$mcs

Automating The Healthcare Industry with Swarms of Agents

The Healthcare Crisis

Current Industry Challenges
Critical issues facing healthcare coding

Medical coding errors cost healthcare $25B annually, with a 9.4% error rate

250,000 preventable deaths yearly due to medical errors

Average coding time of 13 minutes per case creates massive backlogs

Hospitals spend $3.8M yearly on coding with $45-50 cost per error fix

Impact Analysis
The cost of inefficient medical coding

Sequential Swarm Architecture

Input Processing

Medical records are securely ingested and normalized

Sequential Analysis

Each agent processes the data in a coordinated sequence

Verification

Results are cross-validated for accuracy

Code Generation

Final ICD-10 codes are generated and validated

Intelligent Agent Network

Chief Medical Officer Agent
Coordinates the diagnostic process and ensures medical accuracy
  • Medical knowledge validation
  • Diagnostic oversight
  • Quality assurance
Medical Coder Agent
Specializes in ICD-10 code assignment and verification
  • ICD-10 code mapping
  • Code validation
  • Compliance checking
Synthesizer Agent
Combines and validates multiple agent outputs
  • Cross-reference validation
  • Consistency checking
  • Final report generation
HIPAA Compliance & Security
Enterprise-grade security and compliance measures

Data Protection

  • End-to-end encryption
  • Secure key management

Access Control

  • Role-based access
  • Audit logging

Compliance

  • HIPAA certified
  • Secure infrastructure

Developer Integration

REST API
Simple HTTP integration for any stack
curl -X POST https://api.mcs.swarms.world/v1/code \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"diagnosis": "Type 2 diabetes mellitus with diabetic nephropathy"}'
Python SDK
Native Python integration
from mcs import MCSClient

patient_case = """
Patient: 45-year-old White Male
Location: New York, NY

Lab Results:
- egfr 
- 59 ml / min / 1.73
- non african-american
"""

with MCSClient() as client:
    response = client.run_medical_coder("P123", patient_case)
    print(response)
High Availability
  • 99.99% uptime SLA
  • Global edge network
  • Automatic failover
AI-Powered
  • Advanced ML models
  • Continuous learning
  • Adaptive processing
Enterprise Ready
  • HIPAA compliant
  • Rate limiting
  • Usage analytics

Join Our Community

Tokenomics

Token Distribution
Total Supply: 1,000,000,000 $MCS
Team
10% of total supply

Core team allocation

Treasury
10% of total supply

6 month vesting period

Operations
10% of total supply

Operational expenses

Liquidity Pool
70% of total supply

Liquidity Pool Open for purchase

Smart Contract Address:

ALHFgnXSenUv17GMdf3dL9gtFW2KKQTz9avpM2Wypump