seqic_indicator_1 | R Documentation |
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.
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,
...
)
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,
|
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 |
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
|
calculate_ci |
If |
... |
Arguments passed on to
|
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.
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.
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.
Nicolas Foss, Ed.D., MS
# Packages
library(dplyr)
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"
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.