This function identifies and labels all episodes of care for a given patient in chronological order. A new episode begins after a specified gap in therapy has occurred.
It is meant to be used after one has appropriately adjusted dates (propagate_date()) and identified gaps in therapy (identify_gaps()).
rank_episodes(.data, .permissible_gap = NULL, .initial_rank = 1)
| .data | Data frame with a "gap" column appended from |
|---|---|
| .permissible_gap | Integer value suggesting the maximum gap allowed before labeling a new episode of care |
| .initial_rank | Integer value to identify what the indexing rank should be (defaults to 1). |
The initial claims data frame with an episode column appended, which ranks episodes of care in time
This function assumes an adjusted_date column, which is produced by the propagate_date() function and a
gap column, which is produced by identify_gaps(). If you would like to rank episodes of care using other dates and a separate
column for gaps, you'll need to rename those columns before passing the frame to rank_episodes(). Notably, this is on purpose as this step should
almost always come after the former two.
library(adheRenceRX) library(dplyr) toy_claims %>% filter(ID == "D") %>% propagate_date() %>% identify_gaps() %>% rank_episodes(.permissible_gap = 20, .initial_rank = 1)#> # A tibble: 6 x 6 #> ID date days_supply adjusted_date gap episode #> <chr> <date> <dbl> <date> <dbl> <dbl> #> 1 D 2020-01-01 60 2020-01-01 0 1 #> 2 D 2020-01-31 60 2020-03-01 0 1 #> 3 D 2020-03-01 60 2020-04-30 0 1 #> 4 D 2020-05-30 30 2020-06-29 0 1 #> 5 D 2020-08-28 60 2020-08-28 30 2 #> 6 D 2020-09-27 30 2020-10-27 0 2