Currently in development

Introduction

The carecompare package is an R toolkit built to enable US hospitals and health systems to access, explore, and analyze performance in quality measures and payment programs from the Centers for Medicare and Medicaid Services (CMS).

Installation

From source

devtools::install_github("zajichek/carecompare")

Load

require(carecompare)
#> Loading required package: carecompare

Accessing Data

Generic access to the CMS Provider Data Catalog.

# Extract the topics
topics <- pdc_topics()
topics
#>  [1] "Helpful Contacts"                      
#>  [2] "Dialysis facilities"                   
#>  [3] "Home health services"                  
#>  [4] "Hospice care"                          
#>  [5] "Hospitals"                             
#>  [6] "Inpatient rehabilitation facilities"   
#>  [7] "Long-term care hospitals"              
#>  [8] "Nursing homes including rehab services"
#>  [9] "Physician office visit costs"          
#> [10] "Doctors and clinicians"                
#> [11] "Supplier directory"                    
#> [12] "Medicare plan finder"

# Examine the metadata for a given topic
hospital_data <- pdc_datasets("Hospitals")
hospital_data
#> # A tibble: 67 × 7
#>    datasetid topic     title       description issued     modified   downloadurl
#>    <chr>     <chr>     <chr>       <chr>       <date>     <date>     <chr>      
#>  1 4jcv-atw7 Hospitals Ambulatory… A list of … 2022-01-07 2022-01-07 https://da…
#>  2 axe7-s95e Hospitals Ambulatory… This file … 2022-01-07 2022-01-07 https://da…
#>  3 wue8-3vwe Hospitals Ambulatory… This file … 2022-01-07 2022-01-07 https://da…
#>  4 muwa-iene Hospitals CMS Medica… This data … 2020-12-10 2022-01-07 https://da…
#>  5 ynj2-r877 Hospitals Complicati… Complicati… 2020-12-10 2022-01-07 https://da…
#>  6 qqw3-t4ie Hospitals Complicati… Complicati… 2020-12-10 2022-01-07 https://da…
#>  7 bs2r-24vh Hospitals Complicati… Complicati… 2020-12-10 2022-01-07 https://da…
#>  8 tqkv-mgxq Hospitals Comprehens… Comprehens… 2020-12-10 2021-06-15 https://da…
#>  9 bzsr-4my4 Hospitals Data Updat… Lists the … 2020-12-10 2022-03-21 https://da…
#> 10 y9us-9xdf Hospitals Footnote C… The footno… 2020-12-10 2021-09-22 https://da…
#> # … with 57 more rows

# Search for a dataset
hospital_data %>% 
  dplyr::filter(
    title %>%
      stringr::str_detect(
        pattern = "(?i)readmission"
      )
  ) %>%
  knitr::kable(format = "pandoc")

datasetid	topic	title	description	issued	modified	downloadurl
9n3s-kdb3	Hospitals	Hospital Readmissions Reduction Program	In October 2012, CMS began reducing Medicare payments for subsection(d) hospitals with excess readmissions under the Hospital Readmissions Reduction Program (HRRP). Excess readmissions are measured by a ratio, calculated by dividing a hospital’s predicted rate of readmissions for heart attack (AMI), heart failure (HF), pneumonia, chronic obstructive pulmonary disease (COPD), hip/knee replacement (THA/TKA), and coronary artery bypass graft surgery (CABG) by the expected rate of readmissions, based on an average hospital with similar patients.	2020-12-10	2022-01-19	https://data.cms.gov/provider-data/sites/default/files/resources/6862887588c0e2d720f0c821f6ed8e76_1642665920/FY_2022_Hospital_Readmissions_Reduction_Program_Hospital.csv

# Import the data for a given dataset
pdc_read(
  datasetid = "9n3s-kdb3"
)
#> Rows: 19020 Columns: 12
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (11): Facility Name, Facility ID, State, Measure Name, Number of Dischar...
#> dbl  (1): Footnote
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 19,020 × 12
#>    `Facility Name`  `Facility ID` State `Measure Name` `Number of Dis…` Footnote
#>    <chr>            <chr>         <chr> <chr>          <chr>               <dbl>
#>  1 SOUTHEAST HEALT… 010001        AL    READM-30-HIP-… 165                    NA
#>  2 SOUTHEAST HEALT… 010001        AL    READM-30-CABG… 193                    NA
#>  3 SOUTHEAST HEALT… 010001        AL    READM-30-AMI-… 424                    NA
#>  4 SOUTHEAST HEALT… 010001        AL    READM-30-HF-H… 905                    NA
#>  5 SOUTHEAST HEALT… 010001        AL    READM-30-COPD… 310                    NA
#>  6 SOUTHEAST HEALT… 010001        AL    READM-30-PN-H… 504                    NA
#>  7 MARSHALL MEDICA… 010005        AL    READM-30-COPD… 378                    NA
#>  8 MARSHALL MEDICA… 010005        AL    READM-30-AMI-… N/A                    NA
#>  9 MARSHALL MEDICA… 010005        AL    READM-30-HF-H… 223                    NA
#> 10 MARSHALL MEDICA… 010005        AL    READM-30-CABG… N/A                     5
#> # … with 19,010 more rows, and 6 more variables:
#> #   `Excess Readmission Ratio` <chr>, `Predicted Readmission Rate` <chr>,
#> #   `Expected Readmission Rate` <chr>, `Number of Readmissions` <chr>,
#> #   `Start Date` <chr>, `End Date` <chr>

Formatted datasets

hospitals
#> # A tibble: 5,306 × 12
#>    HospitalID Name  Address City  State Zip   County Type  Ownership FullAddress
#>    <chr>      <chr> <chr>   <chr> <chr> <chr> <chr>  <chr> <chr>     <chr>      
#>  1 010001     SOUT… 1108 R… DOTH… AL    36301 HOUST… Acut… Governme… 1108 ROSS …
#>  2 010005     MARS… 2505 U… BOAZ  AL    35957 MARSH… Acut… Governme… 2505 U S H…
#>  3 010006     NORT… 1701 V… FLOR… AL    35630 LAUDE… Acut… Propriet… 1701 VETER…
#>  4 010007     MIZE… 702 N … OPP   AL    36467 COVIN… Acut… Voluntar… 702 N MAIN…
#>  5 010008     CREN… 101 HO… LUVE… AL    36049 CRENS… Acut… Propriet… 101 HOSPIT…
#>  6 010011     ST. … 50 MED… BIRM… AL    35235 JEFFE… Acut… Voluntar… 50 MEDICAL…
#>  7 010012     DEKA… 200 ME… FORT… AL    35968 DE KA… Acut… Propriet… 200 MED CE…
#>  8 010016     SHEL… 1000 F… ALAB… AL    35007 SHELBY Acut… Voluntar… 1000 FIRST…
#>  9 010018     CALL… 1720 U… BIRM… AL    35233 JEFFE… Acut… Voluntar… 1720 UNIVE…
#> 10 010019     HELE… 1300 S… SHEF… AL    35660 COLBE… Acut… Governme… 1300 SOUTH…
#> # … with 5,296 more rows, and 2 more variables: Latitude <dbl>, Longitude <dbl>
cms_payments()
#> # A tibble: 188,806 × 6
#>    HospitalID MSDRGCode Discharges AverageCoveredCharges AverageTotalPayment
#>    <chr>      <chr>          <dbl>                 <dbl>               <dbl>
#>  1 010001     003               14               326515.              62788.
#>  2 010001     023               55               140875.              29767.
#>  3 010001     024               20               109788.              22780.
#>  4 010001     025               23               124579.              24107.
#>  5 010001     027               16                75029.              18216.
#>  6 010001     038               20                73875.               9721.
#>  7 010001     039               45                47281.               6985.
#>  8 010001     054               11                34797.               7782 
#>  9 010001     056               15                66157.              11793.
#> 10 010001     057               38                27677.               7393.
#> # … with 188,796 more rows, and 1 more variable: AverageMedicarePayment <dbl>
cms_msdrg()
#> Rows: 767 Columns: 9
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr (6): MS-DRG, FY 2022 Final Post-Acute DRG, FY 2022 Final Special Pay DRG...
#> dbl (3): Weights, Geometric mean LOS, Arithmetic mean LOS
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 767 × 7
#>    MSDRGCode MSDRGDescription      MSDRGType MajorDiagnostic… Weight GMLOS AMLOS
#>    <chr>     <chr>                 <chr>     <chr>             <dbl> <dbl> <dbl>
#>  1 001       HEART TRANSPLANT OR … SURG      PRE               28.9   30.1  39.1
#>  2 002       HEART TRANSPLANT OR … SURG      PRE               15.0   15.4  18.2
#>  3 003       ECMO OR TRACHEOSTOMY… SURG      PRE               19.1   22.4  30.2
#>  4 004       TRACHEOSTOMY WITH MV… SURG      PRE               11.9   20    24.6
#>  5 005       LIVER TRANSPLANT WIT… SURG      PRE               10.2   14.4  19.4
#>  6 006       LIVER TRANSPLANT WIT… SURG      PRE                4.70   7.5   8.1
#>  7 007       LUNG TRANSPLANT       SURG      PRE               11.6   17.4  20.8
#>  8 008       SIMULTANEOUS PANCREA… SURG      PRE                5.43   9    10.2
#>  9 010       PANCREAS TRANSPLANT   SURG      PRE                3.62   8     9.1
#> 10 011       TRACHEOSTOMY FOR FAC… SURG      PRE                5.02  10.9  13.8
#> # … with 757 more rows