The dramatic increase of diagnostic imaging capabilities over the past decade has contributed to increased radiation
exposure to patient populations. Several factors have contributed to the increase in imaging procedures: wider
availability of imaging modalities, increase in technical capabilities, rise in demand by patients and clinicians,
favorable reimbursement, and lack of guidelines to control utilization. The primary focus of this research is to
provide in depth information about radiation doses that patients receive as a result of CT exams, with the initial
investigation involving abdominal CT exams. Current dose measurement methods (i.e. CTDI<sub>vol</sub> Computed
Tomography Dose Index) do not provide direct information about a patient's organ dose. We have developed a
method to determine CTDI<sub>vol</sub> normalized organ doses using a set of organ specific exponential regression
equations. These exponential equations along with measured CTDI<sub>vol</sub> are used to calculate organ dose estimates
from abdominal CT scans for eight different patient models. For each patient, organ dose and CTDI<sub>vol</sub> were
estimated for an abdominal CT scan. We then modified the DICOM Radiation Dose Structured Report (RDSR) to
store the pertinent patient information on radiation dose to their abdominal organs.
Managing pediatric patients with neurogenic bladder (NGB) involves regular laboratory, imaging, and physiologic testing. Using input from domain experts and current literature, we identified specific data points from these tests to develop the concept of an electronic disease vector for NGB. An information extraction engine was used to extract the desired data elements from free-text and semi-structured documents retrieved from the patient’s medical record. Finally, a Java-based presentation engine created graphical visualizations of the extracted data. After precision, recall, and timing evaluation, we conclude that these tools may enable clinically useful, automatically generated, and diagnosis-specific visualizations of patient data, potentially improving compliance and ultimately, outcomes.