The growing emphasis on pay-for-reporting and pay-for-performance programs, along with the need to identify radiologist-provided value-added aspects of

care and services, spurred the ACR in 2004 to gather a group of quality-focused radiologists in Sun Valley, Idaho, to discuss a road map for improving quality in radiology [15]. Soon thereafter, CMS began to develop a physician quality reporting program and encouraged medical specialty societies to develop quality measures for use in the program. In 2006, the ACR evaluated the need for measure development, and the ACR Metrics Committee was then established to develop radiology performance measures 16 and 17. The Metrics Committee began collaborating with the AMA’s Physician Consortium for Performance Improvement (PCPI) for that purpose [18]. This collaboration resulted in several measure sets with imaging-related measures, many of Bosutinib which are currently used in the CMS PQRS [19]. In this selleck inhibitor paper, we focus on the typical process for the development of performance measures frequently used in such programs. Performance measure development

and implementation is a multiple-step process, beginning with identifying a clinical area that warrants dedicated attention. The project scope may include general imaging and radiology considerations and more specific topics such as radiation exposure and the appropriateness of certain imaging studies. Typically, once a focus area is selected, an environmental scan is conducted to gather relevant clinical practice guidelines and data to provide evidence that an improvement in the focus area is needed. After such a review, a multiple-stakeholder work group is established, composed of experts in various fields pertinent to the focus area. On the basis of the evidence and guidelines collected, the workgroup considers potential measures to draft, begins to develop and refine measure statements, and identifies numerator and denominator populations with any appropriate

exclusion criteria. Technical specifications for refined measures are drafted, FAD and data sources and data collection feasibility are assessed, potentially resulting in modification of the draft measure. After specification, candidate measures are tested for feasibility, reliability, validity, and unintended consequences. Multiple variables carry weight in the final approval, endorsement, use, and sustainability of a measure. These include organizations involved in the measure development process (eg, medical specialties, payers, and consumer representatives), the intended purpose of the measure (eg, quality improvement, accountability, public reporting), and defined settings or levels of care (eg, physician, group, hospital, or system).