The overall objective of this project is to create point-of-care decision support tools using personalized radiation risk assessments to reduce excessive imaging in high-risk patients. Rapidly increasing utilization of diagnostic imaging - and in particular computed tomography (CT) - has markedly increased radiation exposure to the population, heightening concerns about cumulative and subsequent radiation-induced carcinogenesis. Most prior radiation protection work has focused on modifying imaging techniques to reduce radiation exposure throughout the imaged population. Yet little systematic work has been undertaken to specifically address utilization in high-risk groups, beyond the general consensus that radiation exposure should be kept as low as reasonably achievable (ALARA). In our previous work, we have built the tools to extract a patient's radiation exposure history from multiple sources in the hospital network electronic medical record, and have developed simulation methods to calculate a patient's cumulative radiation-induced cancer risk by applying standard radiation risk models to the relevant patient demographics and specific radiation exposure history. Through this work, we have identified high-risk patient groups who undergo recurrent CT imaging for chronic or recurrent symptoms, with high resulting risks of radiation-induced cancers.
Our specific aims for the proposed work are: 1) Design and deploy point-of-care decision support tools to deliver individualized radiation risk information to the ordering physician in real time during electronic order entry. 2) Measure the impact of radiation risk decision support advice on clinical practice patterns. 3) Develop a patient-specific, context-sensitive educational website to display dose and risk assessments from past exposures, and predict the risk impact of alterations in future dose accumulation. 4) Further refine dose tracking and risk assessment methodology to address limitations in the state of the art.
These aims will contribute towards a broader goal of shifting the radiation protection paradigm from a population perspective to the individual patient, permitting better decisions about imaging utilization personalized to each patient. In the long run, with universal adoption of standardized electronic medical records, this work will pave the way for large-scale epidemiologic studies to directly test the underlying cancer risk models of low dose radiation exposure.
The innovative techniques of the proposed work derive from the marriage of informatics tools that extract patient-specific information from the medical record, with radiation risk models to provide point of care decision support feedback to the ordering physician. The significance of this work is in providing unprecedented integration of personalized radiation exposure risks into the imaging risk-benefit analysis to aid identification and protection of high-risk patients. This work will contribute to a paradigm shift from current population-based approaches of radiation protection to individualized patient-specific decision support techniques, which will more appropriately modulate thresholds for study ordering or prompt rational use of alternative imaging approaches.
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|Sodickson, Aaron; Warden, Graham I; Farkas, Cameron E et al. (2012) Exposing exposure: automated anatomy-specific CT radiation exposure extraction for quality assurance and radiation monitoring. Radiology 264:397-405|
|Ikuta, Ichiro; Sodickson, Aaron; Wasser, Elliot J et al. (2012) Exposing exposure: enhancing patient safety through automated data mining of nuclear medicine reports for quality assurance and organ dose monitoring. Radiology 264:406-13|