Healthcare MCP Use Cases Guide

Healthcare MCP Use Cases Guide

Real-world applications of DuckDB MCP integration for healthcare analytics

Population Health Analytics

Use Case 1: Medicare Advantage Market Analysis

Objective: Analyze Medicare Advantage enrollment opportunities across counties

MCP Integration Pattern:

-- Connect to multiple data sources via MCP
ATTACH 'python3' AS census_api (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["scripts/census_mcp_server.py", "--api-key", "${CENSUS_API_KEY}"]'
);

ATTACH 'node' AS medicare_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/medicare_analytics_server.js"]'
);

-- Federated analysis across MCP sources
WITH census_demographics AS (
  SELECT * FROM read_csv('mcp://census_api/file:///data/acs_demographics.csv')
),
medicare_penetration AS (
  SELECT * FROM mcp_call_tool('medicare_server', 'get_ma_penetration',
    '{"geography": "county", "year": 2022}')::TABLE
)
SELECT
  c.county_name,
  c.state,
  c.population_65_plus,
  m.current_ma_enrollment,
  ROUND(100.0 * m.current_ma_enrollment / c.population_65_plus, 2) as penetration_rate,
  c.population_65_plus - m.current_ma_enrollment as opportunity_gap
FROM census_demographics c
JOIN medicare_penetration m ON c.geo_id = m.county_fips
WHERE c.population_65_plus > 5000
ORDER BY opportunity_gap DESC;

Business Value:

• Identify counties with largest MA enrollment opportunities
• Target markets with high senior population but low MA penetration
• Quantify market opportunity by geography

Use Case 2: Healthcare Facility Adequacy Assessment

Objective: Assess healthcare facility coverage relative to population needs

MCP Integration Pattern:

-- Connect to healthcare facility data via MCP
ATTACH 'https://api.healthcare.gov' AS facility_api (
    TYPE mcp,
    TRANSPORT 'https',
    ARGS '["--auth-token", "${HHS_API_TOKEN}"]'
);

-- Assess facility adequacy
SELECT
  demographics.county,
  demographics.state,
  demographics.population_65_plus,
  facility_data.total_facilities,
  facility_data.specialty_facilities,
  ROUND(facility_data.total_facilities * 1000.0 / demographics.population_65_plus, 2) as facilities_per_1k_seniors,
  CASE
    WHEN facility_data.total_facilities * 1000.0 / demographics.population_65_plus < 2.0
    THEN 'Underserved'
    WHEN facility_data.total_facilities * 1000.0 / demographics.population_65_plus > 5.0
    THEN 'Well Served'
    ELSE 'Adequate'
  END as adequacy_rating
FROM demographics
JOIN (
  SELECT
    county_fips,
    COUNT(*) as total_facilities,
    COUNT(CASE WHEN specialty IN ('cardiology', 'oncology', 'orthopedics') THEN 1 END) as specialty_facilities
  FROM read_json('mcp://facility_api/api://facilities/by_county')
  GROUP BY county_fips
) facility_data ON demographics.geo_id = facility_data.county_fips
WHERE demographics.population_65_plus > 1000;

Business Value:

• Identify underserved healthcare markets
• Support facility planning and investment decisions
• Assess competitive landscape for healthcare services

Clinical Data Integration

Use Case 3: FHIR Patient Data Analytics

Objective: Analyze patient demographics and outcomes from FHIR-compliant systems

MCP Integration Pattern:

-- Connect to FHIR server via MCP
ATTACH 'node' AS fhir_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/fhir_mcp_server.js", "--endpoint", "https://fhir.epic.com/interconnect-fhir-oauth"]'
);

-- Process FHIR patient resources
CREATE TABLE patient_analytics AS
SELECT
  JSON_EXTRACT_STRING(patient_resource, '$.id') as patient_id,
  JSON_EXTRACT_STRING(patient_resource, '$.gender') as gender,
  EXTRACT(YEAR FROM CURRENT_DATE) -
    EXTRACT(YEAR FROM CAST(JSON_EXTRACT_STRING(patient_resource, '$.birthDate') AS DATE)) as age,
  JSON_EXTRACT_STRING(patient_resource, '$.address[0].state') as state,
  JSON_EXTRACT_STRING(patient_resource, '$.address[0].postalCode') as zip_code
FROM read_json('mcp://fhir_server/api://Patient?_count=10000', format='array') t(patient_resource);

-- Analyze patient population characteristics
SELECT
  state,
  gender,
  CASE
    WHEN age < 18 THEN 'Pediatric'
    WHEN age BETWEEN 18 AND 64 THEN 'Adult'
    WHEN age >= 65 THEN 'Senior'
  END as age_group,
  COUNT(*) as patient_count,
  AVG(age) as avg_age
FROM patient_analytics
WHERE state IS NOT NULL
GROUP BY state, gender, age_group
ORDER BY state, age_group, gender;

Business Value:

• Understand patient population demographics
• Support population health management
• Enable FHIR-compliant analytics workflows

Use Case 4: Clinical Quality Measures via MCP

Objective: Calculate clinical quality measures using distributed healthcare data

MCP Integration Pattern:

-- Connect to multiple clinical data sources
ATTACH 'python3' AS quality_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/clinical_quality_server.py"]'
);

-- Calculate diabetes care quality measures
WITH diabetes_patients AS (
  SELECT
    patient_id,
    mcp_call_tool('quality_server', 'get_patient_conditions',
      JSON_OBJECT('patient_id', patient_id, 'condition_codes', ['E11', 'E10']))::JSON as conditions
  FROM patient_registry
  WHERE has_diabetes = true
),
quality_measures AS (
  SELECT
    patient_id,
    mcp_call_tool('quality_server', 'calculate_hba1c_control',
      JSON_OBJECT('patient_id', patient_id, 'measurement_period', '2022'))::JSON as hba1c_data,
    mcp_call_tool('quality_server', 'check_eye_exam',
      JSON_OBJECT('patient_id', patient_id, 'measurement_period', '2022'))::JSON as eye_exam_data
  FROM diabetes_patients
)
SELECT
  COUNT(*) as total_diabetes_patients,
  COUNT(CASE WHEN JSON_EXTRACT_STRING(hba1c_data, '$.controlled') = 'true' THEN 1 END) as hba1c_controlled_count,
  COUNT(CASE WHEN JSON_EXTRACT_STRING(eye_exam_data, '$.completed') = 'true' THEN 1 END) as eye_exam_completed_count,
  ROUND(100.0 * COUNT(CASE WHEN JSON_EXTRACT_STRING(hba1c_data, '$.controlled') = 'true' THEN 1 END) / COUNT(*), 2) as hba1c_control_rate,
  ROUND(100.0 * COUNT(CASE WHEN JSON_EXTRACT_STRING(eye_exam_data, '$.completed') = 'true' THEN 1 END) / COUNT(*), 2) as eye_exam_completion_rate
FROM quality_measures;

Business Value:

• Monitor clinical quality performance
• Support value-based care contracts
• Enable population health quality reporting

Public Health Surveillance

Use Case 5: Disease Outbreak Detection

Objective: Monitor and detect potential disease outbreaks using distributed health data

MCP Integration Pattern:

-- Connect to syndromic surveillance systems
ATTACH 'python3' AS surveillance_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/syndromic_surveillance_server.py"]'
);

-- Real-time outbreak detection
CREATE TABLE outbreak_monitoring AS
SELECT
  county_fips,
  surveillance_date,
  syndrome_category,
  case_count,
  LAG(case_count, 7) OVER (
    PARTITION BY county_fips, syndrome_category
    ORDER BY surveillance_date
  ) as cases_week_ago,
  AVG(case_count) OVER (
    PARTITION BY county_fips, syndrome_category
    ORDER BY surveillance_date
    ROWS BETWEEN 21 PRECEDING AND 8 PRECEDING
  ) as baseline_avg,
  STDDEV(case_count) OVER (
    PARTITION BY county_fips, syndrome_category
    ORDER BY surveillance_date
    ROWS BETWEEN 21 PRECEDING AND 8 PRECEDING
  ) as baseline_stddev
FROM read_json('mcp://surveillance_server/api://syndromic/daily', format='array');

-- Detect statistical anomalies
SELECT
  county_fips,
  surveillance_date,
  syndrome_category,
  case_count,
  baseline_avg,
  ROUND((case_count - baseline_avg) / NULLIF(baseline_stddev, 0), 2) as z_score,
  CASE
    WHEN (case_count - baseline_avg) / NULLIF(baseline_stddev, 0) > 2.0 THEN 'Alert'
    WHEN (case_count - baseline_avg) / NULLIF(baseline_stddev, 0) > 1.5 THEN 'Warning'
    ELSE 'Normal'
  END as alert_level
FROM outbreak_monitoring
WHERE surveillance_date = CURRENT_DATE
  AND baseline_avg IS NOT NULL
  AND (case_count - baseline_avg) / NULLIF(baseline_stddev, 0) > 1.5
ORDER BY z_score DESC;

Business Value:

• Early detection of disease outbreaks
• Support public health response planning
• Enable automated surveillance workflows

Use Case 6: Social Determinants of Health Analysis

Objective: Analyze correlation between social factors and health outcomes

MCP Integration Pattern:

-- Connect to multiple social determinant data sources
ATTACH 'python3' AS sdoh_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/social_determinants_server.py"]'
);

-- Comprehensive SDOH analysis
WITH social_factors AS (
  SELECT
    county_fips,
    mcp_call_tool('sdoh_server', 'get_food_access',
      JSON_OBJECT('county', county_fips))::JSON as food_data,
    mcp_call_tool('sdoh_server', 'get_transportation_access',
      JSON_OBJECT('county', county_fips))::JSON as transport_data,
    mcp_call_tool('sdoh_server', 'get_housing_quality',
      JSON_OBJECT('county', county_fips))::JSON as housing_data
  FROM county_list
),
health_outcomes AS (
  SELECT
    county_fips,
    diabetes_prevalence,
    obesity_rate,
    life_expectancy,
    preventable_mortality_rate
  FROM public_health_indicators
)
SELECT
  s.county_fips,
  JSON_EXTRACT_STRING(s.food_data, '$.food_desert_percentage') as food_desert_pct,
  JSON_EXTRACT_STRING(s.transport_data, '$.limited_transport_percentage') as limited_transport_pct,
  JSON_EXTRACT_STRING(s.housing_data, '$.substandard_housing_percentage') as substandard_housing_pct,
  h.diabetes_prevalence,
  h.obesity_rate,
  h.life_expectancy,
  -- Calculate correlation between social factors and health outcomes
  CORR(CAST(JSON_EXTRACT_STRING(s.food_data, '$.food_desert_percentage') AS DECIMAL),
       h.diabetes_prevalence) OVER () as food_diabetes_correlation
FROM social_factors s
JOIN health_outcomes h ON s.county_fips = h.county_fips
WHERE JSON_EXTRACT_STRING(s.food_data, '$.food_desert_percentage') IS NOT NULL;

Business Value:

• Understand root causes of health disparities
• Support targeted intervention programs
• Enable evidence-based policy development

Healthcare Economics

Use Case 7: Medicare Cost Analysis

Objective: Analyze Medicare spending patterns and cost drivers

MCP Integration Pattern:

-- Connect to Medicare cost data via MCP
ATTACH 'https://api.cms.gov' AS cms_api (
    TYPE mcp,
    TRANSPORT 'https',
    ARGS '["--api-key", "${CMS_API_KEY}"]'
);

-- Comprehensive Medicare cost analysis
WITH cost_data AS (
  SELECT * FROM read_json('mcp://cms_api/api://medicare/geographic-variation')
),
utilization_data AS (
  SELECT * FROM read_json('mcp://cms_api/api://medicare/provider-utilization')
)
SELECT
  c.county_name,
  c.state,
  cost_data.total_medicare_spending,
  cost_data.per_capita_spending,
  utilization_data.avg_services_per_beneficiary,
  demographics.population_65_plus as medicare_eligible_pop,
  ROUND(cost_data.total_medicare_spending / demographics.population_65_plus, 2) as spending_per_eligible,
  CASE
    WHEN cost_data.per_capita_spending > (
      SELECT PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY per_capita_spending)
      FROM cost_data
    ) THEN 'High Cost'
    WHEN cost_data.per_capita_spending < (
      SELECT PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY per_capita_spending)
      FROM cost_data
    ) THEN 'Low Cost'
    ELSE 'Average Cost'
  END as cost_category
FROM demographics
JOIN cost_data ON demographics.geo_id = cost_data.county_fips
JOIN utilization_data ON demographics.geo_id = utilization_data.county_fips
WHERE demographics.population_65_plus > 1000;

Business Value:

• Identify cost efficiency opportunities
• Support value-based care initiatives
• Enable Medicare Advantage pricing strategies

Use Case 8: Healthcare ROI Analysis

Objective: Measure return on investment for healthcare interventions

MCP Integration Pattern:

-- Connect to intervention outcome data
ATTACH 'python3' AS outcomes_server (
    TYPE mcp,
    TRANSPORT 'stdio',
    ARGS '["services/intervention_outcomes_server.py"]'
);

-- ROI analysis for diabetes prevention program
WITH intervention_costs AS (
  SELECT
    county_fips,
    intervention_type,
    mcp_call_tool('outcomes_server', 'get_program_costs',
      JSON_OBJECT('county', county_fips, 'program', intervention_type))::JSON as cost_data
  FROM intervention_programs
  WHERE intervention_type = 'diabetes_prevention'
),
health_outcomes AS (
  SELECT
    county_fips,
    mcp_call_tool('outcomes_server', 'calculate_diabetes_incidence_reduction',
      JSON_OBJECT('county', county_fips, 'baseline_year', 2019, 'intervention_year', 2022))::JSON as outcome_data
  FROM intervention_programs
  WHERE intervention_type = 'diabetes_prevention'
)
SELECT
  i.county_fips,
  CAST(JSON_EXTRACT_STRING(i.cost_data, '$.total_program_cost') AS DECIMAL) as program_cost,
  CAST(JSON_EXTRACT_STRING(o.outcome_data, '$.cases_prevented') AS INTEGER) as cases_prevented,
  CAST(JSON_EXTRACT_STRING(o.outcome_data, '$.lifetime_cost_savings') AS DECIMAL) as lifetime_savings,
  ROUND(
    CAST(JSON_EXTRACT_STRING(o.outcome_data, '$.lifetime_cost_savings') AS DECIMAL) /
    NULLIF(CAST(JSON_EXTRACT_STRING(i.cost_data, '$.total_program_cost') AS DECIMAL), 0),
    2
  ) as roi_ratio,
  CASE
    WHEN CAST(JSON_EXTRACT_STRING(o.outcome_data, '$.lifetime_cost_savings') AS DECIMAL) >
         CAST(JSON_EXTRACT_STRING(i.cost_data, '$.total_program_cost') AS DECIMAL)
    THEN 'Positive ROI'
    ELSE 'Negative ROI'
  END as roi_assessment
FROM intervention_costs i
JOIN health_outcomes o ON i.county_fips = o.county_fips;

Business Value:

• Demonstrate value of healthcare interventions
• Support funding and investment decisions
• Enable evidence-based program planning

Real-Time Healthcare Monitoring

Use Case 9: Hospital Capacity Management

Objective: Monitor real-time hospital capacity and resource utilization

MCP Integration Pattern:

-- Connect to real-time hospital systems
ATTACH 'tcp://hospital-data.healthsystem.org:8080' AS hospital_api (
    TYPE mcp,
    TRANSPORT 'tcp'
);

-- Real-time capacity monitoring
CREATE TABLE hospital_capacity_realtime AS
SELECT
  facility_id,
  facility_name,
  county_fips,
  current_timestamp as update_time,
  mcp_call_tool('hospital_api', 'get_current_capacity',
    JSON_OBJECT('facility_id', facility_id))::JSON as capacity_data,
  mcp_call_tool('hospital_api', 'get_patient_acuity',
    JSON_OBJECT('facility_id', facility_id))::JSON as acuity_data
FROM healthcare_facilities
WHERE facility_type = 'hospital';

-- Capacity alerts and predictions
SELECT
  facility_name,
  county_fips,
  JSON_EXTRACT_STRING(capacity_data, '$.total_beds') as total_beds,
  JSON_EXTRACT_STRING(capacity_data, '$.occupied_beds') as occupied_beds,
  JSON_EXTRACT_STRING(capacity_data, '$.available_beds') as available_beds,
  ROUND(
    100.0 * CAST(JSON_EXTRACT_STRING(capacity_data, '$.occupied_beds') AS INTEGER) /
    CAST(JSON_EXTRACT_STRING(capacity_data, '$.total_beds') AS INTEGER),
    1
  ) as occupancy_rate,
  CASE
    WHEN CAST(JSON_EXTRACT_STRING(capacity_data, '$.available_beds') AS INTEGER) < 5
    THEN 'Critical'
    WHEN CAST(JSON_EXTRACT_STRING(capacity_data, '$.available_beds') AS INTEGER) < 10
    THEN 'Warning'
    ELSE 'Normal'
  END as capacity_status
FROM hospital_capacity_realtime
WHERE CAST(JSON_EXTRACT_STRING(capacity_data, '$.occupied_beds') AS INTEGER) /
      CAST(JSON_EXTRACT_STRING(capacity_data, '$.total_beds') AS INTEGER) > 0.85
ORDER BY occupancy_rate DESC;

Business Value:

• Optimize hospital resource allocation
• Support emergency response planning
• Enable proactive capacity management

Use Case 10: Medication Supply Chain Monitoring

Objective: Monitor pharmaceutical supply chain and shortage risks

MCP Integration Pattern:

-- Connect to pharmaceutical supply data
ATTACH 'https://api.fda.gov' AS fda_api (
    TYPE mcp,
    TRANSPORT 'https',
    ARGS '["--api-key", "${FDA_API_KEY}"]'
);

-- Supply chain risk analysis
WITH drug_shortages AS (
  SELECT * FROM read_json('mcp://fda_api/api://drug/shortages')
),
regional_demand AS (
  SELECT
    county_fips,
    drug_name,
    mcp_call_tool('pharmacy_server', 'estimate_regional_demand',
      JSON_OBJECT('county', county_fips, 'drug', drug_name))::JSON as demand_data
  FROM pharmacy_locations
  CROSS JOIN drug_shortages
)
SELECT
  s.drug_name,
  s.shortage_reason,
  s.estimated_resolution_date,
  COUNT(DISTINCT r.county_fips) as affected_counties,
  SUM(CAST(JSON_EXTRACT_STRING(r.demand_data, '$.monthly_demand') AS INTEGER)) as total_regional_demand,
  AVG(CAST(JSON_EXTRACT_STRING(r.demand_data, '$.days_supply_remaining') AS INTEGER)) as avg_days_supply,
  CASE
    WHEN AVG(CAST(JSON_EXTRACT_STRING(r.demand_data, '$.days_supply_remaining') AS INTEGER)) < 30
    THEN 'Critical Shortage'
    WHEN AVG(CAST(JSON_EXTRACT_STRING(r.demand_data, '$.days_supply_remaining') AS INTEGER)) < 60
    THEN 'Shortage Risk'
    ELSE 'Adequate Supply'
  END as supply_status
FROM drug_shortages s
JOIN regional_demand r ON s.drug_name = r.drug_name
GROUP BY s.drug_name, s.shortage_reason, s.estimated_resolution_date
ORDER BY avg_days_supply;

Business Value:

• Proactive shortage identification and mitigation
• Support pharmaceutical procurement planning
• Enable regional supply coordination

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These use cases demonstrate the power of DuckDB MCP integration for comprehensive healthcare analytics, enabling seamless data federation and advanced analytical workflows across diverse healthcare data sources.