This function processes EMS data to calculate the Trauma-01 performance measure, which evaluates the percentage of trauma patients assessed for pain using a numeric scale. The function filters and summarizes the data based on specified inclusion criteria.
Usage
trauma_01(
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_23_col,
evitals_26_col,
evitals_27_col,
edisposition_28_col,
transport_disposition_col,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...
)Arguments
- df
A data frame or tibble containing EMS records. 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
Column name representing the EMS record ID.
- 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 the patient's age in numeric format.
- epatient_16_col
Column name for the unit of age (e.g., "Years", "Months").
- esituation_02_col
Column name indicating if the situation involved an injury.
- eresponse_05_col
Column name for the type of EMS response (e.g., 911 call).
- evitals_23_col
Column name for the Glasgow Coma Scale (GCS) total score.
- evitals_26_col
Column name for AVPU (Alert, Voice, Pain, Unresponsive) status.
- evitals_27_col
Column name for the pain scale assessment.
- edisposition_28_col
Column name for patient care disposition details.
- transport_disposition_col
Column name for transport disposition details.
- confidence_interval
![[Experimental]](figures/lifecycle-experimental.svg)
Logical. If
TRUE, the function calculates a confidence interval for the proportion estimate.- method
- Character. Specifies the
method used to calculate confidence intervals. Options are
"wilson"(Wilson score interval) and"clopper-pearson"(exact binomial interval). Partial matching is supported, so"w"and"c"can be used as shorthand. - conf.level
- Numeric. The confidence
level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
confidence interval). Defaults to 0.95.
- correct
- Logical. If
TRUE, applies a continuity correction to the Wilson score interval whenmethod = "wilson". Defaults toTRUE. - ...
optional additional arguments to pass onto
dplyr::summarize.
Value
A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:
pop: Population type (All, Adults, and Peds).numerator: Count of incidents meeting the measure.denominator: Total count of included incidents.prop: Proportion of incidents meeting the measure.prop_label: Proportion formatted as a percentage with a specified number of decimal places.lower_ci: Lower bound of the confidence interval forprop(ifconfidence_interval = TRUE).upper_ci: Upper bound of the confidence interval forprop(ifconfidence_interval = TRUE).
Examples
# Synthetic test data
test_data <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
epatient_15 = c(34, 5, 45, 2, 60), # Ages
epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
eresponse_05 = rep(2205001, 5),
esituation_02 = rep("Yes", 5),
evitals_23 = rep(15, 5),
evitals_26 = rep("Alert", 5),
evitals_27 = c(0, 2, 4, 6, 8),
edisposition_28 = rep(4228001, 5),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# Run the function
# Return 95% confidence intervals using the Wilson method
trauma_01(
df = test_data,
erecord_01_col = erecord_01,
epatient_15_col = epatient_15,
epatient_16_col = epatient_16,
eresponse_05_col = eresponse_05,
esituation_02_col = esituation_02,
evitals_23_col = evitals_23,
evitals_26_col = evitals_26,
evitals_27_col = evitals_27,
edisposition_28_col = edisposition_28,
transport_disposition_col = edisposition_30,
confidence_interval = TRUE
)
#>
#> ── Trauma-01 ───────────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for Trauma-01 ──
#>
#> Running `trauma_01_population()` [Working on 1 of 14 tasks] ●●●───────────────…
#> Running `trauma_01_population()` [Working on 2 of 14 tasks] ●●●●●─────────────…
#> Running `trauma_01_population()` [Working on 3 of 14 tasks] ●●●●●●●───────────…
#> Running `trauma_01_population()` [Working on 4 of 14 tasks] ●●●●●●●●●●────────…
#> Running `trauma_01_population()` [Working on 5 of 14 tasks] ●●●●●●●●●●●●──────…
#> Running `trauma_01_population()` [Working on 6 of 14 tasks] ●●●●●●●●●●●●●●────…
#> Running `trauma_01_population()` [Working on 7 of 14 tasks] ●●●●●●●●●●●●●●●●──…
#> Running `trauma_01_population()` [Working on 8 of 14 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 9 of 14 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 10 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 11 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 12 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 13 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 14 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating Trauma-01 ──
#>
#>
#> ✔ Function completed in 0.22s.
#>
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#> measure pop numerator denominator prop prop_label lower_ci upper_ci
#> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <dbl>
#> 1 Trauma-01 Adults 3 3 1 100% 0.310 1
#> 2 Trauma-01 Peds 1 1 1 100% 0.0546 1
#> 3 Trauma-01 All 5 5 1 100% 0.463 1