Reference

Algorithm Configuration

The Algorithm configurations are stored in the database and can be managed via the API. Many of the attributes defined on a configuration are limited to a specific set of values, which are defined here.

Features

The Feature enum defines the types of attributes that can be used for matching during the linkage evaluation phase. The following features are supported:

BIRTHDATE: The patient's birthdate (normalized to YYYY-MM-DD).
SEX: The patient's sex (normalized to M or F).
RACE: The patient's race in the format of "AMERICAN_INDIAN", "ASIAN", "BLACK", "HAWAIIAN", "WHITE", "OTHER", "ASKED_UNKNOWN" or "UNKNOWN".
GIVEN_NAME: The patient's given name, this includes first and middle names.
FIRST_NAME: The patient's first name.
LAST_NAME: The patient's last name.
SUFFIX: The patient's name suffix.
ADDRESS: The patient's address.
CITY: The patient's city.
STATE: The patient's state.
ZIP: The patient's 5 digit zip code.
COUNTY: The patient's county.
TELECOM: The patient's phone, email, fax, or other contact information.
PHONE: The patient's phone number (normalized to 10 digits).
EMAIL: The patient's email address.
IDENTIFIER: An identifier for the patient. Matching on this will check if any identifier value/authority/type combination matches.
IDENTIFIER:<type>: The patient's specific identifier type. For example, IDENTIFIER:MR would be the patient's medical record number. Unlike IDENTIFIER, this will ONLY compare values of a specific type. Valid type codes can be found on the R4 FHIR Identifier Type v2 page.

Blocking Key Types

The BlockingKey enum defines the types of blocking values that are generated from the patient data and used during query retrieval. The following blocking key types are supported:

BIRTHDATE (ID: 1): The patients birthdate in the format YYYY-MM-DD.
SEX (ID: 3): The patient's sex in the format of M or F.
ZIP (ID: 4): The patient's 5 digit zip code.
FIRST_NAME (ID: 5): The first 4 characters of the patient's first name.
LAST_NAME (ID: 6): The first 4 characters of the patient's last name.
ADDRESS (ID: 7): The first 4 characters of the patient's address.
PHONE (ID: 8): The last 4 digits of the patient's phone number.
EMAIL (ID: 9): The first 4 characters of the patient's email address.
IDENTIFIER (ID: 10): A colon separated string of the last 4 characters of the value and the identifier type.

Evaluation Functions

These are the functions that can be used to evaluate the matching results as a collection, thus determining it the incoming payload is a match or not to an existing Patient record.

func:recordlinker.linking.matchers.rule_probabilistic_match: Determines whether a given set of feature comparisons matches enough to be the result of a true patient link instead of just random chance. This is represented using previously computed log-odds ratios. A true_match_threshold needs to be set in the kwargs parameter to determine the minimum log-odds ratio that is considered a match. Example: {"kwargs": {"true_match_threshold": 12.5}}

Feature Matching Functions

These are the functions that can be used to compare the values of two features to determine if they are a match or not.

Note: When most features are compared, we are doing a 1 to 1 comparison (e.g. "M" == "M"). However, some features have the ability to have multiple values (e.g. ADDRESS), thus feature matching is designed to compare one list of values to another list of values. For example, an incoming record could have a ADDRESS of [{"address": ["123 Main St", "apt 2"], "city": "Springfield", "state": "IL"}] and want to compare that to an existing Patient with the ADDRESS of [{"address": ["123 Main Street"], "city": "Springfield", "state": "IL"}, {"address": ["456 Elm St"], "state": "IL"}]. In that case we'd want to evaluate "123 Main St" against both "123 Main Street" and "456 Elm St".

func:recordlinker.linking.matchers.compare_probabilistic_exact_match: Determines if a Feature Field has the same value in two different patient records. If the two fields agree exactly (i.e. are exactly the same), then the function returns the full extent of the log-odds weights for the particular field with which it was called. If the two fields do not exactly agree, the function returns 0.0. This is useful when performing probabilistic comparisons (which score a possible match's strength by accumulating a sum of link weights) on fields for which fuzzy similarity doesn't make sense, such as fields defined by an enum (e.g. Sex). Use the kwargs parameter to specify the log-odds ratios based on training. Example: {"kwargs": {"log_odds": {"SEX": 6.8}}}
func:recordlinker.linking.matchers.compare_probabilistic_fuzzy_match: Similar to the above function, but uses a log-odds ratio to determine if the features are a match probabilistically. This is useful when wanting to more robustly compare features by incorporating their predictive power (i.e., the log-odds ratio for a feature represents how powerful of a predictor that feature is in determining whether two patient records are a true match, as opposed to a match by random chance). Use the kwargs parameter to specify the fuzzy match threshold and log-odds ratio based on training. Example: {"kwargs": {"thresholds": {"FIRST_NAME": 0.8}, "log_odds": {"FIRST_NAME": 6.8}}}