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SEQIC Indicator 9 - Emergency Department Transfer Timeliness

Source: R/seqic_9.R
seqic_indicator_9.Rd

[Experimental]

Calculates the proportion of EMS-transferred trauma patients who experienced delayed transfer from the emergency department (ED) based on disposition and decision-to-transfer time frames. This includes both overall rates and stratified results by trauma team activation status, with optional confidence intervals.

Usage

seqic_indicator_9(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  transfer_out_indicator,
  transport_method,
  unique_incident_id,
  trauma_team_activated,
  risk_group,
  ed_LOS,
  ed_decision_LOS,
  ed_decision_discharge_LOS,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Arguments

data

A data frame containing trauma incident records.

level

Column indicating the trauma center designation level (e.g., I, II, III, IV).

included_levels

Character vector indicating what facility levels to include in the analysis. Defaults to c("I", "II", "III", "IV").

transfer_out_indicator

Column name indicating whether the patient was transferred out of the initial trauma center to definitive care. Logical, character, or factor type. Values representing "No" (e.g., FALSE, "No") indicate no transfer out.

transport_method

Column identifying the EMS transport method (e.g., ambulance, private vehicle). Used to exclude non-qualified modes of arrival.

unique_incident_id

Unique identifier for each record.

trauma_team_activated

Column indicating whether the trauma team was activated (character, factor, or logical).

risk_group

A character or factor column indicating the patient's risk group (e.g., "High", "Moderate", "Low"). See risk definitions below.

ed_LOS

Column for the calculated ED length of stay, measured in minutes.

ed_decision_LOS

Numeric column representing minutes from ED arrival to decision to transfer.

ed_decision_discharge_LOS

Numeric column representing minutes from ED decision to discharge to physical discharge.

groups

Additional columns passed as a vector of strings to dplyr::summarize() via the .by argument for grouped summaries. Defaults to NULL.

calculate_ci

If NULL, 95% confidence intervals will not be calculated for the performance estimates. Otherwise, options of "wilson" or "clopper-pearson" can be supplied to utilize the corresponding methods to calculate the confidence intervals for the proportions. Defaults to NULL.

...

Arguments passed on to nemsqar::nemsqa_binomial_confint

conf.level

Numeric value between 0 and 1 indicating the confidence level. Defaults to 0.95 (95% confidence interval).

correct

Logical, indicating whether to apply continuity correction for Wilson intervals. Defaults to TRUE.

Value

A list of four tibbles, with optional 95% confidence intervals:

  • seqic_9_all: Proportion of transferred trauma patients with ED discharge or decision delays >2 or >3 hours, grouped by optional variables.

  • seqic_9_activations: Same proportions as above, further stratified by trauma team activation status.

  • seqic_9_risk: Same proportions as above, further stratified by risk groups.

  • seqic_9_activations_risk: Same proportions as above, further stratified by risk groups and trauma team activation status.

Each tibble includes numerators, denominators, proportions, and (optionally) confidence intervals for:

  • 9a: Delayed discharge >2 hours

  • 9b: Delayed discharge >3 hours

  • 9c: Delayed decision >1 hours

  • 9d: Delayed decision >2 hours

  • 9e: Delayed decision to discharge >1 hour

  • 9f: Delayed decision to discharge >2 hours

Details

This function:

  • Filters the dataset to include only transfers out from trauma centers designated Level I through IV.

  • Deduplicates records using unique_incident_id.

  • Flags records where emergency department decision to discharge occurred more than 60 or 120 minutes after ED arrival.

  • Flags records where physical departure from the ED occurred more than 120 or 180 minutes after ED arrival.

  • Flags records where physical discharge occurred more than 60 or 120 minutes after ED decision to discharge.

  • Stratifies results by trauma team activation status and one or more grouping variables.

  • Stratifies results by risk groups and one or more grouping variables.

  • Returns a summarized tibble with the number of delayed cases (numerator), eligible records (denominator), and the proportion for each delay threshold.

  • Optionally includes 95% confidence intervals if calculate_ci = TRUE.

Note

This function calculates discharge timeliness outcomes for patients transported to trauma centers, stratified by risk of mortality. Risk groups—low, moderate, and high— are defined by the Iowa System Evaluation and Quality Improvement Committee (SEQIC) as described below. Users may also apply alternative risk stratification methods if preferred.

  • Abnormal Physiology Criteria: GCS 3–5; Respirations <5 or >30 per minute; Systolic BP <60 mm Hg

  • High Risk: Probability of Survival < 0.2; ISS > 41; ISS > 24 with abnormal physiology

  • Moderate Risk: Probability of Survival 0.2–0.5; ISS 16–41

  • Low Risk: Probability of Survival > 0.5; ISS < 16; Normal physiology

Users must ensure appropriate column names are passed and data is pre-processed to include the necessary fields without missing critical identifiers or timestamps.

Author

Nicolas Foss, Ed.D., MS

Examples

# Packages
library(dplyr)
library(traumar)

# Simulated dataset for SEQIC Indicator 9
test_data <- tibble::tibble(
  id = as.character(1:10),
  trauma_level = c("I", "II", "III", "IV", "V", "II", "III", "IV", "I",
  "II"),
  transport = c("Ambulance", "Ambulance", "Private Vehicle", "Ambulance",
  "Helicopter", "Ambulance", "Ambulance", "Ambulance", "Ambulance",
  "Ambulance"),
  activated = c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE,
  FALSE),
  ed_LOS = c(120, 180, 90, 60, 200, 130, 110, 160, 95, 220),
  ed_decision = c(55, 125, 65, 30, 190, 80, 70, 45, 61, 130),
  ed_discharge = c(130, 185, 110, 65, 150, 160, 95, 180, 70, 210),
  transfer_out = c(TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE,
  TRUE),
  risk = c("High", "High", "Moderate", "Low", "Moderate", "Low",
           "High", "Low", "Moderate", "High")
)

# Run the function, and store as a list object
seqic_9_result <- traumar::seqic_indicator_9(
  data = test_data,
  level = trauma_level,
  included_levels = c("I", "II", "III", "IV"),
  unique_incident_id = id,
  transport_method = transport,
  transfer_out_indicator = transfer_out,
  ed_LOS = ed_LOS,
  ed_decision_LOS = ed_decision,
  ed_decision_discharge_LOS = ed_discharge,
  trauma_team_activated = activated,
  risk_group = risk
)

# Take a look at the overall output of the function
seqic_9_result$overall |>
tidyr::pivot_longer(cols = -1,
                    names_to = "Indicator",
                    values_to = "Values"
                    )
#> # A tibble: 18 × 3
#>    data              Indicator          Values
#>    <chr>             <chr>               <dbl>
#>  1 population/sample numerator_9a_all    3    
#>  2 population/sample denominator_9a_all  7    
#>  3 population/sample seqic_9a_all        0.429
#>  4 population/sample numerator_9b_all    1    
#>  5 population/sample denominator_9b_all  7    
#>  6 population/sample seqic_9b_all        0.143
#>  7 population/sample numerator_9c_all    5    
#>  8 population/sample denominator_9c_all  7    
#>  9 population/sample seqic_9c_all        0.714
#> 10 population/sample numerator_9d_all    2    
#> 11 population/sample denominator_9d_all  7    
#> 12 population/sample seqic_9d_all        0.286
#> 13 population/sample numerator_9e_all    7    
#> 14 population/sample denominator_9e_all  7    
#> 15 population/sample seqic_9e_all        1    
#> 16 population/sample numerator_9f_all    4    
#> 17 population/sample denominator_9f_all  7    
#> 18 population/sample seqic_9f_all        0.571