R/trauma_01.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Trauma-01 Calculation
#'
#' @description
#'
#' This function processes EMS data to calculate the Trauma-01 performance
#' measure, which evaluates the percentage of trauma patients assessed for pain
#' using a numeric scale. The function filters and summarizes the data based on
#' specified inclusion criteria.
#'
#' @param df A data frame or tibble containing EMS records. Default is `NULL`.
#' @param patient_scene_table A data frame or tibble containing only epatient
#'   and escene fields as a fact table. Default is `NULL`.
#' @param response_table A data frame or tibble containing only the eresponse
#'   fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data.frame or tibble containing only the esituation
#'   fields needed for this measure's calculations. Default is `NULL`.
#' @param disposition_table A data.frame or tibble containing only the
#'   edisposition fields needed for this measure's calculations. Default is
#'   `NULL`.
#' @param vitals_table A data.frame or tibble containing only the evitals fields
#'   needed for this measure's calculations. Default is `NULL`.
#' @param erecord_01_col Column name representing the EMS record ID.
#' @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 name for the patient's age in numeric format.
#' @param epatient_16_col Column name for the unit of age (e.g., "Years",
#'   "Months").
#' @param esituation_02_col Column name indicating if the situation involved an
#'   injury.
#' @param eresponse_05_col Column name for the type of EMS response (e.g., 911
#'   call).
#' @param evitals_23_col Column name for the Glasgow Coma Scale (GCS) total
#'   score.
#' @param evitals_26_col Column name for AVPU (Alert, Voice, Pain, Unresponsive)
#'   status.
#' @param evitals_27_col Column name for the pain scale assessment.
#' @param edisposition_28_col Column name for patient care disposition details.
#' @param transport_disposition_col Column name for 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),
#'     esituation_02 = rep("Yes", 5),
#'     evitals_23 = rep(15, 5),
#'     evitals_26 = rep("Alert", 5),
#'     evitals_27 = c(0, 2, 4, 6, 8),
#'     edisposition_28 = rep(4228001, 5),
#'     edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
#'   )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#'   trauma_01(
#'     df = test_data,
#'     erecord_01_col = erecord_01,
#'     epatient_15_col = epatient_15,
#'     epatient_16_col = epatient_16,
#'     eresponse_05_col = eresponse_05,
#'     esituation_02_col = esituation_02,
#'     evitals_23_col = evitals_23,
#'     evitals_26_col = evitals_26,
#'     evitals_27_col = evitals_27,
#'     edisposition_28_col = edisposition_28,
#'     transport_disposition_col = edisposition_30,
#'     confidence_interval = TRUE
#'   )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
trauma_01 <- function(df = NULL,
                      patient_scene_table = NULL,
                      response_table = NULL,
                      situation_table = NULL,
                      disposition_table = NULL,
                      vitals_table = NULL,
                      erecord_01_col,
                      incident_date_col = NULL,
                      patient_DOB_col = NULL,
                      epatient_15_col,
                      epatient_16_col,
                      esituation_02_col,
                      eresponse_05_col,
                      evitals_23_col,
                      evitals_26_col,
                      evitals_27_col,
                      edisposition_28_col,
                      transport_disposition_col,
                      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 (
    any(
      !is.null(patient_scene_table),
      !is.null(vitals_table),
      !is.null(situation_table),
      !is.null(disposition_table),
      !is.null(response_table)
    ) &&

    is.null(df)

  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # Header
    cli::cli_h1("Trauma-01")

    # Header
    cli::cli_h2("Gathering Records for Trauma-01")

    # Gather the population of interest
    trauma_01_populations <- trauma_01_population(
      patient_scene_table = patient_scene_table,
      response_table = response_table,
      situation_table = situation_table,
      vitals_table = vitals_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 }},
      esituation_02_col = {{ esituation_02_col }},
      eresponse_05_col = {{ eresponse_05_col }},
      evitals_23_col = {{ evitals_23_col }},
      evitals_26_col = {{ evitals_26_col }},
      evitals_27_col = {{ evitals_27_col }},
      edisposition_28_col = {{ edisposition_28_col }},
      transport_disposition_col = {{ transport_disposition_col }}
    )

    # Create a separator
    cli::cli_text("\n")

    # Header for calculations
    cli::cli_h2("Calculating Trauma-01")

    # summarize
    trauma.01 <- results_summarize(
      total_population = trauma_01_populations$initial_population,
      adult_population = trauma_01_populations$adults,
      peds_population = trauma_01_populations$peds,
      population_names = c("all", "adults", "peds"),
      measure_name = "Trauma-01",
      numerator_col = PAIN_SCALE,
      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(trauma.01$denominator < 10) && method == "wilson" && confidence_interval) {

      cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

    }

    return(trauma.01)

  } else if (
    any(
      is.null(patient_scene_table),
      is.null(vitals_table),
      is.null(situation_table),
      is.null(disposition_table),
      is.null(response_table)
    ) &&
    !is.null(df)
  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # Header
    cli::cli_h1("Trauma-01")

    # Header
    cli::cli_h2("Gathering Records for Trauma-01")

    # Gather the population of interest
    trauma_01_populations <- trauma_01_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 }},
      esituation_02_col = {{ esituation_02_col }},
      eresponse_05_col = {{ eresponse_05_col }},
      evitals_23_col = {{ evitals_23_col }},
      evitals_26_col = {{ evitals_26_col }},
      evitals_27_col = {{ evitals_27_col }},
      edisposition_28_col = {{ edisposition_28_col }},
      transport_disposition_col = {{ transport_disposition_col }}
    )

    # Create a separator
    cli::cli_text("\n")

    # Header for calculations
    cli::cli_h2("Calculating Trauma-01")

    # summarize
    trauma.01 <- results_summarize(
      total_population = trauma_01_populations$initial_population,
      adult_population = trauma_01_populations$adults,
      peds_population = trauma_01_populations$peds,
      population_names = c("all", "adults", "peds"),
      measure_name = "Trauma-01",
      numerator_col = PAIN_SCALE,
      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(trauma.01$denominator < 10) && method == "wilson" && confidence_interval) {

      cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

    }

    return(trauma.01)

  }

}