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Make LOS and Readmit Index Summary Tibble

Source: R/data_tbl_functions.R
los_ra_index_summary_tbl.Rd

Create the length of stay and readmit index summary tibble

Usage

los_ra_index_summary_tbl(
  .data,
  .max_los = 15,
  .alos_col,
  .elos_col,
  .readmit_rate,
  .readmit_bench
)

Arguments

.data

The data you are going to analyze.

.max_los

You can give a maximum LOS value. Lets say you typically do not see los over 15 days, you would then set .max_los to 15 and all values greater than .max_los will be grouped to .max_los

.alos_col

The Average Length of Stay column

.elos_col

The Expected Length of Stay column

.readmit_rate

The Actual Readmit Rate column

.readmit_bench

The Expected Readmit Rate column

Value

A tibble

Details

  • Expects a tibble

  • Expects the following columns and there should only be these 4

    • Length Of Stay Actual - Should be an integer

    • Length Of Stacy Benchmark - Should be an integer

    • Readmit Rate Actual - Should be 0/1 for each record, 1 = readmitted, 0 did not.

    • Readmit Rate Benchmark - Should be a percentage from the benchmark file.

  • This will add a column called visits that will be the count of records per length of stay from 1 to .max_los

  • The .max_los param can be left blank and the function will default to 15. If this is not a good default and you don't know what it should be then set it to 75 percentile from the stats::quantile() function using the defaults, like so .max_los = stats::quantile(data_tbl$alos)[[4]]

  • Uses all data to compute variance, if you want it for a particular time frame you will have to filter the data that goes into the .data argument. It is suggested to use timetk::filter_by_time()

  • The index is computed as the excess of the length of stay or readmit rates over their respective expectations.

Author

Steven P. Sanderson II, MPH

Examples


suppressPackageStartupMessages(library(dplyr))

data_tbl <- tibble(
  "alos"            = runif(186, 1, 20)
  , "elos"          = runif(186, 1, 17)
  , "readmit_rate"  = runif(186, 0, .25)
  , "readmit_bench" = runif(186, 0, .2)
)

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 15
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_bench
)
#> # A tibble: 15 × 4
#>    los_group los_index rar_index los_ra_var
#>        <dbl>     <dbl>     <dbl>      <dbl>
#>  1         1     0.134     1.56       1.42 
#>  2         2     0.201     2.43       2.23 
#>  3         3     0.310     1.3        0.990
#>  4         4     0.409     1.62       1.22 
#>  5         5     0.546     1.08       0.538
#>  6         6     0.567     0.929      0.504
#>  7         7     0.793     1.08       0.290
#>  8         8     0.840     1.38       0.535
#>  9         9     0.780     1.22       0.442
#> 10        10     1.15      1.3        0.452
#> 11        11     1.16      1.3        0.455
#> 12        12     1.65      2.33       1.98 
#> 13        13     1.55      1.33       0.881
#> 14        14     1.56      1.36       0.923
#> 15        15     2.16      1.27       1.43 

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 10
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_bench
)
#> # A tibble: 10 × 4
#>    los_group los_index rar_index los_ra_var
#>        <dbl>     <dbl>     <dbl>      <dbl>
#>  1         1     0.134     1.56       1.42 
#>  2         2     0.201     2.43       2.23 
#>  3         3     0.310     1.3        0.990
#>  4         4     0.409     1.62       1.22 
#>  5         5     0.546     1.08       0.538
#>  6         6     0.567     0.929      0.504
#>  7         7     0.793     1.08       0.290
#>  8         8     0.840     1.38       0.535
#>  9         9     0.780     1.22       0.442
#> 10        10     1.72      1.4        1.12