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Medical analytics relating to quality and safety measures have become particularly timely and of high importance in contemporary medical practice. In medical imaging, the dynamic relationship between medical imaging quality and radiation safety creates challenges in quantifying quality or safety independently. By creating a standardized measurement which simultaneously accounts for quality and safety measures (i.e., quality safety index), one can in theory create a standardized method for combined quality and safety analysis, which in turn can be analyzed in the context of individual patient, exam, and clinical profiles. The derived index measures can be entered into a centralized database, which in turn can be used for comparative performance of individual and institutional service providers. In addition, data analytics can be used to create customizable educational resources for providers and patients, clinical decision support tools, technology performance analysis, and clinical/economic outcomes research.

Although image quality is a well-recognized component in the successful delivery of medical imaging services, it has arguably declined over the past decade owing to several technical, economic, cultural, and geographic factors. To improve quality, the radiologist community must take a more proactive role in image quality analysis and optimization; these require analysis of not just the single step of image acquisition but the entire imaging chain. Radiologists can benefit through improved report accuracy, diagnostic confidence, and workflow efficiency. The derived data-driven analyses offer an objective means for provider performance analysis, which can help combat commoditization trends and self-referral by nonradiologist providers., (Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The Institute of Medicine has determined that adverse drug events (ADEs) are the number one cause of medical errors in the United States and that additional efforts are required to improve patient safety. Medical imaging plays an important role in ADEs, most commonly related to the administration of imaging contrast agents. For medical imaging providers to assume a proactive role in patient safety, the creation of objective, data-driven quality metrics is advocated, which analyze all steps in the drug delivery chain, including procurement, prescription, dispersal, administration, monitoring, and management. These metrics would lead to the creation of a quality scorecard, developed to create objective accountability measures for all actors involved in this chain of events, including pharmacists, radiologists, clinicians, physicists, technologists, administrators, nurses, vendors, and patients. The ultimate goal of such an endeavor is to improve patient safety through the reduction of ADEs and the creation and refinement of data-driven best-practice guidelines.

The practice of medical imaging is fraught with inconsistency as it relates to quality assurance, which is due in part to the lack of standardization and objective quality-centric data. By applying the scientific methods of Shewhart and Deming to medical imaging quality assessment, one can devise an objective, data-driven quality model, encompassing the various steps and processes that take place within the medical imaging chain. Through automated recording, tracking, and analysis of these quality data elements, a quantitative scorecard can be derived that provides an objective measure of quality performance, relating to each individual process, participating stakeholder, and technology being used. Through meta-analysis of these quality-centric data, best practice guidelines can be created, which in turn promote quality as the major differentiating feature of service providers.

As the utilization of medical imaging services continues to expand, so does the delivery of ionizing radiation. Although recent efforts within the medical imaging community have begun to focus on radiation dose reduction for computed tomography, very few objective data related to radiation dose are currently being prospectively collected, tracked, or analyzed. Without a comprehensive program aimed at objective and reproducible radiation data analysis, ongoing efforts at dose optimization will, in all likelihood, remain marginalized. The proposed radiation scorecard provides an analysis of events within the imaging chain, attempts to involve all stakeholders, and addresses the various technologies being utilized. The combined data can in turn be used for education and training, certification, research, development of best practice guidelines, and new technology development.

An integral part of cost-benefit analysis within medicine is the comparative assessment of the clinical gains introduced by a service or application compared with the patient costs, which can be measured in economic or clinical terms. When analyzing medical imaging, a number of factors must be included, the most important of which include issues pertaining to patient safety, image quality, and clinical outcomes. Although ionizing radiation has long been recognized as a potential carcinogen capable of introducing harm to patients, the long-term derived benefits have been accepted because of the diagnostic and therapeutic benefits derived from its use. With the increased utilization of high-dose ionizing radiation examinations (such as multidetector computed tomography), it is essential that the medical community create data-driven methodologies to quantify risk in objective terms, develop community-wide radiation standards and "best practice" guidelines (evidence-based medicine), develop new technologies and applications to proactively minimize radiation dose while maintaining quality, and create accountability measures for all pertinent stakeholders.

The existing practice of quality assurance (QA) in medical imaging is problematic because of the subjective manner in which it is performed, the lack of community and industry-wide QA standards, a paucity of supporting technology, and an overall lack of accountability. The solution for optimizing QA lies in the creation of objective and reproducible QA metrics, whose analysis can be automated through the creation of computerized QA software algorithms. The QA data derived from these computerized programs would in turn create the infrastructure for a comprehensive QA database, which can serve as a valuable resource for QA education and training, research, decision support, and technology innovation. The ability to objectively track and analyze QA practice across the wide spectrum of imaging providers creates a mechanism for the creation and refinement of objective QA standards and "best practice" guidelines.

The current focus on the economic bottom line in health care creates the potential for radiology to become a commodity, devoid of qualitative differentiation. This trend toward commoditization has been accelerated by the globalization of imaging services (teleradiology), increased information exchange (eg, Digital Imaging and Communications in Medicine, Integrating the Healthcare Enterprise), and new technology development (eg, picture archiving and communication systems, computer-aided diagnosis). The optimum strategy for avoiding commoditization is the creation of objective quality metrics and standards throughout the medical imaging practice, which will provide a reproducible and objective means with which to differentiate imaging service deliverables on the basis of quality and clinical outcomes. These quality measures can in turn be directly tied to economic incentives (pay for performance), providing further incentive for proactive quality assurance, qualitative differentiation, and technology development centered on quality.