Patients' online searches for imaging costs are increasing, according to a study presented at ACR 2016.
Patients are increasingly comparison shopping when it comes to CT scans, according to a presentation at the 2016 annual meeting of the American College of Radiology.
Researchers from Emory University in Atlanta, GA, sought to determine how U.S. consumers studied online imaging costs. The researchers temporally studied individual user search patterns for costs associated with advanced medical imaging in order to determine and compare search volume indexes (SVIs) from January 2009 through December 2015, focusing specifically on the search terms “CT cost” and “MRI cost.”
The results showed that the SVI for both CT and MRI costs increased each year during the study time period, but more so for CT (59.6%) than MRI (34.6%). The researchers noted that in all years, there was at least twice as much searching for CT costs than MR costs.
The online searches for both CT and MRI have consistently increased over the past few years, although patients seem to be more interested in the cost of CT than MR imaging. The researchers concluded that practices and facilities seeking to meet patient needs should be responsive to increasing interest in cost transparency.
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
April 16th 2025Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
April 15th 2025Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).