201809 FHIR Storage and Analytics

Track Name

FHIR Storage, Query & Analytics ([1])

Track Overview

More and more developers about to start design storage for FHIR data. We hope this track will share experience about FHIR storage implementation, as well as analytics on FHIR datasets.

Please fill out: Track Registration Spreadsheet

Submitting WG/Project/Implementer Group

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Justification

Questions to be answered

• How to store FHIR data?
• What is database schema design?
• Which databases can be used?
• SQL on FHIR
• FHIR data representation for storage
• FHIRPath subset for databases
• What i have to do to be part of it?
• How to approach FHIR search?

Proposed Track Lead

• Nikolai Ryzhikov
• Ryan Brush
• Patrik Sundberg
• Kenny Blanchette

Expected participants

• Nikolai Ryzhikov
• Tim Zallin
• Alexander Zautke
• Ryan Brush
• Patrik Sundberg
• Kenny Blanchette

Roles

• Database Provider: provides access to database with FHIR data
• Database User: design queries

Scenarios

Scenario 1: FHIR search

• Design or take an existing database schema to store Patient, Encounter & Practitioner resources
  • relational (consider schema generation)
  • document oriented
    • postgresql jsonb
    • mongodb
    • big query
  • tripple store (datomic, EAV)
  • xml database (?)
• Load sample data
• Implement FHIR search for
  • Patient by name, address
  • Encounter by date and location/practitioner
  • Encounter include patient/practitioner
  • Encounter chained params
• On fly convertion to FHIR if format is different

Scenario 2: Advanced FHIR search

• Design or take an existing database schema to store Patient & Observation
• Implement search by quantity with respect to system and units

Scenario 3: Complex Queries / CQL

• Implement CQL to SQL (or other query lang) translation (automatic or manual)
• Another analytic queries???

Scenario 4: Analytical databases replication

• Get `transaction log` / history of all CRUD/transaction operations from kafka topic
• Transform and load into analytical databases
  • Click House
  • Elastic Search
  • Vertica
  • Relational databases (MS SQL, Oracle, Postgresql, Mysql)
• Run analytical queries

Scenario 5: Graphql implementaton

• prototype efficient graphql => sql transpilation

Discussion

fhirpath implementation/subset for databases

Assets

• we will provide you with test datasets
• jupyter environment with examples (will be used for demo after track)
• access to existing databases
  • fhirbase
  • Biq Query
  • aidbox
  • HAPI db?
  • ....

Outcomes

• make participants familiar with different approaches
• report/guidelines for implementation of FHIR database
• discuss open questions in a group :)

Databases

Relational

• PostgreSQL
• Big Query

Document databases

• MongoDB

Analytical

• ElasitcSearch
• ClickHouse
• Vertica
• Spark / Hadoop?

Integration bus

• Kafka