diff --git a/CHANGES.rst b/CHANGES.rst
index 80e809eb25..cbe5f5685e 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -17,6 +17,16 @@ datamodels
 
 - Updated schemas for new keywords CROWDFLD, PRIDTYPE, PRIDTPTS, PATTNPTS, SMGRDPAT,
   changed name of SUBPXPNS to SUBPXPTS, and new allowed values for PATTTYPE. [#5618]
+  
+flat_field
+----------
+
+- Added DO_NOT_USE to pixels flagged as NON_SCIENCE for non-nirspec data [#5601]
+
+outlier_detection
+-----------------
+
+- Account for the background subtracted data in the blot image for determining the noise image used in flagging outliers [#5601] 
 
 set_telescope_pointing
 ----------------------
diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py
index adfdd1511d..d657d9939c 100644
--- a/jwst/flatfield/flat_field.py
+++ b/jwst/flatfield/flat_field.py
@@ -215,6 +215,11 @@ def apply_flat_field(science, flat, inverse=False):
     # Combine the science and flat DQ arrays
     science.dq = np.bitwise_or(science.dq, flat_dq)
 
+    # Find all pixels in the flat that have a DQ value of NON_SCIENCE
+    # add the DO_NOT_USE flag to these pixels. We don't want to use these pixels
+    # in futher steps
+    flag_nonsci = np.bitwise_and(science.dq, dqflags.pixel['NON_SCIENCE']).astype(np.bool)
+    science.dq[flag_nonsci] = np.bitwise_or(science.dq[flag_nonsci], dqflags.pixel['DO_NOT_USE'])
 
 #
 # The following functions are for NIRSpec spectrographic data.
diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py
index fd5b6a6da2..fbba1bde2f 100644
--- a/jwst/outlier_detection/outlier_detection.py
+++ b/jwst/outlier_detection/outlier_detection.py
@@ -406,7 +406,7 @@ def flag_cr(sci_image, blot_image, **pars):
     exptime = sci_image.meta.exposure.exposure_time
 
     sci_data = sci_image.data * exptime
-    blot_data = blot_image.data * exptime
+    blot_data = (blot_image.data + subtracted_background) * exptime
     blot_deriv = abs_deriv(blot_data)
 
     err_data = np.nan_to_num(sci_image.err)
@@ -421,7 +421,7 @@ def flag_cr(sci_image, blot_image, **pars):
     #
     # Model the noise and create a CR mask
     diff_noise = np.abs(sci_data - blot_data)
-    ta = np.sqrt(np.abs(blot_data + subtracted_background) + err_data ** 2)
+    ta = np.sqrt(np.abs(blot_data) + err_data ** 2)
     t2 = scl1 * blot_deriv + snr1 * ta
     tmp1 = np.logical_not(np.greater(diff_noise, t2))