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
NULL.- patient_scene_table
A data frame or tibble containing only ePatient and eScene fields as a fact table. Default is
NULL.- procedures_table
A data frame or tibble containing only the eProcedures fields needed for this measure's calculations. Default is
NULL.- vitals_table
A data frame or tibble containing only the eVitals fields needed for this measure's calculations. Default is
NULL.- airway_table
A data frame or tibble containing only the eAirway fields needed for this measure's calculations. 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
Column that contains the incident date. This defaults to
NULLas it is optional in case not available due to PII restrictions.- patient_DOB_col
Column that contains the patient's date of birth. This defaults to
NULLas it is optional in case not available due to PII restrictions.- 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
NULL.- eairway_04_col
Column name for airway procedure data. Default is
NULL.- 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
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
)
#> Running `airway_18_population()` [Working on 1 of 13 tasks] ●●●───────────────…
#> Running `airway_18_population()` [Working on 2 of 13 tasks] ●●●●●●────────────…
#> Running `airway_18_population()` [Working on 3 of 13 tasks] ●●●●●●●●──────────…
#> Running `airway_18_population()` [Working on 4 of 13 tasks] ●●●●●●●●●●────────…
#> Running `airway_18_population()` [Working on 5 of 13 tasks] ●●●●●●●●●●●●●─────…
#> Running `airway_18_population()` [Working on 6 of 13 tasks] ●●●●●●●●●●●●●●●───…
#> Running `airway_18_population()` [Working on 7 of 13 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `airway_18_population()` [Working on 8 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `airway_18_population()` [Working on 9 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `airway_18_population()` [Working on 10 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_18_population()` [Working on 11 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_18_population()` [Working on 12 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_18_population()` [Working on 13 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 12 × 2
#> filter count
#> <chr> <dbl>
#> 1 Invasive airway procedures 5
#> 2 Successful invasive airway procedures 5
#> 3 911 calls 5
#> 4 Successful invasive airway procedures performed after EMS arrival 5
#> 5 Waveform ETCO2 used 5
#> 6 Airway device placement confirmed after airway procedure 5
#> 7 Vitals taken after airway procedure where waveform ETCO2 >= 5 5
#> 8 Waveform ETCO2 >= 5 6
#> 9 Successful invasive airway procedures with waveform ETCO2 confirmed po… 5
#> 10 Adults denominator 2
#> 11 Peds denominator 3
#> 12 Total procedures in dataset 5