Bacancy Cut Relatient’s EHR Integration Maintenance by 65% with a Unified FHIR R4 Engine
Relatient is a US-based patient engagement SaaS that supports hundreds of healthcare groups and reaches millions of patients each year. Its platform spans appointment scheduling, patient messaging, and reminders, which, over time, required integrations with a wide range of EHR systems. The integration layer had grown fragmented, with overlapping connectors slowing engineering work. Bacancy rebuilt it as a single canonical FHIR R4 engine with HL7 v2 fallback, deployed the company’s first MCP server, and shipped a Python-based no-show prediction model.
Canonical FHIR R4 Integration Engine
MCP Server Exposing 18 Tools
ML-Driven No-Show Prediction Model
Automated Patient Intervention
Fragmented EHR integration layer with overlapping connectors slowing engineering velocity across the platform.
Enterprise customers demanded AI assistant access to platform data without internal AI engineering bandwidth.
High patient no-show rates were impacting clinic revenue and reducing daily appointment utilization.
Predictive no-show flags alone could not reduce missed appointments without automated patient intervention workflows.
Bacancy rebuilt the integration layer as a single canonical FHIR R4 engine with HL7 v2 fallback support.
Our MCP developers deployed the company’s first MCP server, which enables 18 tools across appointments, messaging, and reporting.
Our ML engineers shipped a Python prediction model trained on three years of historical appointment data.
Bacancy built an auto-intervention engine that escalates reminder cadence and suggests telehealth swaps based on patient risk.
Appointment Scheduling Engine
Automated Reminder Dispatch
EHR Data Synchronization
Outreach Rule Configuration
4
April 2025 - January 2026
65% reduction in EHR integration maintenance overhead
4x faster engineering velocity across the platform
1.2M saved annually on platform infrastructure costs
9-day EHR onboarding, down from 6 weeks
31% lower no-show rate from ML model
18 MCP tools deployed across workflows
| Frontend | Next.js |
| Backend | Node.js, .NET Core, Python |
| Database | PostgreSQL |
| In-memory Data Store | Redis |
| Cloud Infrastructure | AWS (EKS, RDS, S3) |
| Architecture | Microservices, Event-Driven, MCP Layer |
| Authentication | Keycloak,SSO |
| Healthcare Standards | FHIR R4, HL7 v2 |
| Messaging & Notifications: | Twilio, SendGrid |
| ML & AI: | Python, Model Context Protocol (MCP) |
| API Communication | REST APIs, FHIR R4, HL7 v2 |
| Project & Issue Tracking | Jira |
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