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Editorials |
1 From the Department of Radiology, Veterans Affairs Maryland Healthcare System, Baltimore, Md (B.I.R., E.L.S., K.S., A.M.), and Department of Radiology, University of Maryland School of Medicine, Baltimore, Md (B.I.R., E.L.S., A.M.). Received March 1, 2005; accepted March 11. Address correspondence to: B.I.R., 6 Greenleaf Ln, Seaford, DE 19973 (e-mail: breiner1{at}comcast.net).
Throughout our professional and personal lives we encounter constant paeans to the importance of quality, as if this were the generally accepted and primary focus of attention in any endeavor. We hear advertisements and pronouncements that products are of "top quality" or that "quality is job number one." But is quality really the primary incentive for those who produce goods and services in our economy? When government statistics are released, economic and productivity factors are often emphasized with little or no data on quality. If we were to ask a hospital administrator, chief technologist, and radiologist about their respective highest priorities, they would probably respond (if free to be entirely honest) that productivity is the most important measure of performance.
If our outcomes are defined in quantitative rather than qualitative terms, what does this say about our priorities? The easy answer, of course, is that our first and foremost priority should be patient care, which is the true object of quality assurance (QA) and quality control. Some might argue that an occasional suboptimal-quality image is not really important in the overall scheme of quality health care. The response, of course, is that patient care is compromised by inferior image quality and that there is the potential for medical and legal ramifications that can amount to millions of dollars for a single missed diagnosis. But how can we, as busy practitioners and administrators, justify expensive QA and quality control programs that threaten to diminish operational efficiency within an already overworked and understaffed imaging department?
In radiology, as in other areas of our lives, perceptions are formed from collective experience. Despite the inroads of the digital revolution, most radiologists and technologists still operate in a "filmlike" world. Even after the transition from screen-film to digital radiography, technologist workflow typically replicates the screen-film paradigm. Technologists are located in a centralized work area, which is used to transfer computed radiographic cassettes into a shared computed radiographic plate reader; technologists then review and manipulate images on a shared QA workstation. This centralized, shared workflow approach is often compounded by bottlenecks, errors, and delays. Many radiologists also work in a parallel fashion. They may take a relatively passive role when it comes to image display and optimization, preferring to review digital radiographs in a "single best, as is" presentation state. Rather than actively manipulate images by using the workstation tools and functions that are inherent to filmless operation, radiologists may elect to review and interpret each image in a static manner. The advantage of this approach is that it is quicker and therefore enhances productivity. The disadvantage is that it fails to take advantage of much of the image information that is available on a digital radiograph. By actively manipulating the image, applying advanced processing algorithms, or using decision support software such as computer-aided detection, diagnostic interpretation can be enhanced, with measurable benefits in the quality of patient care.
Because of their collective film-based experience, technologists and radiologists may tend to undervalue or even ignore QA. QA is commonly viewed as a wasteful endeavor that sidetracks valuable resources without noticeable gains. This "hands off" approach to QA actually seemed more justified to many after the transition to digital radiography because image quality is now automatically adjusted through the combination of wider dynamic range, wider exposure latitude, and computer image enhancement. The end result is that QA in the digital environment has become an afterthought at best and is easily ignored by technologists, administrators, and radiologists. Unless we can demonstrate that QA improves both operational efficiency and image quality, this film-based legacy of a negative QA bias will persist.
STATE OF THE GENERAL RADIOGRAPHY MARKETPLACE
Despite escalating technologic innovation in medical imaging, general radiographic examinations account for 65%–70% of all imaging examinations (1). This underscores the continued importance of general radiography at a time when high-technology modalities, such as computed tomography (CT) and magnetic resonance imaging, have become routine elements of imaging practice. Although reimbursement rates for these modalities are relatively high, general radiography is a revenue loser for most imaging departments. By using Medicare global reimbursement rates, Mayo-Smith et al (2) reported an average net loss of $11.11 per examination for outpatient general radiographic examinations even before physician costs were included. This reimbursement deficit is not affected by the way in which the study was acquired or read and serves as a deterrent to the adoption of newer, more expensive technologies. An imaging provider who offers state-of-the-art digital radiography is reimbursed at the same rate as the provider who performs conventional screen-film imaging on a 20-year-old system. At the same time, no financial incentive is provided for the practitioner who incorporates a rigorous QA program into his or her practice. The result is that the governing rules of medical economics are, in most cases, "quality blind" toward general radiography because all providers (radiologists and nonradiologists) are reimbursed at the same level whether they are performing examinations on new equipment or old equipment.
To counteract low reimbursement rates for general radiography, most providers focus their efforts on decreasing the costs per examination by improving technologist and radiologist productivity. This has been the mantra of digital radiography vendors, with many unsubstantiated claims about enhanced productivity. Unfortunately, little attention is given to workflow optimization, which may be as important as the technology itself in enhancing productivity. This point has been emphasized at our institution, where productivity and operational efficiency gains were achieved through a combination of automation, integration, and simplification. Siegel and Reiner (3) reported that these efforts reduced the number of workflow steps from 59 to nine in ordering, image acquisition, and reporting for inpatient radiographic chest examinations.
Lipoti and Orrison (4) described an innovative QA program that resulted in improved image quality, reduced radiation doses, and fewer repeat examinations. The study used the existing Mammography Quality Standards Act as a model for the performance of general radiography. After completion of the 2-year study period, researchers reported a dose reduction of approximately 35% and an image quality improvement of 22%. Although this study did not address the economic and productivity concerns of general radiography, it illustrated the clear importance of QA in the clinical environment. Patient safety has taken on an additional urgency in imaging, with widely cited publications that indicate increased cancer risks from CT and other examinations (5) and continued concerns about the effects of low-level radiation (6,7). Although patient concerns about radiation exposure have been somewhat muted in the United States, coverage of these issues in the media and on the Internet may produce an increasing public awareness about radiation doses and calls for more aggressive QA measures in the performance of general radiographic examinations.
DATA FROM A MULTICENTER TRIAL ON COMPUTED RADIOGRAPHY AND DIRECT RADIOGRAPHY
In a recent multicenter evaluation on the productivity and economics of computed radiography and direct radiography (8,9), a number of interesting observations were made that highlighted the current QA dilemma. First, the largest time inefficiency observed in the performance of computed radiography was postacquisition processing, which occurred immediately after the completion of the last exposure and was a part of the technologist's QA responsibilities. This step alone accounted for 41%–48% of the total examination time for general radiography and for 57%–100% of the total time differences between computed radiography and direct radiography. If the time spent by technologists in postacquisition processing could be greatly reduced or eliminated, technologist productivity would be dramatically enhanced, thereby improving the economics of digital radiography. A number of possible methods for addressing the time-intensive technologist postacquisition processing include (a) elimination of QA entirely, (b) reallocation of postacquisition processing to a dedicated QA subspecialist, and (c) automation of postacquisition processing through the development of automated QA software.
The first approach may seem drastic, but it is not totally absurd when we consider that retake rates after the transition from screen-film to digital radiography have been documented to decrease by as much as 10% to 0.8% (10,11). These figures suggest that only eight out of every 1000 computed radiographic and direct radiographic examinations would require repeating—not bad odds at all if the focus were on economics and productivity alone. If such an approach is undertaken, technologist productivity would almost double owing to the elimination of as much as 48% of the total examination time; this, in turn, could cut technologist staffing requirements in half and significantly improve the existing economic shortfall that is inherent in general radiography. The problem with this approach is that it is quantitatively driven and ignores issues of image quality and patient safety.
The second strategy allows for somewhat smaller technologist productivity and economic gains without sacrificing image quality or patient safety. By employing a specially trained QA subspecialist, overall image quality and consistency should be improved because this individual applies a consistent set of standards for all images. The technologist who is motivated to "pass through" a suboptimal image would no longer be the final quality inspector. In addition, the designated QA specialist could provide valuable educational feedback to other technologists. Data could be collected to identify department QA trends, as well as individual deficiencies, in image quality. The downside of such an approach is that it creates a new position that incurs additional personnel costs or reduces the number of technologists available to perform image acquisition. Although individual technologist productivity would undoubtedly improve, the economic gains would be partially offset by the additional specialist.
The third and most attractive solution would be the development of an automated QA software program that would, in effect, replace the need of and expense for a QA subspecialist. In addition to the obvious economic incentive, this approach would collectively offer the opportunity to improve image quality, reduce radiation dose, and improve technologist and radiologist workflow. A QA database could be created to facilitate improved practice management, trending analyses, technologist education, and radiation dose optimization. Such an approach also would encourage a multidisciplinary effort with participation from technologists, radiologists, administrators, and vendors. This QA team could create a comprehensive set of rules with adjustable QA thresholds to accommodate the specific idiosyncrasies of the institution and practitioners. Criteria could be established for an "acceptable" image, with defined limits for exposure, artifacts, positioning, motion, collimation, and supporting data. Such criteria are currently being developed in digital radiography for the purpose of dose reduction (12,13).
The development of an automated QA program could result in additional reimbursements for users, much like the added fee currently provided to those who adopt computer-aided detection systems for mammography. Scientific results that demonstrate improved clinical outcomes owing to the integration of automated QA and digital radiography are needed to make a reasonable case for such added fees. Imaging practices that adopt proactive QA programs could then differentiate themselves from their counterparts. In the end, quality would be financially rewarded and, most important, patients would benefit.
Some insurers have already indicated their intentions to mandate referrals to facilities with specific QA accreditation. To date, mammography is the only imaging modality that has instituted strict QA requirements under the United States Food and Drug Administration's Mammography Quality Standards Act Final Rules (14).
CONCLUSION
Medical imaging departments have a dual mission that requires them to maintain the highest quality and consistency of patient care while maximizing efficiency, productivity, and profitability. These goals are sometimes viewed as mutually exclusive in a climate where reimbursement rates decrease, technology costs increase, and staffing shortages abound. In an effort to address these competing demands, many imaging providers have elected to focus their attention on productivity and economic measures at the expense of image quality. This approach is both myopic and self-defeating, as is shown by the previously published data on productivity for computed radiography and direct radiography (8,9). These data suggest that technologist workflow and operational efficiency can be improved by redefining the traditional assignment of QA to each technologist. This redefinition can be accomplished by outsourcing QA to a dedicated subspecialist or by incorporating an automated QA software program. By reinventing QA in the filmless environment, a number of improvements can be realized in productivity, economics, image quality, and dose reduction. This approach requires the imaging provider to reinvent workflow within the digital imaging department by letting go of the legacy of film-based operation. By taking this alternative approach, QA becomes the "missing link" in the workflow paradigm that simultaneously improves quantitative and qualitative performance measures.
References
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