Each call-out meant manual texts, credential checks, and policy work—often after hours. Swift handles logistics; managers keep the judgment.
An integrated healthcare provider operating 11 hospitals across Georgia, where Nurse Managers carry joint responsibility for patient care quality and day-to-day unit staffing.
When nurses called out unexpectedly, Nurse Managers had to manually text eligible staff, verify credentials, check policy compliance, and coordinate...
“As nurse leaders, you’re expected to be present for your teams and your patients. But scheduling interruptions can pull you away at any moment. It wasn’t unusual for staffing issues to follow you home or cut into time that should have been spent supporting your teams.”
Integrated healthcare system operating hospitals and medical facilities.
Mapped scheduling workflows and built an AI scheduling copilot on Azure for Wellstar
Enterprise software, cloud infrastructure, and consumer electronics platform.
Wellstar Health System's Shift scheduling is part of this use case:
Related implementations across industries and use cases
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Charting ate into patient time and kept nurses late. Ambient listening now captures notes in real time as they move room to room.
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Fragmented data and manual workflows slowed care. Now, AI helps staff optimize surgical scheduling and physicians diagnose rare diseases.
Heavy reporting volumes bottlenecked understaffed finance teams. Now, staff use AI to draft emails and summarize financial reports.
Charting ate into patient time and kept nurses late. Ambient listening now captures notes in real time as they move room to room.
Reports read one by one, cohorts assembled in spreadsheets—weeks of work. Now physicians ask in plain language; answers arrive in seconds.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
PDFs and emailed guides left field techs scrambling. Now QR codes on machines surface short, localized videos in seconds.
Each call-out meant manual texts, credential checks, and policy work—often after hours. Swift handles logistics; managers keep the judgment.
An integrated healthcare provider operating 11 hospitals across Georgia, where Nurse Managers carry joint responsibility for patient care quality and day-to-day unit staffing.
When nurses called out unexpectedly, Nurse Managers had to manually text eligible staff, verify credentials, check policy compliance, and coordinate...
“As nurse leaders, you’re expected to be present for your teams and your patients. But scheduling interruptions can pull you away at any moment. It wasn’t unusual for staffing issues to follow you home or cut into time that should have been spent supporting your teams.”
Integrated healthcare system operating hospitals and medical facilities.
Mapped scheduling workflows and built an AI scheduling copilot on Azure for Wellstar
Enterprise software, cloud infrastructure, and consumer electronics platform.
Wellstar Health System's Shift scheduling is part of this use case:
Related implementations across industries and use cases
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Charting ate into patient time and kept nurses late. Ambient listening now captures notes in real time as they move room to room.
Clinicians spent 1-2 hours after shifts typing notes. Now, AI drafts records from natural dialogue, restoring eye contact with patients.
Fragmented data and manual workflows slowed care. Now, AI helps staff optimize surgical scheduling and physicians diagnose rare diseases.
Heavy reporting volumes bottlenecked understaffed finance teams. Now, staff use AI to draft emails and summarize financial reports.
Charting ate into patient time and kept nurses late. Ambient listening now captures notes in real time as they move room to room.
Reports read one by one, cohorts assembled in spreadsheets—weeks of work. Now physicians ask in plain language; answers arrive in seconds.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
PDFs and emailed guides left field techs scrambling. Now QR codes on machines surface short, localized videos in seconds.