Filters data down to the target populations for Trauma-08, and categorizes records to identify needed information for the calculations.
Identifies key categories to records that are 911 request for patients whose pain score was lowered during the EMS encounter. based on specific criteria and calculates related ECG measures. This function segments the data by age into adult and pediatric populations.
Usage
trauma_03_population(
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_01_col,
evitals_27_col = NULL,
evitals_27_initial_col = NULL,
evitals_27_last_col = NULL,
edisposition_28_col,
transport_disposition_col
)Arguments
- df
A data frame or tibble containing EMS data with all relevant columns. Default is
NULL.- 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.- situation_table
A data frame or tibble containing only the esituation fields needed for this measure's calculations. Default is
NULL.- disposition_table
A data frame or tibble containing only the edisposition 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.- erecord_01_col
The column representing the EMS record unique 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
The column for patient age numeric value.
- epatient_16_col
The column for patient age unit (e.g., "Years", "Months").
- esituation_02_col
The column containing information on the presence of injury.
- eresponse_05_col
The column representing the 911 response type.
- evitals_01_col
The column for the time of pain scale measurement.
- evitals_27_col
The column for the pain scale score. Default is
NULL.- evitals_27_initial_col
The column for the initial pain scale score. Default is
NULL.- evitals_27_last_col
The column for the last pain scale score. Default is
NULL.- edisposition_28_col
The column for patient care disposition details.
- transport_disposition_col
The column for patient transport disposition.
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
# 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)
)
# situation table
situation_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
esituation_02 = rep("Yes", 5),
)
# vitals table for a single pain scale column
vitals_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
evitals_01 = lubridate::as_datetime(c("2025-01-01 12:00:00", "2025-01-05
18:00:00", "2025-02-01 06:00:00", "2025-01-01 01:00:00", "2025-06-01
14:00:00"))
) |>
tidyr::uncount(weights = 2) |> # Duplicate each row twice
# Assign pain scores
dplyr::mutate(evitals_27 = c(0, 0, 2, 1, 4, 3, 6, 5, 8, 7)) |>
dplyr::group_by(erecord_01) |>
dplyr::mutate(
# Lower score = later time
time_offset = dplyr::if_else(dplyr::row_number() == 1, -5, 0),
evitals_01 = evitals_01 + lubridate::dminutes(time_offset)
) |>
dplyr::ungroup() |>
dplyr::select(-time_offset) # Remove temporary column
# disposition table
disposition_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
edisposition_28 = rep(4228001, 5),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# test the success of the function
# use the single pain scale column
result <- trauma_03_population(patient_scene_table = patient_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
disposition_table = disposition_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,
esituation_02_col = esituation_02,
evitals_01_col = evitals_01,
evitals_27_initial_col = NULL,
evitals_27_last_col = NULL,
evitals_27_col = evitals_27,
edisposition_28_col = edisposition_28,
transport_disposition_col = edisposition_30
)
#> Running `trauma_03_population()` [Working on 1 of 17 tasks] ●●●───────────────…
#> Running `trauma_03_population()` [Working on 2 of 17 tasks] ●●●●●─────────────…
#> Running `trauma_03_population()` [Working on 3 of 17 tasks] ●●●●●●────────────…
#> Running `trauma_03_population()` [Working on 4 of 17 tasks] ●●●●●●●●──────────…
#> Running `trauma_03_population()` [Working on 5 of 17 tasks] ●●●●●●●●●●────────…
#> Running `trauma_03_population()` [Working on 6 of 17 tasks] ●●●●●●●●●●●●──────…
#> Running `trauma_03_population()` [Working on 7 of 17 tasks] ●●●●●●●●●●●●●─────…
#> Running `trauma_03_population()` [Working on 8 of 17 tasks] ●●●●●●●●●●●●●●●───…
#> Running `trauma_03_population()` [Working on 9 of 17 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `trauma_03_population()` [Working on 10 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()` [Working on 11 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()` [Working on 12 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()` [Working on 13 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()` [Working on 17 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 10 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 Non-missing vital sign date-time with initial pain score > 0 4
#> 3 Transports 5
#> 4 Injury cases 5
#> 5 Patient evaluated and care provided 5
#> 6 Pain scale decreased 4
#> 7 Adults denominator 1
#> 8 Peds denominator 3
#> 9 Initial population 4
#> 10 Total dataset 5