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SEQIC Indicator 12 - Timeliness of Data Entry Post-Discharge

Source: R/seqic_12.R
seqic_indicator_12.Rd

[Experimental]

Calculates the proportion of trauma cases where data were entered into the trauma registry within a defined number of days post-discharge. This measure supports trauma system quality improvement by identifying facilities meeting timely documentation expectations.

Usage

seqic_indicator_12(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  facility_id,
  exclude_facility_list = NULL,
  unique_incident_id,
  data_entry_time,
  data_entry_standard = 60,
  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").

facility_id

Numeric, character, or factor. Column giving the unique facility identifiers in the trauma dataset.

exclude_facility_list

Optional. Numeric, character, or factor. List of facilities to exclude from analysis due to known data quality issues or other justifiable reasons. Defaults to NULL.

unique_incident_id

Unique identifier for each record.

data_entry_time

Numeric. Column representing the time in days between patient discharge and trauma registry data entry.

data_entry_standard

Numeric. The maximum allowable number of days between discharge and data entry. Records entered within this threshold are considered timely. Default is 60.

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 12 results. Includes numerator, denominator, and performance rate. 95% confidence intervals are included if requested.

Details

This function:

  • Filters to include only patients treated at Level I–IV trauma centers.

  • Excludes records from facilities specified by the user, if applicable.

  • Deduplicates by unique_incident_id to ensure each incident is counted once.

  • Flags records where data entry occurred within data_entry_standard days of discharge.

  • Optionally calculates confidence intervals using methods from nemsqa_binomial_confint().

  • Returns a tibble with numerator, denominator, and proportion of timely entries, with optional confidence intervals and population/sample labels.

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 data for SEQIC Indicator 12
test_data <- tibble::tibble(
  id = as.character(1:10),
  trauma_level = c("I", "II", "III", "IV", "II", "I", "IV", "III", "II",
  "I"),
  facility = c("A", "B", "C", "D", "A", "C", "B", "A", "C", "D"),
  data_entry_delay = c(30, 65, 10, 70, 45, 20, 80, 15, 55, 90)
)

# Run the function
traumar::seqic_indicator_12(
  data = test_data,
  level = trauma_level,
  included_levels = c("I", "II", "III", "IV"),
  facility_id = facility,
  unique_incident_id = id,
  exclude_facility_list = c("D"),
  data_entry_time = data_entry_delay,
  data_entry_standard = 60,
  calculate_ci = "wilson"
)
#> # A tibble: 1 × 6
#>   data              numerator_12 denominator_12 seqic_12 lower_ci_12 upper_ci_12
#>   <chr>                    <int>          <int>    <dbl>       <dbl>       <dbl>
#> 1 population/sample            6              8     0.75       0.356       0.955