Transforming Medicaid & Public Health with AI
Discovering the Art of the Possible with Maryland Department of Health.
RFI BPM052131
Originally issued by Maryland Department of Health, Office of Enterprise Technology
The Maryland Department of Health (MDH) seeks to discover the “Art of the Possible” in Medicaid programs and broader operations through innovative AI Agents, specialized Large Language Model (LLM), Robotic Process Automation (RPA), Process Mining(PM), Communications Mining(CM), and Artificial Intelligence (AI) solutions.
In response to RFI BPM052131, we showcase specific use cases demonstrating the art of the possible related to Maryland’s Medicaid system, replacing legacy COBOL-based infrastructure (e.g., MMIS), and enhancing public health and IT operations. We show that AI and Machine Learning can be harnessed to reduce costs, improve efficiency, and ensure compliance with HIPAA, Federal Laws, and Maryland state laws.
Each use case below demonstrates functionality using synthetic data.
Use Case 13 RPA for Data Entry and Record Reconciliation
Solution Description and Purpose
Pinnacle’s Healthcare Data Reconciliation RPA is an intelligent automation platform specifically designed for Maryland’s complex healthcare data environment. Our solution automates the extraction, validation, and reconciliation of patient records, grants data, and administrative information across multiple MDH systems, significantly reducing manual processing time while ensuring data accuracy and HIPAA compliance.
The platform leverages advanced OCR capabilities, machine learning validation algorithms, and real-time database integration to process unstructured documents (PDFs, scanned forms, emails) and seamlessly update target databases with validated information.
Technical Details
The solution implements a dual-path extraction pipeline optimized for reliability in on-premises Medicaid environments: a primary LLM extraction path that enforces JSON-only responses with strict schema validation; and a deterministic fallback path using PDF text parsing, medical regex patterns, and NLP to guarantee field coverage when LLM output is incomplete or non-JSON, ensuring continuous operation even during modeldrift or network constraints.
All extractions are normalized into a canonical schema: patientName (string), dateOfBirth (ISO‑8601), medicaidId (MD+[9–12 digits] pattern), diagnosis (string), procedures (array of strings), claimAmount (standardized currency), and provider (string), enabling idempotent database updates and high-fidelity reconciliation across target systems.
Per-field confidence scores combine model confidence with rule-based validators (format checks for dates and currency, ICD‑10/CPT detection, Medicaid ID patterns, and cross-field plausibility between diagnosis and procedure), with configurable thresholds that route low-confidence records to human review for auditability and quality control.
Architecture: On-premises deployment with secure cloud integration options; Logical Architecture (on-premises first): Ingestion (PDF/text) → Extraction Service (medical LLM with JSON-only prompt discipline and schema validation) → Validation Layer (format, code sets, cross-field rules) → Compliance Engine (policy packs and evidence generation) → Connectors (secure adapters for EHR/databases and ticketing/reporting) → Audit Log Store (immutable) → Operations UI for monitoring and human-in-the-loop reviews.
Integration: Native connectivity to current systems, databases, and document management systems
Compliance: Full HIPAA compliance with encryption, audit logging, and role-based access controls
Technology Stack: .NET Core, Python ML libraries, OCR engines, SQL Server/PostgreSQL compatibility
Security: End-to-end encryption, secure API interfaces, comprehensive audit trails
Demonstration Video
Our demonstration showcases:
- PDF Data Extraction: RPA bot automatically extracts patient demographics, financial information, and clinical data from mock healthcare PDFs
- Intelligent Validation: Machine learning algorithms validate extracted data against business rules and data quality standards
- Database Integration: Seamless update of multiple healthcare databases with validated information
- Error Handling: Automatic flagging of discrepancies and routing to human reviewers
- Compliance Reporting: Generation of audit reports and compliance documentation
Video Access: Click here to download
Service Level Agreements
- Accuracy Rate: 99.5% data extraction accuracy guaranteed
- Processing Speed: 500+ documents per hour per bot instance
- Uptime: 99.9% system availability
- Response Time: < 2 seconds for data validation processes
- Support: 24/7 technical support with 4-hour response SLA
Licensing and Pricing Structure
Licensing Model: Per-bot subscription with volume discounts
Pricing Drivers:
- Number of concurrent bot instances
- Volume of documents processed monthly
- Integration complexity (number of source/target systems)
- Customization requirements
- Support level (standard vs. premium)
Structure: [Base platform fee] + [per-bot licensing] + [optional professional services for customization and integration]
Use Case 15 AI Agent for Regulatory Compliance Monitoring
Solution Description and Purpose
Pinnacle’s Intelligent Compliance Monitoring Agent is a sophisticated AI-powered platform that continuously monitors MDH operations for regulatory compliance across HIPAA, FISMA, CMS guidelines, and Maryland state regulations. The system proactively identifies compliance gaps, generates actionable insights, and provides real-time alerts to prevent violations before they occur.
Our AI agent employs natural language processing, pattern recognition, and predictive analytics to analyze operational processes, audit logs, documentation, and staff activities, ensuring comprehensive compliance coverage across all MDH departments and functions.
Technical Details
The Compliance Rules Engine applies versioned policy packs spanning HIPAA privacy safeguards, CMS documentation completeness, and Maryland state requirements, generating evidence-linked findings and audit-ready justifications for each rule evaluation.
Alerts are standardized by severity (LOW/MEDIUM/HIGH/CRITICAL) with owner routing, SLA timers, and escalation workflows, and are summarized into an overall Compliance Score (0–100) with drill-downs to remediation steps and policy references.
Architecture: On-premises deployment with secure cloud integration options; Logical Architecture (on-premises first): Ingestion (PDF/text) → Extraction Service (medical LLM with JSON-only prompt discipline and schema validation) → Validation Layer (format, code sets, cross-field rules) → Compliance Engine (policy packs and evidence generation) → Connectors (secure adapters for EHR/databases and ticketing/reporting) → Audit Log Store (immutable) → Operations UI for monitoring and human-in-the-loop reviews.
Deployment: Hybrid architecture supporting both on-premises and secure cloud deployment
Integration: RESTful APIs connecting to audit systems, document management, and workflow systems
Compliance: HIPAA, SOC 2 Type II compliant with FedRAMP-ready architecture
AI Technology: Custom-trained models for healthcare compliance, NLP engines, anomaly detection algorithms
Monitoring: Real-time process monitoring with configurable alerting and escalation workflows
Demonstration Video
Our demonstration includes:
- Process Auditing: AI agent automatically reviews mock healthcare processes and identifies potential compliance issues
- Violation Detection: Real-time identification of HIPAA privacy violations and CMS regulatory gaps
- Risk Assessment: Automated calculation of compliance risk scores and impact analysis
- Alert Generation: Immediate notification system with prioritization and routing capabilities
- Report Creation: Comprehensive compliance reports with remediation recommendations
Video Access: Click here to download
Service Level Agreements
- Detection Accuracy: 95% compliance issue identification rate
- Alert Response Time: Real-time alerts with < 30-second notification delivery
- False Positive Rate: < 5% to minimize alert fatigue
- Report Generation: Automated reports within 24 hours of audit completion
- System Uptime: 99.95% availability with redundant monitoring
Licensing and Pricing Structure
Licensing Model: Per-bot subscription with volume discounts
Pricing Drivers:
- Number of concurrent bot instances
- Volume of documents processed monthly
- Integration complexity (number of source/target systems)
- Customization requirements
- Support level (standard vs. premium)
Structure: [Base platform fee] + [per-bot licensing] + [optional professional services for customization and integration]
Custom AI Solutions.
insight Extraction
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Benefit from our complimentary, commitment-free review of your complex data challenge. We’ll conduct a comprehensive analysis of your issue and propose ways that AI can help solve it.
Frequently Asked Questions
The best first step for your business is to identify a specific, well-defined challenge or process you want to improve. Pinnacle Thrive Solutions can conduct an initial AI consulting assessment to understand your needs and data. We’ll then recommend practical AI solutions that offer clear benefits and a strong return on investment, guiding you through a seamless integration process tailored for local businesses.
Many DC and Baltimore businesses struggle with data overload. AI is incredibly powerful at analyzing vast, complex datasets far faster than humans. Pinnacle Thrive Solutions uses AI to extract actionable insights from your data, identifying hidden patterns, trends, and opportunities. This helps your local business make smarter, data-driven decisions for everything from marketing to operations.
Absolutely. AI excels at predictive modeling, using historical data to forecast future outcomes with high accuracy. For your business, this means AI can help predict sales trends, optimize inventory levels to reduce waste, and even forecast demand for your services. This proactive approach allows you to make better strategic decisions and enhance efficiency.
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