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Automating Patient Intake for a Multi-Clinic Network

HealthcareProcess AutomationCustom AIDocument Processing
8 min
Avg intake time (was 45 min)
−12pts
No-show rate reduction
2 FTEs
Per clinic redeployed to care
Timeline: 12 weeks including EHR integration and clinical workflow validation
Team: 3 AiGiri engineers + 1 clinical workflow consultant + 1 client IT lead
Industry: Healthcare Technology

The Challenge

A 12-clinic healthcare network processed patient intake forms, insurance verification, and pre-appointment questionnaires manually. Each clinic required 2 dedicated staff members for intake, and new patient onboarding averaged 45 minutes — creating waiting-room backlogs and contributing to a 19% no-show rate as patients grew frustrated before they'd even been seen.

The Solution

AiGiri built a custom intake automation agent trained on the network's intake protocols, insurance verification workflows, and clinical questionnaire logic. The agent handled document collection, insurance pre-verification, and appointment preparation autonomously, with staff reviewing exceptions rather than processing every case.

Implementation approach

The system was designed around exception-based review: the AI processed routine cases without staff involvement and escalated only the 12% of cases that required human judgment. Integration with the network's EHR system meant patient data flowed directly into clinical records without manual re-entry. SMS and email reminders were triggered automatically 48 and 24 hours before appointments, personalised with the patient's name, provider, and preparation instructions.

Key technical decisions

Exception-based model (AI handles routine cases, staff review exceptions) preserved clinical safety
EHR integration via HL7 FHIR API to avoid double data entry
Personalised reminder messaging reduced no-shows more than generic reminders
Phased rollout by clinic size to manage change management risk

The Outcome

Average intake time dropped from 45 minutes to 8 minutes. Staff previously dedicated to intake processing were redeployed to patient care roles. The no-show rate fell from 19% to 7% within 90 days of deployment.

Product used

Enterprise Productivity

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