Nothing
R/safety_02.R
In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations
Defines functions
safety_02
Documented in
safety_02
#' @title Safety-02 Calculation
#'
#' @description
#'
#' The `safety_02` function calculates the Safety-02 metric, evaluating the
#' proportion of emergency medical calls involving transport where no lights and
#' sirens were used. This function categorizes the population into adult and
#' pediatric groups based on their age, and summarizes results with a total
#' population count as well.
#'
#' @param df A data frame where each row is an observation, and each column
#' represents a feature.
#' @param patient_scene_table A data.frame or tibble containing only epatient
#' and escene fields as a fact table.
#' @param response_table A data.frame or tibble containing only the eresponse
#' fields needed for this measure's calculations.
#' @param disposition_table A data.frame or tibble containing only the
#' edisposition fields needed for this measure's calculations.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param incident_date_col Column that contains the incident date. This
#' defaults to `NULL` as it is optional in case not available due to PII
#' restrictions.
#' @param patient_DOB_col Column that contains the patient's date of birth. This
#' defaults to `NULL` as it is optional in case not available due to PII
#' restrictions.
#' @param epatient_15_col Column giving the calculated age value.
#' @param epatient_16_col Column giving the provided age unit value.
#' @param eresponse_05_col Column giving response codes, identifying 911
#' responses.
#' @param edisposition_18_col Column giving transport mode descriptors,
#' including possible lights-and-sirens indicators.
#' @param edisposition_28_col Column giving patient evaluation and care
#' categories for the EMS response.
#' @param transport_disposition_cols One or more unquoted column names (such as
#' edisposition.12, edisposition.30) containing transport disposition details.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#' method used to calculate confidence intervals. Options are `"wilson"`
#' (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#' Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (All,
#' Adults and Peds) with the following columns:
#' - `pop`: Population type (All, Adults, and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#' of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # Synthetic test data
#' test_data <- tibble::tibble(
#' erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#' epatient_15 = c(34, 5, 45, 2, 60), # Ages
#' epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#' eresponse_05 = rep(2205001, 5),
#' edisposition_18 = rep(4218015, 5),
#' edisposition_28 = rep(4228001, 5),
#' edisposition_30 = rep(4230001, 5)
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' safety_02(
#' df = test_data,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' edisposition_18_col = edisposition_18,
#' edisposition_28_col = edisposition_28,
#' transport_disposition_cols = edisposition_30,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
safety_02 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
disposition_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
edisposition_18_col,
edisposition_28_col,
transport_disposition_cols,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if (
all(
!is.null(patient_scene_table),
!is.null(response_table),
!is.null(disposition_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Safety-02")
# header
cli::cli_h2("Gathering Records for Safety-02")
# gather the population of interest
safety_02_populations <- safety_02_population(patient_scene_table = patient_scene_table,
response_table = response_table,
disposition_table = disposition_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
edisposition_18_col = {{ edisposition_18_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_cols = {{ transport_disposition_cols }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Safety-02")
# summary
safety.02 <- results_summarize(total_population = safety_02_populations$initial_population,
adult_population = safety_02_populations$adults,
peds_population = safety_02_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Safety-02",
numerator_col = NO_LS_CHECK,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(safety.02$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(safety.02)
} else if(
all(
is.null(patient_scene_table),
is.null(response_table),
is.null(disposition_table)
) && !is.null(df)
# utilize a dataframe to analyze the data for the measure analytics
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Safety-02")
# header
cli::cli_h2("Gathering Records for Safety-02")
# gather the population of interest
safety_02_populations <- safety_02_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
edisposition_18_col = {{ edisposition_18_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_cols = {{ transport_disposition_cols }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Safety-02")
# summary
safety.02 <- results_summarize(total_population = safety_02_populations$initial_population,
adult_population = safety_02_populations$adults,
peds_population = safety_02_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Safety-02",
numerator_col = NO_LS_CHECK,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(safety.02$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(safety.02)
}
}
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Defines functions safety_02
Documented in safety_02
#' @title Safety-02 Calculation
#'
#' @description
#'
#' The `safety_02` function calculates the Safety-02 metric, evaluating the
#' proportion of emergency medical calls involving transport where no lights and
#' sirens were used. This function categorizes the population into adult and
#' pediatric groups based on their age, and summarizes results with a total
#' population count as well.
#'
#' @param df A data frame where each row is an observation, and each column
#' represents a feature.
#' @param patient_scene_table A data.frame or tibble containing only epatient
#' and escene fields as a fact table.
#' @param response_table A data.frame or tibble containing only the eresponse
#' fields needed for this measure's calculations.
#' @param disposition_table A data.frame or tibble containing only the
#' edisposition fields needed for this measure's calculations.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param incident_date_col Column that contains the incident date. This
#' defaults to `NULL` as it is optional in case not available due to PII
#' restrictions.
#' @param patient_DOB_col Column that contains the patient's date of birth. This
#' defaults to `NULL` as it is optional in case not available due to PII
#' restrictions.
#' @param epatient_15_col Column giving the calculated age value.
#' @param epatient_16_col Column giving the provided age unit value.
#' @param eresponse_05_col Column giving response codes, identifying 911
#' responses.
#' @param edisposition_18_col Column giving transport mode descriptors,
#' including possible lights-and-sirens indicators.
#' @param edisposition_28_col Column giving patient evaluation and care
#' categories for the EMS response.
#' @param transport_disposition_cols One or more unquoted column names (such as
#' edisposition.12, edisposition.30) containing transport disposition details.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#' method used to calculate confidence intervals. Options are `"wilson"`
#' (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#' Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (All,
#' Adults and Peds) with the following columns:
#' - `pop`: Population type (All, Adults, and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#' of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # Synthetic test data
#' test_data <- tibble::tibble(
#' erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#' epatient_15 = c(34, 5, 45, 2, 60), # Ages
#' epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#' eresponse_05 = rep(2205001, 5),
#' edisposition_18 = rep(4218015, 5),
#' edisposition_28 = rep(4228001, 5),
#' edisposition_30 = rep(4230001, 5)
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' safety_02(
#' df = test_data,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' edisposition_18_col = edisposition_18,
#' edisposition_28_col = edisposition_28,
#' transport_disposition_cols = edisposition_30,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
safety_02 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
disposition_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
edisposition_18_col,
edisposition_28_col,
transport_disposition_cols,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if (
all(
!is.null(patient_scene_table),
!is.null(response_table),
!is.null(disposition_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Safety-02")
# header
cli::cli_h2("Gathering Records for Safety-02")
# gather the population of interest
safety_02_populations <- safety_02_population(patient_scene_table = patient_scene_table,
response_table = response_table,
disposition_table = disposition_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
edisposition_18_col = {{ edisposition_18_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_cols = {{ transport_disposition_cols }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Safety-02")
# summary
safety.02 <- results_summarize(total_population = safety_02_populations$initial_population,
adult_population = safety_02_populations$adults,
peds_population = safety_02_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Safety-02",
numerator_col = NO_LS_CHECK,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(safety.02$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(safety.02)
} else if(
all(
is.null(patient_scene_table),
is.null(response_table),
is.null(disposition_table)
) && !is.null(df)
# utilize a dataframe to analyze the data for the measure analytics
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Safety-02")
# header
cli::cli_h2("Gathering Records for Safety-02")
# gather the population of interest
safety_02_populations <- safety_02_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
edisposition_18_col = {{ edisposition_18_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_cols = {{ transport_disposition_cols }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Safety-02")
# summary
safety.02 <- results_summarize(total_population = safety_02_populations$initial_population,
adult_population = safety_02_populations$adults,
peds_population = safety_02_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Safety-02",
numerator_col = NO_LS_CHECK,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(safety.02$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(safety.02)
}
}
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