The quality/safety medical index: a standardized method for concurrent optimization of radiation dose and image quality in medical imaging.

Abstract

Data analytics referable to quality and safety in medical practice have recently taken on increased importance as healthcare consumers push for increased healthcare accountability through increased data access, transparency, participation in medical decision-making, and payment models in which reimbursements are in part tied to quality [1–4]. In medical imaging practice, radiation dose and image quality are critical components of these analyses, with the former gaining increased attention as computed tomography (CT) utilization and resulting radiation dose measures dramatically rise [5–7]. At the heart of radiation dose management is the concept of ALARA, which is an acronym for “as low as reasonably achievable” [8]. This well-established mandate calls for radiology providers to achieve high levels of image quality while maintaining low and medically acceptable radiation dose. In the past decade, a shift has taken place from the principle of “image quality as good as possible” to “image quality as good as needed” [9]. This has in effect modified the traditional tenets of ALARA by placing a greater emphasis on radiation dose (i.e., safety) and less emphasis on image quality, as long as image quality is adequate to enable an accurate diagnosis [10, 11]. As medical imaging service and technology providers strive to achieve these principles for maximal radiation dose reduction and “clinically adequate” image quality, it becomes clear that a static approach to radiation dose reduction is neither practical nor prudent. Both radiation dose and image quality are inherently dynamic in nature and affected by individual patient attributes, technology, clinical context, and exam type. For these reasons, it is important that any methodology used to optimize quality and safety in tandem takes into account the dynamic nature and multiplicity of factors affecting quality and safety. In order to accomplish this challenge, standardized data is required to create a referenceable database, which provides for objective metadata analysis and creation of best practice guidelines.