AtlantiCare
Clinical documentation
Providers were glued to screens, not patients. AI now drafts notes automatically, saving over an hour of clerical work daily.
- 66 mins daily savings per provider
- 41% reduction in documentation time
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
A New Jersey-based healthcare system employing up to 9,999 staff members to manage daily clinical operations and patient care.
Clinicians and advanced practice providers spent one to two hours after their shifts completing documentation, reviewing messages, and finalizing...
“We really needed to restore the joy in practice and let the technology do what it should which is remove friction, remove that cognitive burden of documentation, not add to it.”
Academic health system providing primary, specialty, and tertiary medical care.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Cooper University Health Care's Clinical documentation is part of this use case:
Related implementations across industries and use cases
Providers were glued to screens, not patients. AI now drafts notes automatically, saving over an hour of clerical work daily.
Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Providers were glued to screens, not patients. AI now drafts notes automatically, saving over an hour of clerical work daily.
Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
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 spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
A New Jersey-based healthcare system employing up to 9,999 staff members to manage daily clinical operations and patient care.
Clinicians and advanced practice providers spent one to two hours after their shifts completing documentation, reviewing messages, and finalizing...
“We really needed to restore the joy in practice and let the technology do what it should which is remove friction, remove that cognitive burden of documentation, not add to it.”
Academic health system providing primary, specialty, and tertiary medical care.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Cooper University Health Care's Clinical documentation is part of this use case:
Related implementations across industries and use cases
Providers were glued to screens, not patients. AI now drafts notes automatically, saving over an hour of clerical work daily.
Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
Heavy charting forced clinicians to type during visits or after hours. Now, ambient AI drafts EHR notes, restoring patient connections.
Providers were glued to screens, not patients. AI now drafts notes automatically, saving over an hour of clerical work daily.
Clinicians lost hours typing patient charts. Now, an AI assistant listens to visits and auto-drafts clinical notes in the EHR.
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.