In the area of using HL7 for patient record storage, there are use cases to involve various sources of patient information who are involved in the care for one patient. For these people, we need to be able to offer a synchronization between multiple HL7-repositories. Are there any implementations of a synchronization engine between HL7 repositories?
There is no single product that provides a solution like this. Typically, a working solution like this involves a great deal of custom business logic, and such solutions are usually solved using a mixture of interface engines, scripting, and bespoke code and services developed in some programming language of choice. See Why use an interface engine?
This is a common problem that has been solved more than once in a variety of ways with a myriad of products.
Here’s an overview of the challenge:
If by synchronization we mean just “replication” from A to B, then A needs to be able to send and B needs to receive messages or service calls. If by synchronization we mean two-way “symmetric” synchronization then you have to add logic to prevent “‘rattling” (where the same event gets triggered back and forth). An integration engine can provide the transformations between DB records and messages, but in general the concept codes and identifiers must still be reconciled between the systems.
For codes, an “interlingua” like SNOMED, LOINC, etc. is helpful if one or both of the systems uses local codes. The participants may implement translations (lookups) to map to the other participant or to the interlingua (it acts as the mediating correlator) The interface engine can call services, or perform the needed lookups. “Semantic” mapping incorporates extra logic for mapping concepts that are divided into their aspects (like LOINC, body system, substance, property, units, etc. Naturally if all participants actually support the interlingua natively the problem goes away. For identifiers, a correlating EMPI at each end can find-or-register patients based on matching rules. If a simplistic matching rule is sufficient and the receiving repository is just a database, then the integration engine alone could map the incoming demographic profile to a query against the patients table and look up the target patient – and add one if it’s new.
But if the target repository has numerous patients, with probabilistic matching rules (to maximize the rate of unattended matches, i.e. not bringing a human registrar into the loop to do merges), then the receiving system should implement a service of some kind (using HL7/OMG IXS standard, OMG PIDS (ref?), or FHIR), and the integration engine can translate the incoming demographic into a find-or-register call to that service. Such a project will of course require some analysis and configuration, but with most interface engines, there will be no need for conventional programming. Rather, you have (or make) trees that describe the message segments, tables, or service calls, and then you map (drag/drop) the corresponding elements from sources to targets.
An MDM or EMPI product worth its salt will implement a probabilistic matching engine and implement a web-callable interface (SOAP or REST) as described. If the participants are organizationally inside the same larger entity (a provider health system), then the larger organization may implement a mediating correlator just like the interlingua for terminology. The “correlating” EMPI assigns master identifiers in response to incoming feeds (carrying local ids) from source systems; Then that EMPI can service “get corresponding ids” requests to support the scenario you describe. An even tighter integration results if one or both participants actually uses that “master” id domain as its patient identifiers.
Here’s some example projects along these lines:
- dbMotion created a solution that would allow a clinical workstation to access information about a common patient from multiple independent EMRs. It accomplished this by placing an adapter on top of EHR that exposed its data content in a common format (based upon the RIM) that their workstation application was able to query and merge the patient data from all the EMR into a single desktop view. The actual data in the source EHR were never modified in any way. This was implemented in Israel and then replicated in the US one RHIO at a time. (Note: dbMotion has since been acquired by Allscripts)
- California State Immunization created a solution that facilitated synchronization of patient immunization history across the nine different immunization registries operating within the state. The solution was based upon a family of HL7 v2 messages that enabled each registry to request patient detail from another and use the query result to update its own record. This solution was eventually replaced by converting all the registries to a common technical platform and then creating a central instance of the system that served all of the regional registries in common (so synchronization was no longer an issue now that there was a single database of record, which is much simpler to maintain).
- LA County IDR is an architecture put in place in Los Angles County to integrate data from the 19+ public health information system both as a means of creating a master database that could be used for synchronization and could be used as a single source to feed data analytics. The Integrated Data Repository was built using a design that was first envisioned as part of the CDC PHIN project. The IDR is a component of the CDC’s National Electronic Disease Surveillance System (NEDSS) implemented in at least 16 state health departments.