Diagnostic imaging utilization has grown at an astounding pace in the last decade, accounting for 60 percent of radiology costs and 80 percent of radiology cost increases.¹ But while this exponential growth has undoubtedly produced positive outcomes, some industry experts question whether it's truly merited, arguing that unnecessary imaging procedures cost this country between $3 billion and $10 billion annually.²

Integrated clinical decision-support systems (CDSS) are proving to be effective in combating this unchecked growth. These systems combine clinical information provided by the referring physician at the time of ordering with patient demographics to produce a "utility score" for the requested exam. The higher the score, the more diagnostically appropriate the exam. If a low score is presented, the physician may select an alternate procedure with a higher score or continue with the low-utility exam and provide justification for the order. Some of these systems also offer back-end outcomes analysis and validation tools to track and monitor ordering scores and patterns.

Consider, for example, a Web-based application in which referring physicians enter patient indications into a secure, Web-based portal or through a decision-support interface within their existing computerized physician order entry (CPOE) system. Let's assume the patient is a 59-year-old male with sciatic leg pain as well as a known diagnosis of spinal stenosis and an abnormal previous bone scan. The referring physician enters a request for a spine CT and provides all of the clinical conditions. Before completing the order, the system presents the physician with a utility score for the order. In this case, the referring physician learns that this request received a marginal rating, and it is suggested that an order be placed for an alternate exam-either a spine MR or X-ray, both of which are indicated to be more diagnostically appropriate. The physician may change the exam to one of the recommended alternatives, proceed with the spine CT or revise the indications.

American College of Radiology (ACR) Appropriateness Criteria is the basis for many applications on the market. These rules, developed over the last 15 years, cover a wide array of high-tech diagnostic imaging procedures and provide guidelines for radiology decision-making based on scientific analysis and broad-based consensus techniques.³ The format of the rules takes shape as a color-coded rating scale that ranges in score from one to nine. Commercial solution providers are working with leading medical institutions to expand these rules and provide decision support for even greater exam coverage.

Until recently, most imaging utilization has been controlled by radiology benefits management (RBM) companies. Health care payers have looked to these groups to manage utilization and rein in costs. Now, more payers are turning to technology advances to address high-tech imaging utilization. Using evidence-based ordering techniques and having the ability to track outcomes and ordering patterns is a compelling advantage of clinical decisions support systems. While traditional RBMs have proven to be effective at controlling costs and utilization, the lack of evidence-based analysis provides an opportunity for decision support systems. A seven-year time series analysis published in Radiology concludes that computerized physician order entry (CPOE) with integrated decision support resulted in a substantial decrease in high-tech outpatient volumes and growth at a large metropolitan academic medical center.⁴ The progression of this technology and expansion of rules, combined with supporting research and data, has lead to clinical decision support being a viable, and cost-effective, solution for managing high-tech diagnostic imaging utilization.

In addition to payers, many providers have embraced these systems because they provide a more efficient way to order imaging tests without the need for lengthy approval phone calls and because they lend themselves to improving the quality of patient care through placement of diagnostically appropriate exams and exam warnings. Determination of medical necessity of exams/procedures and potential overexposure to radiation are issues of great concern.

Health care is moving toward data-driven management. Combining proven front-end decision support tools with back-end data analytics software leads to better utilization management and provides powerful safeguards for managing advanced imaging services in today's challenging health care environment.

1. Salganik, M.W. (2007). Boom in medical scans presses health payers. The Baltimore Sun. Accessed online and dated May 13.

2. Stein, C. (2003). Code red partners program aims to rein in skyrocketing costs of diagnostic imaging. The Boston Globe. Accessed online and dated June 27.

3. American College of Radiology. (2008). ACR Appropriateness Criteria Background and Development. Accessed via www.acr.org; dated Sept. 29.

4. Christopher, L., Sistrom, P.A., Dang, J.B., Weilburg, K.J. (2009.) Effect of Computerized Order Entry with Integrated Decision Support on the Growth of Outpatient Procedure Volumes: Seven-year Time Series Analysis. Radiology, Feb.12, 2009.