https://github.com/validatehealth/cmshcc

This R package calculates CMS-HCC risk scores as an alternative to using SAS
https://github.com/validatehealth/cmshcc

hcc medicare medicare-data risk-scores

Last synced: about 2 months ago
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This R package calculates CMS-HCC risk scores as an alternative to using SAS

• Host: GitHub
• URL: https://github.com/validatehealth/cmshcc
• Owner: validatehealth
• Created: 2015-06-29T00:35:42.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2019-07-01T14:49:41.000Z (over 5 years ago)
• Last Synced: 2024-08-13T07:13:25.995Z (5 months ago)
• Topics: hcc, medicare, medicare-data, risk-scores
• Language: R
• Homepage: https://www.ValidateHealth.com
• Size: 2.25 MB
• Stars: 25
• Watchers: 13
• Forks: 16
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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• jimsghstars - validatehealth/cmshcc - This R package calculates CMS-HCC risk scores as an alternative to using SAS (R)

README

        
Based on presentation from:

http://ase.uva.nl/binaries/content/assets/subsites/amsterdam-school-of-economics/r-in-insurance/webster-risk-adjustment-in-r.pdf

1) install.packages("devtools") if not already installed

2) install cmshcc package using:  devtools::install_github("healthactuary/cmshcc")

3) load the cmshcc package using library(cmshcc)

4) generate a sample PERSON data frame or use your own with same format: PERSON <- generate_PERSON()

5) load the CMS-HCC diagnosis code to HCC mapping: cmshcc_map <- load_cmshcc_map()

6) create a diagnosis data frame using mapping or use your own data with same format: DIAG <- generate_DIAG(cmshcc_map)

7) Evaluate the 2017 V22 CMS-HCC models:

  Community_NonDual_Aged = evaluate_v22_2017(PERSON, DIAG, "Community_NonDual_Aged")

  Community_NonDual_Disabled = evaluate_v22_2017(PERSON, DIAG, "Community_NonDual_Disabled")

  Community_FBDual_Aged = evaluate_v22_2017(PERSON, DIAG, "Community_FBDual_Aged")

  Community_FBDual_Disabled = evaluate_v22_2017(PERSON, DIAG, "Community_FBDual_Disabled")

  Community_PBDual_Aged = evaluate_v22_2017(PERSON, DIAG, "Community_PBDual_Aged")

  Community_PBDual_Disabled = evaluate_v22_2017(PERSON, DIAG, "Community_PBDual_Disabled")

  Institutional = evaluate_v22_2017(PERSON, DIAG, "Institutional")

  Bind together results in one dataframe if preferable:

  

  results <- cbind(Community_NonDual_Aged, Community_NonDual_Disabled, Community_FBDual_Aged, Community_FBDual_Disabled, Community_PBDual_Aged, Community_PBDual_Disabled, Institutional)