diff --git a/R/dr.lab.functions.R b/R/dr.lab.functions.R
index 36df2c88..85218a4a 100644
--- a/R/dr.lab.functions.R
+++ b/R/dr.lab.functions.R
@@ -578,7 +578,9 @@ clean_lab_data_regional <- function(lab_data,
   cli::cli_process_start("Filtering nonsensical dates")
   lab_data5 <- lab_data5 |>
     dplyr::filter(
-      (DateStoolCollected >= ParalysisOnsetDate | is.na(ParalysisOnsetDate)),
+      (is.na(DateStoolCollected) |
+         DateStoolCollected >= ParalysisOnsetDate |
+         is.na(ParalysisOnsetDate)),
       # (lubridate::year(DateFinalCellCultureResult) <= 2023 | is.na(DateFinalCellCultureResult)),
       # remove a blank specimen row
       !is.na(EPID)
diff --git a/R/kpi.main.functions.R b/R/kpi.main.functions.R
index 4bbd522e..27015a07 100644
--- a/R/kpi.main.functions.R
+++ b/R/kpi.main.functions.R
@@ -329,7 +329,7 @@ generate_kpi_template <- function(output_path, name, edav) {
     "# Obtaining shapefiles in long format ----",
     "# Ensure st.year is the year of your start date.",
     ctry_sf, dist_sf, "\n",
-    "# Cleaning lab data ----",
+    "# Use cleaned lab data ----",
     paste0("start_date <- ", start_date), paste0("end_date <- ", end_date), "\n",
     'lab_data <- clean_lab_data(lab_data, start_date, end_date, raw_data$afp)\n',
     "# Generate GPSAP C1-C4 tables ----",
diff --git a/R/kpi.table.functions.R b/R/kpi.table.functions.R
index 5c4a882c..697a9afc 100644
--- a/R/kpi.table.functions.R
+++ b/R/kpi.table.functions.R
@@ -313,10 +313,9 @@ generate_wild_vdpv_summary <- function(raw_data, start_date, end_date,
 #' @returns `tibble` lab data with timeliness columns.
 #'
 #' @keywords internal
-generate_kpi_lab_timeliness <- function(lab_data, start_date, end_date, afp_data) {
+generate_kpi_lab_timeliness <- function(lab_data, start_date, end_date, afp_data = NULL) {
   start_date <- lubridate::as_date(start_date)
   end_date <- lubridate::as_date(end_date)
-  lab_data <- clean_lab_data(lab_data, start_date, end_date, afp_data)
 
   lab_data <- lab_data |>
     dplyr::mutate(
diff --git a/inst/rmarkdown/templates/desk-review-template/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/desk-review-template/skeleton/skeleton.Rmd
index a873ddd3..5b858d51 100644
--- a/inst/rmarkdown/templates/desk-review-template/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/desk-review-template/skeleton/skeleton.Rmd
@@ -119,10 +119,8 @@ For ISS/eSURV data, it is advisable to take a look at the data before cleaning,
 
 ```{r data.cleaning}
 ctry.data <- clean_ctry_data(ctry.data)
-lab_data <- clean_lab_data(lab_data, start_date, end_date, 
-                                     ctry.data$afp.all.2, Sys.getenv("DR_COUNTRY")
-                                     )
-# NOTE: if lab data is cleaned, you may need to manually filter to the countries of interest
+lab_data <- sirfunctions::edav_io("read", file_loc = get_constant("CLEANED_LAB_DATA")) |>
+  dplyr::filter(!is.na(EPID))
 ctry.data$iss.data <- clean_iss_data(ctry.data$iss.data, start_date, end_date)
 ```
 
diff --git a/man/generate_kpi_lab_timeliness.Rd b/man/generate_kpi_lab_timeliness.Rd
index 09803944..c22f2647 100644
--- a/man/generate_kpi_lab_timeliness.Rd
+++ b/man/generate_kpi_lab_timeliness.Rd
@@ -4,8 +4,7 @@
 \alias{generate_kpi_lab_timeliness}
 \title{Generate KPI lab intervals}
 \usage{
-generate_kpi_lab_timeliness(lab_data, start_date, end_date, afp_data)
-}
+generate_kpi_lab_timeliness(lab_data, start_date, end_date, afp_data = NULL)
 \arguments{
 \item{lab_data}{\code{tibble} Lab data containing information of ES or AFP samples.}