Creating accountability in image quality analysis part 3: creation of a standardized image-centric mark-up and annotation tool.

Abstract

While quality assurance (QA) has existed in some form from the inception of modern medicine, its importance and degree of scrutiny is greater than ever today, as evidenced by numerous quality-centric mandates issued from the Institute of Medicine (IOM) [1–3]. These IOM mandates have in turn fostered numerous healthcare and legislative quality initiatives including evidence-based medicine (EBM), meaningful use, and pay for performance [4–7]. The common denominator to these quality initiatives is data, which serves as the means with which measurement takes place and performance is judged. The goal of this data-driven analysis and intervention is to improve quality in healthcare delivery, which in turn is expected to improve clinical outcomes. Unfortunately, in its present form, a great deal of medical data exists in nonstandardized formats, which precludes creation of referenceable databases and meta-analysis [8, 9]. At the same time, despite the “digitization” of vast amounts of medical data, data integration and accessibility remains a problem due to the relative lack of integration between disparate information systems [10, 11]. The combined inabilities to record, access, correlate, and analyze standardized data in medical practice adversely affects these quality initiatives. Despite almost universal support for the principles behind EBM, its widespread applicability is limited by these existing data deficiencies. Attempts to improve quality and standardize data in medical imaging practice have been limited to date by a number of factors including the preferred method of radiology reporting (i.e., nonstandardized and narrative free text), inconsistency of QA standards (with the exception of mammography), lack of supporting quality-centric technology, and heightened emphasis on productivity and workflow enhancement in the face of declining reimbursements and increasing data volume and complexity [12–15]. If the IOM mandates are to be addressed in medical imaging practice, radical innovation is required which simultaneously addresses issues of data standardization and quality improvement without sacrificing workflow and productivity.