Creating Accountability in Image Quality Analysis Part 1: the Technology Paradox

Abstract

Medical technology development is typically thought of as a catalyst for improving outcomes, whether measured in operational efficiency, cost-efficacy, or clinical terms. In medical imaging and informatics, technology innovations have resulted in a number of dramatic advancements, including new methods for disease detection and analysis (e.g., functional MRI), improved data storage and retrieval (e.g., picture archiving and communication system (PACS)), more timely information delivery (e.g., speech recognition), and computer-assisted diagnosis (e.g., CAD). While these technologic advancements have all had an undeniably positive impact on service deliverables, there has not been an equally positive effect on the technical quality of medical imaging data, which in some respects has paradoxically worsened with the transition from film-based (i.e., analog) to filmless (i.e., digital) imaging [1]. The current practice of quality assurance (QA) in everyday medical imaging practice is often idiosyncratic and inconsistent, with the priorities of imaging service providers often focused on productivity and workflow, which can come at the expense of quality. Since quality in itself is not directly revenue generating, the imaging market and technology producers often downplay the intrinsic value of quality-centric technologies, resulting in a gradual deterioration in QA practice. At the same time, the accreditation process for medical imaging service providers is relatively lax and infrequent (i.e., typically triennial), further compounding this untoward effect on image quality. While the vast majority of medical imaging providers understand the criticality of quality on clinical outcomes, the reality is that QA in its present form is limited at best and inherently flawed at worse. In order to reverse this negative QA trend and reprioritize quality, we should begin by analyzing the temporal changes in QA and technology and then explore innovation strategies aimed at continuous QA measurement, analysis, and intervention. In the end, the future success of medical imaging is in large part tied to quality deliverables. It is time to put QA back into QA practice.