News | March 10, 2009

Appropriateness Practice Guidelines Reduce Imaging Use, Cost

March 10, 2009 – The Medical Imaging Technology Alliance said today that a recent study conducted by researchers at the University of Florida Health Center and Massachusetts General Hospital demonstrates that using appropriateness criteria to help curb growth rates in advanced imaging utilization, according to a study set for publication in an upcoming issue of Radiology.

“This study provides groundbreaking evidence affirming that appropriateness criteria is the key to ensuring patients get the right scan at the right time,” said Ilyse Schuman, managing director, MITA. “MITA applauds the work of Dr. Sistrom and his team, whose research demonstrates that when appropriateness criteria is integrated into physicians’ practices, imaging utilization and its associated cost are significantly reduced, while still ensuring patients have access to the services they need.”

“This study confirms that the appropriateness criteria provisions in last year’s Medicare bill, and not pre-authorization requirements delivered by radiology benefit managers, are the right way for policymakers to ensure the proper use of advanced imaging equipment and generate savings without compromising access to life-saving diagnostic services.”

The study, led by Dr. Christopher L. Sistrom, evaluated the effect that certain appropriateness criteria measures – specifically a computerized radiology entry (ROE) and decision support (DS) system – have on the growth rates of outpatient CT, magnetic resonance (MR) imaging and ultrasonography (US) procedures over time. The ROE system was introduced in 2001 to assist physicians in making their decisions ordering high-cost imaging tests. DS was implemented three years later, providing physicians with a 1-9 appropriateness score[1] for their diagnostic recommendation after clinical indications for the patient had been provided.

Based on a statistical analysis of data accumulated between October 2000 and December 2007, Sistrom et al found that the implementation of the ROE and DS system led to a drastic decrease in high-cost imaging growth. Even as outpatient visits increased at a compound annual rate of nearly 5 percent, the annual outpatient CT growth rate decreased from 12 to 1 percent, while MR imaging and US annual growth rates each decreased by 5 percent, from 12 to 7 percent and 9 to 4 percent, respectively. Researchers concluded that, “introducing computerized ROE with DS…may substantially reduce the growth rate of high-cost outpatient imaging volumes.”

For more information: www.medicalimaging.org

Related Content

Video Plus Brochure Helps Patients Make Lung Cancer Scan Decision

Image courtesy of the American Thoracic Society

News | Lung Cancer | April 19, 2019
A short video describing the potential benefits and risks of low-dose computed tomography (CT) screening for lung...
FDA Clears GE's Deep Learning Image Reconstruction Engine
Technology | Computed Tomography (CT) | April 19, 2019
GE Healthcare has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) of its Deep Learning Image...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Ebit and DiA Imaging Analysis Partner on AI-based Cardiac Ultrasound Analysis
News | Cardiovascular Ultrasound | April 16, 2019
DiA Imaging Analysis has partnered with the Italian healthcare IT company Ebit (Esaote Group), to offer DiA’s LVivo...
360 Photos | 360 View Photos | April 12, 2019
This 360 degree view shows staff at the ...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
DiA Imaging Analysis Introduces LVivo SAX Ultrasound Analysis Tool
Technology | Cardiovascular Ultrasound | April 09, 2019
DiA Imaging Analysis announced the launch of LVivo SAX, a cardiac analysis tool that helps clinicians quickly and...
SuperSonic Imagine Highlights Aixplorer Mach 30 Breast Ultrasound at SBI/ACR Breast Imaging Symposium
News | Ultrasound Women's Health | April 03, 2019
SuperSonic Imagine will introduce the new generation of its Aixplorer Mach 30 breast ultrasound solution at the 2019...
Videos | RSNA | April 03, 2019
ITN Editor Dave Fornell takes a tour of some of the most interesting new medical imaging technologies displa
Johns Hopkins Medicine First in U.S. to Install Canon Medical's Aquilion Precision
News | Computed Tomography (CT) | March 26, 2019
March 26, 2019 — Johns Hopkins Medicine now has access to the first...