The Challenge
Global Health Network, one of North America's largest integrated care providers, operated across 47 hospitals and over 300 clinics. Their patient experience was fractured across a dozen legacy systems — patients juggled different logins for scheduling, records, billing, and telehealth, and providers spent up to 30% of their day reconciling data between siloed tools. Wait times were rising, satisfaction scores were falling, and the leadership team knew that incremental fixes would not close the gap.
Our Approach
We partnered with their executive team and clinical informatics group over a 14-month engagement to design and deliver a unified, cloud-native telemedicine and patient-engagement platform. Work was sequenced in three phases:
- Discovery & architecture — interviews with 80+ clinicians, patient journey mapping, and a HIPAA-aligned cloud reference architecture on AWS with end-to-end encryption.
- Platform build — a React/TypeScript front end, microservices on EKS, FHIR-native data layer, and an event bus connecting EHR, billing, scheduling, and pharmacy systems.
- AI augmentation — clinical decision-support models, an intake triage assistant, and predictive no-show scoring to optimise scheduling.
Key Capabilities Delivered
- Single sign-on patient portal with unified medical history and real-time messaging.
- Video consultations with adaptive bitrate streaming, working reliably down to 1.5 Mbps.
- AI-assisted clinical notes that reduced documentation time by 22 minutes per shift.
- Smart scheduling with automated insurance eligibility checks.
Results
Within 9 months of rollout the platform was serving more than 500,000 active patients across 12 states. Patient satisfaction (NPS) climbed 85% versus the prior baseline, average consultation handling time dropped 40%, and the organisation eliminated four legacy point solutions — recovering an estimated $6.4M in annual licensing and support costs.
What's Next
The team is now layering in remote patient monitoring for chronic conditions and an in-house AI model trained on de-identified clinical notes to surface care-plan recommendations.