async def process_healthcare_revenue_cycle(patient_encounter):
┌─────────────────────────────────────┐
│ [ML] ORCHESTRATOR.initialize() │
└─────────────────┬───────────────────┘
│
▼
pre_visit_tasks = [
await SchedulingAgent.optimize_appointment(patient_encounter),
await NoShowPredictor.calculate_risk_score(patient_encounter),
await PriorAuthAgent.check_authorization(patient_encounter)
]
if pre_visit_tasks[1].risk_score > 0.7:
await InterventionSystem.execute_prevention_protocol()
class SchedulingAgent:
@optimization_algorithm
def optimize_appointment(self, patient):
┌─────────────────────────────┐
│ INPUT: patient_preferences │
└────────────┬────────────────┘
│
┌────────────▼────────────────┐
│ PROCESS: ML optimization │
│ - Resource allocation │
│ - Wait time minimization │
│ - Provider matching │
└────────────┬────────────────┘
│
┌────────────▼────────────────┐
│ OUTPUT: optimal_slot │
└─────────────────────────────┘
constraints = {
'provider_availability': self.get_provider_slots(),
'patient_preferences': patient.preferences,
'resource_capacity': self.check_resources()
}
return OptimizationEngine.solve(constraints)
class MedicalCodingAgent:
@transformer_algorithm
def auto_code_encounter(self, clinical_notes):
┌──────────────────────────────────┐
│ [BERT] Clinical Text Analysis │
├──────────────┬───────────────────┤
│ Diagnoses │ Procedures │
└──────┬───────┴────────┬──────────┘
│ │
┌──────▼───────┐ ┌──────▼───────┐
│ ICD-10 Map │ │ CPT Map │
└──────┬───────┘ └──────┬───────┘
│ │
┌──────▼────────────────▼───────┐
│ Validated Code Output │
└───────────────────────────────┘
embeddings = self.bert_model.encode(clinical_notes)
entities = self.ner_model.extract_medical_entities(embeddings)
return {
'icd10': self.map_to_icd10(entities.diagnoses),
'cpt': self.map_to_cpt(entities.procedures),
'confidence': entities.confidence_scores
}
async def execute_visit_phase(encounter):
┌─────────────────┐
│ Visit Encounter │
└────────┬────────┘
│
┌────────────┼────────────┬────────────┐
▼ ▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐
│Patient │ │Insurance│ │Medical │ │Charge │
│Reg │ │Verify │ │Coding │ │Capture │
└─────────┘ └─────────┘ └─────────┘ └─────────┘
tasks = await asyncio.gather(
PatientRegAgent.validate(encounter),
InsuranceAgent.verify(encounter),
CodingAgent.auto_code(encounter),
ChargeAgent.capture(encounter)
)
return VisitResults.compile(tasks)
class DenialManagementAgent:
@reinforcement_learning
def process_denial(self, denial):
┌────────────────────────────────────┐
│ DENIAL RECEIVED │
└─────────────┬──────────────────────┘
│
┌─────────────▼──────────────────────┐
│ [ML] Classify Denial Reason │
│ • Medical necessity │
│ • Authorization │
│ • Coding error │
│ • Timely filing │
└─────────────┬──────────────────────┘
│
┌─────────────▼──────────────────────┐
│ [RL] Select Optimal Action │
│ Based on historical success │
└─────────────┬──────────────────────┘
│
┌─────────────▼──────────────────────┐
│ Generate & Submit Appeal │
└────────────────────────────────────┘
state = self.extract_state_features(denial)
action = self.q_network.predict_best_action(state)
appeal = self.appeal_generator.create(action)
self.q_network.update(state, action, reward)
return appeal
def intelligent_error_handler(agent_name, error):
┌──────────────────────┐
│ ERROR DETECTED │
└──────────┬───────────┘
│
┌──────────▼───────────┐
│ Classify Error Type │
└──────────┬───────────┘
│
┌──────────▼───────────────────────┐
│ if recoverable: │
│ → Retry with backoff │
│ elif partial_failure: │
│ → Continue with degraded mode │
│ else: │
│ → Escalate to human review │
└──────────────────────────────────┘
try:
return fallback_strategies[agent_name](error)
except:
return HumanReviewQueue.add(error)
METRICS = {
'throughput': 'claims/hour',
'accuracy': 'error_rate < 0.01',
'latency': 'p99 < 100ms',
'cost_savings': 'automation_rate > 95%'
}