safety_01_population: Safety-01 Populations

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

Safety-01 Populations

Description

Filters data down to the target populations for Safety-01, and categorizes records to identify needed information for the calculations.

Identifies key categories related to 911 responses where "lights and sirens" were not used in an EMS dataset. This function segments the data by age into adult and pediatric populations.

Usage

safety_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eresponse_24_col
)

Arguments

df	A data frame or tibble containing EMS data.
patient_scene_table	A data frame or tibble containing only epatient and escene fields as a fact table. Default is NULL.
response_table	A data frame or tibble containing only the eresponse fields needed for this measure's calculations. Default is NULL.
erecord_01_col	Column name containing the unique patient record identifier.
incident_date_col	Date or POSIXct column indicating the date of the incident.
patient_DOB_col	Date or POSIXct column for the patient’s date of birth
epatient_15_col	Column containing age.
epatient_16_col	Column for age units.
eresponse_05_col	Column containing response mode codes (e.g., 911 response codes).
eresponse_24_col	Column detailing additional response descriptors as text.

Value

A list that contains the following:

• a tibble with counts for each filtering step,

• a tibble for each population of interest

• a tibble for the initial population

• a tibble for the total dataset with computations

Author(s)

Nicolas Foss, Ed.D., MS

Examples

# create tables to test correct functioning

# patient table
  patient_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    incident_date = as.Date(c("2025-01-01", "2025-01-05",
                              "2025-02-01", "2025-01-01",
                              "2025-06-01")
                              ),
    patient_dob = as.Date(c("2000-01-01", "2020-01-01",
                            "2023-02-01", "2023-01-01",
                            "1970-06-01")
                            ),
    epatient_15 = c(25, 5, 2, 2, 55),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Years", "Years")

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eresponse_05 = rep(2205001, 5),
    eresponse_24 = rep("No Lights or Sirens", 5)

  )

# Run the function
result <- safety_01_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              erecord_01_col = erecord_01,
                              incident_date_col = incident_date,
                              patient_DOB_col = patient_dob,
                              epatient_15_col = epatient_15,
                              epatient_16_col = epatient_16,
                              eresponse_05_col = eresponse_05,
                              eresponse_24_col = eresponse_24
                        )

# show the results of filtering at each step
result$filter_process

nemsqar documentation built on Aug. 8, 2025, 6:15 p.m.