Courses - Become a health data wizard! 🧙‍♀️

We're excited to partner with HEALS Academy to master the art of healthcare data analysis

Partnering with

We're on a mission to empower healthcare data analysts and scientists to do more with publicly available data. Our exciting partnership with Heals Academy brings you hands-on, practical courses that turn complex healthcare data challenges into streamlined solutions.

Ready to become a healthcare data wizard? More courses are coming soon as we continue to expand our partnership with Heals Academy. Join the waitlist to be the first to know about new offerings!

📚 Available Courses

RAS-FHIR 201: HCC-In-FHIR Basics

Perfect for getting started with HCC risk scoring

This class introduces you to how to transform CMS Explanation of Benefit (EOB) data into accurate HCC risk scores using the hccinfhir package — no complex coding needed. Get a ready-to-deploy workflow in just three sessions, with tools for revenue forecasting and compliance-ready results.

📅 Mode: Cohort
📅 Dates: Aug 18-20th 2025
⏰ Times: 7-8PM ET
✅ Status: Registration Open

RAS-FHIR 202: HCC-In-FHIR Intermediate

Take your HCC skills to the executive level

Build on your RAS-FHIR 201 foundation and turn CMS EOB and other data (e.g. claims) into rock-solid HCC risk scores that executives can bank on. In just three one-hour sessions you'll master the hccinfhir package to create revenue-forecasting analytics, compliance-ready audit traces, and research that scale with you and your organization's growth.

📅 Mode: Cohort
📅 Dates: Aug 25-27th 2025
⏰ Times: 7-8PM ET
✅ Status: Registration Open

🛠️ Get a Taste of What You'll Learn

Here's a sneak peek at the hccinfhir package that powers these courses:

# Perfect for RAS-FHIR 201: Simple risk scoring
from hccinfhir.model_calculate import calculate_raf

# Patient with diabetes and heart failure

diagnosis_codes = ['E119', 'I509']  
age = 67
sex = 'F'
model_name = "CMS-HCC Model V24"

result = calculate_raf(
diagnosis_codes=diagnosis_codes,
model_name=model_name,
age=age,
sex=sex
)

print(f"Risk Score: {result['risk_score']}")
print(f"HCC Categories: {result['hcc_list']}")

# RAS-FHIR 202: Full pipeline from FHIR to risk scores
from hccinfhir.hccinfhir import HCCInFHIR
from hccinfhir.datamodels import Demographics

# Initialize processor with filtering
hcc_processor = HCCInFHIR(
    filter_claims=True,
    model_name="CMS-HCC Model V28"
)

# Demographics
demographics = {
    'age': 67,
    'sex': 'F'
}

# Process FHIR EOB resources
raf_result = hcc_processor.run(eob_list, demographics)

print(f"Final RAF Score: {raf_result['risk_score']}")
print(f"Applied HCCs: {raf_result['hcc_list']}")
print(f"CC to Diagnosis Mapping: {raf_result['cc_to_dx']}")

# Advanced workflow: Transform service data to HCC scores
from hccinfhir.hccinfhir import HCCInFHIR

# Service level data format
service_data = [{
    "procedure_code": "99214",
    "claim_diagnosis_codes": ["E11.9", "I10"],
    "claim_type": "71",
    "provider_specialty": "01",
    "service_date": "2024-01-15"
}]

# Process through HCC pipeline
hcc_processor = HCCInFHIR(filter_claims=True)
result = hcc_processor.run_from_service_data(
    service_data,
    {'age': 67, 'sex': 'F'}
)

# Revenue-ready results
print(f"Patient Risk Score: {result['risk_score']}")
print(f"Applied Coefficients: {result['coefficients']}")

-- Query mimilabs data lakehouse for HCC reference data
-- (Used to build the ra_dx_to_cc_mapping_2026.csv file)

SELECT
  diagnosis_code,
  cc,
  model_name
FROM mimi_ws_1.cmspayment.ra_dx_to_cc_mapping
WHERE model_type = 'Initial'
  AND year = 2026
  AND model_name = 'CMS-HCC Model V28'
ORDER BY diagnosis_code
LIMIT 10;

🎯 Why Our Courses Work

🔥 Battle-tested in the real world - Our hccinfhir package is used in production by healthcare organizations
⚡ Results in hours, not months - Three 1-hour sessions get you production-ready
📊 Executive-ready outputs - Generate reports that decision-makers trust
🛡️ Compliance-first approach - Built-in audit trails and documentation

🔗 Learn More

Want to dive deeper into the technical details? Check out how the hccinfhir package works:

Ready to become a healthcare data wizard? More courses are coming soon as we continue to expand our partnership with Heals Academy. Join the waitlist to be the first to know about new offerings!

Did you enjoy the courses? Here is the testimonial form: https://love.mimilabs.ai