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Safety-02 Calculation

Source: R/safety_02.R
safety_02.Rd

The safety_02 function calculates the Safety-02 metric, evaluating the proportion of emergency medical calls involving transport where no lights and sirens were used. This function categorizes the population into adult and pediatric groups based on their age, and summarizes results with a total population count as well.

Usage

safety_02(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  disposition_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  edisposition_18_col,
  edisposition_28_col,
  transport_disposition_cols,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame where each row is an observation, and each column represents a feature.

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.

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

Column that contains the incident date. This defaults to NULL as 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 NULL as it is optional in case not available due to PII restrictions.

epatient_15_col

Column giving the calculated age value.

epatient_16_col

Column giving the provided age unit value.

eresponse_05_col

Column giving response codes, identifying 911 responses.

edisposition_18_col

Column giving transport mode descriptors, including possible lights-and-sirens indicators.

edisposition_28_col

Column giving patient evaluation and care categories for the EMS response.

transport_disposition_cols

One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition details.

confidence_interval

[Experimental] Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

[Experimental]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

[Experimental]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

[Experimental]Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

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 for prop (if confidence_interval = TRUE).

  • upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author

Nicolas Foss, Ed.D., MS

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),
    edisposition_18 = rep(4218015, 5),
    edisposition_28 = rep(4228001, 5),
    edisposition_30 = rep(4230001, 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  safety_02(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    edisposition_18_col = edisposition_18,
    edisposition_28_col = edisposition_28,
    transport_disposition_cols = edisposition_30,
    confidence_interval = TRUE
  )
#> 
#> ── Safety-02 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Safety-02 ──
#> 
#> Running `safety_02_population()`  [Working on 1 of 11 tasks] ●●●●──────────────…
#> Running `safety_02_population()`  [Working on 2 of 11 tasks] ●●●●●●────────────…
#> Running `safety_02_population()`  [Working on 3 of 11 tasks] ●●●●●●●●●─────────…
#> Running `safety_02_population()`  [Working on 4 of 11 tasks] ●●●●●●●●●●●●──────…
#> Running `safety_02_population()`  [Working on 5 of 11 tasks] ●●●●●●●●●●●●●●●───…
#> Running `safety_02_population()`  [Working on 6 of 11 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `safety_02_population()`  [Working on 7 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()`  [Working on 8 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()`  [Working on 9 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()`  [Working on 10 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()`  [Working on 11 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Safety-02 ──
#> 
#> 
#> ✔ Function completed in 0.17s.
#> 
#> 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 Safety-02 Adults         3           3     1 100%         0.310         1
#> 2 Safety-02 Peds           1           1     1 100%         0.0546        1
#> 3 Safety-02 All            5           5     1 100%         0.463         1