Sam Brusco, Associate Editor09.07.23
GE HealthCare and Mass General Brigham have announced as part of their initial partnership, they will co-develop an artificial intelligence (AI) algorithm to boost healthcare institution operations effectiveness and productivity.
The 10-year commitment between GE HealthCare and Mass General Brigham was first signed in 2017 to explore use of AI across a range of diagnostic and treatment paradigms.
The two said the first application will be a schedule predictions dashboard of Radiology Operations Module (ROM), a digital imaging tool that helps optimize scheduling, lower cost, and free providers from administrative burden and allow more time for the clinician-patient relationship.
The actionable insights from AI and machine learning are engineered to boost 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.
"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,” Parminder Bhatia, chief AI officer of GE HealthCare told the press.
When a patient misses an appointment, fails to schedule a follow up or is late, also known as missed care opportunities (MCO), impact can be significant. The co-developed algorithm aims to predict MCO and late arrivals, which may help boost flexibility and streamline administrative operations, improve patient satisfaction, and better accommodate urgent, inpatients, or walk-in appointments. In preliminary tests, the company said the algorithm was able to predict the missed care opportunity correctly, at rates of up to 96%, with limited false positives.
“Utilizing operational AI and machine learning can bring providers together and streamline data sets,” said Keith Dreyer, DO, Ph.D., 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.”
The 10-year commitment between GE HealthCare and Mass General Brigham was first signed in 2017 to explore use of AI across a range of diagnostic and treatment paradigms.
The two said the first application will be a schedule predictions dashboard of Radiology Operations Module (ROM), a digital imaging tool that helps optimize scheduling, lower cost, and free providers from administrative burden and allow more time for the clinician-patient relationship.
The actionable insights from AI and machine learning are engineered to boost 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.
"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,” Parminder Bhatia, chief AI officer of GE HealthCare told the press.
When a patient misses an appointment, fails to schedule a follow up or is late, also known as missed care opportunities (MCO), impact can be significant. The co-developed algorithm aims to predict MCO and late arrivals, which may help boost flexibility and streamline administrative operations, improve patient satisfaction, and better accommodate urgent, inpatients, or walk-in appointments. In preliminary tests, the company said the algorithm was able to predict the missed care opportunity correctly, at rates of up to 96%, with limited false positives.
“Utilizing operational AI and machine learning can bring providers together and streamline data sets,” said Keith Dreyer, DO, Ph.D., 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.”
