Alexis is director of content at AIMed, with responsibility for the research, development and delivery of products across events, digital and publishing. A highly experienced events executive with a career focus on the intersection between healthcare and technology, he is also a school governor leading on teaching, learning, and quality of education.
Intermountain Healthcare has established a Data Science and Artificial Intelligence Center of Excellence to ensure ethical AI standards and set goals to further improve patient care as interest grows in how artificial intelligence, advanced analytics, and machine learning can help advance and improve healthcare.
This new center builds on the rich legacy of Intermountain caregivers using computers to improve patient outcomes, which were pioneered in the 1950s at LDS Hospital in Salt Lake City by Homer Warner, MD. Dr Warner and his colleagues went on to develop one of the nation’s first electronic medical records, and designed a system in the 1970s to assist clinicians in bedside decision-making.
Intermountain’s center of excellence is a new integrated model, which brings experts together from multiple disciplines – such as data analytics, applied mathematics and statistics, computer science, behavioral sciences, econometrics, computational linguistics and clinical informatics – along with expertise from various clinical specialties.
Their goal is to further improve the quality and affordability of healthcare, proactively identify disparities in care, enhance patient experience, and ensure responsible and ethical AI.
“At Intermountain, we utilize machine learning algorithms that emulate human cognition to help providers improve their diagnosis and treatment of medical conditions, predict and identify diseases and infections in their early stages (including COVID-19) and prevent hospital-readmissions,” said Albert Marinez, chief analytics officer at Intermountain.
“American healthcare needs to be better. The work of this new Center of Excellence – which brings together the right algorithms and data at the right time – enables our caregivers to care for people with the best evidence and decision-support in the moment,” said Mark Briesacher, MD, senior vice president and chief physician executive for Intermountain. “People expect and deserve the best experience and care in every moment with their healthcare providers.”
“Responsible AI applications automate routine tasks and create time for providers and caregivers to listen, see, and feel what patients are sharing and need. AI presents providers with data-driven insights and suggested next actions for evidence-based care plans, treatments and interventions for patients,” said Diego Ize-Ludlow, MD, Intermountain’s chief health information officer.
Some of the AI-centered projects at Intermountain have included an ePneumonia protocol that has saved approximately 1,166 lives per year since 2015, and a program that identifies hospital patients who are at risk of declining, which has led to lower mortality rates.
“We’ve developed an AI Playbook as a framework to deploy and scale human-centered AI that is transparent, equitable, ethical, and above all, ensures patient privacy. The playbook outlines goals to establish appropriate AI governance, set validation and documentation standards, detect inherent bias, ensure data integrity and promote AI literacy among caregivers,” said Greg Nelson, assistant vice president for analytics services at Intermountain.
“It’s unique for a health system not associated with an educational institution to be at the forefront of innovation for data analytics, machine learning and artificial intelligence. Establishing these AI standards is a natural outgrowth of Intermountain’s focus on becoming a model health system and developing ethical healthcare leaders,” Nelson added.
Any data product used at Intermountain Healthcare follows a validation process following guidelines based on U.S. and international best practices. Concepts are derived from the U.S. Food and Drug Administration’s definition of validation and require verification and documentation to meet predetermined specifications and quality attributes.
Across the industry today, there are more than 130 algorithms that have been FDA approved or cleared as AI-enabled processes for conditions ranging from breast cancer to sickle cell disease to schizophrenia.