The purpose of the entity matching service is to enable matching in a single list of patients, health workers, facilities or other entities or to find potential matches between two lists of the same entities.
The service receives a FHIR message with the entity to be matched and returns zero to 10 matches and their scores. We are supporting FHIR through Hapi FHIR.
We envision the following potential use cases:
Ensuring the the entity doesn't exist when entering a new instance of the entity
Duplicate checking during bulk imports.
Analysis of potential duplicates in an existing data set.
Mapping one data set of entities to their corresponding value in another data set.
Depending upon the use case, we envision that there might be a spectrum of implementation options. We expect to learn from the first implementations and refine the use patterns based upon experience. For now, we imagine the following types of architectural implementations:
Tight coupling - A tightly coupled implementation might be one where the matching service software library is incorporated into the architecture component.
Medium - This type of implementation could be one where the service interacts directly with the architecture component's data source.
Loose - This type of service may load data into the service's data base and analyze the data from there.
http://gforge.hl7.org/gf/project/fhir/tracker/?action=TrackerItemEdit&tracker_item_id=9685&start=0
https://testmap.ohie.org/registry/fhir/Location/$match
<Parameters xmlns="http://hl7.org/fhir"> <parameter> <name value="location"/> <resource> <Location xmlns="http://hl7.org/fhir"> <contained> <Location xmlns="http://hl7.org/fhir"> <id value="1"/> <identifier> <value value="a.bc.1.sample"/> </identifier> <name value="simple health"/> </Location> </contained> <identifier> <value value="117"/> </identifier> <name value="simple clinic"/> <position> <longitude value="10"/> <latitude value="100"/> </position> <partOf> <reference value="#1"/> </partOf> </Location> </resource> </parameter> <parameter> <name value="count"/> <valueInteger value="5"/> </parameter> </Parameters> |
<Bundle xmlns="http://hl7.org/fhir"> <entry> <resource> <Location xmlns="http://hl7.org/fhir"> <id value="1000010"/> <contained> <Location xmlns="http://hl7.org/fhir"> <id value="con31"/> <identifier> <value value="A.BC.1.SAMPLE"/> </identifier> <name value="SAMPLE HEALTH"/> </Location> </contained> <extension url="http://ohie.org/fhir/StructureDefinition/datim-mechid"> <valueString value="1111"/> </extension> <identifier> <value value="117"/> </identifier> <name value="SIMPLE CLINIC"/> <position> <longitude value="10.0"/> <latitude value="100.0"/> </position> <partOf> <reference value="#con31"/> </partOf> </Location> </resource> <search> <score value="0.99762179871785583440413347489084117114543914794921875"/> </search> </entry> </Bundle> |
https://tools.regenstrief.org/stash/users/amartin/repos/registry/browse
There are multiple ways to determine a match.
Example Actors:
This is one example of a possible workflow:
participant Entity Searcher as ES participant Interoperability\nLayer as IL participant Entity Matching Service as EMS participant Entity Authority as EA loop configuration time of refresh managed in IL IL->EMS: trigger refresh of entity cache EMS->EA: request updates to FHIR entity\nsince last refresh\nusing the search transaction EA->EMS: return FHIR bundle of\n upated entities EMS->EMS: update local cache EMS->EMS: retune matching parameters end ES->EMS: execute FHIR $match service EMS->ES: return possible matches |
(CL: didn't see the ability to add a web sequence diagram directly on this page for some reason)
Different interfaces will need to be created to instantiate different use cases that call the service.
While the entity matching service currently implements a sophisticated probabilistic algorithm, a key overarching goal of the entity matching service is to accommodate a variety matching methods. The current algorithm can be configured for matching different types of entities.
The matching service is highly configurable. Shaun Grannis Andrew Martin - please advise here.
When configuring the matching service to run against an existing database, one will likely have existing tools for loading data into the database. However, an importer is included with the matching service. This can be helpful if one creates a new database to be used by the matching service. The importer can take a flat file and import data into the database.
If you are using the data source provided with the service, Identifiers are stored in a separate data structure that includes the value and the type of value it is. There can be multiple identifiers stored for a single entity.
A: The matching service is divided into two basic steps: coarse blocking and fine-grained matching handled in Java. The blocking step is for performance, so that the service doesn't need to apply the fine-grained matching algorithm to every row in the database. It’s less flexible than the fine-grained matching step and is designed to allow fast queries based on typical database indexes. For example, an index on the name column will make this query fast:
select * from organisationunit where name=?
But a normal database won’t be able to quickly run a query to search for rows based on a Levenshtein score. The <blockingScheme> element defines how the matching service will handle this coarse blocking.
The <caseMode> element can be used with these possible values: