Safety-02 Populations — safety_02

Filters data down to the target populations for Safety-02, and categorizes records to identify needed information for the calculations.

Identifies key categories related to a 911 request during which lights and sirens were not used during patient transport. This function segments the data by age into adult and pediatric populations.

Usage

safety_02_population(
  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_col,
  transport_disposition_cols = lifecycle::deprecated()
)

Arguments

df: A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.
patient_scene_table: A data.frame or tibble containing at least ePatient, and eScene as a fact table.
response_table: A data.frame or tibble containing at least 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 representing the patient's numeric age agnostic of unit.
epatient_16_col: Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").
eresponse_05_col: Column that contains eResponse.05 or the response type.
edisposition_18_col: Column giving documentation of transport mode techniques for this EMS response.
edisposition_28_col: Column giving patient disposition for an EMS event identifying whether a patient was evaluated and care or services were provided.
transport_disposition_col: One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition for an EMS event identifying whether a transport occurred and by which unit.
transport_disposition_cols: Use transport_disposition_col instead.

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
a tibble with a summary of missingness for each column in each table

Author

Nicolas Foss, Ed.D., MS

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)

  )

  # disposition table
  disposition_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    edisposition_18 = rep(4218015, 5),
    edisposition_28 = rep(4228001, 5),
    edisposition_30 = rep(4230001, 5)
  )

  # test the success of the function
  result <- safety_02_population(patient_scene_table = patient_table,
                        response_table = response_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,
                        edisposition_18_col = edisposition_18,
                        edisposition_28_col = edisposition_28,
                        transport_disposition_col = edisposition_30
                        )
#> 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] ●●●●●●●●●●●●●●●●●…
#> 

# show the results of filtering at each step
result$filter_process
#> # A tibble: 8 × 2
#>   filter                               count
#>   <chr>                                <int>
#> 1 911 calls                                5
#> 2 Patients evaluated and care provided     5
#> 3 Transport runs                           5
#> 4 No lights and sirens                     5
#> 5 Adults denominator                       2
#> 6 Peds denominator                         3
#> 7 Initial population                       5
#> 8 Total dataset                            5