending threat of commoditization within radiology. Structured reporting offers a number of unique opportunities and advantages over traditional free text reporting and will provide a means for the radiology community to add value to its most important service deliverable the radiology report.

In an attempt to maximize productivity within the medical imaging department, increasing importance and attention is being placed on workflow. Workflow is the process of analyzing individual steps that occur during a single event, such as the performance of an MRI exam. The primary focus of workflow optimization within the imaging department is automation and task consolidation, however, a number of other factors should be considered including the stochastic nature of the workload, availability of human resources, and the specific technologies being employed. The purpose of this paper is to determine the complex relationship that exists between information technology and the radiologic technologist, in an attempt to determine how workflow can be optimized to improve technologist productivity. This relationship takes on greater importance as more imaging departments are undergoing the transition from filmbased to filmless operation. A nationwide survey was conducted to compare technologist workflow in filmbased and filmless operations, for all imaging modalities. The individual tasks performed by technologists were defined, along with the amount of time allocated to these tasks. The index of workflow efficiency was determined to be the percentage of overall technologist time allocated to image acquisition, since this is the primary responsibility of the radiologic technologist. Preliminary analysis indicates technologist workflow in filmless operation is enhanced when compared with film-based operation, for all imaging modalities. The specific tasks that require less technologist time in filmless operation are accessing data and retake rates (due to both technical factors and lost exams). Surprisingly, no significant differences were reported for the task of image processing, when comparing technologist workflow in film-based and filmless operations. Additional research is planned to evaluate the potential workflow gains achievable through workflow optimization software, improved systems integration, and automation of advanced image processing techniques.

No abstract

Uncertainty has been the perceived Achilles heel of the radiology report since the inception of the free-text report. As a measure of diagnostic confidence (or lack thereof), uncertainty in reporting has the potential to lead to diagnostic errors, delayed clinical decision making, increased cost of healthcare delivery, and adverse outcomes. Recent developments in data mining technologies, such as natural language processing (NLP), have provided the medical informatics community with an opportunity to quantify report concepts, such as uncertainty. The challenge ahead lies in taking the next step from quantification to understanding, which requires combining standardized report content, data mining, and artificial intelligence; thereby creating Knowledge Discovery Databases (KDD). The development of this database technology will expand our ability to record, track, and analyze report data, along with the potential to create data-driven and automated decision support technologies at the point of care. For the radiologist community, this could improve report content through an objective and thorough understanding of uncertainty, identifying its causative factors, and providing data-driven analysis for enhanced diagnosis and clinical outcomes.

The transformation from film-based to filmless operation has become more and more challenging, as imaging studies expand in size and complexity. To adapt to these changes, radiologists must proactively develop new workflow strategies to compensate for increasing work demands and the existing workforce shortage. This article addresses the evolutionary changes underway in the radiology interpretation process and reviews changes that have occurred in the past decade. These include a number of developments in soft-copy interpretation, which is migrating from a relatively static process, duplicating film-based interpretation, to a dynamic process, using multi-planar reconstructions, volumetric navigation, and electronic decision support tools. The result is optimization of the human-computer interface with improved productivity, diagnostic confidence, and interpretation accuracy.KEY WORDS: Evolution of radiology practice, radiology interpretation, Transforming the Radiology Interpretation Process (TRIP)T HE EVOLUTIONARY FORCES underway within radiology are occurring at a faster pace than ever before. Technology expansion is evident throughout all medical disciplines, but no area is more affected than radiology, the only medical specialty that is 100% technology driven. This dependence on technology is a double-edged sword for medical practitioners. It creates a unique opportunity for economic growth and expansion while solidifying the position of its specialists within the medical community. At the same time, however, new imaging and computer technologies present an entirely new set of clinical, educational, and political challenges for the radiologist. As new technologies are introduced into the practice of radiology, so are heightened expectations concerning the timeliness of information delivery, the accuracy of radiologic diagnosis, and the overall standard of patient care.The evolving technologies within medical imaging take on a variety of forms including imaging modalities to information systems, and picture archiving and communication systems (PACS). Radiologists, clinicians, technologists, and information technology (IT) personnel are bombarded daily with new medical imaging and computer applications that surpass their predecessors in speed, complexity, and sophistication. This creates a series of economic, educational, integration, and implementation challenges. The long-term success of these professionals will be tied in large part to their ability to incorporate the changing technologies into their workplace. This article, written from the perspective of the clinical radiologist, discusses how these evolutionary pressures are changing the way radiology is being practiced. We begin by analyzing current trends and then attempt to predict how future technology developments will change radiologist workflow and the interpretation process.

No abstract