Robbie Freeman, Chief Digital Transformation Officer, Mount Sinai Health System, is steering a consolidated digital-and-AI agenda that aims to simplify experiences for patients, clinicians, and a 48,000-person workforce, while tightening the link between experimentation and enterprise scale. In a wide-ranging discussion, he outlined a governance model that blends top-down priorities with bottom-up discovery, a disciplined approach to pilots, and an emphasis on training and workflow change to ensure real adoption.
This interview was conducted as part of our recently published
Special Report on AI
In his role, Freeman oversees digital and AI product teams responsible for the myMountSinai app, omnichannel access (phone, text, web, app), and tools supporting clinicians at the point of care, alongside a new “employee digital front door” intended to streamline routine questions and tasks. He said that the workforce front door—powered by generative interfaces—will draw answers from policies and business systems, letting staff ask not only “what is our PTO policy?” but also “what is my PTO balance?”
“We’re pulling that together to create one seamless front door just for our workforce,” he said.
In describing the system’s scope, he framed the work as three “journeys”: the patient journey (access and navigation), the care journey (clinician-facing tools such as ambient documentation), and the workforce journey (HR and work-enablement). He emphasized that each journey is prioritized using a consistent approach, with experience research (focus groups, rounding, asynchronous polling) determining the pain points most worth solving now.
Governance Meets the Front Line
At Mount Sinai, digital and AI review structures have been merged to drive faster, clearer decision-making and to align investments with enterprise goals. Freeman said the unified body evaluates requests through an “experience-led” lens and balances “moonshots” with near-term fixes. The result, he noted, is a portfolio that can flex quarterly while preserving a common operating model for safety, bias testing, and measurement.
He stressed that outcomes hinge on people and process more than technology, and that success rides on the intensity of education, support and feedback loops built into rollouts. “I like to say that our projects, this work, is just 5% technology and 95% the people, process, and change management.” To keep that people focus, the team invites ideas from any staff member and feeds them through a centralized intake, risk scoring, and guardrail process that calibrates assurance requirements to the use case—lighter for back-office productivity tasks, heavier for clinical decision support.
From Pilot to Scale
Mount Sinai runs innovation units to incubate new solutions with enhanced on-the-ground support, then moves promising tools into progressively broader environments once adoption and performance criteria are met. Freeman pointed to an internally developed AI model that flags patients at highest risk for pressure injuries, noting that it outperformed the legacy Braden score used historically by bedside teams. “We’re able to show that we could outperform that with AI,” he said. Early successes, he added, are just a starting point: the team co-designs workflows with frontline users, launches on a single unit, validates onboarding and adherence, and only then phases rollouts across sites.
He described a “silent pilot” pattern for higher-risk clinical use cases: the model runs in real time behind the scenes, its outputs are compared with clinical decisions, and equity checks and operating thresholds are tuned before any intervention reaches clinicians. In parallel, policy requirements spell out expectations for measurement, human oversight, and documentation.
