hccpy 0.0.2

hccpy is a Python implementation of HCC

Hierachical Condition Categories Python Package (hccpy)

This module implements the Hierachical Condition Categories that are used for adjusting risks for the Medicare population.

Installing

Installing from the source:

$ git clone [e-mail]:yubin-park/hccpy
$ cd hccpy
$ python setup.py develop

Or, simply using pip:

$ pip install hccpy

Code Examples

hccpy is really simple to use. Please see some examples below:

>>> import json
>>> from hccpy.hcc import HCCEngine
>>> he = HCCEngine()
>>> print(he.profile.__doc__)
Returns the HCC risk profile of a given patient information.

        Parameters
        ----------
        dx_lst : list of str
                 A list of ICD10 codes for the measurement year.
        age : int or float
              The age of the patient.
        sex : str 
              The sex of the patient; {"M", "F"}
        elig : str
               The eligibility segment of the patient.
               Allowed values are as follows:
               - "CFA": Community Full Benefit Dual Aged
               - "CFD": Community Full Benefit Dual Disabled
               - "CNA": Community NonDual Aged
               - "CND": Community NonDual Disabled
               - "CPA": Community Partial Benefit Dual Aged
               - "CPD": Community Partial Benefit Dual Disabled
               - "INS": Long Term Institutional
               - "NE": New Enrollee
               - "SNPNE": SNP NE
        orec: str
              Original reason for entitlement code.
              - "0": Old age and survivor's insurance
              - "1": Disability insurance benefits
              - "2": End-stage renal disease 
              - "3": Both DIB and ESRD
        medicaid: bool
                  If the patient is in Medicaid or not.
>>>
>>> rp = he.profile(["E1169", "I5030", "I509", "I211", "I209", "R05"])
>>> print(json.dumps(rp, indent=2))
{
  "risk_score": 1.314,
  "details": {
    "CNA_M70_74": 0.379,
    "CNA_HCC18": 0.318,
    "CNA_HCC85": 0.323,
    "CNA_HCC88": 0.14,
    "CNA_HCC85_gDiabetesMellit": 0.154
  },
  "parameters": {
    "age": 70,
    "sex": "M",
    "elig": "CNA",
    "medicaid": false,
    "disabled": 0,
    "origds": 0
  }
}
>>>
>>> rp = he.profile([], elig="NE", age=65)
>>> print(json.dumps(rp, indent=2))
{
  "risk_score": 0.514,
  "details": {
    "NE_NMCAID_NORIGDIS_NEM65": 0.514
  },
  "parameters": {
    "age": 65,
    "sex": "M",
    "elig": "NE_NMCAID_NORIGDIS_NE",
    "medicaid": false,
    "disabled": 0,
    "origds": 0
  }
}
>>>
>>> rp = he.profile(["E1169", "I5030", "I509", "I209"], elig="INS")
>>> print(json.dumps(rp, indent=2))
{
  "risk_score": 2.6059999999999994,
  "details": {
    "INS_M70_74": 1.323,
    "INS_HCC88": 0.497,
    "INS_HCC85": 0.191,
    "INS_HCC18": 0.441,
    "INS_DIABETES_CHF": 0.154
  },
  "parameters": {
    "age": 70,
    "sex": "M",
    "elig": "INS",
    "medicaid": false,
    "disabled": 0,
    "origds": 0
  }
}

Authors

• Yubin Park, PhD

References

• https://www.nber.org/data/cms-risk-adjustment.html
• https://www.cms.gov/medicare/health-plans/medicareadvtgspecratestats/risk-adjustors.html
• https://github.com/calyxhealth/pyriskadjust
• https://github.com/AlgorexHealth/hcc-python
• https://github.com/galtay/hcc_risk_models