# Detect PHI Use the **DetectPHI** operation when you only want to detect Protected Health Information (PHI) data when scanning the clinical text. To detect all available entities in the clinical text use **DetectEntitiesV2**. This API is best for a use case where only detecting PHI entities is required. For information about information in the non-PHI categories, see [Detect entities (Version 2)](textanalysis-entitiesv2.md). **Important** Amazon Comprehend Medical provides confidence scores that indicate the level of confidence in the accuracy of the detected entities. Evaluate these confidence scores and identify the right confidence threshold for your use case. For specific compliance use cases, we recommend that you use additional human review or other methods to confirm the accuracy of detected PHI. Under the HIPAA act, PHI that is based on a list of 18 identifiers must be treated with special care. Amazon Comprehend Medical detects entities associated with these identifiers but these entities don't map 1:1 to the list specified by the Safe Harbor method. Not all identifiers are contained in unstructured clinical text, but Amazon Comprehend Medical does cover all the relevant identifiers. These identifiers consist of data that can be used to identify an individual patient, including the following list. For more information, see [Health Information Privacy](https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html) on the *U.S. Government Health and Human Services* website. Each PHI-related entity includes a score (`Score` in the response) that indicates the level of confidence Amazon Comprehend Medical has in the accuracy of the detection. Identify the right confidence threshold for your use case and filter out entities that do not meet it. When identifying occurrences of PHI, it may be better to use a low confidence threshold for filtering to capture more potential detected entities. This is especially true when not using the values of the detected entities in compliance use cases. The following PHI-related entities can be detected by running the **DetectPHI** or **DetectEntitiesV2** operations: **Detected PHI Entities** | Entity | Description | HIPAA Category | | --- | --- | --- | | AGE | All components of age, spans of age, and any age mentioned, be it patient or family member or others involved in the note. Default is in years unless otherwise noted. | 3. Dates related to an individual | | DATE | Any date related to patient or patient care. | 3. Dates related to an individual | | NAME | All names mentioned in the clinical note, typically belonging to patient, family, or provider. | 1. Name | | PHONE\$1OR\$1FAX | Any phone, fax, pager; excludes named phone numbers such as 1-800-QUIT-NOW as well as 911. | 4. Phone number 5. FAX number | | EMAIL | Any email address. | 6. Email addresses | | ID | Any sort of number associated with the identity of a patient. This includes their social security number, medical record number, facility identification number, clinical trial number, certificate or license number, vehicle or device number. It also includes biometric numbers, and numbers identifying the place of care or provider. | 7. Social Security Number 8. Medical Record number 9. Health Plan number 10. Account numbers 11. Certificate/License numbers 12. Vehicle identifiers 13. Device numbers 16. Biometric information 18. Any other identifying characteristics | | URL | Any web URL. | 14. URLs | | ADDRESS | This includes all geographical subdivisions of an address of any facility, named medical facilities, or wards within a facility. | 2. Geographic location | | PROFESSION | Includes any profession or employer mentioned in a note as it pertains to the patient or the patient’s family. | 18. Any other identifying characteristics | **Example** The text "Patient is John Smith, a 48-year-old teacher and resident of Seattle, Washington." returns: + "John Smith" as an *entity* of type `NAME` in the `PROTECTED_HEALTH_INFORMATION` category. + "48" as an *entity* of type `AGE` in the `PROTECTED_HEALTH_INFORMATION` category. + "teacher" as an *entity* of type `PROFESSION` (identifying characteristic) in the `PROTECTED_HEALTH_INFORMATION` category. + "Seattle, Washington" as an `ADDRESS` *entity* in the `PROTECTED_HEALTH_INFORMATION` category. In the Amazon Comprehend Medical console, this is shown like this: ![\[Patient information card displaying name, age, profession, and address details.\]](http://docs.aws.amazon.com/comprehend-medical/latest/dev/images/patient.png) When using the **DetectPHI** operation, the response appears like this. When you use the **StartPHIDetectionJob** operation, Amazon Comprehend Medical creates a file in the output location with this structure. ``` { "Entities": [ { "Id": 0, "BeginOffset": 11, "EndOffset": 21, "Score": 0.997368335723877, "Text": "John Smith", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "NAME", "Traits": [] }, { "Id": 1, "BeginOffset": 25, "EndOffset": 27, "Score": 0.9998362064361572, "Text": "48", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "AGE", "Traits": [] }, { "Id": 2, "BeginOffset": 37, "EndOffset": 44, "Score": 0.8661606311798096, "Text": "teacher", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "PROFESSION", "Traits": [] }, { "Id": 3, "BeginOffset": 61, "EndOffset": 68, "Score": 0.9629441499710083, "Text": "Seattle", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "ADDRESS", "Traits": [] }, { "Id": 4, "BeginOffset": 78, "EndOffset": 88, "Score": 0.38217034935951233, "Text": "Washington", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "ADDRESS", "Traits": [] } ], "UnmappedAttributes": [] } ```