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Open Client Registry (OpenCR) vs pyJedAI

Open Client Registry (OpenCR)

openclientregistry.org

pyJedAI

github.com

Side by side

FigureOpen Client Registry (OpenCR)pyJedAI
Price from—Free
Free plan—Yes
Free trial——
Open source——
WebYes—
WindowsYesYes
macOSYes—
LinuxYesYes
APIYes—
Self-hostedYesYes
Plans published01
Spec score · Identity Resolution Software7.4 (#13)7.6 (#10)
IntegrationsThe documentation describes FHIR R4, HAPI FHIR, OpenHIM, and OpenSearch or Elasticsearch as components or connections in an OpenCR deployment.The project uses Python data-science and machine-learning packages, including NLTK, pandas, transformers, sentence-transformers, and FAISS CPU.
PurposeOpenCR links patient identities across information systems to support clinical safety, continuity of care, and accurate reporting.pyJedAI is an open-source Python framework for building end-to-end entity resolution workflows.
SupportThe About page directs users to contact [email protected] and join the OpenCR Slack community.The project recommends GitHub Discussions for general questions and discussions and GitHub Issues for bugs.

Plans

Open Client Registry (OpenCR)

No plans published.

Open Client Registry (OpenCR) pricing →

pyJedAI

  • pyJedAI (open-source library)Free

pyJedAI pricing →

In their own words

Open Client Registry (OpenCR)

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.

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pyJedAI

pyJedAI is a free, open-source Python framework for creating entity-resolution workflows. It addresses record linkage, entity resolution and link discovery across structured and semi-structured data. Its collection of algorithms includes supervised and unsupervised machine-learning methods. The project says its blocking and matching methods can apply deep learning and NLP without requiring users to provide labeled instances. Installation is available through pip, Git checkout or Docker. The README says pyJedAI has been tested on Windows and Linux and requires Python 3.8 or later; Windows also requires Microsoft Visual C++ 14.0. It uses packages including NLTK, pandas, transformers, sentence-transformers and FAISS CPU. pyJedAI is licensed under Apache-2.0. The project describes it as suitable for both expert and novice users, and its package metadata also identifies developer and science/research audiences. GitHub Discussions are recommended for general questions, while bugs can be reported through GitHub Issues. The README points Java users to JedAI, which includes a web app for building workflows interactively.

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