diff --git a/src/constants/regex_patterns.py b/src/constants/regex_patterns.py
index 45861af..44b0af8 100644
--- a/src/constants/regex_patterns.py
+++ b/src/constants/regex_patterns.py
@@ -40,7 +40,7 @@ NPI_PATTERN = r"\b(?:\d\s*){10}\b"
 TIN_OCR_PATTERN = r"\b[O0lI1S5G6B8gbo\d]{9}\b|\b[O0lI1S5G6B8gbo\d]{2}[-.][O0lI1S5G6B8gbo\d]{7}\b|\b[O0lI1S5G6B8gbo\d]{3}[-.][O0lI1S5G6B8gbo\d]{2}[-.][O0lI1S5G6B8gbo\d]{4}\b"
 
 # OCR-Correctable NPI Pattern
-# Strategy: Match 10-character sequences containing digits and OCR-prone letters,
+# Strategy: Match 10-character sequences containing digits and OCR-prone letters; also Matches "chained" NPIs separated by '1', 'l', or 'I'.
 # then apply corrections post-match to validate it becomes a proper 10-digit NPI.
 #
 # Examples that WILL be captured and corrected:
@@ -48,6 +48,7 @@ TIN_OCR_PATTERN = r"\b[O0lI1S5G6B8gbo\d]{9}\b|\b[O0lI1S5G6B8gbo\d]{2}[-.][O0lI1S
 # - "l234567890" → "1234567890" (l at start)
 # - "12345O7890" → "1234507890" (O in middle)
 # - "123456789l" → "1234567891" (l at end)
+# - "123456789011234567890" (seen as NPI + "1" + NPI)
 #
 # Examples that will NOT match:
 # - "123456789" (only 9 characters)
@@ -57,7 +58,7 @@ TIN_OCR_PATTERN = r"\b[O0lI1S5G6B8gbo\d]{9}\b|\b[O0lI1S5G6B8gbo\d]{2}[-.][O0lI1S
 # Note: NPIs are always unformatted 10-digit numbers, so no hyphen/dot variations.
 # Validation happens post-match by applying OCR_SUBSTITUTIONS and checking
 # that result is exactly 10 digits.
-NPI_OCR_PATTERN = r"\b[O0lI1S5G6B8gbo\d]{10}\b"
+NPI_OCR_PATTERN = r"\b(?:[O0lI1S5G6B8gbo\d]{10}[1lI])*[O0lI1S5G6B8gbo\d]{10}\b"
 
 # OCR Substitution Mapping
 OCR_SUBSTITUTIONS = {
diff --git a/src/pipelines/shared/extraction/tin_npi_funcs.py b/src/pipelines/shared/extraction/tin_npi_funcs.py
index 03073dd..d6f7bef 100644
--- a/src/pipelines/shared/extraction/tin_npi_funcs.py
+++ b/src/pipelines/shared/extraction/tin_npi_funcs.py
@@ -66,6 +66,22 @@ def get_all_matches_with_ocr(
     # Get OCR-correctable candidates (even if we found exact matches)
     ocr_candidates = get_all_matches(text, ocr_pattern)
     for candidate in ocr_candidates:
+
+        # Checks if length follows pattern: 10*N + (N-1) -> (len + 1) % 11 == 0
+        if (
+            identifier_type == "NPI"
+            and len(candidate) > 10
+            and (len(candidate) + 1) % 11 == 0
+        ):
+            # Split into chunks of 10, skipping every 11th character (the separator)
+            chunks = [candidate[i : i + 10] for i in range(0, len(candidate), 11)]
+            for chunk in chunks:
+                corrected, is_valid = attempt_ocr_correction(chunk, identifier_type)
+                if is_valid and corrected not in seen_values:
+                    results.append((corrected, "OCR_CORRECTED"))
+                    seen_values.add(corrected)
+            continue
+
         logging.debug(f"OCR candidate for {identifier_type}: {candidate}")
         corrected, is_valid = attempt_ocr_correction(candidate, identifier_type)
         if is_valid and corrected not in seen_values: