Mount Sinai Health System is estimating that it will see a $50 million bottom-line impact from its AI portfolio this year, reporting more than a 3-to-1 return on investment as the health system scales the technology across clinical and operational workflows.
New York City-based Mount Sinai ties all AI initiatives to measurable value metrics, including financial impact, patient experience, operational efficiency, quality and safety, Robbie Freeman, DNP, RN, chief digital transformation officer, told Becker’s.
“One of the changes we made at Mount Sinai a little over a year ago was combining our digital and AI governance into one unified structure,” Dr. Freeman said.
The health system now manages what he described as a unified “digital AI experience portfolio,” evaluating projects based on organizational priorities and measurable outcomes.
“We also have both top-down and bottom-up innovation processes. Anyone in the organization can submit an idea for an AI or digital tool,” he said. “The ideas go through our governance and OKR framework, and for the highest-impact ideas, we co-develop solutions directly with frontline staff.”
Among the strongest examples of AI-driven ROI has been pressure injury prevention. Dr. Freeman said a frontline wound care nurse proposed using AI to help identify patients at risk for pressure injuries, which can cost hospitals roughly $50,000 per case.
“We partnered with that nurse and built an AI-enabled solution internally,” he said. “Today, it’s deployed across multiple hospitals in our health system.”
Dr. Freeman said the organization also has seen measurable gains from AI-powered patient navigation tools and ambient clinical documentation technology.
The health system launched an AI-powered symptom checker that directs patients to the appropriate level of care using evidence-based treatment guidelines.
“We heard from patients that when they start feeling unwell, they’re often unsure where to seek care. So we built an AI-powered symptom checker integrated into our digital platform,” Dr. Freeman said.
With this tool, patients can enter symptoms, and AI matches them with evidence-based treatment guidance and recommends where they should go for care.
“That helps improve access and attract patients into the health system, and we’re able to track downstream utilization and impact,” he said.
Ambient documentation has also been a major workforce and operational win for the health system. Mount Sinai has scaled ambient AI across the organization and is seeing improvements in note quality and financial performance. The organization’s physicians also are benefiting, as some who were considering retirement have decided to stay in practice because the technology gives them time back, according to Dr. Freeman.
“They’re no longer spending their evenings finishing documentation,” he said. “That wellness impact is harder to quantify financially, but it’s incredibly important for clinicians and for the organization overall.”
To ensure projects deliver measurable results, Mount Sinai has implemented formal governance structures and predefined success metrics for AI pilots.
“We want to fail fast if something isn’t working,” Dr. Freeman said. “We have organization-wide governance, and depending on where the AI is being used, it goes through different governance pathways.”
For example, clinician-facing AI tools go through Mount Sinai’s care journey governance committee, which has formal decision-making authority around whether projects move forward or stop.
“One of the biggest lessons we’ve learned is the importance of defining objective success criteria before a pilot begins,” Dr. Freeman said. “Historically, organizations struggle when they launch pilots without clearly defining what success looks like. If you don’t establish measurable outcomes upfront, it becomes difficult to know whether the pilot succeeded.”
With the pressure injury project, one of the health system’s first success metrics was achieving greater than 80% adoption among frontline care team members. If not, the tool would not scale beyond the initial pilot areas until the health system met that benchmark.
“Having those predefined metrics makes the decision-making process much more objective,” Dr. Freeman said.
Mount Sinai also has established an independent “assurance lab” that prospectively tests higher-risk AI models before deployment.
“We don’t want the same people building AI to also be responsible for validating it,” he said. “The assurance lab risk-scores every AI solution. For higher-risk models, the AI runs prospectively behind the scenes before anyone uses it in production.”
The assurance lab then reviews performance and provides recommendations back to governance committees before deployment decisions are made.
“That independent validation process has been incredibly important for maintaining rigor and safety,” Dr. Freeman said.
Despite the financial gains, he emphasized that not every AI investment is evaluated solely on direct monetary return.
“Not every AI initiative has direct financial ROI,” Dr. Freeman said. “Some projects are worthwhile because they improve safety, quality or experience. That’s why we evaluate return on value across the entire portfolio rather than expecting every individual project to generate direct financial return.”
At the same time, technology costs are increasing just like every other cost category in healthcare — whether that’s labor, supplies or pharmaceuticals. Because of this, Dr. Freeman said the health system is trying to use AI strategically to save where it can so the organization can “invest where we should.”
“For example, we’ve rationalized portions of our technology portfolio and reduced vendor redundancy to create savings that support AI investment,” he said.
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