SEQIC Indicator 1 – Trauma Team Response Evaluation

This function calculates System Evaluation and Quality Improvement Committee (SEQIC) Indicator 1 (subparts a through f). These indicators assess the timeliness and type of provider response (e.g., surgeon, mid-level, physician) to trauma alerts based on trauma team activation level, hospital trauma level, and time to provider presence. Confidence intervals can optionally be calculated for the proportion, using either the Wilson or Clopper-Pearson method.

Usage

seqic_indicator_1(
  data,
  trauma_team_activation_level,
  trauma_team_physician_service_type,
  level,
  included_levels = c("I", "II", "III", "IV"),
  unique_incident_id,
  response_time,
  trauma_team_activation_provider,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Arguments

data

A data frame containing trauma incident records.

trauma_team_activation_level

Column identifying trauma team activation level (e.g., Level 1, Level 2).

trauma_team_physician_service_type

Column indicating the type of medical provider (e.g., Surgery/Trauma, Emergency Medicine). For indicators 1a, 1b, and 1c, seqic_indicator_1() will only look for records with the trauma team member service type documented as marked as 'Surgery/Trauma'. For Indicators 1d, 1e, and 1f, seqic_indicator_1() will look for the following service types:

"Surgery/Trauma",
"Emergency Medicine",
"Family Practice",
"Nurse Practitioner",
"Physician Assistant",
"Surgery Senior Resident",
"Hospitalist",
"Internal Medicine"

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").

unique_incident_id

Unique identifier for each record.

response_time

Numeric variable representing the time (in minutes) to provider response.

trauma_team_activation_provider

Column identifying the responding provider for trauma activation.

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 tibble summarizing SEQIC Indicator 1 results across sub-measures (1a–1f). Includes numerators, denominators, and performance rate for each indicator. 95% confidence intervals are provided optionally.

Details

This function filters and summarizes trauma records to calculate SEQIC Indicators 1a through 1f:

1a: Proportion of Level 1 activations at Level I/II centers with surgical response <= 15 minutes.
1b: Same as 1a, but includes Level III centers and uses <= 30 minutes.
1c: Proportion of Level 1 activations with missing surgical response time.
1d/e: Response within 5 and 20 minutes, respectively, for specific provider types and activation levels, includes level I-IV trauma centers.
1f: Proportion of missing response times among the group in 1d/e, includes level I-IV trauma centers.

Note

This function:

Filters trauma records to those with a trauma team activation level of "Level 1" and/or "Level 2" based on the indicator.
Restricts provider type to surgical, physician, and mid-level provider roles.
Filters trauma center levels to I–IV based on the measure.
Calculates the proportion of cases where the response time is within 5, 15, or 30 minutes, depending on the indicator.
Computes proportions for trauma activation times, including missing times and within thresholds.

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)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(traumar)

# Data
data <- tibble::tibble(
  incident_id = 1:6,
  activation_level = c("Level 1", "Level 1", "Level 2", "Level 1", "Level 2",
  "Level 1"),
  provider_type = c("Surgery/Trauma", "Emergency Medicine", "Physician
  Assistant", "Surgery/Trauma", "Surgery/Trauma", "Family Practice"),
  trauma_level = c("I", "II", "III", "I", "III", "IV"),
  response_minutes = c(12, 25, 6, NA, 18, 22),
  provider = c("Dr. A", "Dr. B", "PA C", "Dr. D", "Dr. E", "NP F")
)

# Run the function
traumar::seqic_indicator_1(
  data = data,
  trauma_team_activation_level = activation_level,
  trauma_team_physician_service_type = provider_type,
  level = trauma_level,
  unique_incident_id = incident_id,
  response_time = response_minutes,
  trauma_team_activation_provider = provider,
  calculate_ci = "wilson"
) |>
tidyr::pivot_longer(cols = -1,
                    names_to = "Indicator",
                    values_to = "Values"
                    )
#> # A tibble: 30 × 3
#>    data              Indicator      Values
#>    <chr>             <chr>           <dbl>
#>  1 population/sample numerator_1a   1     
#>  2 population/sample denominator_1a 1     
#>  3 population/sample seqic_1a       1     
#>  4 population/sample lower_ci_1a    0.0546
#>  5 population/sample upper_ci_1a    1     
#>  6 population/sample numerator_1b   1     
#>  7 population/sample denominator_1b 1     
#>  8 population/sample seqic_1b       1     
#>  9 population/sample lower_ci_1b    0.0546
#> 10 population/sample upper_ci_1b    1     
#> # ℹ 20 more rows