Quality in cardiovascular imaging remains a necessary and important goal that all laboratories should seek. Yet the substance of what defines "quality" is elusive and not well established. In the face of increasing imaging volume and a paucity of data documenting clinical value, especially related to patient outcome, serious questions have been raised regarding not only the utility of imaging, but also its quality.1–4 It is likely that poor quality imaging, however defined, may cause harm to patients, possibly adversely impacting patient outcome.
The inclusion of this chapter in the textbook is testimony to the increased recognition of the importance on quality in cardiac imaging. The goal of this chapter is to provide the reader with a contemporary view of quality metrics as they are related to imaging and to specifically address the value of test/patient selection, by means of appropriate use criteria (AUC). Furthermore, laboratory accreditation and physician certification are addressed in this context.
Care metrics have been well defined in many areas of cardiology5; they encourage the provision of high-quality care and have a defined goal. Performance measures are a subset of quality metrics that are evidence-based and suitable for public reporting and payment-for-performance initiatives. Quality metrics usually are self-assessment quality initiatives and lack the high standard of performance measures. Interestingly, not even quality metrics have been well defined for cardiac imaging, although great efforts are underway, as shown in this chapter, to define information necessary to develop quality metrics in imaging.
The elements that define quality in imaging include many components in the process. Several years ago, a conference was held to define and focus quality initiatives for cardiovascular imaging, the American College of Cardiology/Duke University Think Tank for Quality in Cardiovascular Imaging.2 The purpose of this conference, attended by practitioners, educational leaders, medical societies, and industry executives, was to define the key components of quality in cardiovascular imaging and to define measures that may serve to demonstrate quality and improve the practice of imaging. The approach was modeled after the Donabedian method6and consists of the following components: (1) structural measures, such as equipment and protocols, (2) process measures including patient selection and image acquisition, and (3) outcome, such as the impact on clinical decision-making. This construct permitted the development of a plan of quality assessment, with several key steps and a number of quality parameters along the path (Fig. 9-1). This is outlined in the subsequent sections.
Components of quality, moving from the selection of the correct test for a specific patient, through a variety of process and structural measures, and ending up with improved outcomes. (Adapted with permission from Ref 2. Copyright © Elsevier.)
THE PATH TO QUALITY IMAGING