Nothing
R/vec-service-line.R
In healthyR: Hospital Data Analysis Workflow Tools
Defines functions
service_line_vec
Documented in
service_line_vec
#' Service Line Grouper Vectorized Function
#'
#' @family Vector Function
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes a few arguments from a data.frame/tibble and returns a service line
#' vector for a set of patients.
#'
#' @details
#' This is a vectorized function in that it returns a vector. It can be applied
#' inside of a `mutate` statement when using `dplyr` if desired. A data.frame/tibble
#' is required, along with a principal diagnosis column, a principal procedure column,
#' and a column for the DRG number. These are needed so that the function can
#' join the dx_cc_mapping and px_cc_mapping columns to provide the service line.
#' This function only works on visits that are coded using ICD Version 10 only.
#'
#' Lets take an example discharge, the DRG is 896 and the Principal Diagnosis code
#' maps to DX_660, then this visit would get grouped to `alcohol_abuse`
#'
#' DRG 896: ALCOHOL, DRUG ABUSE OR DEPENDENCE WITHOUT REHABILITATION THERAPY WITH
#' MAJOR COMPLICATION OR COMORBIDITY (MCC)
#'
#' DX_660 Maps to the following ICD-10 Codes ie F1010 Alcohol abuse, uncomplicated:
#'
#' ```
#' library(healthyR)
#' dx_cc_mapping %>%
#' filter(CC_Code == "DX_660", ICD_Ver_Flag == "10")
#' ```
#'
#' @param .data The data being passed that will be augmented by the function.
#' @param .dx_col The column containing the Principal Diagnosis for the discharge.
#' @param .px_col The column containing the Principal Coded Procedure for the discharge.
#' It is possible that this could be blank.
#' @param .drg_col The DRG Number coded to the inpatient discharge.
#'
#' @examples
#' df <- data.frame(
#' dx_col = "F10.10",
#' px_col = NA,
#' drg_col = "896"
#' )
#'
#' service_line_vec(
#' .data = df,
#' .dx_col = dx_col,
#' .px_col = px_col,
#' .drg_col = drg_col
#' )
#'
#' @return
#' A vector of service line assignments.
#'
#' @export
#'
service_line_vec <- function(.data, .dx_col, .px_col, .drg_col) {
# Tidyeval ----
# Grab the columns necessary
dx_col <- rlang::enquo(.dx_col)
px_col <- rlang::enquo(.px_col)
drg_col <- rlang::enquo(.drg_col)
# Checks ----
if(!is.data.frame(.data)){
stop(call. = FALSE, ".data must be supplied.")
}
if(rlang::quo_is_missing(dx_col) | rlang::quo_is_missing(px_col) |
rlang::quo_is_missing(drg_col)){
stop(call. = FALSE, "The columns .dx_col, .px_col and .drg_col must be supplied.")
}
# Copy data
data <- tibble::as_tibble(.data)
# Rearrange data and adjust internal table as desired.
data <- data %>%
dplyr::select(
{{dx_col}},
{{px_col}},
{{drg_col}},
dplyr::everything()
) %>%
dplyr::mutate(
dx_col = gsub({{dx_col}}, pattern = "\\.", replacement = "") %>%
stringr::str_squish(),
px_col = gsub({{px_col}}, pattern = "\\.", replacement = "") %>%
stringr::str_squish(),
drg_col = stringr::str_squish({{drg_col}}) %>% as.numeric()
) %>%
dplyr::mutate(
dplyr::across(
.cols = tidyselect::vars_select_helpers$where(is.character),
.fns = stringr::str_squish
)
)
# Join dx_cc_mapping and px_cc_mapping
df <- data %>%
dplyr::left_join(
y = healthyR::dx_cc_mapping %>%
dplyr::filter(ICD_Ver_Flag == '10'),
by = c("dx_col" = "ICDCode")
) %>%
dplyr::left_join(
y = healthyR::px_cc_mapping %>%
dplyr::filter(ICD_Ver_Flag == "10"),
by = c("px_col" = "ICDCode")
) %>%
dplyr::select(dx_col:drg_col, CC_Code.x, CC_Code.y) %>%
dplyr::rename(
dx_cc_code = CC_Code.x,
px_cc_code = CC_Code.y
) %>%
dplyr::as_tibble()
# split off inpatient and outpatient and assign service line appropirately
ip_df <- df %>%
dplyr::filter(!is.na(drg_col))
op_df <- df %>%
dplyr::filter(is.na(drg_col))
# Main if statement ----
# Inpatient assignment
ip_df <- ip_df %>%
dplyr::mutate(
service_line = dplyr::case_when(
drg_col %in% c("896", "897") &
dx_cc_code == "DX_660" ~ "alcohol_abuse",
drg_col %in% c("231", "232", "233", "234", "235", "236") ~ "cabg",
drg_col %in% c("34", "35", "36", "37", "38", "39") &
px_cc_code %in% c("PX_51", "PX_59") ~ "carotid_endarterectomy",
drg_col %in% c("602", "603") &
dx_cc_code == "DX_197" ~ "cellulitis",
drg_col == "313" &
dx_cc_code == "DX_102" ~ "chest_pain",
drg_col %in% c("291,292,293") &
dx_cc_code %in% c("DX_108", "DX_99") ~ "chf",
drg_col %in% c("190", "191", "192") &
dx_cc_code %in% c("DX_127", "DX_128") ~ "copd",
drg_col %in% c("765", "766") ~ "c_section",
drg_col %in% c("61", "62", "63", "64", "65", "66") ~ "cva",
drg_col %in% c("619", "620", "621") &
px_cc_code == "PX_74" ~ "bariatric_surgery_inpatient",
drg_col %in% c("377", "378", "379") ~ "gi_hemorrhage",
drg_col %in% c("739", "740", "741", "742", "743") &
px_cc_code == "PX_124" ~ "hysterectomy",
drg_col %in% c("469", "470") &
px_cc_code %in% c("PX_152", "PX_153") &
!dx_cc_code %in% c(
"DX_237", "DX_238", "DX_230", "DX_229",
"DX_226", "DX_225", "DX_231", "DX_207"
) ~ "joint_replacement",
drg_col %in% c("417", "418", "419") ~ "laproscopic_cholecystectomy_inpatient",
drg_col %in% c("582", "583", "584", "585") &
px_cc_code == "PX_167" ~ "mastectomy",
drg_col %in% c("280", "281", "282", "283", "284", "285") ~ "mi",
drg_col == "795" &
dx_cc_code == "DX_218" ~ "newborn",
drg_col %in% c("193", "194", "195") ~ "pneumonia",
drg_col %in% c("881", "885") &
dx_cc_code == "DX_657" ~ "major_depression_bipolar_disorders",
drg_col %in% c("885") &
dx_cc_code == "DX_659" ~ "schizophrenia",
drg_col %in% c("246", "247", "248", "249", "250", "251") &
px_cc_code == "PX_45" ~ "ptca",
drg_col %in% c("945", "946") ~ "rehab",
drg_col == "312" ~ "syncope",
drg_col %in% c("67", "68", "69") ~ "tia",
drg_col %in% c("774", "775") ~ "vaginal_delivery",
drg_col %in% c("216", "217", "218", "219", "220", "221", "266", "267") ~ "valve_procedure",
(drg_col >= 1 & drg_col <= 8) |
(drg_col >= 10 & drg_col <= 14) |
(drg_col %in% c(16, 17)) |
(drg_col >= 20 & drg_col <= 42) |
(drg_col >= 113 & drg_col <= 117) |
(drg_col >= 129 & drg_col <= 139) |
(drg_col >= 163 & drg_col <= 168) |
(drg_col >= 215 & drg_col <= 265) |
(drg_col >= 326 & drg_col <= 358) |
(drg_col >= 405 & drg_col <= 425) |
(drg_col >= 453 & drg_col <= 519) |
(drg_col == 520) |
(drg_col >= 570 & drg_col <= 585) |
(drg_col >= 614 & drg_col <= 630) |
(drg_col >= 652 & drg_col <= 675) |
(drg_col >= 707 & drg_col <= 718) |
(drg_col >= 734 & drg_col <= 750) |
(drg_col >= 765 & drg_col <= 780) |
(drg_col >= 782 & drg_col <= 804) |
(drg_col >= 820 & drg_col <= 830) |
(drg_col >= 853 & drg_col <= 858) |
(drg_col == 876) |
(drg_col >= 901 & drg_col <= 909) |
(drg_col >= 927 & drg_col <= 929) |
(drg_col >= 939 & drg_col <= 941) |
(drg_col >= 955 & drg_col <= 959) |
(drg_col >= 969 & drg_col <= 970) |
(drg_col >= 981 & drg_col <= 989) ~ "surgical",
TRUE ~ "medical"
)
)
# Outpatient assignment
op_df <- op_df %>%
dplyr::mutate(
service_line = dplyr::case_when(
px_col %in% c("4468", "4495", "43770", "43770") ~ "bariatric_surgery_outpatient",
px_col %in% c(
"3721", "3722", "3723", "36013", "93451",
"93452", "93456", "93457", "93458", "93549",
"93460", "93461", "93462", "93560", "93531",
"93532", "93533"
) ~ "cardiac_catheterization",
px_col %in% c(
"1311", "1319", "132", "133", "1341",
"1342", "1343", "1351", "1359", "1364",
"1365", "1366", "66820", "66821", "66830",
"66840", "66850", "66852", "66920", "66930",
"66940", "66982", "66983", "66984"
) ~ "cataract_removal",
px_col %in% c(
"4513", "4514", "4516", "4523", "4524",
"4525", "4542", "4543", "43235", "43236",
"43237", "43238", "43239", "43240", "43241",
"43242", "43257", "43259", "44100", "44360",
"44361", "44370", "44377", "44378", "44379",
"44385", "44386", "45317", "45320", "45330",
"45331", "45332", "45333", "45334", "45335",
"45338", "45339", "45341", "45342", "45345",
"45378", "45379", "45380", "45381", "45382",
"45383", "45384", "45385", "45391", "45392",
"G0104", "G0105", "G0121", "S0601"
) ~ "colonoscopy_endoscopy",
px_col %in% c("5123", "5124", "47562", "47563", "47564") ~ "laproscopic_cholecystectomy_outpatient",
px_col %in% c(
"0066", "92920", "92924", "92928", "92933",
"92937", "92941", "92943", "C9600", "C9602",
"C9604", "C9606", "C9607"
) ~ "ptca_outpatient",
TRUE ~ "general_outpatient"
)
)
# rbind ip and op back together
df_final_tbl <- rbind(ip_df, op_df)
# Get the vector of service lines
svc_line_vec <- df_final_tbl$service_line
# Return ----
return(svc_line_vec)
}
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Defines functions service_line_vec
Documented in service_line_vec
#' Service Line Grouper Vectorized Function
#'
#' @family Vector Function
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes a few arguments from a data.frame/tibble and returns a service line
#' vector for a set of patients.
#'
#' @details
#' This is a vectorized function in that it returns a vector. It can be applied
#' inside of a `mutate` statement when using `dplyr` if desired. A data.frame/tibble
#' is required, along with a principal diagnosis column, a principal procedure column,
#' and a column for the DRG number. These are needed so that the function can
#' join the dx_cc_mapping and px_cc_mapping columns to provide the service line.
#' This function only works on visits that are coded using ICD Version 10 only.
#'
#' Lets take an example discharge, the DRG is 896 and the Principal Diagnosis code
#' maps to DX_660, then this visit would get grouped to `alcohol_abuse`
#'
#' DRG 896: ALCOHOL, DRUG ABUSE OR DEPENDENCE WITHOUT REHABILITATION THERAPY WITH
#' MAJOR COMPLICATION OR COMORBIDITY (MCC)
#'
#' DX_660 Maps to the following ICD-10 Codes ie F1010 Alcohol abuse, uncomplicated:
#'
#' ```
#' library(healthyR)
#' dx_cc_mapping %>%
#' filter(CC_Code == "DX_660", ICD_Ver_Flag == "10")
#' ```
#'
#' @param .data The data being passed that will be augmented by the function.
#' @param .dx_col The column containing the Principal Diagnosis for the discharge.
#' @param .px_col The column containing the Principal Coded Procedure for the discharge.
#' It is possible that this could be blank.
#' @param .drg_col The DRG Number coded to the inpatient discharge.
#'
#' @examples
#' df <- data.frame(
#' dx_col = "F10.10",
#' px_col = NA,
#' drg_col = "896"
#' )
#'
#' service_line_vec(
#' .data = df,
#' .dx_col = dx_col,
#' .px_col = px_col,
#' .drg_col = drg_col
#' )
#'
#' @return
#' A vector of service line assignments.
#'
#' @export
#'
service_line_vec <- function(.data, .dx_col, .px_col, .drg_col) {
# Tidyeval ----
# Grab the columns necessary
dx_col <- rlang::enquo(.dx_col)
px_col <- rlang::enquo(.px_col)
drg_col <- rlang::enquo(.drg_col)
# Checks ----
if(!is.data.frame(.data)){
stop(call. = FALSE, ".data must be supplied.")
}
if(rlang::quo_is_missing(dx_col) | rlang::quo_is_missing(px_col) |
rlang::quo_is_missing(drg_col)){
stop(call. = FALSE, "The columns .dx_col, .px_col and .drg_col must be supplied.")
}
# Copy data
data <- tibble::as_tibble(.data)
# Rearrange data and adjust internal table as desired.
data <- data %>%
dplyr::select(
{{dx_col}},
{{px_col}},
{{drg_col}},
dplyr::everything()
) %>%
dplyr::mutate(
dx_col = gsub({{dx_col}}, pattern = "\\.", replacement = "") %>%
stringr::str_squish(),
px_col = gsub({{px_col}}, pattern = "\\.", replacement = "") %>%
stringr::str_squish(),
drg_col = stringr::str_squish({{drg_col}}) %>% as.numeric()
) %>%
dplyr::mutate(
dplyr::across(
.cols = tidyselect::vars_select_helpers$where(is.character),
.fns = stringr::str_squish
)
)
# Join dx_cc_mapping and px_cc_mapping
df <- data %>%
dplyr::left_join(
y = healthyR::dx_cc_mapping %>%
dplyr::filter(ICD_Ver_Flag == '10'),
by = c("dx_col" = "ICDCode")
) %>%
dplyr::left_join(
y = healthyR::px_cc_mapping %>%
dplyr::filter(ICD_Ver_Flag == "10"),
by = c("px_col" = "ICDCode")
) %>%
dplyr::select(dx_col:drg_col, CC_Code.x, CC_Code.y) %>%
dplyr::rename(
dx_cc_code = CC_Code.x,
px_cc_code = CC_Code.y
) %>%
dplyr::as_tibble()
# split off inpatient and outpatient and assign service line appropirately
ip_df <- df %>%
dplyr::filter(!is.na(drg_col))
op_df <- df %>%
dplyr::filter(is.na(drg_col))
# Main if statement ----
# Inpatient assignment
ip_df <- ip_df %>%
dplyr::mutate(
service_line = dplyr::case_when(
drg_col %in% c("896", "897") &
dx_cc_code == "DX_660" ~ "alcohol_abuse",
drg_col %in% c("231", "232", "233", "234", "235", "236") ~ "cabg",
drg_col %in% c("34", "35", "36", "37", "38", "39") &
px_cc_code %in% c("PX_51", "PX_59") ~ "carotid_endarterectomy",
drg_col %in% c("602", "603") &
dx_cc_code == "DX_197" ~ "cellulitis",
drg_col == "313" &
dx_cc_code == "DX_102" ~ "chest_pain",
drg_col %in% c("291,292,293") &
dx_cc_code %in% c("DX_108", "DX_99") ~ "chf",
drg_col %in% c("190", "191", "192") &
dx_cc_code %in% c("DX_127", "DX_128") ~ "copd",
drg_col %in% c("765", "766") ~ "c_section",
drg_col %in% c("61", "62", "63", "64", "65", "66") ~ "cva",
drg_col %in% c("619", "620", "621") &
px_cc_code == "PX_74" ~ "bariatric_surgery_inpatient",
drg_col %in% c("377", "378", "379") ~ "gi_hemorrhage",
drg_col %in% c("739", "740", "741", "742", "743") &
px_cc_code == "PX_124" ~ "hysterectomy",
drg_col %in% c("469", "470") &
px_cc_code %in% c("PX_152", "PX_153") &
!dx_cc_code %in% c(
"DX_237", "DX_238", "DX_230", "DX_229",
"DX_226", "DX_225", "DX_231", "DX_207"
) ~ "joint_replacement",
drg_col %in% c("417", "418", "419") ~ "laproscopic_cholecystectomy_inpatient",
drg_col %in% c("582", "583", "584", "585") &
px_cc_code == "PX_167" ~ "mastectomy",
drg_col %in% c("280", "281", "282", "283", "284", "285") ~ "mi",
drg_col == "795" &
dx_cc_code == "DX_218" ~ "newborn",
drg_col %in% c("193", "194", "195") ~ "pneumonia",
drg_col %in% c("881", "885") &
dx_cc_code == "DX_657" ~ "major_depression_bipolar_disorders",
drg_col %in% c("885") &
dx_cc_code == "DX_659" ~ "schizophrenia",
drg_col %in% c("246", "247", "248", "249", "250", "251") &
px_cc_code == "PX_45" ~ "ptca",
drg_col %in% c("945", "946") ~ "rehab",
drg_col == "312" ~ "syncope",
drg_col %in% c("67", "68", "69") ~ "tia",
drg_col %in% c("774", "775") ~ "vaginal_delivery",
drg_col %in% c("216", "217", "218", "219", "220", "221", "266", "267") ~ "valve_procedure",
(drg_col >= 1 & drg_col <= 8) |
(drg_col >= 10 & drg_col <= 14) |
(drg_col %in% c(16, 17)) |
(drg_col >= 20 & drg_col <= 42) |
(drg_col >= 113 & drg_col <= 117) |
(drg_col >= 129 & drg_col <= 139) |
(drg_col >= 163 & drg_col <= 168) |
(drg_col >= 215 & drg_col <= 265) |
(drg_col >= 326 & drg_col <= 358) |
(drg_col >= 405 & drg_col <= 425) |
(drg_col >= 453 & drg_col <= 519) |
(drg_col == 520) |
(drg_col >= 570 & drg_col <= 585) |
(drg_col >= 614 & drg_col <= 630) |
(drg_col >= 652 & drg_col <= 675) |
(drg_col >= 707 & drg_col <= 718) |
(drg_col >= 734 & drg_col <= 750) |
(drg_col >= 765 & drg_col <= 780) |
(drg_col >= 782 & drg_col <= 804) |
(drg_col >= 820 & drg_col <= 830) |
(drg_col >= 853 & drg_col <= 858) |
(drg_col == 876) |
(drg_col >= 901 & drg_col <= 909) |
(drg_col >= 927 & drg_col <= 929) |
(drg_col >= 939 & drg_col <= 941) |
(drg_col >= 955 & drg_col <= 959) |
(drg_col >= 969 & drg_col <= 970) |
(drg_col >= 981 & drg_col <= 989) ~ "surgical",
TRUE ~ "medical"
)
)
# Outpatient assignment
op_df <- op_df %>%
dplyr::mutate(
service_line = dplyr::case_when(
px_col %in% c("4468", "4495", "43770", "43770") ~ "bariatric_surgery_outpatient",
px_col %in% c(
"3721", "3722", "3723", "36013", "93451",
"93452", "93456", "93457", "93458", "93549",
"93460", "93461", "93462", "93560", "93531",
"93532", "93533"
) ~ "cardiac_catheterization",
px_col %in% c(
"1311", "1319", "132", "133", "1341",
"1342", "1343", "1351", "1359", "1364",
"1365", "1366", "66820", "66821", "66830",
"66840", "66850", "66852", "66920", "66930",
"66940", "66982", "66983", "66984"
) ~ "cataract_removal",
px_col %in% c(
"4513", "4514", "4516", "4523", "4524",
"4525", "4542", "4543", "43235", "43236",
"43237", "43238", "43239", "43240", "43241",
"43242", "43257", "43259", "44100", "44360",
"44361", "44370", "44377", "44378", "44379",
"44385", "44386", "45317", "45320", "45330",
"45331", "45332", "45333", "45334", "45335",
"45338", "45339", "45341", "45342", "45345",
"45378", "45379", "45380", "45381", "45382",
"45383", "45384", "45385", "45391", "45392",
"G0104", "G0105", "G0121", "S0601"
) ~ "colonoscopy_endoscopy",
px_col %in% c("5123", "5124", "47562", "47563", "47564") ~ "laproscopic_cholecystectomy_outpatient",
px_col %in% c(
"0066", "92920", "92924", "92928", "92933",
"92937", "92941", "92943", "C9600", "C9602",
"C9604", "C9606", "C9607"
) ~ "ptca_outpatient",
TRUE ~ "general_outpatient"
)
)
# rbind ip and op back together
df_final_tbl <- rbind(ip_df, op_df)
# Get the vector of service lines
svc_line_vec <- df_final_tbl$service_line
# Return ----
return(svc_line_vec)
}
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