Package 'nemsqar'

Description

Calculates the NEMSQA Airway-01 measure.

Calculates the proportion of times when the first endotracheal intubation attempt is successful with no peri-intubation hypoxia or hypotension.

Usage

airway_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  earrest_01_col,
  eresponse_05_col,
  evitals_01_col,
  evitals_06_col,
  evitals_12_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  eprocedures_05_col,
  eprocedures_06_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

Value

A data.frame summarizing results for two population groups (Adults and Peds) with the following columns:

• pop: Population type (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(s)

Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
    "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
    "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
    "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
    evitals_06 = rep(c(90, 100, 102, 103, 104), 2),
    evitals_12 = rep(c(90, 91, 92, 93, 94), 2)

  )

# arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    earrest_01 = rep("No", 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2),
    eprocedures_05 = rep(1, 10),
    eprocedures_06 = rep(9923003, 10)

  )

# Run the function
# Return 95% confidence intervals using the Wilson method
airway_01(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         arrest_table = arrest_table,
         response_table = response_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         eprocedures_05_col = eprocedures_05,
         eprocedures_06_col = eprocedures_06,
         earrest_01_col = earrest_01,
         evitals_01_col = evitals_01,
         evitals_06_col = evitals_06,
         evitals_12_col = evitals_12,
         confidence_interval = TRUE
         )

Description

This function processes and analyzes the dataset to generate the populations of interest needed to perform calculations to obtain performance data.

Usage

airway_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  earrest_01_col,
  eresponse_05_col,
  evitals_01_col,
  evitals_06_col,
  evitals_12_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  eprocedures_05_col,
  eprocedures_06_col
)

Arguments

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(s)

Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
    "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
    "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
    "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
    evitals_06 = rep(c(90, 100, 102, 103, 104), 2),
    evitals_12 = rep(c(90, 91, 92, 93, 94), 2)

  )

# arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    earrest_01 = rep("No", 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2),
    eprocedures_05 = rep(1, 10),
    eprocedures_06 = rep(9923003, 10)

  )

# Run the function
result <- airway_01_population(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         arrest_table = arrest_table,
         response_table = response_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         eprocedures_05_col = eprocedures_05,
         eprocedures_06_col = eprocedures_06,
         earrest_01_col = earrest_01,
         evitals_01_col = evitals_01,
         evitals_06_col = evitals_06,
         evitals_12_col = evitals_12
         )

# show the results of filtering at each step
result$filter_process

Description

Calculates the NEMSQA Airway-05 measure.

Calculates the proportion of endotracheal intubation attempts with adequate oxygenation.

Usage

airway_05(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  earrest_01_col,
  eresponse_05_col,
  evitals_01_col,
  evitals_12_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

Value

A data.frame summarizing results for two population groups (Adults and Peds) with the following columns:

• pop: Population type (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(s)

Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
    "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
    "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
    "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
    evitals_12 = rep(c(94, 95, 96, 97, 98), 2)

  )

# arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    earrest_01 = rep("No", 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2)

  )

# Run the function
# Return 95% confidence intervals using the Wilson method
airway_05(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         arrest_table = arrest_table,
         response_table = response_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         earrest_01_col = earrest_01,
         evitals_01_col = evitals_01,
         evitals_12_col = evitals_12,
         confidence_interval = TRUE
         )

Description

This function processes and analyzes the dataset to generate the populations of interest needed to perform calculations to obtain performance data.

Usage

airway_05_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  earrest_01_col,
  eresponse_05_col,
  evitals_01_col,
  evitals_12_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col
)

Arguments

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(s)

Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
    "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
    "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
    "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
    evitals_12 = rep(c(90, 91, 92, 93, 94), 2)

  )

# arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    earrest_01 = rep("No", 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2)

  )

# Run the function
result <- airway_05_population(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         arrest_table = arrest_table,
         response_table = response_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         earrest_01_col = earrest_01,
         evitals_01_col = evitals_01,
         evitals_12_col = evitals_12
         )

# show the results of filtering at each step
result$filter_process

Description

This function processes and analyzes the dataset to calculate the "Airway-18" NEMSQA metric. It includes cleaning and transforming several columns related to patient data, airway procedures, and vital signs, and it returns a cleaned dataset with the relevant calculations. The final calculation is an assessment of the successful last invasive airway procedures performed during an EMS response originating from a 911 request in which waveform capnography is used for tube placement confirmation.

Usage

airway_18(
  df = NULL,
  patient_scene_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  airway_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  eprocedures_06_col,
  eairway_02_col = NULL,
  eairway_04_col = NULL,
  evitals_01_col,
  evitals_16_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

Value

A data.frame summarizing results for two population groups (Adults and Peds) with the following columns:

• pop: Population type (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(s)

Nicolas Foss, Ed.D., MS, Samuel Kordik, BBA, BS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:01:00", "2025-01-01 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:06:00")),
    evitals_16 = rep(c(5, 6, 7, 8, 9), 2)

  )

  # airway table
  airway_table <- tibble::tibble(
  erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
  eairway_02 = rep(lubridate::as_datetime(c("2025-01-01 23:05:00",
    "2025-01-05 12:02:00", "2025-02-01 19:03:00", "2025-01-01 05:04:00",
    "2025-06-01 13:06:00")), 2),
  eairway_04 = rep(4004019, 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2),
    eprocedures_06 = rep(9923003, 10)

  )

# Run the function
# Return 95% confidence intervals using the Wilson method
airway_18(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         response_table = response_table,
         airway_table = airway_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         eprocedures_06_col = eprocedures_06,
         evitals_01_col = evitals_01,
         evitals_16_col = evitals_16,
         eairway_02_col = eairway_02,
         eairway_04_col = eairway_04,
         confidence_interval = TRUE
         )

Description

This function processes and analyzes the dataset to generate the populations of interest needed to perform calculations to obtain performance data.

Usage

airway_18_population(
  df = NULL,
  patient_scene_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  airway_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  eprocedures_06_col,
  eairway_02_col = NULL,
  eairway_04_col = NULL,
  evitals_01_col,
  evitals_16_col
)

Arguments

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(s)

Nicolas Foss, Ed.D., MS, Samuel Kordik, BBA, BS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")), 2),
    patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
                                "2023-01-01", "1970-06-01")), 2),
    epatient_15 = rep(c(25, 5, 2, 2, 55), 2),  # Ages
    epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:01:00", "2025-01-01 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:06:00")),
    evitals_16 = rep(c(5, 6, 7, 8, 9), 2)

  )

  # airway table
  airway_table <- tibble::tibble(
  erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
  eairway_02 = rep(lubridate::as_datetime(c("2025-01-01 23:05:00",
    "2025-01-05 12:02:00", "2025-02-01 19:03:00", "2025-01-01 05:04:00",
    "2025-06-01 13:06:00")), 2),
  eairway_04 = rep(4004019, 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2),
    eprocedures_06 = rep(9923003, 10)

  )

# Run the function
result <- airway_18_population(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         response_table = response_table,
         airway_table = airway_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         eprocedures_06_col = eprocedures_06,
         evitals_01_col = evitals_01,
         evitals_16_col = evitals_16,
         eairway_02_col = eairway_02,
         eairway_04_col = eairway_04
         )

# show the results of filtering at each step
result$filter_process

Description

Calculates the NEMSQA Asthma-01 measure.

Calculates key statistics related to asthma-related incidents in an EMS dataset, specifically focusing on cases where 911 was called for respiratory distress, and certain medications were administered. This function segments the data by age into adult and pediatric populations, computing the proportion of cases that received beta-agonist treatment.

Usage

asthma_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  medications_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  emedications_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

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(s)

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),
  esituation_11 = c("Respiratory Distress", "Respiratory Distress",
  "Chest Pain", "Respiratory Distress", "Respiratory Distress"),
  esituation_12 = c("Asthma", "Asthma", "Other condition", "Asthma", "Asthma"),
  emedications_03 = c("Albuterol", "Albuterol", "Epinephrine", "None",
  "Albuterol")
)

# Run the function
# Return 95% confidence intervals using the Wilson method
asthma_01(
  df = test_data,
  erecord_01_col = erecord_01,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col = epatient_15,
  epatient_16_col = epatient_16,
  eresponse_05_col = eresponse_05,
  esituation_11_col = esituation_11,
  esituation_12_col = esituation_12,
  emedications_03_col = emedications_03,
  confidence_interval = TRUE
)

Description

Filters data down to the target populations for Asthma-01, and categorizes records to identify needed information for the calculations.

Identifies key categories related to asthma-related incidents in an EMS dataset, specifically focusing on cases where 911 was called for respiratory distress, and certain medications were administered. This function segments the data by age into adult and pediatric populations, computing the proportion of cases that received beta-agonist treatment.

Usage

asthma_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  medications_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  emedications_03_col
)

Arguments

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(s)

Nicolas Foss, Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

# patient table
patient_table <- tibble::tibble(

  erecord_01 = 1:3,
  incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01")),
  patient_dob = as.Date(c("2000-01-01", "2020-01-01", "2023-01-01")),
  epatient_15 = c(25, 5, 2),
  epatient_16 = c("years", "years", "months")

)

# response table
response_table <- tibble::tibble(

  erecord_01 = 1:3,
  eresponse_05 = c("2205001", "2205009", "2205003")

)

# situation table
situation_table <- tibble::tibble(

  erecord_01 = 1:3,
  esituation_11 = c("weakness", "asthma", "bronchospasm"),
  esituation_12 = c("asthma", "weakness", "weakness")
)

# medications table
medications_table <- tibble::tibble(

  erecord_01 = 1:3,
  emedications_03 = c("albuterol", "levalbuterol", "metaproterenol")

)

# test the success of the function
result <- asthma_01_population(patient_scene_table = patient_table,
                               response_table = response_table,
                               situation_table = situation_table,
                               medications_table = medications_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_11_col = esituation_11,
                               esituation_12_col = esituation_12,
                               emedications_03_col = emedications_03
                               )

# show the results of filtering at each step
result$filter_process

Description

The hypoglycemia_01 function calculates the NEMSQA measure evaluating how often hypoglycemic patients with altered mental status receive hypoglycemia treatment.

Usage

hypoglycemia_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  evitals_18_col,
  evitals_23_col,
  evitals_26_col,
  emedications_03_col,
  eprocedures_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

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(s)

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),
  esituation_11 = c(rep("E13.64", 3), rep("E16.2", 2)),
  esituation_12 = c(rep("E13.64", 2), rep("E16.2", 3)),
  emedications_03 = c(372326, 376937,
                      377980, 4850,
                      4832),
  evitals_18 = c(60, 59, 58, 57, 56),
  evitals_23 = c(16, 15, 14, 13, 12),
  evitals_26 = c("Alert", "Painful", "Verbal", "Unresponsive", "Alert"),
  eprocedures_03 = rep("710925007", 5)
)

# Run the function
# Return 95% confidence intervals using the Wilson method
hypoglycemia_01(
  df = test_data,
  erecord_01_col = erecord_01,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col = epatient_15,
  epatient_16_col = epatient_16,
  eresponse_05_col = eresponse_05,
  esituation_11_col = esituation_11,
  esituation_12_col = esituation_12,
  emedications_03_col = emedications_03,
  evitals_18_col = evitals_18,
  evitals_23_col = evitals_23,
  evitals_26_col = evitals_26,
  eprocedures_03_col = eprocedures_03,
  confidence_interval = TRUE
)

Description

Filters data down to the target populations for Hypoglycemia-01, and categorizes records to identify needed information for the calculations.

Identifies key categories related to diabetes/hypoglycemia incidents in an EMS dataset, specifically focusing on cases where 911 was called for diabetes/hypoglycemia distress, certain medications were administered, and a weight is taken. This function segments the data into pediatric populations, computing the proportion of cases that have a documented weight.

Usage

hypoglycemia_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  evitals_18_col,
  evitals_23_col,
  evitals_26_col,
  emedications_03_col,
  eprocedures_03_col
)

Arguments

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(s)

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)

)

# situation table
situation_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  esituation_11 = c(rep("E13.64", 3), rep("E16.2", 2)),
  esituation_12 = c(rep("E13.64", 2), rep("E16.2", 3))
)

# medications table
medications_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  emedications_03 = c(372326, 376937, 377980, 4850, 4832),

)

# vitals table
vitals_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  evitals_18 = c(60, 59, 58, 57, 56),
  evitals_23 = c(16, 15, 14, 13, 12),
  evitals_26 = c("Alert", "Painful", "Verbal", "Unresponsive", "Alert")

)

# procedures table
procedures_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  eprocedures_03 = rep("710925007", 5)

)

# test the success of the function
result <- hypoglycemia_01_population(patient_scene_table = patient_table,
                            response_table = response_table,
                            situation_table = situation_table,
                            medications_table = medications_table,
                            vitals_table = vitals_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,
                            esituation_11_col = esituation_11,
                            esituation_12_col = esituation_12,
                            emedications_03_col = emedications_03,
                            evitals_18_col = evitals_18,
                            evitals_23_col = evitals_23,
                            evitals_26_col = evitals_26,
                            eprocedures_03_col = eprocedures_03
                            )

# show the results of filtering at each step
result$filter_process

Description

Computes confidence intervals for binomial proportions using either the Wilson or Clopper-Pearson method. This function supports vectorized operations and allows optional correction for continuity. The Wilson interval is computed using stats::prop.test(), while the Clopper-Pearson interval is computed using stats::binom.test().

Usage

nemsqa_binomial_confint(
  data = NULL,
  x,
  n,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE
)

Arguments

Details

The Wilson confidence interval is calculated using stats::prop.test(), which provides an improved approximation to the binomial proportion confidence interval by avoiding the instability of the Wald interval (Wilson, 1927). The Clopper-Pearson interval, computed using stats::binom.test(), is an exact method based on the cumulative probabilities of the binomial distribution (Clopper & Pearson, 1934).

The use of match.arg() within nemsqar::nemsqa_binomial_confint() allows users to specify the method using partial matching, meaning they can enter just "w" instead of "wilson" or "c" instead of "clopper-pearson".

Value

A tibble containing the estimated proportion (prop), lower confidence interval (lower_ci), upper confidence interval (upper_ci), and a formatted proportion label (prop_label). If data is provided, these columns are appended to data via dplyr::bind_cols().

References

Clopper, C. J. & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413. doi:10.2307/2331986.

Wilson, E.B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22, 209–212. doi:10.2307/2276774.

Examples

# Example without a data frame
nemsqa_binomial_confint(data = NULL,
                        x = c(5, 10, 20),
                        n = c(50, 100, 200),
                        method = "wilson"
                        )

# Example with a data.frame
data <- data.frame(successes = c(5, 10, 20), trials = c(50, 100, 200))
nemsqa_binomial_confint(data,
                        x = successes,
                        n = trials,
                        method = "clopper-pearson"
                        )

Description

This dataset provides completely synthetic test data for the airway-related fields in the National EMS Information System (NEMSIS). It is not specific to any single function but can be used to test multiple functions in the nemsqar package. Users are encouraged to experiment with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Usage

data(nemsqar_airway_table)

Format

A data frame with 10,000 rows and 8 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Airway Indications For Invasive Management List (eAirway.01)

List of indications for invasive airway management (character).

Airway Device Placement Confirmation Date Time (eAirway.02)

Timestamp of airway device placement confirmation (datetime).

Airway Device Being Confirmed (eAirway.03)

Type of airway device being confirmed (character).

Patient Airway Device Being Confirmed List (eAirway.03)

List of airway devices being confirmed (character).

Airway Device Placement Confirmed Method (eAirway.04)

Primary method used to confirm airway device placement (character).

Airway Device Placement Confirmed Method List (eAirway.04)

List of methods used to confirm airway device placement (character).

Examples

data(nemsqar_airway_table)
head(nemsqar_airway_table)

Description

This dataset provides completely synthetic test data for the cardiac arrest-related fields in the National EMS Information System (NEMSIS). It is not specific to any single function but can be used to test multiple functions in the nemsqar package. Users are encouraged to experiment with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Usage

data(nemsqar_arrest_table)

Format

A data frame with 10,000 rows and 28 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Cardiac Arrest During EMS Event With Code (eArrest.01)

Indicates whether cardiac arrest occurred during the EMS event (character).

Cardiac Arrest Etiology With Code (eArrest.02)

Suspected cause of the cardiac arrest (character).

Cardiac Arrest Indications Resuscitation Attempted By EMS (eArrest.03)

Whether resuscitation was attempted by EMS (character).

Cardiac Arrest Indications Resuscitation Attempted By EMS With Code List (eArrest.03)

Detailed reasons for resuscitation decisions (character).

Cardiac Arrest Witnessed By (eArrest.04)

Who witnessed the cardiac arrest (character).

Cardiac Arrest Witnessed By List (eArrest.04)

List of all witnesses to the cardiac arrest (character).

Cardiac Arrest CPR Provided Prior To EMS Arrival (3.4=eArrest.05/3.5=itArrest.105)

Whether CPR was provided before EMS arrival (character).

Cardiac Arrest Who Provided CPR Prior To EMS (3.4=eArrest.06/3.5=itArrest.106)

Who performed CPR before EMS arrival (character).

Cardiac Arrest Who Provided CPR Prior To EMS Arrival With Code List (3.4=eArrest.06/3.5=itArrest.106)

List of responders who provided CPR before EMS arrival (character).

Cardiac Arrest AED Use Prior To EMS Arrival (eArrest.07)

Whether an AED was used before EMS arrival (character).

Cardiac Arrest Who Used AED Prior To EMS (3.4=eArrest.08/3.5=itArrest.108)

Who used the AED before EMS arrival (character).

Cardiac Arrest Who Used AED Prior To EMS Arrival With Code List (3.4=eArrest.08/3.5=itArrest.108)

List of responders who used the AED before EMS arrival (character).

Cardiac Arrest Type Of CPR Provided List (eArrest.09)

List of types of CPR performed (character).

Cardiac Arrest First Monitored Arrest Rhythm Of Patient (eArrest.11)

First recorded cardiac rhythm during arrest (character).

Cardiac Arrest First Monitored Arrest Rhythm Of Patient With Code (eArrest.11)

Coded representation of the first monitored cardiac rhythm (character).

Cardiac Arrest Any Return Of Spontaneous Circulation (eArrest.12)

Whether the patient regained spontaneous circulation (character).

Cardiac Arrest Any Return Of Spontaneous Circulation With Code List (eArrest.12)

List of codes indicating ROSC status (character).

Cardiac Arrest Date Time (eArrest.14)

Timestamp of cardiac arrest occurrence (datetime).

Cardiac Arrest Resuscitation Discontinued Date Time (eArrest.15)

Timestamp of resuscitation discontinuation (datetime).

Cardiac Arrest Reason CPR Resuscitation Discontinued (eArrest.16)

Reason for discontinuing resuscitation (character).

Cardiac Arrest Rhythm On Arrival At Destination List (eArrest.17)

Recorded cardiac rhythm upon arrival at the destination (character).

Cardiac Arrest Patient Outcome At End Of EMS Event (eArrest.18)

Patient's condition at the end of the EMS event (character).

Cardiac Arrest Initial CPR Date Time (eArrest.19)

Timestamp of initial CPR administration (datetime).

Cardiac Arrest Who Initiated CPR With Code (3.4=itArrest.008/3.5=eArrest.20)

Who initiated CPR (character).

Cardiac Who First Applied The AED (3.4=itArrest.015/3.5=eArrest.21)

Who first applied the AED (character).

Cardiac Who First Defibrillated The Patient (3.4=itArrest.013/3.5=eArrest.22)

Who performed the first defibrillation (character).

Examples

data(nemsqar_arrest_table)
head(nemsqar_arrest_table)

Description

This dataset provides completely synthetic test data for evaluating disposition-related functions in the nemsqar package. It includes key variables related to patient disposition, transport mode, hospital capabilities, and team pre-arrival alerts. The dataset is intended to assist users in testing the expected input structure for disposition-related measures.

Usage

data(nemsqar_disposition_table)

Format

A data frame with 10,000 rows and 13 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Disposition Position Of Patient During Transport List (eDisposition.14)

Position of the patient during transport (character).

Disposition Additional Transport Mode Descriptor (eDisposition.18)

Additional transport mode details (character).

Disposition Additional Transport Mode Descriptor List (eDisposition.18)

List of additional transport mode descriptors (character).

Hospital Capability (3.4=itDisposition.105/3.5=eDisposition.23)

Primary hospital capability associated with the incident (character).

Hospital Capability List (3.4=itDisposition.105/3.5=eDisposition.23)

List of hospital capabilities at the destination facility (character).

Disposition Team Pre Arrival Alert (eDisposition.24)

Indicates whether a pre-arrival alert was triggered for the team (character).

Disposition Destination Team Pre-arrival Alert Activation (eDisposition.24)

Indicates the specific type of pre-arrival alert activated (character).

Destination Trauma General Team Pre-arrival Activation (eDisposition.24)

Indicates whether a general trauma team was activated pre-arrival (logical).

Patient Evaluation/Care (3.4=itDisposition.100/3.5=eDisposition.28)

Evaluation or care provided to the patient (character).

Transport Disposition (3.4=itDisposition.102/3.5=eDisposition.30)

Final transport disposition of the patient (character).

Disposition Incident Patient Disposition With Code (3.4=eDisposition.12/3.5=itDisposition.112)

Final disposition of the patient, including standardized codes (character).

Details

Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Examples

data(nemsqar_disposition_table)
head(nemsqar_disposition_table)

Description

This dataset provides completely synthetic test data for evaluating patient examination-related functions in the nemsqar package. It includes key variables related to patient weight, assessment findings, lung and chest examinations, and neurological assessments. The dataset is intended to assist users in testing the expected input structure for examination-related measures.

Usage

data(nemsqar_exam_table)

Format

A data frame with 10,000 rows and 11 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Patient Weight In Kilograms (eExam.01)

Patient's weight in kilograms (numeric).

Patient Length Based Color (eExam.02)

Color coding based on patient length for pediatric patients (character).

Patient Assessment Date Time (eExam.03)

Timestamp for patient assessment (datetime).

Patient Extremity Assessment Findings List (eExam.16)

List of findings related to extremity assessment (character).

Patient Neurological Assessment Findings List (eExam.20)

List of neurological assessment findings (character).

Patient Lung Assessment Finding Location (3.4=itExam.099/3.5=eExam.22)

Location of lung assessment findings (character).

Patient Lung Assessment Findings List (3.4=itExam.100/3.5=eExam.23)

List of lung assessment findings (character).

Patient ChestExclusive Assessment Finding Location (3.4=itExam.101/3.5=eExam.24)

Location of chest-exclusive assessment findings (character).

Patient ChestExclusive Assessment Findings List (3.4=itExam.102/3.5=eExam.25)

List of chest-exclusive assessment findings (character).

Details

Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Examples

data(nemsqar_exam_table)
head(nemsqar_exam_table)

Description

This dataset provides completely synthetic test data for evaluating injury-related functions in the nemsqar package. It includes key variables related to the cause of injury, trauma triage criteria, vehicular risk factors, and height of falls. The dataset is intended to assist users in testing the expected input structure for injury-related measures.

Usage

data(nemsqar_injury_table)

Format

A data frame with 10,000 rows and 8 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Injury Cause of Injury (eInjury.01)

General description of the cause of injury (character).

Injury Cause Of Injury Description And Code List (eInjury.01)

Detailed description and coding of injury causes (character).

Injury Trauma Center/Triage Criteria (Steps 1 and 2) List (eInjury.03)

List of trauma triage criteria met in Steps 1 and 2 (character).

Injury Vehicular Pedestrian Or Other Injury Risk Factor/Triage Criteria (Steps 3 and 4) (eInjury.04)

Primary vehicular or other risk factors for injury (character).

Injury Vehicular Pedestrian Or Other Injury Risk Factor/Triage Criteria (Steps 3 and 4) List (eInjury.04)

Detailed list of vehicular or pedestrian injury risk factors (character).

Injury Height Of Fall In Feet (eInjury.09)

Height of fall in feet when applicable (numeric).

Details

Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Examples

data(nemsqar_injury_table)
head(nemsqar_injury_table)

Description

This dataset provides completely synthetic test data for evaluating medication-related functions in the nemsqar package. It includes key variables related to medication administration, timing, route, and standardized coding. The dataset is designed to assist users in testing the expected input structure for medication-related measures.

Usage

data(nemsqar_medications_table)

Format

A data frame with 10,000 rows and 8 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Medication Administered Date Time (eMedications.01)

Date and time the medication was administered (datetime).

Medication Administered Prior To EMS Unit Care (eMedications.02)

Indicator of whether medication was administered before EMS arrival (character).

Medication Given or Administered Description And RXCUI Code (eMedications.03)

Name of medication administered with its associated RXCUI code (character).

Patient Medication Given or Administered Description And RXCUI Codes List (eMedications.03)

List of all medications administered with RXCUI codes (character).

Medication Administered Route (eMedications.04)

Method by which the medication was administered (character).

Medication Administered Route Code (eMedications.04)

Standardized code for medication administration route (character).

Details

Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Examples

data(nemsqar_medications_table)
head(nemsqar_medications_table)

Description

This dataset provides synthetic patient information from the ePatient section of the National Emergency Medical Services Information System (NEMSIS). It contains example patient demographics and scene-related details that can be used for testing various functions within the nemsqar package. Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Usage

nemsqar_patient_scene_table

Format

A tibble with 10,000 rows and 6 variables:

⁠Incident Patient Care Report Number - PCR (eRecord.01)⁠

(chr) Unique identifier for the patient care report.

⁠Incident Date⁠

(date) The date of the EMS incident.

⁠Patient Age (ePatient.15)⁠

(dbl) Patient's age at the time of the incident.

⁠Patient Age Units (ePatient.16)⁠

(chr) The units of measurement for patient age (e.g., Years, Months, Hours, etc.).

⁠Patient Date Of Birth (ePatient.17)⁠

(date) The patient's date of birth.

⁠Patient Gender (ePatient.13)⁠

(chr) The patient's gender as recorded in the EMS report.

Details

The data in this table are entirely synthetic and intended solely for testing purposes. These data do not represent real patients, incidents, or outcomes and should not be used for research or operational decision-making.

Examples

data(nemsqar_patient_scene_table)
dplyr::glimpse(nemsqar_patient_scene_table)

Description

This dataset provides synthetic procedure-related information from the eProcedures section of the National Emergency Medical Services Information System (NEMSIS). It contains example procedure details that can be used for testing various functions within the nemsqar package. Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Usage

nemsqar_procedures_table

Format

A tibble with 10,000 rows and 8 variables:

⁠Incident Patient Care Report Number - PCR (eRecord.01)⁠

(chr) Unique identifier for the patient care report.

⁠Incident Date⁠

(date) The date of the EMS incident.

⁠Procedure Performed Date Time (eProcedures.01)⁠

(dttm) The date and time the procedure was performed.

⁠Procedure Performed Prior To EMS Care (eProcedures.02)⁠

(chr) Indicates whether the procedure was performed before EMS arrival.

⁠Procedure Performed Description And Code (eProcedures.03)⁠

(chr) Description and code of the performed procedure.

⁠Patient Attempted Procedure Descriptions And Codes List (eProcedures.03)⁠

(chr) List of attempted procedures with descriptions and codes.

⁠Procedure Number Of Attempts (eProcedures.05)⁠

(dbl) Number of attempts made to perform the procedure.

⁠Procedure Successful (eProcedures.06)⁠

(chr) Indicates whether the procedure was successful.

Details

The data in this table are entirely synthetic and intended solely for testing purposes. These data do not represent real patients, incidents, or outcomes and should not be used for research or operational decision-making.

Examples

data(nemsqar_procedures_table)
dplyr::glimpse(nemsqar_procedures_table)

Description

A synthetic dataset representing the eResponse section of the National Emergency Medical Services Information System (NEMSIS). This dataset is intended for testing purposes only and does not contain real patient or incident data. Users are encouraged to test functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Usage

data(nemsqar_response_table)

Format

A tibble with 10,000 rows and 5 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Character. Unique identifier for the patient care report.

Incident Date

Date. The date of the EMS incident.

Response Type Of Service Requested With Code (eResponse.05)

Character. The type of service requested, including a coded value.

Response Type Of Scene Delay List (eResponse.10)

Character. A list of delay types encountered at the scene.

Response Additional Response Mode Descriptors List (eResponse.24)

Character. A list of additional response mode descriptors.

Description

This dataset provides completely synthetic test data for evaluating situation-related functions in the nemsqar package. It includes variables related to patient complaints, symptoms, injury status, and provider impressions. The dataset is designed to assist users in testing the expected input structure for situation-related measures.

Usage

data(nemsqar_situation_table)

Format

A data frame with 10,000 rows and 18 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique identifier for the incident report (character).

Incident Date

Date of the incident (Date).

Situation Symptom Onset Date Time (eSituation.01)

Date and time when symptoms began (datetime).

Situation Possible Injury With Code (eSituation.02)

Indicates whether an injury is possible, including coded response (character).

Situation Complaint Type (eSituation.03)

Classification of the patient's complaint (character).

Situation Complaint Statement (eSituation.04)

Primary complaint reported by the patient (character).

Situation Primary Complaint Statement List (eSituation.04)

List of primary complaints (character).

Situation Complaint Duration (eSituation.05)

Duration of the complaint (numeric).

Situation Complaint Duration Time Units (eSituation.06)

Units of time associated with the complaint duration (character).

Situation Chief Complaint Anatomic Location (eSituation.07)

Anatomic location of the primary complaint (character).

Situation Chief Complaint Organ System (eSituation.08)

Organ system affected by the chief complaint (character).

Situation Primary Symptom (eSituation.09)

Primary symptom reported by the patient, including ICD code (character).

Situation Other Associated Symptom Description (eSituation.10)

Description of additional symptoms (character).

Situation Other Associated Symptom ICD Code (eSituation.10)

ICD code for associated symptoms (character).

Situation Other Associated Symptoms List (eSituation.10)

List of additional symptoms reported (character).

Situation Provider Primary Impression Code And Description (eSituation.11)

Primary impression of the provider, including ICD code (character).

Situation Provider Secondary Impression Description And Code (eSituation.12)

Secondary provider impression, including ICD code (character).

Situation Provider Secondary Impression Description And Code List (eSituation.12)

List of secondary provider impressions (character).

Details

Users are encouraged to test these functions with this dataset, but results should not be interpreted as meaningful. Some outputs may be nonsensical, which is expected since this data is only intended to demonstrate the expected structure of input data.

Examples

data(nemsqar_situation_table)
head(nemsqar_situation_table)

Description

This dataset contains synthetic data for the eVitals section of the National Emergency Medical Services Information System (NEMSIS). It is designed for testing functions within the nemsqar package. The data structure follows the expected format for eVitals fields but does not produce meaningful clinical results.

Usage

nemsqar_vitals_table

Format

A tibble with 10,000 rows and 19 variables:

Incident Patient Care Report Number - PCR (eRecord.01)

Unique incident identifier (character).

Incident Date

Date of the EMS incident (Date).

Vitals Signs Taken Date Time (eVitals.01)

Timestamp of vital signs measurement (Datetime).

Vitals ECG Type (eVitals.04)

ECG type recorded (character).

Vitals Systolic Blood Pressure SBP (eVitals.06)

Systolic blood pressure (numeric).

Vitals Diastolic Blood Pressure DBP (eVitals.07)

Diastolic blood pressure (numeric).

Vitals Heart Rate (eVitals.10)

Heart rate in beats per minute (numeric).

Vitals Pulse Oximetry (eVitals.12)

Oxygen saturation percentage (numeric).

Vitals Respiratory Rate (eVitals.14)

Respiratory rate in breaths per minute (numeric).

Vitals Respiratory Effort (eVitals.15)

Observed respiratory effort (character).

Vitals Carbon Dioxide CO2 (eVitals.16)

End-tidal CO2 measurement (numeric).

Vitals Blood Glucose Level (eVitals.18)

Blood glucose level in mg/dL (numeric).

Vitals Glasgow Coma Score GCS Motor (eVitals.21)

GCS motor response score (character).

Vitals Total Glasgow Coma Score GCS (eVitals.23)

Total Glasgow Coma Score (numeric).

Vitals Level Of Responsiveness AVPU (eVitals.26)

AVPU scale assessment (character).

Vitals Pain Scale Score (eVitals.27)

Pain scale score (numeric).

Vitals Pain Scale Score Range Sort Order (eVitals.27)

Sort order for pain scale (numeric).

Vitals Stroke Scale Score (eVitals.29)

Stroke scale assessment result (character).

Vitals Stroke Scale Type (eVitals.30)

Type of stroke scale used (character).

Details

Users are encouraged to use this dataset to test functions, but the outputs may be nonsensical, as the data is solely intended to demonstrate the expected input structure.

Examples

data(nemsqar_vitals_table)
dplyr::glimpse(nemsqar_vitals_table)

Description

The function calculates a pediatric metric focused on EMS responses, specifically targeting responses that involve patients under 18 years of age, where certain weight-based medications were administered. This function filters EMS data to identify relevant 911 responses and further narrows down the dataset to cases involving children, calculating the proportion of cases with documented weight among those where weight-based medications were administered.

Usage

pediatrics_03b(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  exam_table = NULL,
  medications_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eexam_01_col,
  eexam_02_col,
  emedications_03_col,
  emedications_04_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

Value

A data.frame summarizing results for two population groups (Peds) with the following columns:

• pop: Population type (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(s)

Nicolas Foss, Ed.D., MS

Examples

# Synthetic test data
test_data <- tibble::tibble(
  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
  "2025-06-01", "2025-12-15")),
  patient_dob = as.Date(c("2021-01-01", "2020-01-01", "2022-02-01",
  "2023-06-01", "2019-12-15")),
  epatient_15 = c(4, 5, 3, 2, 6),  # Ages
  epatient_16 = c("Years", "Years", "Years", "Years", "Years"),
  eresponse_05 = rep(2205001, 5),
  emedications_03 = rep("stuff", 5),
  emedications_04 = c("Inhalation", "pill", "liquid", "pill", "liquid"),
  eexam_01 = c(60, 59, 58, 57, 56),
  eexam_02 = c("Red", "Purple", "Grey", "Yellow", "Orange")
)

# Run the function
# Return 95% confidence intervals using the Wilson method
pediatrics_03b(
  df = test_data,
  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,
  emedications_03_col = emedications_03,
  emedications_04_col = emedications_04,
  eexam_01_col = eexam_01,
  eexam_02_col = eexam_02,
  confidence_interval = TRUE
)

Description

Filters data down to the target populations for Pediatrics-03B, and categorizes records to identify needed information for the calculations.

Identifies key categories related to diabetes/hypoglycemia incidents in an EMS dataset, specifically focusing on cases where 911 was called for diabetes/hypoglycemia distress, certain medications were administered, and a weight is taken. This function segments the data into pediatric populations, computing the proportion of cases that have a documented weight.

Usage

pediatrics_03b_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  exam_table = NULL,
  medications_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eexam_01_col,
  eexam_02_col,
  emedications_03_col,
  emedications_04_col
)

Arguments

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(s)

Nicolas Foss, Ed.D., MS

Examples

# create tables to test correct functioning
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 <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  eresponse_05 = rep(2205001, 5)

)

exam_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  eexam_01 = c(60, 59, 58, 57, 56),
  eexam_02 = c("Red", "Purple", "Grey", "Yellow", "Orange")
)

medications_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  emedications_03 = rep("stuff", 5),
  emedications_04 = c("Inhalation", "pill", "liquid", "pill", "liquid"),

)

# test the success of the function

result <- pediatrics_03b_population(patient_scene_table = patient_table,
                           response_table = response_table,
                           exam_table = exam_table,
                           medications_table = medications_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,
                           emedications_03_col = emedications_03,
                           emedications_04_col = emedications_04,
                           eexam_01_col = eexam_01,
                           eexam_02_col = eexam_02
                           )

# show the results of filtering at each step
result$filter_process

Description

This function formats numeric variables as percentages with a specified number of decimal places. It refines the output by removing unnecessary trailing zeros after the decimal point and ensures the percentage sign is correctly applied without extraneous characters, resulting in a polished, human-readable percentage representation.

Usage

pretty_percent(variable, n_decimal = 1)

Arguments

Value

A character vector containing the formatted percentages.

Author(s)

Nicolas Foss, Ed.D., MS

Examples

# Example usage:
pretty_percent(0.12345)  # Default decimal places
pretty_percent(0.12345, n_decimal = 2)  # Two decimal places
pretty_percent(c(0.1, 0.25, 0.3333), n_decimal = 1)  # Vector input

Description

The respiratory_01 function filters and analyzes data related to emergency 911 respiratory distress incidents, providing summary statistics for adult and pediatric populations. This function uses specific data columns for 911 response codes, primary and secondary impressions, and vital signs to calculate the proportion of cases with complete vital signs recorded, stratified by age.

Usage

respiratory_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  evitals_12_col,
  evitals_14_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

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(s)

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),
  esituation_11 = c(rep("J80", 3), rep("I50.9", 2)),
  esituation_12 = c(rep("J80", 2), rep("I50.9", 3)),
  evitals_12 = c(60, 59, 58, 57, 56),
  evitals_14 = c(16, 15, 14, 13, 12)
)

# Run the function
# Return 95% confidence intervals using the Wilson method
respiratory_01(
  df = test_data,
  erecord_01_col = erecord_01,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col = epatient_15,
  epatient_16_col = epatient_16,
  eresponse_05_col = eresponse_05,
  esituation_11_col = esituation_11,
  esituation_12_col = esituation_12,
  evitals_12_col = evitals_12,
  evitals_14_col = evitals_14,
  confidence_interval = TRUE
)

Description

The respiratory_01_population function filters and analyzes data related to emergency 911 respiratory distress incidents, providing the adult, pediatric, and initial populations. This function uses specific data columns for 911 response codes, primary and secondary impressions, and vital signs to filter a dataset down to the populations of interest.

Usage

respiratory_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  evitals_12_col,
  evitals_14_col
)

Arguments

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(s)

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)

)

# situation table
situation_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  esituation_11 = c(rep("J80", 3), rep("I50.9", 2)),
  esituation_12 = c(rep("J80", 2), rep("I50.9", 3))
)

# vitals table
vitals_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  evitals_12 = c(60, 59, 58, 57, 56),
  evitals_14 = c(16, 15, 14, 13, 12)

)

# Run the function
result <- respiratory_01_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              situation_table = situation_table,
                              vitals_table = vitals_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_11_col = esituation_11,
                              esituation_12_col = esituation_12,
                              evitals_12_col = evitals_12,
                              evitals_14_col = evitals_14
                             )

# show the results of filtering at each step
result$filter_process

Description

The respiratory_02 function calculates metrics for pediatric and adult respiratory populations based on pre-defined criteria, such as low oxygen saturation and specific medication or procedure codes. It returns a summary table of the overall, pediatric, and adult populations, showing counts and proportions.

Usage

respiratory_02(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  evitals_12_col,
  emedications_03_col,
  eprocedures_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

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(s)

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),
    emedications_03 = c("Oxygen", "Oxygen", "Oxygen", "Oxygen", "Oxygen"),
    evitals_12 = c(60, 59, 58, 57, 56),
    eprocedures_03 = rep("applicable thing", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  respiratory_02(
    df = test_data,
    erecord_01_col = erecord_01,
    incident_date_col = NULL,
    patient_DOB_col = NULL,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    emedications_03_col = emedications_03,
    evitals_12_col = evitals_12,
    eprocedures_03_col = eprocedures_03,
    confidence_interval = TRUE
  )

Description

The respiratory_02_population function calculates metrics for pediatric and adult respiratory populations based on pre-defined criteria, such as low oxygen saturation and specific medication or procedure codes. It returns a summary table of the overall, pediatric, and adult populations, showing counts and proportions.

Usage

respiratory_02_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  evitals_12_col,
  emedications_03_col,
  eprocedures_03_col
)

Arguments

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(s)

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)

  )

  # medications table
  medications_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    emedications_03 = c("Oxygen", "Oxygen", "Oxygen", "Oxygen", "Oxygen")

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    evitals_12 = c(60, 59, 58, 57, 56),

  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eprocedures_03 = rep("applicable thing", 5)

  )

# Run the function
result <- respiratory_02_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              medications_table = medications_table,
                              vitals_table = vitals_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,
                              emedications_03_col = emedications_03,
                              evitals_12_col = evitals_12,
                              eprocedures_03_col = eprocedures_03
                             )

# show the results of filtering at each step
result$filter_process

Description

The safety_01 function calculates the proportion of 911 responses where "lights and sirens" were not used in an EMS dataset. It generates age-based population summaries, calculating the count and proportion of "lights and sirens" responses among all incidents, and within adult and pediatric groups.

Usage

safety_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eresponse_24_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)