第18章 医疗健康MCP应用
前言
健康关系生命。本章展示如何通过MCP将Claude集成到医疗健康系统中，实现临床决策支持、患者健康管理和医疗数据分析的智能化升级，同时严格遵循HIPAA等医疗隐私法规。
18.1 案例1：临床决策支持系统
18.1.1 应用场景
graph TB
A["患者信息"] --> B["病历系统"]
A --> C["检查报告"]
A --> D["用药记录"]
E["医学知识"] --> E1["临床指南"]
E --> E2["医学文献"]
E --> E3["最佳实践"]
F["Claude临床助手"] --> F1["病情分析"]
F --> F2["诊断建议"]
F --> F3["用药检查"]
F --> F4["治疗方案"]
B --> F
C --> F
D --> F
E1 --> F
E2 --> F
E3 --> F
F --> G["医生决策支持"]
18.1.2 医疗数据模型
from typing import Dict, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import hashlib
class MedicationWarningLevel(Enum):
"""药物警告等级"""
NORMAL = "normal"
WARNING = "warning"
CRITICAL = "critical"
CONTRAINDICATED = "contraindicated"
@dataclass
class Patient:
"""患者档案（已脱敏）"""
patient_id: str # 使用hash而不是真实ID
age: int
gender: str
weight: float # kg
height: float # cm
allergies: List[str] = field(default_factory=list)
comorbidities: List[str] = field(default_factory=list)
current_medications: List[str] = field(default_factory=list)
@dataclass
class ClinicalRecord:
"""临床记录"""
record_id: str
patient_id: str
visit_date: datetime
chief_complaint: str
vital_signs: Dict[str, float]
physical_exam: str
assessment: str
plan: str
icd_codes: List[str] = field(default_factory=list)
class ClinicalDecisionSupport:
"""临床决策支持系统"""
def __init__(self):
self.patients: Dict[str, Patient] = {}
self.records: Dict[str, ClinicalRecord] = {}
self.clinical_guidelines: Dict[str, Dict] = {}
self.drug_interactions: Dict[str, List[str]] = {}
async def analyze_patient_condition(self, patient_id: str,
chief_complaint: str) -> Dict:
"""
分析患者病情
Args:
patient_id: 患者ID
chief_complaint: 主诉
Returns:
病情分析结果
"""
patient = self.patients.get(patient_id)
if not patient:
return {"error": "Patient not found"}
# 基于主诉和患者信息的初步分析
keywords = chief_complaint.lower().split()
differential_diagnoses = []
# 简单的关键词匹配（实际应使用ML模型）
condition_keywords = {
"chest pain": ["心肌梗死", "心绞痛", "肺栓塞"],
"shortness": ["哮喘", "肺炎", "心力衰竭"],
"fever": ["感染", "流感", "细菌感染"],
"headache": ["偏头痛", "紧张性头痛", "脑膜炎"],
"abdominal": ["胆囊炎", "阑尾炎", "胃溃疡"]
}
for key, diagnoses in condition_keywords.items():
if key in chief_complaint.lower():
differential_diagnoses.extend(diagnoses)
# 考虑患者既往史
relevant_history = [c for c in patient.comorbidities
if any(d.startswith(c[:3]) for d in differential_diagnoses)]
return {
"patient_id": patient_id,
"chief_complaint": chief_complaint,
"patient_age": patient.age,
"patient_comorbidities": patient.comorbidities,
"differential_diagnoses": list(set(differential_diagnoses))[:5],
"relevant_history": relevant_history,
"recommended_tests": self._recommend_tests(chief_complaint),
"clinical_guidelines": self._get_relevant_guidelines(chief_complaint),
"confidence": "medium"
}
def _recommend_tests(self, complaint: str) -> List[str]:
"""推荐检查"""
tests_map = {
"chest pain": ["ECG", "Troponin", "Chest X-ray", "CT angiography"],
"shortness": ["Chest X-ray", "ABG", "D-dimer", "Echocardiogram"],
"fever": ["CBC", "Blood culture", "Chest X-ray"],
"headache": ["CT head", "Lumbar puncture", "MRI"],
"abdominal": ["Abdominal ultrasound", "CT abdomen", "Lipase"]
}
for key, tests in tests_map.items():
if key in complaint.lower():
return tests
return ["Labs", "Imaging"]
def _get_relevant_guidelines(self, complaint: str) -> List[Dict]:
"""获取相关临床指南"""
return [
{
"guideline": "AHA/ACC Chest Pain Guidelines",
"url": "guideline_reference",
"relevance": "high"
},
{
"guideline": "ACCP VTE Guidelines",
"url": "guideline_reference",
"relevance": "medium"
}
]
async def check_medication_safety(self, patient_id: str,
proposed_medication: str,
dose: float) -> Dict:
"""
检查药物安全性
Args:
patient_id: 患者ID
proposed_medication: 提议的药物
dose: 剂量
Returns:
药物安全检查结果
"""
patient = self.patients.get(patient_id)
if not patient:
return {"error": "Patient not found"}
warnings = []
contraindications = []
# 检查过敏
if proposed_medication.lower() in [a.lower() for a in patient.allergies]:
contraindications.append(f"患者对{proposed_medication}过敏")
# 检查药物相互作用
for current_med in patient.current_medications:
if proposed_medication in self.drug_interactions.get(current_med, []):
warnings.append(f"{proposed_medication}与{current_med}存在相互作用")
# 检查剂量（简化版）
dose_limits = {
"aspirin": {"min": 75, "max": 500},
"metformin": {"min": 500, "max": 2000},
"lisinopril": {"min": 10, "max": 80}
}
limits = dose_limits.get(proposed_medication.lower())
if limits:
if dose < limits["min"] or dose > limits["max"]:
warnings.append(f"剂量{dose}mg可能超出推荐范围")
# 检查肾功能和肝功能（简化版）
if patient.age > 65:
warnings.append("患者年龄>65岁，需考虑老年患者用药特点")
# 确定警告等级
if contraindications:
level = MedicationWarningLevel.CONTRAINDICATED
elif len(warnings) > 2:
level = MedicationWarningLevel.CRITICAL
elif warnings:
level = MedicationWarningLevel.WARNING
else:
level = MedicationWarningLevel.NORMAL
return {
"patient_id": patient_id,
"proposed_medication": proposed_medication,
"proposed_dose": dose,
"warning_level": level.value,
"warnings": warnings,
"contraindications": contraindications,
"recommendation": "禁忌" if contraindications else "谨慎使用" if warnings else "安全"
}
async def generate_treatment_plan(self, patient_id: str,
diagnosis: str) -> Dict:
"""
生成治疗方案
Args:
patient_id: 患者ID
diagnosis: 诊断
Returns:
治疗方案
"""
patient = self.patients.get(patient_id)
if not patient:
return {"error": "Patient not found"}
# 生成基于诊断的治疗方案
treatment_options = {
"高血压": {
"first_line": ["Lisinopril 10mg", "Amlodipine 5mg"],
"lifestyle": ["减钠摄入", "规律运动", "压力管理"],
"monitoring": ["每周测血压", "3个月后复查"]
},
"糖尿病": {
"first_line": ["Metformin 500mg", "GLP-1受体激动剂"],
"lifestyle": ["控制饮食", "规律锻炼", "血糖监测"],
"monitoring": ["每周测血糖", "3个月后测HbA1c"]
}
}
plan = treatment_options.get(diagnosis, {
"first_line": ["根据具体情况选择"],
"lifestyle": ["健康生活方式改变"],
"monitoring": ["定期随访"]
})
return {
"patient_id": patient_id,
"diagnosis": diagnosis,
"treatment_plan": {
"pharmacological": plan.get("first_line", []),
"lifestyle": plan.get("lifestyle", []),
"monitoring": plan.get("monitoring", []),
"follow_up": "2周后门诊随访"
},
"contraindications": patient.allergies,
"baseline_vitals": {"BP": "140/90", "HR": 75}
}
class ClinicalMCPServer:
"""临床MCP服务器"""
def __init__(self, cds: ClinicalDecisionSupport):
self.cds = cds
def get_tools(self) -> List[Dict]:
"""定义工具"""
return [
{
"name": "analyze_condition",
"description": "分析患者病情",
"inputSchema": {
"type": "object",
"properties": {
"patient_id": {"type": "string"},
"chief_complaint": {"type": "string"}
},
"required": ["patient_id", "chief_complaint"]
}
},
{
"name": "check_medication",
"description": "检查药物安全性",
"inputSchema": {
"type": "object",
"properties": {
"patient_id": {"type": "string"},
"medication": {"type": "string"},
"dose": {"type": "number"}
},
"required": ["patient_id", "medication", "dose"]
}
},
{
"name": "treatment_plan",
"description": "生成治疗方案",
"inputSchema": {
"type": "object",
"properties": {
"patient_id": {"type": "string"},
"diagnosis": {"type": "string"}
},
"required": ["patient_id", "diagnosis"]
}
}
]
async def call_tool(self, tool_name: str, arguments: Dict) -> str:
"""调用工具"""
import json
try:
if tool_name == "analyze_condition":
result = await self.cds.analyze_patient_condition(
arguments["patient_id"],
arguments["chief_complaint"]
)
elif tool_name == "check_medication":
result = await self.cds.check_medication_safety(
arguments["patient_id"],
arguments["medication"],
arguments["dose"]
)
elif tool_name == "treatment_plan":
result = await self.cds.generate_treatment_plan(
arguments["patient_id"],
arguments["diagnosis"]
)
else:
return json.dumps({"error": f"Unknown tool: {tool_name}"})
return json.dumps(result, ensure_ascii=False, indent=2)
except Exception as e:
return json.dumps({"error": str(e)})
18.2 案例2：患者健康管理系统
18.2.1 个人健康数据管理
@dataclass
class HealthRecord:
"""健康记录"""
record_id: str
patient_id: str
record_date: datetime
metrics: Dict[str, float] # BP, Weight, Blood Sugar等
symptoms: List[str]
medications_taken: List[str]
class PatientHealthManager:
"""患者健康管理"""
def __init__(self):
self.health_records: Dict[str, List[HealthRecord]] = {}
self.health_goals: Dict[str, Dict] = {}
async def track_vital_signs(self, patient_id: str,
vitals: Dict[str, float]) -> Dict:
"""
追踪生命体征
Args:
patient_id: 患者ID
vitals: 生命体征字典
Returns:
追踪结果
"""
if patient_id not in self.health_records:
self.health_records[patient_id] = []
record = HealthRecord(
record_id=f"HR_{len(self.health_records[patient_id])}",
patient_id=patient_id,
record_date=datetime.now(),
metrics=vitals,
symptoms=[],
medications_taken=[]
)
self.health_records[patient_id].append(record)
# 分析趋势
alerts = self._check_vital_alerts(vitals)
return {
"patient_id": patient_id,
"recorded_vitals": vitals,
"timestamp": record.record_date.isoformat(),
"alerts": alerts,
"trend": self._analyze_vital_trends(patient_id)
}
def _check_vital_alerts(self, vitals: Dict[str, float]) -> List[str]:
"""检查生命体征告警"""
alerts = []
# 血压告警
if "systolic_bp" in vitals:
if vitals["systolic_bp"] > 160:
alerts.append("收缩压过高，建议立即就医")
elif vitals["systolic_bp"] > 140:
alerts.append("收缩压升高，需要监测")
# 血糖告警
if "blood_glucose" in vitals:
if vitals["blood_glucose"] > 300:
alerts.append("血糖危险升高")
elif vitals["blood_glucose"] < 70:
alerts.append("血糖过低")
# 心率告警
if "heart_rate" in vitals:
if vitals["heart_rate"] > 120:
alerts.append("心率过快")
elif vitals["heart_rate"] < 50:
alerts.append("心率过缓")
return alerts
def _analyze_vital_trends(self, patient_id: str) -> Dict:
"""分析生命体征趋势"""
records = self.health_records.get(patient_id, [])
if len(records) < 2:
return {"status": "insufficient_data"}
# 获取最近两条记录
recent = records[-1].metrics
previous = records[-2].metrics
trends = {}
for key in recent:
if key in previous:
change = recent[key] - previous[key]
trend = "increasing" if change > 0 else "decreasing"
trends[key] = {
"trend": trend,
"change": f"{abs(change):.1f}"
}
return trends
async def get_health_recommendations(self, patient_id: str) -> Dict:
"""
获取健康建议
Args:
patient_id: 患者ID
Returns:
健康建议
"""
records = self.health_records.get(patient_id, [])
if not records:
return {"error": "No health records found"}
latest = records[-1]
recommendations = []
# 基于最新记录生成建议
if latest.metrics.get("systolic_bp", 0) > 130:
recommendations.append("需要控制盐摄入和加强运动")
if latest.metrics.get("weight", 0) > 90: # 示例体重
recommendations.append("建议健康饮食和规律运动")
if latest.metrics.get("blood_glucose", 0) > 125:
recommendations.append("需要咨询营养师和内分泌科医生")
return {
"patient_id": patient_id,
"latest_record": latest.record_date.isoformat(),
"recommendations": recommendations,
"next_checkup": "1周后"
}
class HealthMCPServer:
"""健康MCP服务器"""
def __init__(self, manager: PatientHealthManager):
self.manager = manager
def get_tools(self) -> List[Dict]:
"""定义工具"""
return [
{
"name": "track_vitals",
"description": "追踪生命体征",
"inputSchema": {
"type": "object",
"properties": {
"patient_id": {"type": "string"},
"vitals": {"type": "object"}
},
"required": ["patient_id", "vitals"]
}
},
{
"name": "health_recommendations",
"description": "获取健康建议",
"inputSchema": {
"type": "object",
"properties": {
"patient_id": {"type": "string"}
},
"required": ["patient_id"]
}
}
]
async def call_tool(self, tool_name: str, arguments: Dict) -> str:
"""调用工具"""
import json
try:
if tool_name == "track_vitals":
result = await self.manager.track_vital_signs(
arguments["patient_id"],
arguments["vitals"]
)
elif tool_name == "health_recommendations":
result = await self.manager.get_health_recommendations(
arguments["patient_id"]
)
else:
return json.dumps({"error": f"Unknown tool: {tool_name}"})
return json.dumps(result, ensure_ascii=False, indent=2)
except Exception as e:
return json.dumps({"error": str(e)})
18.3 医疗隐私与合规
class HealthcarePrivacyManager:
"""医疗隐私管理"""
def __init__(self):
self.audit_logs: List[Dict] = []
self.access_controls: Dict[str, List[str]] = {}
def anonymize_patient_data(self, patient_data: Dict) -> Dict:
"""
匿名化患者数据
Args:
patient_data: 患者数据
Returns:
匿名化后的数据
"""
import hashlib
# 创建伪ID
pseudo_id = hashlib.sha256(
patient_data.get("patient_id", "").encode()
).hexdigest()[:8]
return {
"pseudo_patient_id": pseudo_id,
"age_range": f"{patient_data.get('age', 0) // 10 * 10}-{patient_data.get('age', 0) // 10 * 10 + 9}",
"condition_type": patient_data.get("diagnosis", "").split()[0],
"gender": patient_data.get("gender"),
# 不包含：真实姓名、住址、电话、医保号等
}
def check_hipaa_compliance(self, data_access: Dict) -> Dict:
"""
检查HIPAA合规性
Args:
data_access: 数据访问记录
Returns:
合规检查结果
"""
# 检查最小必要原则
accessed_fields = data_access.get("fields", [])
allowed_fields = data_access.get("role") == "provider"
# 记录审计日志
self.audit_logs.append({
"timestamp": datetime.now().isoformat(),
"user": data_access.get("user_id"),
"action": "access_patient_data",
"patient": data_access.get("patient_id"),
"status": "allowed" if allowed_fields else "denied"
})
return {
"compliant": allowed_fields,
"audit_logged": True,
"fields_accessed": accessed_fields if allowed_fields else [],
"reason_if_denied": "Insufficient authorization" if not allowed_fields else None
}
def get_audit_trail(self, patient_id: str, days: int = 30) -> List[Dict]:
"""
获取审计日志
Args:
patient_id: 患者ID
days: 天数
Returns:
审计日志
"""
cutoff = datetime.now().timestamp() - (days * 86400)
return [
log for log in self.audit_logs
if log.get("patient") == patient_id
]
18.4 医疗工作流示例
class ClinicialWorkflow:
"""临床工作流"""
async def patient_visit_workflow(self, patient_id: str,
chief_complaint: str) -> Dict:
"""
患者就诊工作流
Args:
patient_id: 患者ID
chief_complaint: 主诉
Returns:
就诊结果
"""
workflow = {
"patient_id": patient_id,
"steps": [],
"summary": None
}
# 第1步：病情分析
workflow["steps"].append({"name": "Condition Analysis", "status": "running"})
# 执行分析
workflow["steps"][-1]["status"] = "completed"
# 第2步：检查建议
workflow["steps"].append({"name": "Test Recommendation", "status": "running"})
workflow["steps"][-1]["status"] = "completed"
# 第3步：药物检查
workflow["steps"].append({"name": "Drug Safety Check", "status": "running"})
workflow["steps"][-1]["status"] = "completed"
# 第4步：治疗方案
workflow["steps"].append({"name": "Treatment Plan", "status": "running"})
workflow["steps"][-1]["status"] = "completed"
# 第5步：随访安排
workflow["steps"].append({"name": "Follow-up Scheduling", "status": "running"})
workflow["steps"][-1]["status"] = "completed"
workflow["summary"] = {
"status": "completed",
"patient_id": patient_id,
"chief_complaint": chief_complaint,
"next_appointment": "2周后"
}
return workflow
本章总结
| 关键点 | 说明 |
|---|---|
| 病情分析 | 基于主诉和病史的鉴别诊断 |
| 药物安全 | 过敏、相互作用、剂量检查 |
| 治疗方案 | 个性化治疗计划生成 |
| 健康追踪 | 生命体征监测和趋势分析 |
| 健康建议 | 个性化健康建议 |
| 隐私保护 | HIPAA合规、数据匿名化 |
| 审计日志 | 完整的访问追踪 |
常见问题
Q1: 如何保证医疗数据安全？ A: 加密存储、访问控制、审计日志、HIPAA合规、数据匿名化。
Q2: 如何处理患者隐私？ A: 最小必要原则、伪ID、字段级访问控制、定期审计。
Q3: 如何实现临床决策支持？ A: 循证医学、临床指南集成、ML模型辅助诊断。
Q4: 如何检查药物相互作用？ A: 药物数据库、交叉检查、阈值告警、定期更新。
Q5: 如何优化患者随访？ A: 自动提醒、健康指标监测、智能推荐、远程医疗集成。
下一章预告：第19章将讲述教育与培训MCP应用！
