01
Frontend & User Experience
3 technologies
React 19TypeScriptTailwind CSS
MMHC
AI-Powered Clinical Documentation Review and Quality Assurance
ExpertsCloud developed MMHC, a clinical documentation quality management platform that combines AI-powered note validation, structured human review, role-based approvals, and audit logging. Built with React, AdonisJS, TypeScript, MySQL, and AWS AI services, the platform helps healthcare practices standardize note reviews, track recurring issues, and monitor documentation quality.
Technologies:AdonisJS (v6)Amazon BedrockAWSAWS SageMakerNode.jsReact NativeRemotionTailwind CSSTypeScript
Business Focus: Clinical Documentation QA
Case Study Summary
MMHC streamlines clinical note quality assurance through AI-assisted validation, structured human review, manager approvals, and centralized audit trails. The platform also supports recurring issue tracking, shared review templates, user management, and performance reporting.
Industry
Healthcare & HealthTech
Timeline
12 Months
Platform
Web App
Architecture
Role-Based AI Review Platform
Business Challenges
01
Inconsistent Clinical Documentation
Manual note preparation and inconsistent documentation practices make it difficult to maintain complete, accurate, and standardized session records.
02
Time-Consuming Review Processes
Reviewing every session note manually creates operational overhead and makes it harder to identify missing information, recurring issues, and potential billing code discrepancies efficiently.
03
Limited Review Visibility and Accountability
Practices need a structured process for assigning reviews, tracking corrections, recording approvals, and maintaining a clear history of who reviewed and approved each note.
04
Fragmented Quality Management
Managing reviewer guidance, recurring documentation issues, user permissions, and performance reports across disconnected workflows makes consistent quality oversight more difficult.
What ExpertsCloud Built
01
AI-Powered Note Validation
Implemented AI-assisted clinical note checks to identify missing information, assess documentation completeness, suggest billing codes, and flag common documentation issues for further review.
02
Structured Human Review and Approval
Built review queues that allow trained reviewers to validate AI findings, correct notes, flag issues, and submit reviewed documentation for manager or administrator approval.
03
Role-Based Access and Audit Logging
Developed role-specific workspaces for practitioners, reviewers, managers, administrators, and subject-matter experts, with access controls and audit records for significant changes.
Quality Analytics and Workflow Automation
Integrated background task processing, recurring issue tracking, shared review templates, notifications, and reporting capabilities to support ongoing documentation quality management.
How We Solved the Challenge
01
Centralized Clinical Note Intake
Enabled practitioners to submit session notes directly through the platform or receive documentation from a connected records system, bringing clinical records into a consistent workflow.
02
Automated AI Quality Checks
Connected AI services through AWS to assess note completeness, identify common issues, and provide suggested billing codes. Background queues handle longer-running tasks without blocking the user interface.
03
Human Review and Approval Workflows
Developed dedicated reviewer queues, correction and flagging actions, and manager approval workflows. Feedback and review outcomes are retained to support traceability and ongoing quality improvement.
04
Reporting and Reliable Background Processing
Implemented centralized MySQL records, Redis and BullMQ task queues, audit logging, and Google BigQuery reporting to support operational visibility, asynchronous processing, and review performance analysis.
Tech Stack
02
Backend & Data Processing
4 technologies
AdonisJS 6Node.jsTypeScriptMySQL
03
Cloud, AI & Quality Assurance
3 technologies
AWSAmazon BedrockAmazon S3
Before vs. After
Before
- Manual documentation quality checks
- Time-consuming note review and correction
- Limited visibility into recurring issues
- Fragmented approvals and quality reporting
VS
After
- AI-assisted documentation validation
- Structured reviewer queues and approvals
- Centralized issue tracking and guidance
- Auditable workflows and reporting
Results & Outcomes
Improving Clinical Documentation Quality and Review Efficiency
- AI-assisted validation helps identify incomplete notes, common issues, and potential billing code discrepancies before final approval.
- Structured queues and role-based workflows create a consistent path from note submission through AI checks, human review, and final sign-off.
- Centralized audit logs and recorded review decisions help practices trace documentation changes, reviewer findings, and approval actions.
- Centralized issue tracking, shared templates, background processing, and reporting capabilities support ongoing quality monitoring and workflow management.
FREQUENTLY ASKED QUESTIONS
MMHC is a healthcare documentation quality management platform that combines AI-assisted note checks, human review, administrative approvals, and audit logging to help practices manage clinical session notes.
Notes are submitted by practitioners or received from a connected records system. AI checks for completeness, common issues, and suggested billing codes. Reviewers then validate findings, correct or flag notes, and submit them for final approval.
The platform uses React 19, TypeScript, AdonisJS 6, Node.js, MySQL, Redis, BullMQ, AWS AI services including Amazon Bedrock and SageMaker, Amazon S3, and Google BigQuery.
Role-based access controls, review queues, recorded corrections, approval workflows, and audit logs provide traceability across significant documentation and review actions.