Presentation about open science, the FAIR principles, and medical evidence generation with the OHDSI COVID-19 study-a-thon as an example. I've used variations on this deck in a couple of classroom and online courses for PhD and master students early 2020.

1. Kees van Bochove, Founder, The Hyve
Open science and
medical evidence generation
@keesvanbochove
Apr 28, 2020

2. We enable open science by
developing and implementing
open source solutions and FAIRifying
data in life sciences

3. Software engineers, data scientists, project managers & staff; expertise in
bioinformatics, medical informatics, biostatistics, knowledge management etc.
Fast-growing
Started in 2012
40+ people by now
Core values
Share
Reuse
Specialize
Customer Segments
Pharma
Life Sciences
Healthcare
Interdisciplinary team

4. Fast-growing
Started in 2012
40+ people by now
Core values
Share
Reuse
Specialize
Customer Segments
Pharma
Life Sciences
Healthcare
▶Translational Research
▶Cancer Genomics
▶Real World Data
▶Real World Evidence
▶Wearable Sensors
▶Data & Knowledge Management/FAIR Data
Focus areas at The Hyve

5. 5
A Year at
58 Projects
13 pharma companies
10 hospitals
11consortia/biobanks/health
networks/patient organisations
33 Projects with
patient level data
7Clinical Data
Models

6. Outline
1. Introduction to Open Science & Data
Stewardship
2. Medical evidence generation is
changing through open science
3. Example: COVID-19 studyathon

7. Science is broken
Statement #1
@keesvanbochove @TheHyveNL
but it can be fixed

8. What’s with the academic career path?
https://danco.substack.com/p/can-twitter-save-science

9. What’s with the academic career path?
https://danco.substack.com/p/can-twitter-save-science

10. Science is broken?

11. Altmetrics.org

12. 12
#OpenScience

13. 13
Research Data
Lifecycle
UK Data Service / JISC

14. The roots of FAIR
►Public-private partnership to advance:
►Open Science
► Sustainability & reuse of data
►Workshop in Leiden in 2014
►Towards a Modular Blueprint ‘Floor-plan’ of a safe
and fair Data Stewardship, Trading and Routing
environment, provisionally called the Data
FAIRPORT
https://www.lorentzcenter.nl/lc/web/2014/602/info.php3?wsid=602

15. Findable:
F1. (meta)data are assigned a globally
unique and persistent identifier;
F2. data are described with rich metadata;
F3. metadata clearly and explicitly include
the identifier of the data it describes;
F4. (meta)data are registered or indexed
in a searchable resource;
http://www.nature.com/articles/sdata201618
FAIR Data
Accessible:
A1. (meta)data are retrievable by their
identifier using a standardized
communications protocol;
A1.1 the protocol is open, free, and
universally implementable;
A1.2. the protocol allows for an
authentication and authorization procedure,
where necessary;
Reusable:
R1. meta(data) are richly described with a plurality of
accurate and relevant attributes;
R1.1. (meta)data are released with a clear and accessible
data usage license;
R1.2. (meta)data are associated with detailed
provenance;
R1.3. (meta)data meet domain-relevant community
standards;
Interoperable:
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles;
I3. (meta)data include qualified references to other
(meta)data;

16. 16
Open
Science

17. 17
Data Stewardship
https://ds-wizard.org
Rob Hooft

18. The fundamental change behind FAIR
Data
management
Data
stewardship
scope: project scope: organization
/ discipline

19. Science is
broken, but it can
be fixed
Statement #1
● The classical way of doing
science has too many
perverse incentives
● Digital collaboration can
change that for the better
● From data management to
data stewardship
@keesvanbochove @TheHyveNL

20. Medical evidence
generation is
changing through
open science
Statement #2
@keesvanbochove @TheHyveNL
● Scale
● Speed
● Impact

21. 21
OBJECTIVES OF THE EHDEN PROJECT
Harmonisation
Harmonise in excess of 100 million anonymised health
records to the OMOP common data model, supported by
an ecosystem of certified SMEs, and technical architecture
for a federated network
Evidence
Impact our understanding of, and improvement of, clinical
outcomes for patients within diverse healthcare systems
in the EU
Community
Establish a self-sustaining open science collaboration in
Europe, supporting academia, industry, regulators, payers,
government, NGOs and others

22. 22
THE JOURNEY TO REAL WORLD EVIDENCE
Patient-level data
in source system
Reliable evidence
Reproducible data flow
Documented manipulations and procedures.
Automated, end-to-end analysis code.
Common data model & standardised vocabulary
Two-step process: standardisation before analysis.
ETL & source code separated from analysis.
Re-use of data & analysis.
Patient-level data in
Common data
model

23. 23
WHY IS THIS NOT CURRENT PRACTICE?
Analytical method
Link to data
Data interoperability Standardised analytics Data network Strong community
What will it require?
The data…
Source - Christian Reich (IQVIA)

24. 24
THE OMOP COMMON DATA MODELStandardizedclinicaldata
Standardized
healtheconomies
Standardizedderived
elements
Standardizedvocabularies
Standardized meta-dataStandardized health system dataPerson
Observation period
Specimen
Death
Visit occurrence
Procedure occurrence
Drug exposure
Device exposure
Condition occurrence
Measurement
Observation
Note
Note NLP
Fact relationship
Care site
Payer plan period
CDM source
Concept
Vocabulary
Domain
Concept class
Concept relationship
Relationship
Condition era
Drug era
Dose era
Location
Cost
Cohort
Cohort attribute
Concept synonym
Concept ancestor
Source-to-concept map
Drug strength
Cohort definition
Attribute definition
Provider
Patient-centric
Tabular
Extendable
Built for analytics
Relational design
v 5.0.1

25. 25
STANDARDIZED VOCABULARIES

26. 26
OHDSI METHODS LIBRARY (R CODE) https://ohdsi.github.io/MethodsLibrary

27. 27
LARGE-SCALE OBSERVATIONAL RESEARCH IS FEASIBLE.
“Characterizing treatment pathways at scale using the OHDSI network.”
George Hripcsak et al. - PNAS (2016)27:7329–7336
11 Data sources
4 Countries
> 250 million
patients
Hypertension DepressionT2 Diabetes
Mellitus

28. 28
LANCET PAPER FROM OHDSI-LEGEND COLLABORATION

29. 29

30. The COVID-19
Study-a-thon
The power of Interoperability
● Background
● OMOP CDM:
Interoperable Data
Assets
● OHDSI tooling:
Reusable Analytics

31. Fast observational research is feasible
March 26-29, 2020
● Virtual event
● >300 collaborators
from 30 countries
● Four time zones
● 37 healthcare
databases
● Twelve concurrent
network studies

32. Interoperable data network

33. Key pharmacology terms
● Efficacy is the capacity of a drug to produce an
effect (e.g., lower blood pressure).
○ Typically studied in RCTs (under ‘ideal’ conditions)
● Effectiveness differs from efficacy in that it takes into
account how well a drug works in real-world use
● Safety concerns the type and likelihood of adverse
effects that a drug has.

34. Three focus areas, Twelve Questions
“Safety of hydroxychloroquine, alone and in
combination with azithromycin, in light of rapid wide-
spread use for COVID-19: a multinational, network
cohort and self-controlled case series study”
https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine

35. Standardized Analytical tooling

36. Standardized Analytical tooling

37. OHDSI Methods Library
● Reusable code
packages
● Implementing
best practices
for
observational
research

38. Standardized Analytical tooling
37databases

39. Interoperable data network
1)
2)
3)
4)

40. Standardised Dissemination of Results
https://data.ohdsi.org/Covid19EstimationHydroxychloroquine/

41. Within weeks: Paper in pre-print
https://www.medrxiv.org/content/10.1101/2020.04.08.20054551v1
https://www.sciencemag.org/news/2020/04/antimalarials-widely-used-against-covid-19-heighten-risk-cardiac-arrest-how-can-doctors

42. 42
STUDY-A-THON WEBINAR
https://youtu.be/
X5yuoJoL6xs
Review the webinar for a
run-through of an earlier
study-a-thon with the
organizer, Dani Prieto from
University of Oxford.
https://thehyve.nl/
resources

43. 43
COVID-19 RESOURCES
https://www.pscp.tv/w/1LyxBNwZBlExN
https://www.ohdsi.org/88-hours/
https://www.ohdsi.org/covid-19-updates/

44. Medical evidence
generation is
changing through
open science
Statement #2
@keesvanbochove @TheHyveNL
● The scale is changing: “a
paradigm shift from single
study and single estimate
medical research to large-
scale systematic evidence
generation”
https://ohdsi.github.io/TheBookOfOhdsi/
OpenScience.html
● The speed is changing:
study-a-thon example

45. Job opportunities https://thehyve.nl/about-us/vacancies/