Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
The largest nonprofit health system in the Intermountain West operates 34 hospitals and over 400 clinics, employing more than 68,000 caregivers who handle nearly a million emergency visits annually.
Clinicians relied on a mix of legacy speech tools, dictation, and templates that failed to reduce the administrative burden of complex documentation...
“Our people love it. I have so many people coming to me saying, ‘Wow, I was going to retire with this EHR shift, now with Dragon Copilot, and my ambient dictation, I'm going to hang around. I think I can serve my patients for a few more years.’”
Nonprofit health system providing medical services and hospital care in the Western US.
Designed and executed a comprehensive adoption strategy and provided training for Intermountain.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Intermountain Health's Clinical documentation is part of this use case:
Related implementations across industries and use cases
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Manual charting bottlenecked rural clinicians. Now, they use a mobile AI scribe to capture visits and instantly draft EMR records.
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
The largest nonprofit health system in the Intermountain West operates 34 hospitals and over 400 clinics, employing more than 68,000 caregivers who handle nearly a million emergency visits annually.
Clinicians relied on a mix of legacy speech tools, dictation, and templates that failed to reduce the administrative burden of complex documentation...
“Our people love it. I have so many people coming to me saying, ‘Wow, I was going to retire with this EHR shift, now with Dragon Copilot, and my ambient dictation, I'm going to hang around. I think I can serve my patients for a few more years.’”
Nonprofit health system providing medical services and hospital care in the Western US.
Designed and executed a comprehensive adoption strategy and provided training for Intermountain.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Intermountain Health's Clinical documentation is part of this use case:
Related implementations across industries and use cases
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Manual charting bottlenecked rural clinicians. Now, they use a mobile AI scribe to capture visits and instantly draft EMR records.
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.