diff --git a/README.md b/README.md
index 6f7c82f..72e92d2 100644
--- a/README.md
+++ b/README.md
@@ -11,10 +11,35 @@ These are basic Python based tools for working with medical images and text, spe
 The cleaning method used here mirrors the one by CTP in that we can identify images based on known
 locations. We are looking for collaborators to develop and validate an OCR cleaning method! Please reach out if you would like to help work on this.
 
+## HIPAA-safe Quickstart (use synthetic data only)
+
+**Never test with real Protected Health Information (PHI).** Start with synthetic DICOM files so you can practice safely.
+
+Install:
+pip install deid deid-data pydicom
+
+Minimal check in Python (loads one synthetic DICOM file):
+from deid.data import get_dataset
+from deid.dicom import get_files, DicomCleaner
+
+    # synthetic sample dataset name: "dicom-cookies"
+    data_dir = get_dataset("dicom-cookies")
+    test_file = next(get_files(data_dir))
+
+    cleaner = DicomCleaner()      # create a simple cleaner
+    cleaner.detect(test_file)     # analyze for potential burned-in PHI regions
+    # cleaner.clean(...)          # optional: perform cleaning based on a recipe
+    # cleaner.save_dicom(...)     # optional: save a cleaned copy
+
+Notes:
+
+- Use only synthetic datasets during evaluation (e.g., "dicom-cookies").
+- Before sharing outputs or logs, verify they don’t contain identifiers.
 
 ## Installation
 
 ### Local
+
 For the stable release, install via pip:
 
 ```bash
@@ -35,4 +60,5 @@ docker run pydicom/deid --help
 ```
 
 ## Issues
+
 If you have an issue, or want to request a feature, please do so on our [issues board](https://www.github.com/pydicom/deid/issues).