airway_18_population {nemsqar} | R Documentation |
Airway-18 Populations
Description
This function processes and analyzes the dataset to generate the populations of interest needed to perform calculations to obtain performance data.
Usage
airway_18_population(
df = NULL,
patient_scene_table = NULL,
procedures_table = NULL,
vitals_table = NULL,
airway_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,
eprocedures_01_col,
eprocedures_02_col,
eprocedures_03_col,
eprocedures_06_col,
eairway_02_col = NULL,
eairway_04_col = NULL,
evitals_01_col,
evitals_16_col
)
Arguments
df |
A data frame or tibble containing the dataset to be processed.
Default is |
patient_scene_table |
A data frame or tibble containing only ePatient
and eScene fields as a fact table. Default is |
procedures_table |
A data frame or tibble containing only the
eProcedures fields needed for this measure's calculations. Default is
|
vitals_table |
A data frame or tibble containing only the eVitals fields
needed for this measure's calculations. Default is |
airway_table |
A data frame or tibble containing only the eAirway fields
needed for this measure's calculations. Default is |
response_table |
A data frame or tibble containing only the eResponse
fields needed for this measure's calculations. Default is |
erecord_01_col |
Column name containing the unique patient record identifier. |
incident_date_col |
Column that contains the incident date. This
defaults to |
patient_DOB_col |
Column that contains the patient's date of birth. This
defaults to |
epatient_15_col |
Column name for patient information (exact purpose unclear). |
epatient_16_col |
Column name for patient information (exact purpose unclear). |
eresponse_05_col |
Column name for emergency response codes. |
eprocedures_01_col |
Column name for procedure times or other related data. |
eprocedures_02_col |
Column name for whether or not the procedure was performed prior to EMS care being provided. |
eprocedures_03_col |
Column name for procedure codes. |
eprocedures_06_col |
Column name for procedure success codes. |
eairway_02_col |
Column name for airway procedure data (datetime).
Default is |
eairway_04_col |
Column name for airway procedure data. Default is
|
evitals_01_col |
Column name for vital signs data (datetime). |
evitals_16_col |
Column name for additional vital signs data. |
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
Nicolas Foss, Ed.D., MS, Samuel Kordik, BBA, BS
Examples
# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble
# create tables to test correct functioning
# patient table
patient_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
"2025-01-01", "2025-06-01")), 2),
patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
"2023-01-01", "1970-06-01")), 2),
epatient_15 = rep(c(25, 5, 2, 2, 55), 2), # Ages
epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)
)
# response table
response_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
eresponse_05 = rep(2205001, 10)
)
# vitals table
vitals_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
evitals_01 = lubridate::as_datetime(c("2025-01-01 23:02:00",
"2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
"2025-06-01 13:01:00", "2025-01-01 23:02:00",
"2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
"2025-06-01 13:06:00")),
evitals_16 = rep(c(5, 6, 7, 8, 9), 2)
)
# airway table
airway_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
eairway_02 = rep(lubridate::as_datetime(c("2025-01-01 23:05:00",
"2025-01-05 12:02:00", "2025-02-01 19:03:00", "2025-01-01 05:04:00",
"2025-06-01 13:06:00")), 2),
eairway_04 = rep(4004019, 10)
)
# procedures table
procedures_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
"2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
"2025-06-01 13:00:00")), 2),
eprocedures_02 = rep("No", 10),
eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
673005), 2),
eprocedures_06 = rep(9923003, 10)
)
# Run the function
result <- airway_18_population(df = NULL,
patient_scene_table = patient_table,
procedures_table = procedures_table,
vitals_table = vitals_table,
response_table = response_table,
airway_table = airway_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,
eprocedures_01_col = eprocedures_01,
eprocedures_02_col = eprocedures_02,
eprocedures_03_col = eprocedures_03,
eprocedures_06_col = eprocedures_06,
evitals_01_col = evitals_01,
evitals_16_col = evitals_16,
eairway_02_col = eairway_02,
eairway_04_col = eairway_04
)
# show the results of filtering at each step
result$filter_process