Clinical Decision Support Agent
Build an AI agent that performs real-time drug safety checks during prescription workflows. Autonomous contraindication detection, interaction warnings, and dosage verification with complete FDA traceability.
The Problem
Clinical Workflows Need Real-Time Safety Verification
Manual lookup is too slow: Clinicians can't search FDA labels manually for every prescription. Real-time decisions need instant verification.
Generic drug databases lack context: Static databases show interactions but don't reason about patient-specific scenarios or provide FDA evidence.
Liability concerns: Incorrect safety checks lead to medication errors, adverse events, and malpractice exposure. Need verifiable decision support.
No audit trail: When adverse events occur, impossible to prove what information the system provided at decision time.
The Solution
AI Agent with Lemma Reasoning API
Deploy an autonomous AI agent that queries Lemma's Reasoning API in real-time during prescription workflows. The agent checks contraindications, drug interactions, and warnings with structured FDA evidence — all in under 2 seconds.
Instant safety checks with multi-hop reasoning over FDA data
Structured results with severity levels and relationship types
Complete FDA evidence for every warning with fingerprints
Automatic audit trail for compliance and liability protection
How It Works
Complete Integration Flow
Agent Receives Patient Context
Agent Queries Lemma Reasoning API
{
"question": "Does warfarin interact with aspirin?"
}Lemma Returns Structured Relationship
{
"results": [
{
"drug": {
"name": "warfarin",
"brand_names": ["Coumadin"]
},
"relationship": {
"type": "interaction",
"condition": "increased bleeding risk with NSAIDs and antiplatelet agents",
"severity": "serious"
},
"evidence": [
{
"text": "NSAIDs and antiplatelet agents increase the risk of bleeding...",
"fingerprint": "11001100110011001100110011001100...",
"section": "Drug Interactions"
}
]
}
]
}Agent Generates Alert
⚠️ SERIOUS DRUG INTERACTION DETECTED
Warfarin interacts with patient's current medications (aspirin, ibuprofen). Increased bleeding risk, especially with history of GI bleeding.
FDA Evidence:
"NSAIDs and antiplatelet agents increase the risk of bleeding..."
[fp:11001100110011001100110011001100...]
Implementation Code
import requests
from typing import List, Dict
LEMMA_API_KEY = "your_api_key"
class ClinicalSafetyAgent:
"""
Autonomous agent for real-time drug safety checks.
Queries Lemma Reasoning API to verify prescriptions.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.lemma.la/v1"
def check_prescription(self, new_drug: str, current_meds: List[str],
patient_history: Dict) -> Dict:
"""
Perform comprehensive safety check for new prescription.
Returns: alerts with severity levels and FDA evidence.
"""
alerts = []
# Check 1: Drug interactions with current medications
for med in current_meds:
interaction = self._check_interaction(new_drug, med)
if interaction:
alerts.append(interaction)
# Check 2: Contraindications based on patient history
for condition in patient_history.get('conditions', []):
contraindication = self._check_contraindication(new_drug, condition)
if contraindication:
alerts.append(contraindication)
# Prioritize by severity
alerts.sort(key=lambda x: self._severity_score(x['severity']), reverse=True)
return {
"drug": new_drug,
"safe": len(alerts) == 0,
"alerts": alerts,
"audit_trail": self._generate_audit_trail(alerts)
}
def _check_interaction(self, drug1: str, drug2: str) -> Dict:
"""Check if two drugs interact using Reasoning API."""
response = requests.post(
f"{self.base_url}/graph",
headers={
"x-api-key": self.api_key,
"Content-Type": "application/json"
},
json={
"question": f"Does {drug1} interact with {drug2}?"
}
)
response_data = response.json()
data = response_data.get('data', response_data)
if data.get('results'):
result = data['results'][0]
return {
"type": "interaction",
"severity": result['relationship']['severity'],
"condition": result['relationship']['condition'],
"evidence": result['evidence'],
"recommendation": self._generate_recommendation(result)
}
return None
def _check_contraindication(self, drug: str, condition: str) -> Dict:
"""Check if drug is contraindicated for condition."""
response = requests.post(
f"{self.base_url}/graph",
headers={
"x-api-key": self.api_key,
"Content-Type": "application/json"
},
json={
"question": f"Is {drug} contraindicated with {condition}?"
}
)
response_data = response.json()
data = response_data.get('data', response_data)
if data.get('results'):
result = data['results'][0]
return {
"type": "contraindication",
"severity": result['relationship']['severity'],
"condition": result['relationship']['condition'],
"evidence": result['evidence']
}
return None
def _severity_score(self, severity: str) -> int:
"""Convert severity to numeric score for prioritization."""
scores = {
"life_threatening": 4,
"serious": 3,
"moderate": 2,
"mild": 1
}
return scores.get(severity, 0)
def _generate_recommendation(self, result: Dict) -> str:
"""Generate clinical recommendation based on finding."""
severity = result['relationship']['severity']
if severity in ['life_threatening', 'serious']:
return "DO NOT PRESCRIBE - Consider alternative medication"
elif severity == 'moderate':
return "CAUTION - Monitor patient closely if prescribed"
else:
return "AWARE - Inform patient of potential interaction"
def _generate_audit_trail(self, alerts: List[Dict]) -> List[Dict]:
"""Generate audit trail with fingerprints for compliance."""
return [
{
"alert_type": alert['type'],
"severity": alert['severity'],
"fingerprints": [e['fingerprint'] for e in alert['evidence']],
"timestamp": datetime.now().isoformat()
}
for alert in alerts
]
# Example usage
agent = ClinicalSafetyAgent(LEMMA_API_KEY)
result = agent.check_prescription(
new_drug="warfarin",
current_meds=["aspirin", "ibuprofen"],
patient_history={
"conditions": ["gi bleeding", "atrial fibrillation"],
"age": 72
}
)
if not result['safe']:
print(f"⚠️ ALERTS DETECTED: {len(result['alerts'])}")
for alert in result['alerts']:
print(f" • {alert['severity'].upper()}: {alert['condition']}")
print(f" Recommendation: {alert['recommendation']}")
else:
print("✓ No safety concerns detected")Why This Works
Ready to Build?
Deploy clinical decision support agents that verify prescriptions in real-time with complete FDA traceability.