This function processes EMS data to generate the population needed to calculated the Trauma-04 NEMSQA measure.
Usage
trauma_04_population(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
situation_table = NULL,
vitals_table = NULL,
exam_table = NULL,
procedures_table = NULL,
injury_table = NULL,
disposition_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,
eresponse_10_col,
transport_disposition_col,
edisposition_23_col,
evitals_06_col,
evitals_10_col,
evitals_12_col,
evitals_14_col,
evitals_15_col,
evitals_21_col,
eexam_16_col,
eexam_20_col,
eexam_23_col,
eexam_25_col,
eprocedures_03_col,
einjury_01_col,
einjury_03_col,
einjury_04_col,
einjury_09_col
)Arguments
- df
A data frame or tibble containing EMS data with all relevant columns.
- patient_scene_table
A data.frame or tibble containing only epatient and escene fields as a fact table.
- response_table
A data.frame or tibble containing only the eresponse fields needed for this measure's calculations.
- situation_table
A data.frame or tibble containing only the esituation fields needed for this measure's calculations.
- vitals_table
A data.frame or tibble containing only the evitals fields needed for this measure's calculations.
- exam_table
A data.frame or tibble containing only the eexam fields needed for this measure's calculations.
- procedures_table
A data.frame or tibble containing only the eprocedures fields needed for this measure's calculations.
- injury_table
A data.frame or tibble containing only the einjury fields needed for this measure's calculations.
- disposition_table
A data.frame or tibble containing only the edisposition fields needed for this measure's calculations.
- erecord_01_col
The column representing the EMS record unique identifier.
- incident_date_col
The column indicating the incident date. Must be of class
Dateor similar.- patient_DOB_col
The column representing the patient's date of birth. Must be of class
Dateor similar.- 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.
- eresponse_10_col
Column name containing scene delay information.
- transport_disposition_col
The column for patient transport disposition.
- edisposition_23_col
Column name containing trauma hospital verification information.
- evitals_06_col
Column name containing systolic blood pressure (SBP) values.
- evitals_10_col
Column name containing heart rate values.
- evitals_12_col
Column name containing pulse oximetry values.
- evitals_14_col
Column name containing capillary refill information.
- evitals_15_col
Column name containing respiratory effort values.
- evitals_21_col
Column name containing Glasgow Coma Scale (GCS) Motor values.
- eexam_16_col
Column name containing extremities assessment details.
- eexam_20_col
Column name containing neurological assessment details.
- eexam_23_col
Column name containing lung assessment details.
- eexam_25_col
Column name containing chest assessment details.
- eprocedures_03_col
Column name containing airway management or tourniquet usage details.
- einjury_01_col
Column name containing injury cause details.
- einjury_03_col
Column name containing trauma triage steps 1 and 2 information.
- einjury_04_col
Column name containing trauma triage steps 3 and 4 information.
- einjury_09_col
Column name containing fall height information.
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),
eresponse_10 = rep(2210011, 5)
)
# situation table
situation_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
esituation_02 = rep("Yes", 5),
)
# vitals table
vitals_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
evitals_06 = c(100, 90, 80, 70, 85),
evitals_10 = c(110, 89, 88, 71, 85),
evitals_12 = c(50, 60, 70, 80, 75),
evitals_14 = c(30, 9, 8, 7, 31),
evitals_15 = c("apneic", "labored", "rapid", "shallow", "weak/agonal"),
evitals_21 = c(5, 4, 3, 2, 1)
)
# disposition table
disposition_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
edisposition_23 = c(9908029, 9908027, 9908025, 9908023, 9908021),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# injury table
injury_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
einjury_01 = c("V20", "V36", "V86", "V39", "V32"),
einjury_03 = c(2903011, 2903009, 2903005, 3903003, 2903001),
einjury_04 = c(2904013, 2904011, 2904009, 2904007, 2904001),
einjury_09 = c(11, 12, 13, 14, 15)
)
# exam table
exam_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
eexam_16 = c(3516043, 3516067, 3516043, 3516067, 3516067),
eexam_20 = c(3520045, 3520043, 3520019, 3520017, 3520017),
eexam_23 = c(3523011, 3523003, 3523001, 3523011, 3523003),
eexam_25 = c(3525039, 3525023, 3525005, 3525039, 3525023)
)
# procedures table
procedures_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
eprocedures_03 = c(424979004, 427753009, 429705000, 47545007, 243142003)
)
# test the success of the function
result <- trauma_04_population(patient_scene_table = patient_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
disposition_table = disposition_table,
exam_table = exam_table,
injury_table = injury_table,
procedures_table = procedures_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_10_col = eresponse_10,
esituation_02_col = esituation_02,
evitals_06_col = evitals_06,
evitals_10_col = evitals_10,
evitals_12_col = evitals_12,
evitals_14_col = evitals_14,
evitals_15_col = evitals_15,
evitals_21_col = evitals_21,
eexam_16_col = eexam_16,
eexam_20_col = eexam_20,
eexam_23_col = eexam_23,
eexam_25_col = eexam_25,
edisposition_23_col = edisposition_23,
transport_disposition_col = edisposition_30,
eprocedures_03_col = eprocedures_03,
einjury_01_col = einjury_01,
einjury_03_col = einjury_03,
einjury_04_col = einjury_04,
einjury_09_col = einjury_09
)
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#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 29 × 2
#> filter count
#> <chr> <int>
#> 1 Situation possible injury 5
#> 2 911 calls 5
#> 3 Transports 5
#> 4 GCS Motor 1-5 5
#> 5 Breath sounds absent, decreased, increased respiratory effort 5
#> 6 Flail segment, retraction, accessory muscles used in breathing 5
#> 7 Respiratory effort apneic, labored, mech. assist, rapid, shallow, weak… 5
#> 8 Airway management procedures 5
#> 9 Pulse oximetry < 90 5
#> 10 SBP < 110 5
#> # ℹ 19 more rows