Information overload: when less is more in medical imaging

Abstract

In medicine, data collection and analysis provide the information needed to reduce diagnostic uncertainty. An examination of how medical imaging data is collected and then transformed into diagnostic information provides testable ideas for better managing this dynamic process. In other fields, process data is systematically assessed for differences between observed and predicted values. For studies that expose patients to the potentially harmful effects of ionizing radiation, monitoring imaging studies/illness, images/imaging study and radiation exposure/image would be steps towards developing radiation dose budgets for the diagnosis and treatment of common conditions. Random variation within the expected range would signal a high quality process. Conversely, single outlying cases or nonrandom variation within the expected range would trigger an investigation for a possible underlying cause. Such investigations would provide insights into how to continually improve this important aspect of healthcare.

Keywords: computed tomography; diagnostic error; information theory; ionizing radiation; patient safety; process improvement; radiology; shared decision making.