Surging demand overwhelmed staff. AI agents now handle refills, cutting call volume 15% so teams can focus on complex diagnoses.
A top-ranked academic health system in Chicago comprising three hospitals and an extensive outpatient network handling thousands of monthly patient support calls.
Rising care demand overwhelmed the contact center, where representatives struggled with data silos and complex routing rules across departments....
“The first part of our journey was combining data in Health Cloud and Data 360, normalizing it, and putting it in a place where we could actually work with it.”
Academic health system for hospital care, medical education, and research.
Acted as the implementation partner for Rush's Agentforce and agentic AI deployment.
AI-powered CRM platform for sales, service, marketing, and commerce teams.
Rush University System for Health's Patient support automation is part of this use case:
Related implementations across industries and use cases
Reviewing scattered medical notes took a half-day per patient. Now, care teams use AI to generate full summaries in seconds.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Rigid phone trees bottlenecked support. Now, an AI agent resolves inquiries, sums balances across systems, and routes to human staff.
High case volumes bottlenecked symptom triage. Now, an AI agent instantly filters routine inquiries, freeing vet staff for urgent care.
Reps manually calculated variances across three systems. AI now compares statements in seconds, spotting fee changes instantly.
Typing notes broke eye contact and caused late nights. Now, AI drafts records from spoken exam findings so vets stay present.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.
Surging demand overwhelmed staff. AI agents now handle refills, cutting call volume 15% so teams can focus on complex diagnoses.
A top-ranked academic health system in Chicago comprising three hospitals and an extensive outpatient network handling thousands of monthly patient support calls.
Rising care demand overwhelmed the contact center, where representatives struggled with data silos and complex routing rules across departments....
“The first part of our journey was combining data in Health Cloud and Data 360, normalizing it, and putting it in a place where we could actually work with it.”
Academic health system for hospital care, medical education, and research.
Acted as the implementation partner for Rush's Agentforce and agentic AI deployment.
AI-powered CRM platform for sales, service, marketing, and commerce teams.
Rush University System for Health's Patient support automation is part of this use case:
Related implementations across industries and use cases
Reviewing scattered medical notes took a half-day per patient. Now, care teams use AI to generate full summaries in seconds.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Rigid phone trees bottlenecked support. Now, an AI agent resolves inquiries, sums balances across systems, and routes to human staff.
High case volumes bottlenecked symptom triage. Now, an AI agent instantly filters routine inquiries, freeing vet staff for urgent care.
Reps manually calculated variances across three systems. AI now compares statements in seconds, spotting fee changes instantly.
Typing notes broke eye contact and caused late nights. Now, AI drafts records from spoken exam findings so vets stay present.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Manually evaluating high call volumes was slow and inaccurate. Now, AI extracts sentiment so sales teams can refine their outreach.
Manual call evaluations delayed insights by weeks. Now, bots score calls at scale, empowering analysts to synthesize data in hours.