Patient Matching

Patient matching supports consistent identification of individuals when patient data is submitted, evaluated, queried, or retrieved. Matching lets systems associate clinical data with the correct individual across identity-driven workflows.

Health Gorilla connects your organization to multiple health information networks. Accurate, complete demographics improve match confidence and reduce ambiguity wherever matching happens, whether Health Gorilla is normalizing data you submit or a responding organization is evaluating a query.

How Matching Works

Health Gorilla standardizes the demographics your organization submits, then uses them to identify the right individual across connected networks.

• Health Gorilla normalizes submitted demographics so formatting differences don't cause avoidable mismatches.
• When Health Gorilla queries a connected network, the responding organization evaluates the request with its own patient matching logic and returns candidate patients or no match. Matching methodologies, thresholds, and response behaviors vary by organization and aren't controlled by Health Gorilla.
• Matching behavior can vary by network. Some networks apply an additional check that requires an exact match on core demographics, plus at least one shared contact detail or a matching street address, before Health Gorilla automatically links a record.

Because responding organizations match independently, results depend on the demographics provided, the data available at each organization, and local system behavior. A query can return a single high-confidence match, multiple candidates when demographics are ambiguous, or no match when the individual isn't found or the submitted demographics are too sparse to evaluate reliably.

Demographics Used for Matching

Patient matching doesn't rely on any single required field. The methodology weighs multiple demographic factors, so no one field is mandatory to attempt a match. More complete and accurate demographics improve match confidence and reduce the chance of multiple-candidate results.

Required and Optional Fields

Two fields are worth calling out because integrators often ask about them:

• Social Security number: Not a required field for matching. Health Gorilla doesn't include it in the demographics sent to connected networks for patient discovery.
• Gender: Not an expressly required field for matching. The administrative sex in an inbound HL7 v2 admission, discharge, transfer (ADT) message maps to the patient's gender. When it isn't supplied, Health Gorilla sets gender to unknown, a standard value in the FHIR administrative gender code set rather than a placeholder, so a missing gender doesn't block matching. This field reflects administrative gender used for record matching and administrative purposes, per the FHIR specification, not clinical sex or gender identity. Supplying gender when it's known removes ambiguity and reduces the chance of multiple-candidate results.

Core Demographics

The following demographics are strongly recommended for reliable matching. At a minimum, first name, last name, date of birth, and gender improve match confidence, and address improves it further.

• First name and last name
• Date of birth
• Gender
• Address, standardized, with a five-digit ZIP Code. Many networks won't return a candidate without a matching ZIP Code, so treat address as effectively required for reliable matching, not merely a confidence booster.

Submitting first name, last name, date of birth, gender, and address together significantly improves match confidence and reduces ambiguous outcomes and the likelihood of multiple-candidate results.

Supplemental Demographics

Additional attributes improve disambiguation in complex scenarios:

• Phone number
• Email address
• Middle name or suffix
• Prior address
• Medical record number (MRN) with assigning authority, when applicable

Required Data for Creating a Patient

The data required to create a patient record depends on how the record enters Health Gorilla:

• From an inbound HL7 v2 ADT feed your organization sends to Health Gorilla: first name, last name, and date of birth are required for Health Gorilla to create or match a patient record. Without them, the ADT event is retained but no patient record is created or linked. When gender isn't supplied, Health Gorilla sets it to unknown rather than blocking record creation.
• Through the FHIR API, POST /fhir/R4/Patient: first name, last name, date of birth, and gender are required. A request that omits gender, or supplies a date of birth without a day, is rejected.

The two intake paths apply different validation rules by design. These minimums are for creating a record and are separate from the demographics that drive match confidence.

Demographic Normalization

To reduce mismatches caused by formatting differences, Health Gorilla standardizes demographic values used for matching.

Normalization includes:

• Names normalized for casing, punctuation, and diacritics
• Dates of birth standardized to a consistent format
• Gender normalized to standard values
• Phone numbers normalized to a consistent format for comparison
• Email addresses evaluated case-insensitively
• Addresses standardized to a consistent representation, with punctuation and casing removed and ZIP Codes truncated to five digits

Common Matching Scenarios

These scenarios show how patient matching behaves in practice.

Patient Moves

Address changes are common. When your organization submits an updated address, the most recent address is used for matching and discovery. Prior addresses can remain relevant when historical records exist at other organizations, so address recency and relevance factor into evaluation.

Note: When prior addresses are known, including them as supplemental demographics can improve matching across organizations that hold historical records.

Twins

Twins can share name, date of birth, gender, and address. In these cases, additional differentiating attributes such as phone number, email address, MRN, middle name, or suffix improve disambiguation. When ambiguity remains, matching can return multiple candidate patients rather than force a match, which reduces the risk of incorrect identity association.

Similar Naming Conventions

Some populations have higher rates of shared or similar names. Normalization supports spelling and formatting variations, but supplemental attributes such as phone, email, and address are critical for reducing false matches. Complete demographics improve match confidence across organizations.

Address Formatting

Address formats vary across systems. Health Gorilla standardizes addresses before evaluation to reduce mismatches caused by punctuation, casing, or ZIP Code formatting differences.

Matching Best Practices

Complete and accurate demographics improve matching and discovery outcomes. Your organization should:

• Submit all available core demographic elements.
• Include phone number or email whenever possible.
• Update demographics promptly when patient information changes.
• Include prior addresses when recent moves are known.
• Avoid placeholder or default values that don't reflect the patient's actual identity.