Open Client Registry (OpenCR) vs Splink

Open Client Registry (OpenCR) links patient identities across information systems to support care coordination, continuity of care and reporting. It offers configurable matching using probabilistic and deterministic methods, and can assign or look up unique identifiers and accept FHIR messages from point-of-service systems such as labs and EMRs. Its interface lets users inspect records and break matches. A deployment uses the OpenCR Service and HAPI FHIR Server with OpenSearch or Elasticsearch; OpenHIM is optional. Transactions, including queries, are stored as audit events in HAPI FHIR and can be viewed in the UI. OpenCR is open source and is listed for API, Linux, macOS, self-hosted, web and Windows environments. It is not a shared health record and does not contain patient clinical data. The production installation guide expects Linux, command-line familiarity and Linux administration expertise; HAPI FHIR needs a production database backend such as PostgreSQL or MySQL. JWT and other user authentication solutions are not provided out of the box and can be added for a deployment. The project describes itself as a community project intended for adaptation to different use cases.

Splink is a free Python package for probabilistic record linkage, used to deduplicate records and connect them when unique identifiers are absent. Its linkage method follows the Fellegi-Sunter model and can be trained without labeled examples. Matching can include term-frequency adjustments and user-defined fuzzy logic. Splink creates SQL for a selected backend, with documented support for DuckDB, Spark, SQLite, and PostgreSQL. DuckDB is recommended for most users; Spark is recommended for very large linkages or when a Spark cluster is more accessible. The maker says it can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records. It works best with standardized data across several columns that are not highly correlated, rather than a single bag-of-words column. Interactive visualizations help examine and diagnose models, predictions, and clusters. Install with pip or conda, with optional backend-specific installation for Spark and PostgreSQL. It runs on Linux, macOS, and Windows, as well as self-hosted environments.