GE, Mass General Brigham Collaborate on Operational AI-Enabled Tools

As part of a 10-year commitment to drive innovation, GE HealthCare and Mass General Brigham have co-developed an artificial intelligence (AI) algorithm that will help increase operations effectiveness and productivity. The first innovative AI application from the collaboration, first signed in 2017, 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. ROM is commercially available to healthcare institutions.

The actionable insights driven by AI and machine learning are designed to help improve both departmental and enterprise-wide productivity and administrative efficiency. By 2025, the U.S. is estimated to have a shortage of approximately 446,000 home health aides, 95,000 nursing assistants, 98,700 medical, and lab technologists and technicians, and more than 29,000 nurse practitioners, according to a report conducted by industry market analytic firm Mercer. Health systems will need to rely on technology to help solve some of these challenges.

"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. Through the fusion of distinctive datasets and cutting-edge machine learning methods, harnessing the synergy of clinical and technical proficiency, we are ushering in unprecedented healthcare advancements,” said Parminder Bhatia, Chief AI Officer of GE HealthCare.

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 help increase flexibility and streamline administrative operations, improve patient satisfaction, and 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% with limited false positives, based on testing at one Mass General Brigham location.

“Utilizing operational AI and machine learning can bring providers together and streamline data sets,” said Keith Dreyer, DO, PhD, Chief Data Science Officer, Mass General Brigham. “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.”

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