End-to-End Implementation Framework for Scale Healthcare’s AI-Based Solutions
This document outlines the complete end-to-end implementation framework for Scale Healthcare’s AI-based solutions deployed in client-hosted environments. It serves as a single source of truth detailing the lifecycle of AI model delivery—starting from initial assessment and planning, through implementation, customization, and go-live, to long-term support and future expansion. The framework is divided into 9 clearly defined phases, each capturing the objectives, responsibilities, workflows, and deliverables required to ensure a successful AI rollout. For each phase, we have also included triggers for professional services (beyond the standard scope) to maintain transparency and enable clear contractual alignment with our clients. Where applicable, this document includes links to supporting Excel-based checklists, dashboards, and tracking tools. These resources are intended to help streamline execution, track progress, assign responsibilities, and generate insights in a structured and auditable manner.
It outlines a complete lifecycle—from initial assessment through implementation and long-term support—organized into 10 structured phases, each with detailed objectives, responsibilities, workflows, and deliverables.
📌 Overview of Phases
- Assessment & Discovery
- Pre-Requisites from Client
- Implementation
- Customization & Adjustments
- Client Testing & Validation
- Go-Live
- Post-Launch Stabilization
- Handover
- Maintenance & Support
🔍 Phase 1: Assessment & Discovery
Scale Healthcare initiates with a structured questionnaire evaluating AI readiness, EMR systems, data architecture, RCM workflows, KPIs, and compliance requirements.
Scale Healthcare will begin by sharing a structured questionnaire covering key areas like AI readiness, EMR systems, data architecture, infrastructure, RCM workflows, KPIs, and compliance. Clients are expected to complete this in detail and provide supporting documentation where needed. Based on the responses, Scale will analyze and prepare a comprehensive report that outlines. This assessment report will directly inform Phase 4: Customization & Adjustments, where out-of-scope items are planned and estimated via a separate SOW. What is covered under the standard implementation & What falls under professional services due to customization needs or process complexity
🧱 Phase 2: Pre-Requisites from Client
Objective: Establish infrastructure and access framework for deployment.
To establish the technical foundation, access framework, and cross-functional alignment necessary to deploy, configure, and adapt the AI models within the client's environment. This checklist is designed by Scale Healthcare to ensure all necessary infrastructure, access, and organizational elements are in place to begin implementation of the AI model. It helps identify: Gaps that could delay setup or integration, Key client-side responsibilities, Required SME and IT involvement, Security, compliance, and access controls. All checklist items must be addressed before moving into the active implementation phase.
| Category | Client Action Item | Details / Examples |
|---|---|---|
| Cloud Infrastructure | Provision cloud subscription on Azure / AWS / GCP | Include tenant ID, subscription ID, and contact for access |
| Cloud Infrastructure | Create dedicated resource group for AI implementation | Use naming conventions and tagging standards |
| Cloud Infrastructure | Configure compute, networking (VNet), and storage | Define dev/test/prod environments |
| Cloud Infrastructure | Assign temporary access (IAM role) to Scale team | Contributor or admin role recommended |
| Data & System Access | List all EMR/PM systems in use with version info | e.g., Athena v2023.1, Greenway, eCW |
| Data & System Access | Provide API keys / DB credentials / secure access | Ensure read or write permission is defined |
| Data & System Access | Confirm integration method per system (API, DB, HL7) | Choose for each EMR/data source |
| Data & System Access | Enable VPN or IP whitelisting for Scale team | Secure access to EMRs or databases |
| RCM Workflow Alignment | Share SOPs for billing, charge capture, denials | Used to align AI agents to workflow |
| RCM Workflow Alignment | Share known workflow gaps or RCM challenges | E.g., denial backlogs, charge lag |
| RCM Workflow Alignment | List number of sites, EMRs, data fragmentation | e.g., 6 clinics, 2 EMRs, 1 clearinghouse |
| Security & Compliance | Share HIPAA/PHI policies & BAA agreement | Mandatory before go-live |
| Security & Compliance | Confirm audit & logging mechanisms are in place | e.g., Splunk, LogDNA, Azure Monitor |
| Security & Compliance | Assign compliance SPOC to coordinate reviews | e.g., privacy officer |
| Team & Stakeholders | Assign IT SPOC for infra & cloud management | e.g., DevOps lead |
| Team & Stakeholders | Assign RCM SME for denial, charge, and payment | Needed for validation & feedback |
| Team & Stakeholders | Nominate executive sponsor from leadership | Decision maker to unblock issues |
| Automation & Bots | Provide bot logins or service accounts | Scoped to write where needed |
| Automation & Bots | Confirm write access on EMR for agents | e.g., appointment notes, denial status |
| Automation & Bots | Provide sandbox/test instance for bot validation | Prevents test writes in production |
| Automation & Bots | Define session/concurrent user limits | Avoid system conflicts |
| Automation & Bots | Provide SOP for user provisioning/deactivation | Required for bot user IDs |
| Infra Control & Handover | Agree on infra handover post-stabilization | Timeline for internal IT transition |
| Infra Control & Handover | Indicate if Scale to continue managing post-handover | If yes, new SOW to be created |
⚙️ Phase 3: Implementation
Secure AI model deployment and adaptation, including data ingestion, normalization, bot setup, and testing.
In this phase, Scale Healthcare deploys and adapts AI models within the client’s environment through a structured, step-by-step process. Key activities include secure data ingestion from EMRs and other systems, normalization of data to align with AI model requirements, and agent configuration to reflect the client’s operational workflows. Bots are integrated for EMR automation, with robust fail-safe protocols to ensure system stability and data integrity. The process concludes with full system testing, performance validation, and client-led User Acceptance Testing (UAT). ✅ All items identified during the Discovery Phase that fall outside the standard implementation scope will be carried forward to the next phase—Customization and Adjustments. A separate Statement of Work (SOW) will be required to execute these additional efforts. Note: Triggers such as extra EMR integrations, fragmented data, or complex business rule customization may also initiate professional services engagement beyond standard implementation.
Phase Step
Description
Client Dependencies
Data Ingestion
Establish secure access and pipelines to EMR, PM, and other data sources. Handle PHI securely with access control.
Credentials, schema access, and sandbox/testing environments required from client.
Data Normalization
Map extracted data into standardized formats for downstream AI processing and use across modules.
Client must share data dictionaries or support mapping logic where schema is custom.
Mandatory Field Validation
Ensure critical fields (e.g., service date, CPT, payer, patient ID) are validated and standardized.
Data team to confirm and validate that required fields are consistently populated.
Agent Configuration
Adapt AI agents (e.g., DenialShield, FrontDeskIQ) to client workflows, EMR logic, and specific triggers/actions.
Client must share SOPs, escalation rules, routing rules, and customizations if any.
Bot Integration (API/RPA)
Configure bots for EMR interaction (read/write), EMR validation, task creation, and UI automation where required.
Client to provide bot user accounts and required system access with elevated privileges.
🧩 Phase 4: Customization & Adjustments
Execute non-standard development efforts tailored to client’s workflows and systems.
This phase is dedicated to executing all custom development items that fall outside the standard implementation scope. These are typically identified during the Assessment and Implementation phases and are essential to align the AI solution with the client’s unique operational workflows, systems, and strategic goals. Customization efforts may include additional EMR or third-party system integrations, advanced business rule logic, data remediation, non-standard SOP alignment, hybrid RPA configurations, and specialized reporting or automation features. The goal is to ensure the AI model performs optimally within the client's complex and evolving environment. Each customization requirement is documented, scoped, and delivered under a separate Statement of Work (SOW) with clearly defined deliverables, timelines, and cost structure. This approach provides transparency, ensures flexibility, and prevents delays in the core AI rollout. Key Benefits of This Phase: Extends the AI model’s capabilities to support unique business needs, Enables smoother EMR automation and intelligent workflow execution, Supports better change management with custom training and governance planning, Provides clients the flexibility to prioritize and phase custom work based on urgency and budget This phase allows Scale Healthcare to deliver a deeply tailored AI deployment while maintaining clarity and control around scope, timelines, and value.
✅ Phase 5: Client Testing & Validation
User Acceptance Testing (UAT) phase to validate functional, operational, and compliance readiness.
This phase ensures that the deployed AI solution performs reliably across the client's unique workflows, systems, and data scenarios. Through structured User Acceptance Testing (UAT), we validate that the AI models, automations, and UI/UX meet functional expectations, compliance needs, and operational readiness. It involves simulation of real-world use cases, system responsiveness checks, error handling validation, and feedback collection across departments. Scale facilitates this phase using a guided checklist, SME engagement, and collaborative issue resolution—ensuring smooth transition toward go-live.
🚀 Phase 6: Go-Live
Controlled deployment into production with support from Scale and client stakeholders.
This phase ensures the safe, stable, and coordinated deployment of the AI solution into the client's production environment. The Scale team works closely with client stakeholders to validate infrastructure readiness, deploy models, confirm workflow alignment, set up monitoring, and activate end-user access. Key activities include running dry-run simulations, issuing runbooks, coordinating internal communications, and assigning hypercare support teams. Additional responsibilities like rollback planning, compliance checks, UAT documentation, and final sign-off ensure risk-free execution and smooth transition. Client and Scale teams must align on final validations and have escalation paths, baseline metrics, and bot writebacks verified before full transition to production.
📈 Phase 7: Post-Launch Stabilization
Monitoring and tuning during early production usage.
This phase ensures the AI solution is performing optimally in the client’s live environment. Scale focuses on real-time monitoring, system tuning, issue resolution, and reinforcing training, while the client supports adoption, feedback, and internal escalation. All stabilization activities are tracked weekly, and outputs like updated SOPs, onboarding logs, and performance dashboards are shared. The phase concludes with client sign-off once usage is consistent, KPIs are validated, and the system is considered stable.
🔄 Phase 8: Handover
Transition of infrastructure, admin access, and operational control to client teams.
The Handover Phase is the formal transition of infrastructure, admin access, and operational control of the AI model from Scale Healthcare to the client. Its objective is to ensure the client’s internal IT and operations teams are fully equipped to manage the deployed solution independently, with all required tools, documentation, and support structures in place. This transition is governed by a detailed Handover Checklist, which outlines Scale’s responsibilities—such as delivering infrastructure credentials, admin setup, operational runbooks, and agent lifecycle documentation—as well as client responsibilities like validating access, reviewing documentation, and formally accepting system ownership. The checklist also ensures mutual accountability and is signed off once all items are complete. If the client is not fully prepared to take ownership—due to internal IT readiness or operational limitations—Scale offers a Managed Support SOW to temporarily retain infra access and oversight. In such cases, the handover can be staged in two parts: administrative access followed by full operational control once readiness is achieved. This phase ensures a clean, accountable, and flexible transfer, enabling long-term sustainability whether the client chooses internal management or ongoing support from Scale.
🛠️ Phase 9: Maintenance & Support
Ensures continued performance, security, and compatibility.
The Maintenance & Support phase begins post-handover and ensures that the AI solution remains stable, secure, and high performing in the client environment. Scale Healthcare provides proactive support, including regular model performance tuning, security patching, UI bug fixes, and monitoring for model drift. Compatibility with third-party systems is maintained, and all basic documentation, user support, and uptime tracking are handled within this phase. A detailed maintenance checklist governs what is included—along with specific update cycles—ensuring expectations are clear. If additional needs arise (like new agents, complex UI redesigns, or data expansion), these are treated as professional services and scoped separately under a new SOW. Operationally, support tickets are triaged via a structured process, and all maintenance activities are logged and reviewed periodically. This approach allows clients to operate with confidence while retaining flexibility to expand or evolve the solution with Scale’s support.
Maintenance & Support – Covered Services
| Category | Covered Services | Update Frequency / Notes |
|---|---|---|
| Model Drift Monitoring | Monitoring data inputs for signs of AI model drift | Monthly checks; quarterly adjustments if needed |
| Vulnerability Management | Security patching for known dependencies and frameworks | Monthly or as vulnerabilities are disclosed |
| Dependency Support | Managing compatibility with APIs, EMR versions, and third-party libraries | Tracked quarterly; urgent upgrades handled on case-by-case basis |
| UI Bug Fixes | Resolving usability issues, broken screens, or front-end errors | Bi-weekly patch window or within 3 business days of report |
| Performance Optimization | Tuning slow queries, memory issues, or response delays | Monthly performance audit or case-based response |
| Agent Runtime Monitoring | Uptime, error logs, alert triaging, and recovery | Real-time dashboard; weekly reporting |
| Basic Report Health Checks | Ensuring scheduled reports, KPIs, and dashboards run correctly | Monthly validation |
| Basic User Management | Helpdesk-level support for onboarding new users or resetting credentials | On-demand support (during business hours) |
| Standard Documentation | Updating SOPs, runbooks, and admin documentation for ongoing operations | Semi-annually or with major system updates |
Maintenance & Support – Not Covered (Examples)
| Category | Examples |
|---|---|
| New Agent Development | Creation of additional AI agents beyond current scope |
| Major UI/UX Enhancements | Redesigning workflows, building new screens, or introducing new feature sets |
| Custom Dashboard/Report Builds | Reports requiring new KPIs, cross-EMR logic, or client-specific logic |
| Advanced Retraining Requests | Model re-training due to strategic business shifts, not standard drift |
| Data Source Expansion | Adding new EMRs, billing systems, or third-party APIs not originally covered |
| Compliance Upgrades | Support for new regulatory frameworks or enhanced audit log implementation (e.g., SOC2, ISO) |
| Major Workflow Reconfigurations | Business rule rewrites due to change in org structure or operational practices |
| Disaster Recovery Planning | Setup of backup systems, geo-redundancy, or business continuity simulation |
| Client IT Ownership Support | Ongoing ops if client chooses not to take over infra after handover |
📂 Supporting Materials
- Excel checklists
- Dashboards
- Runbooks
- Performance trackers
- Issue and change logs
🔒 Final Notes
This playbook offers a transparent, scalable framework for AI deployment in healthcare. Adhering to the structured phases ensures clarity, accountability, and long-term success.
💬 Have Questions?
Please contact your Scale Healthcare delivery representative for guidance on any phase or customization scope.
