August 18, 2026, (Inside AI) — Mass General Brigham, one of the nation's largest academic health systems, is scaling an AI-powered scribe to curb physician burnout. The technology records patient conversations, transcribes them, and drafts clinical notes, freeing clinicians from keyboard work during visits.
Harvard Business School Associate Professor Susanna Gallani and co-authors Rob Huckman, Suraj Srinivasan, and Asaf Bitton examine the rollout in a new case study. The case, titled "The AI Scribe: Enhancing Physician Presence and Curbing Burnout at Mass General Brigham," explores how leadership weighed risks and benefits before scaling.
The stakes are high. Mass General Brigham employs roughly 12,000 physicians. Burnout, turnover, and administrative overload are endemic. Gallani describes the scribe as a rare innovation that improves care without asking clinicians to do more.
"This is the first time that we're trying to do more and better in healthcare while taking work off the physician's plate, the clinician's plate," Susanna Gallani, Associate Professor, Harvard Business School said.
The technology is simple in concept. A clinician places a phone on the table, records the visit with patient consent, and an AI engine transcribes the conversation. It then populates the electronic health record and suggests billing codes and prescriptions. The physician must review and sign the note. The audio recording is deleted shortly after.
Early results were promising. A pilot with about 800 self-selected clinicians showed a significant drop in burnout. Users reported more family time and better patient engagement.
But scaling has been uneven. When the program opened to primary care, the group with the highest burnout, many license holders did not use the tool. The organization set a rule: if a clinician did not use the scribe for three months, they were asked why and could lose the license. Each license carries a monthly fee.
Resistance came from several directions. Some physicians disliked how the AI phrased notes. Others worried that verbalizing clinical reasoning aloud, a requirement for the AI to capture it, could confuse or frighten patients. Some questioned what leadership would do with the time saved.
"You are now giving me back time. What are you going to ask me to do with that time?" Susanna Gallani said, quoting a physician's concern.
That fear is not unfounded. In the past, human scribes came with a productivity expectation: see more patients to cover the cost. Gallani said the same concern shadows the AI rollout. If leaders demand higher volume, the burnout reduction goal would be lost.
Patient trust is another hurdle. Recording conversations raises questions about data use, privacy, and whether patients will speak naturally. Gallani said healthcare data carries deeper sensitivity than most commercial data.
The case also surfaces a training dilemma. Should residents learn to work with AI or remain independent of it? The same question is emerging in law firms and other knowledge-intensive fields. Gallani said there is no clear answer yet.
Mass General Brigham's leadership was explicit about its primary goal. It was not productivity, though that was measured. The driver was reducing clinician burnout.
"The main reason why they wanted to try this technology was to reduce the burnout among their clinicians," Susanna Gallani said.
Gallani's broader lesson for any organization: start with the human problem, not the technology. Build trust first. Use AI to elevate workers, not replace them. And do not rush adoption before the organization is ready.
"We have to really be specific of what problem we're trying to solve," Susanna Gallani said.
The case does not offer a final verdict on the scribe's long-term impact. But it frames a central tension in AI adoption across industries: how to measure returns that are both financial and deeply human.
