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).

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 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_OR_FAX	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:

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": []
}