The document summarizes the Valencia Region Biobank Project, which aims to facilitate interoperability between different biobank information management systems (BIMS) in the Valencia region of Spain. It describes the GIBIO platform, which provides services to allow different BIMS to share sample and health record data using standardized forms and terminology mappings. The platform generates simplified schemas from archetypes to facilitate data collection and defines transformations between local codes and standard terminologies like SNOMED-CT. It discusses using the platform and archetypes to perform semantic queries and analyze data quality across multiple biobanks.

1. Valencia Region Biobank Project
openEHR day Valencia, 2/10/2019
Diego Boscá Tomás, PhD.

2. Valencia Region Biobank project
VeraTech for Health
• UPV spin-off
• Born in 2010
• Focused in providing solutions for semantic
interoperability & data quality
• “Transforming data into decisions”

3. Valencia Region Biobank project
Interoperability platform

4. Valencia Region Biobank project
Biobanks
• Biobanks are non for profit public and private
entities which preserve high quality biological
samples for their use in biomedical research
• Biobanks offer both the biological samples
and related relevant clinical information

5. Valencia Region Biobank project
GIBIO platform
• Biobank Interoperability Manager (GIBIO) is a
platform to manage the data and terminology
transformations to be applied to each one of
the Biobank Information Management System
(BIMS) part of the project
• GIBIO platform provides a set of services to
allow different BIMS to send samples data and
the health records context for this samples

6. Valencia Region Biobank project
GIBIO platform
• This project is a joint project between Consellería de
Sanitat and the Foundation for the Promotion of
Health and Biomedical Research of Valencia Region
(FISABIO)
• Public Procurement for Innovative Technology
• It was developed in the context of the Valencia
Biobank Network (RVB) which is part of the Spanish
Biobank Network (Red Biobancos)

7. Valencia Region Biobank project
Project context
• Biobanks main origin is in the tumour banks
from anatomical pathology hospital services
• The number of samples for specific diseases
can be low in each one of the sites, which
empathises the need of integration
• Each biobank must report to the RVB Biobank
management system (SGI-RVB) when new
samples are available
• Connected to region patient identifier system
to get basic data such as age and gender

8. Valencia Region Biobank project
Project context
• What kind of data is being shared?
– Donor
• Patient demographics, reproductive status, cause of
death, etc.
– Donation
• Diagnosis
• Context of the donation event
– Sample
• Type of sample
• Pre-analytical variables
• Processing variables
• Storage details

9. Valencia Region Biobank project
Project context
• Heterogeneous non-normalized distributed
BIMS
• BIMS A != BIMS B
• A mixture of in-house and commercial
Biobank Information Management Systems
• Each BIMS may contain a different set of
variables

10. Valencia Region Biobank project
GIBIO Platform

11. Valencia Region Biobank project
GIBIO Platform

12. Valencia Region Biobank project
GIBIO Platform

13. Valencia Region Biobank project
GIBIO Platform

14. Valencia Region Biobank project
GIBIO Platform
• Focus on semantics
– Model standardized forms with archetypes
• Archetypes (and specifically Integration Archetypes)
play a key role in the platform as they also describe
which transformations are allowed
– Define the translations between local codes in
each BIMS and standard terminologies
– Local domain terminologies (such as SPREC or
MIABIS) are mapped to clinical domain
terminologies such as SNOMED-CT
• Hard task: create subsets with existing codes and define
national extensions when needed.

15. Valencia Region Biobank project
GIBIO Platform
• Example use cases:
– Spanish Lung Cancer Checklist
– SPREC

16. Valencia Region Biobank project
Example 1: Spanish Lung Cancer Check-list
• Each one of the sample types has its own
metadata variables that are filled by the donor
organization
• It’s critical to model these sets of variables so
every BIMS can collect data with same
meaning
• Link data points to terminology subsets when
possible

17. Valencia Region Biobank project
Example 1: Spanish Lung Cancer Check-list

18. Valencia Region Biobank project
Example 1: Spanish Lung Cancer Check-list
• Each archetype contains numerous data points
– Can we expect that every BIMS will be able to provide
all data points?
– Can we expect that every BIMS is able to generate
normalized data instances?
• Solution: provide BIMS with canonical (simplified)
schemas
– XSD or JsonSchema
• In the near future, JsonFlat
– Can provide schemas with high optionality, to allow
constraints to be checked later on

19. Valencia Region Biobank project
Example 1: Spanish Lung Cancer Check-list
• Canonical schemas
autogenerated from the
archetype
• Process also generates the
transformation program
between simplified schema
and target normalized
archetype

20. Valencia Region Biobank project
Example 2: SPREC
• SPREC (Standard PREanalytical Code) is a standardized set of
codes to facilitate the documentation and communication of
the most important preanalytical quality parameters for
different types of biospecimens used for research
– Type of sample
– Type of primary container
– Pre-centrifugation
– Centrifugation
– 2nd centrifugation
– Post-centrifugation delay
– Long term storage
• Can be used for fluid and solid samples

21. Valencia Region Biobank project
Example 2: SPREC
• SPREC codes encode all these axis in a single 11 letter
string
– E.g. CSFPXDZNNNJ, U24ACDXAAXA, URNLHGXNIXC
• To ease the understanding of the SPREC codes and they query
we can model SPREC axis as an archetype

22. Valencia Region Biobank project
Example 2: SPREC
• By splitting the SPREC code into its different axis and searching the
terminology service for the terms associated with that SPREC code we can
easily get a meaningful representation of the code
TERCPDZFIIZ

23. Valencia Region Biobank project
Example 2: SPREC
• Moreover, by using a terminology service with terminology
mapping capabilities this transformation can be enriched to
obtain the corresponding SNOMED-CT mappings for each
code

24. Valencia Region Biobank project
Next step: Data Quality
• Once data has been normalized both in structure,
codes, and semantics, domain wide quality analysis
can be performed
• Archetypes are the means to define both structural
and terminology constraints
• Archetype constraints can be refined in order to
obtain valid data compliant with each use case data
quality requirements

25. Valencia Region Biobank project
Next step: Data Quality
• Archetypes allow the definition of additional rules
• The rules syntax can be improved to deal with
terminology and Snomed expression syntax in an
easy way
• Archetype constraints and rules are translated to
schematron to validate data
• Still an evolving specification, only a subset is
currently supported

26. Valencia Region Biobank project
Next step: Data Quality

27. Valencia Region Biobank project
Next step: Data Quality
Uniqueness
Completness
Consistency
Correctness
Temporal stability
Multi-source stability
Is there repeated data?
Is there missing data?
Data follows the specified constraints?
Is there data not fitting to the real world?
Does data vary during time?
Does data vary depending on source?

28. Valencia Region Biobank project
Next step: Semantic queries
• Use of snomed expression constraint language
can be used to improve queries to the
available samples and related clinical
information
– “All blood samples of patients with a cancer
diagnosis”
– “Patients with liquid substance samples in this
past year”
• Use available services to enrich data
– Calculators (scores, risk, percentiles…)
– Services to detect drug interactions, etc.

29. Valencia Region Biobank project
Conclusions
• Creating a SNOMED-CT extension is a time consuming
effort
• As the platform is generic, it should be possible to use it for
other interoperability and normalization scenarios
– Get other kinds of information from EHR, such as medication
information, family history, consent, etc.
– Normalize the information of other healthcare areas with lots of
‘information silos’, e.g. private clinics, legacy systems, etc.
• Model and terminology reuse: Both generated archetypes
and Snomed extensions are meant to be reused in other
regional and national biobank related projects
• While the end goal of this project was to include the data
into an existing system (SGI-RVB), deploying the resulting
normalization in an openEHR data repository is one REST
service call away

30. Innovación y nuevos desarrollos de VeraTech