Intermountain, University of Utah Health awarded $3.8M to build an automated cancer screening tool

An African-American female technician positions a Caucasian woman at an imaging machine to receive a mammogram.
An automated tool developed by Intermountain and the University of Utah aims to detect patients at risk for breast cancer.

Two prominent Utah health systems have joined forces to create a new cancer screening tool that integrates EHR and clinical decision support technology.

Backed by a $3.8 million grant from the National Cancer Institute, researchers with Intermountain Healthcare and the University of Utah Health plan to build an open source screening tool that automatically detects patients at high risk for breast and colorectal cancers.

Researchers will also tap expertise from the University of Utah’s Huntsman Cancer Institute to develop a standardized platform that can be integrated across a variety of healthcare organizations with disparate EHR systems.

RELATED: Industry coalition finalizes clinical decision support guidelines aimed at self-regulation

“It is crucial that primary care physicians who are the frontline of care identify patients who are at high risk of developing cancer,” the project's co-investigator Scott Narus, Ph.D., medical informatics director and chief clinical systems architect for Intermountain Healthcare, said in a release. “Early diagnosis and screening of cancer greatly increases the chances for successful treatment.”

The initiative will target several pain points in healthcare including limited clinical decision support capabilities within existing EHR systems and CDS systems that aren’t interoperable across various organizations. The two organizations plan to develop and test the platform in their respective hospitals to verify its interoperability.

Screening tools that can integrate patient data in to predictive analytics could have a major impact on patient care. National efforts through the Cancer Moonshot are currently underway to overhaul the industry’s approach to cancer diagnosis by sifting through patient data.

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