hcuppy

A Python package for H-CUP Tools and Software. The modules implemented in this package are as follows:

• "CCS (Clinical Classification Software)" converts ICD-10 diagnosis and procedure codes to clinically meaningful groups
• "CCI (Chronic Condition Indicator)" identifies chronic conditions from ICD-10 diagnosis codes
• "Elixhauser Comordibity Index" calculates both readmission and mortality risks using a set of ICD-10 diagnosis codes
• "Procedure Classes" identify if a given ICD-10 procedure code is Minor/Major Diagnosis/Therapeutic
• "Utilization Flags" identify if a combination of UB40 revenue codes and ICD-10 procedure codes indicates (or implies) a certain resource utilization e.g. Intensive Care Unit, Ultrasound, X-Ray, etc.
• "Surgery Flags" identify if a CPT code is a surgery related or not. NOTE that, to use this module, users must agree to an additional license agreement with the AMA for using CPT codes here.

NOTE that this package does not support for ICD-9.

Installing

Installing from the source:

$ git clone git@github.com:yubin-park/hcuppy.git
$ cd hcuppy
$ python setup.py develop

Or, simply using pip:

$ pip install hcuppy

File Structure

• hcuppy/: The package source code is located here.
  • data/: The raw data files downloaded from the H-CUP website.
  • ccs.py: a module for CCS
  • cci.py: a module for CCI
  • elixhauser.py: a module for Elixhauser Comorbidity Index
  • prcls.py: a module for Procedure Class
  • uflag.py: a module for Utilization Flags
  • sflag.py: a module for Surgery Flags
  • utils.py: utility functions for reading data files.
• tests/: test scripts to check the validity of the outputs.
• LICENSE.txt: Apache 2.0.
• README.md: This README file.
• setup.py: a set-up script.

Code Examples

hcuppy is really simple to use. Please see some examples below. NOTE that all functions used below have docstrings. If you want to see the input parameter specifications, please type print(<instance>.<function>.__doc__).

Using CCS

>>> import json
>>> from hcuppy.ccs import CCSEngine
>>> ce = CCSEngine(mode="dx")
>>> out = ce.get_ccs(["E119", "I10"])
>>> print(json.dumps(out, indent=2))
[
  {
    "ccs": "49",
    "ccs_desc": "Diabetes mellitus without complication",
    "ccs_lv1": "3",
    "ccs_lv1_desc": "Endocrine; nutritional; and metabolic diseases and immunity disorders",
    "ccs_lv2": "3.2",
    "ccs_lv2_desc": "Diabetes mellitus without complication [49.]"
  },
  {
    "ccs": "98",
    "ccs_desc": "Essential hypertension",
    "ccs_lv1": "7",
    "ccs_lv1_desc": "Diseases of the circulatory system",
    "ccs_lv2": "7.1",
    "ccs_lv2_desc": "Hypertension"
  }
]
>>>

Using CCI

>>> from hcuppy.cci import CCIEngine
>>> ce = CCIEngine()
>>> out = ce.get_cci(["E119"])
>>> print(json.dumps(out, indent=2))
[
  {
    "is_chronic": true,
    "body_system": "3",
    "body_system_desc": "Endocrine, nutritional, and metabolic diseases and immunity disorders"
  }
]

Using Elixhauser Comorbidity Index

>>> from hcuppy.elixhauser import ElixhauserEngine
>>> ee = ElixhauserEngine()
>>> out = ee.get_elixhauser(["E119", "E108", "I10", "I110", "Z944"])
>>> print(json.dumps(out, indent=2))
{
  "cmrbdt_lst": [
    "LIVER",
    "DMCX",
    "HTNCX",
    "CHF"
  ],
  "rdmsn_scr": 31,
  "mrtlt_scr": 9
}
>>>

Using Procedure Class

>>> from hcuppy.prcls import PrClsEngine
>>> pce = PrClsEngine()
>>> out = pce.get_prcls(["B231Y0Z"])
>>> print(json.dumps(out, indent=2))
[
  {
    "class": "1",
    "desc": "Minor Diagnostic"
  }
]
>>>

Using Utilization Flag

>>> from hcuppy.uflag import UFlagEngine
>>> ufe = UFlagEngine()
>>> out = ufe.get_uflag(rev_lst=["0380"], pr_lst=["BB0DZZZ"])
>>> print(json.dumps(out, indent=2))
[
  "Blood",
  "Chest X-Ray"
]
>>>

Please refer to the test scripts under the tests/ folder if you want to see other example use cases.

License

Apache 2.0

Authors

Yubin Park, PhD

References

• https://www.hcup-us.ahrq.gov/
• https://www.hcup-us.ahrq.gov/tools_software.jsp
• https://cran.r-project.org/web/packages/comorbidity/vignettes/comorbidityscores.html
• https://github.com/modusdatascience/ccs