A structured evaluation of your AI readiness, EMR systems, data architecture, RCM workflows, KPIs, and compliance requirements — establishing a precise baseline before any work begins.
Establishing the infrastructure and access framework required for deployment — including environment provisioning, data access, and stakeholder alignment before implementation begins.
Secure AI model deployment and adaptation within the client-hosted environment — covering data ingestion, normalization, bot setup, integration configuration, and initial testing.
Non-standard development efforts tailored to the client's specific workflows, payer configurations, and systems — with clear scope documentation to distinguish from standard delivery.
User Acceptance Testing (UAT) to validate functional, operational, and compliance readiness — with structured test plans, issue logging, and formal sign-off before go-live.
Controlled deployment into production with active support from SCALE and client stakeholders — monitored closely with rollback protocols in place to protect operational continuity.
Intensive monitoring and performance tuning during early production usage — rapid-response issue resolution and daily status reporting to ensure a stable, high-performance environment.
Formal transition of infrastructure ownership, admin access, and operational control to client teams — with complete documentation, training, and SCALE oversight during the handoff period.
Continued performance assurance, security updates, model compatibility, and access to SCALE's RCM experts — ensuring long-term reliability and adaptability as your environment evolves.
Every phase is backed by a structured asset library to track progress, assign responsibility, and maintain a fully auditable record of your implementation.
Pre-flight
Live Metrics
Protocols
Milestones
Governance
