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201809 FHIR Storage and Analytics

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Return to Fall 2018 Proposals



Previous tracks:


Track Name

FHIR Storage & Analytics ([1])

Track Overview

With this track we want to put together people, which are interested in implementation and usage of FHIR persistence layer, implementing FHIR servers on top and analysing FHIR data.

We hope this group can produce useful feedback for FHIR community and potentially propose new aspects for standard - like SQL on FHIR or FHIRPath for databases.

Another goal is to collect solutions and best practices to store and query FHIR data, using popular databases.


Please fill out: TODO [TODO Track Registration Spreadsheet]

Submitting WG/Project/Implementer Group

We want to create S&A working group or FHIR Foundation Project.


Justification

Initial set of questions to be answered or discussed

  • How we can organise our group (WG || FHIR Foundation Project || Just informal meetings)?
  • How to store FHIR data?
  • What is database schema design?
  • SQL (as common query language) on FHIR
  • FHIR data representation for storage
  • FHIRPath subset for databases
  • How to approach FHIR search?
  • Best practices for popular databases (postgresq, spark, mongo, bigquery etc)?

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

Discussions

SQL on FHIR

Initial notes for creating a standard representation of FHIR that is directly usable in scalable SQL-based systems.

Original document by Ryan Brush - https://docs.google.com/document/d/1IPyI-4GYlF400vmZvrtjDYL6jauHdjeLfjGJMo6yWtA/edit#heading=h.ch8y6lqurlaj

FHIR data representation in database

JSON and other JSON like formats like yaml, edn, avro, protobuf are very popular in avantе-garde of programmers, some modern databases now could understad JSON as first-class data-structures, that’s why we think, good design for it is strictly required for FHIR adoption.

Original github - https://github.com/fhir-fuel/fhir-fuel.github.io/issues

FHIRPath subset for databases

It worth to discuss subset of FHIRPath, which is implementable in databases

Issue to discuss - https://github.com/fhir-fuel/fhir-fuel.github.io/issues/10


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


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