The research journal Nature recently ranked Mount Sinai Health System No. 1 on its 2024 AI Index of leading health care institutions. Today, Mount Sinai has a large portfolio of artificial intelligence (AI) products, including many that intersect with nursing to contribute to important improvements in practice and care. Many more are still in the pipeline.
“Our teams think of AI as augmented intelligence, versus artificial intelligence,” says Robbie Freeman, DNP, RN, Vice President of Digital Experience and Chief Nursing Informatics Officer at the Mount Sinai Health System. “The goal is to leverage AI as a supportive tool to enhance clinical decision-making—not to replace it.”
He adds, “Risk assessment models and tools for guiding care have always been integral to nursing practice. By combining nursing expertise and critical thinking with the ability to analyze vast amounts of data, AI is transforming how we deliver care, elevating quality and safety to unprecedented levels. In the coming years, this technology will continue to support nursing practice by enabling the creation of highly targeted, personalized care plans for every patient.”
Shown from let: Eric Kim; Prem Timsina, ScD; Arianna Goldman; Dhaval Patel; Maria ‘Vickee’ Sevillano, RN; Kim-Anh-Nhi Nguyen, MSc; Robbie Freeman, DNP, RN; and Arash Kia, MD, MsC
“Every AI project starts with a working group,” says Dr. Freeman, “and that working group always includes the people who are delivering care. For example, if we’re working on a falls-related initiative, we sit down with front-line nurses, with geriatricians, with nurse leaders, and from day one we’re talking together about what might be helpful.”
Currently, Mount Sinai nurses are using a machine learning model that predicts which patients are most likely to fall while in the hospital. The data behind this tool largely came from examining electronic medical record (EMR) data. By combing through nursing notes using recognition algorithms, Mount Sinai also developed an AI tool to better identify which patients are at higher risk for becoming delirious while in the hospital so that tailored preventive interventions could be put in place at the earliest opportunity.
From left: Prem Timsina, ScD; David Reich, MD, Chief Clinical Officer, Mount Sinai Health System, and President of The Mount Sinai Hospital; Robbie Freeman; Matt Levin, MD, and Arash Kia, MD, MsC
Mount Sinai is leading the world in developing a variety of AI products that support nurses and keep patients safe, according to Dr. Freeman.
During the summer of 2024, a multidisciplinary Mount Sinai team won the national AI Health Prize from Hearst Health and the UCLA Center for SMART Health for an internally developed product called NutriScan AI. The AI tool facilitates faster identification and treatment of malnutrition in hospitalized patients. It has been deployed across six Mount Sinai hospitals using the Epic electronic medical record, and the Health System is now 2.5 to 3 times more likely to identify malnutrition.
Another AI product came about when Maria ‘Vickee’ Sevillano, BSN, RN, CWCN, COCN, a Mount Sinai clinical nurse, proposed an idea focused on the prevention of pressure injuries, also known as bed sores.
“We embraced the idea, collaboratively explored its nuances through a co-design process, and partnered with our internal data scientists and software engineers to transform it into a fully realized product,” says Dr. Freeman. “We tested and fine-tuned it, and in early 2024 the idea brought forward was introduced to the clinical setting. This predictive software is currently embedded in our EMR at The Mount Sinai Hospital, and we hope to expand its use as we continue to evaluate the product.”
Mount Sinai has also done a lot of work with a new type of AI called large language models, which, among other tasks, can recognize and generate large amounts of text. One particular study involved examining nursing triage notes to identify predictors for which Emergency Department patients were likely to be admitted to the hospital.
“In many cases the nursing documentation can really power AI,” says Dr. Freeman. “Much of nursing documentation data reflects their expert observations and has predictive power. So, using things like natural language processing algorithms, the nursing observations and assessments are really helpful in the development of AI tools that have broader use and impact. ”Mount Sinai is also using AI to help reduce the amount of time nurses spend doing documentation by rolling out macros—a sequence of computer instructions to automate a task—and tools that can streamline the process and relieve the documentation burden.
Kim-Anh-Nhi Nguyen, MSc, left, and Maria ‘Vickee’ Sevillano, BSN, RN, CWCN, COCN
As this emerging field continues to grow, Dr. Freeman says it is important to note that Mount Sinai has governance in place to ensure there is a solid understanding of how these tools work, that they are safe, and that they are being used in ways that are ethical and sound before they are being used in patient care.
“There’s a science and methodology for ensuring AI products are used responsibly,” Dr. Freeman says. The shared decision-making structure plays a critical role. Mount Sinai is also part of the nonprofit Coalition for Health AI, which brings together a diverse array of stakeholders to drive the development, evaluation, and appropriate use of AI in health care.
“AI is here and has proven it holds promise for thoughtfully revolutionizing care delivery in ways never imagined,” he says.