GE Healthcare, Mass General Brigham Co-Develop Radiology Operations Algorithm

GE HealthCare and Mass General Brigham in Boston have co-developed an AI algorithm intended to predict “missed care opportunities” and late arrivals in radiology operations, which could help increase flexibility and streamline administrative operations.

In 2017, Chicago-based GE HealthCare and Mass General Brigham began a 10-year commitment to explore the use of AI across a broad range of diagnostic and treatment paradigms. At the time, the organizations said the initial focus of the relationship would be on the development of applications aimed to improve clinician productivity and patient outcomes in diagnostic imaging. Over time, the groups said they would create new business models for applying AI to healthcare and develop products for additional medical specialties like molecular pathology, genomics and population health.

The first innovative AI application from the collaboration is the schedule predictions dashboard of Radiology Operations Module (ROM), a digital imaging tool that helps optimize scheduling, reduce cost, and free providers from administrative burden, allowing more time for the clinician-patient relationship, the groups said.  ROM is commercially available to healthcare institutions. 

"Amid the vast sea of data and the heavy tasks that divert healthcare providers from patient care, our collaboration with Mass General Brigham is groundbreaking,” said Parminder Bhatia, chief AI officer of GE HealthCare, in a statement. “Through the fusion of distinctive data sets and cutting-edge machine learning methods, harnessing the synergy of clinical and technical proficiency, we are ushering in unprecedented healthcare advancements,”  

Operational AI-enabled tools can address challenges that often pose a threat to patient care such as cost of care, and hospital inefficiencies. When a patient misses an appointment, fails to schedule a follow up or is late, also known as missed care opportunities (MCO), the impact can be significant. The co-developed algorithm is intended to predict MCO and late arrivals, which could better accommodate urgent, inpatients, or walk-in appointments. In preliminary tests, the algorithm was able to predict the missed care opportunity correctly, at rates of up to 96 percent, with limited false positives.

“Utilizing operational AI and machine learning can bring providers together and streamline data sets,” said Keith Dreyer, D.O., Ph.D., chief data science officer, Mass General Brigham, in a statement. “The strategic use of AI offers great potential for the future of healthcare and we’re proud to be at the forefront of the movement. This technology has the potential to reduce burnout and allow physicians to spend more time with patients, which may ultimately lead to better outcomes.”