In this presentation, Kees van Bochove, founder & CEO of The Hyve, a services company in biomedical open source software, presents a number of different types of healthcare data. As an example, he also provides details of a project in which The Hyve participates and which uses that kind of data. Covered are: translational medicine data using tranSMART and cBioPortal, population health data using OMOP and OHDSI, and personal health data processing using open mHealth Shimmer and Apache Kafka.

1. Using healthcare data
for biomedical research
MAY 27, 2016
Kees van Bochove, CEO & Founder, The Hyve

2. Open Source
u Source code openly accessible and reusable for everyone
u Enables pre-competitive collaboration: both academics and
industry can use and enhance it
u Transparency: verification (scientific as well as IT security) can be
done by anyone, no ‘black box’

3. 3
The Hyve
u Professional support for open source so*ware for bioinforma1cs and transla1onal
research so5ware, such as tranSMART, cBioPortal, i2b2, Galaxy, ADAM and OHDSI
Mission
Enable pre-compe11ve
collabora1on
in life science R&D
by leveraging
open source so*ware
Core values
Share
Reuse
Specialize
Office Loca5ons
Utrecht, Netherlands
Cambridge, MA, United States
Services
So5ware development
Data science services
Consultancy
Hos1ng / SLAs
Fast-growing
Started in 2012
35 people by now

4. Interdisciplinary team
so5ware engineers, data scien1sts, project managers & staff; exper1se in
bioinforma1cs, medical informa1cs, so5ware engineering, biosta1s1cs etc.
4

5. YoungCB meeting @ The Hyve
5

6. New The Hyve offices being built at the Arthur van Schendellaan
in Utrecht, The Netherlands
6

7. 7
3 Health Data Areas The Hyve is active in
u Translational Research Data
(‘Clinical & bioinformatics data’)
u Population Health Data
(‘Real world data’)
u Personal Health Data
(‘Wearable sensors data’)
Example project:

8. 1.
TRANSLATIONAL RESEARCH DATA
8

9. 9
Center for Translational Molecular Medicine (CTMM)
u Public-private consortium
u Dedicated to the development of Molecular
Diagnostics and Molecular Imaging technologies
u Focusing on the translational aspects of molecular
medicine.
u 120 partners
u universities, academic medical centers, medical
technology enterprises and chemical and
pharmaceutical companies.
u Budget 300 M€
u 22 projects / research consortia
u TraIT is the Translational Research IT project
supporting these projects with a joint IT infrastructure

10. 10
TraIT Consortium
Growing TraIT project team

11. TraIT data workflow
Hospital (IT) Translational Research (IT)
data domains
clinical data
imaging data
experimental data
biobanking
integrated data translational
analytics
workbench
HIS
PACS
LIS
Galaxy
tranSMART/
cohort explorer
R
tranSMART/i2b2
datawarehouseCBM-NL
OpenClinica
NBIA + AIM
e.g. PhenotypeDB,
Annai Systems
e.g.
Galaxy, Chipster
Samples (IT)
P
s
e
u
d
o
n
y
m
i
z
a
t
i
o
n
Public Data
BIMS

12. 12
TranSMART Platform: Scientific Function
CLINICAL GENETICS SENSORS IMAGING
DATA
UNDER
STANDING
BIOLOGY MEDICINE

13. 13
TranSMART Open Source History
u February 2012: J&J releases tranSMART as open
source on GitHub under GPL v3
u December 2012: CTMM TraIT project decides to use
tranSMART as core infrastructure component
u January 2013: IMI eTRIKS starts, uses tranSMART as
core infrastructure component
u February 2013: kickoff of tranSMART Foundation, U.
Michigan publishes PostgreSQL port
u March 2014: IMI EMIF kickoff, tranSMART is used as
data integration component

14. Contributors

15. Amsterdam, June 2013: tranSMART Workshop
Attendees from 10 Pharma companies, 11 University Medical Centers and 12 IT companies
http://lanyrd.com/2013/transmart
15
VUmc
Sanofi
Recombina
nt / Deloitte
University
of
Michigan
Thomson
Reuters Pfizer
Astra
Zeneca
CDISC
University of
Luxembourg
Philips
Johnson
&
Johnson
The Hyve
70

16. Ann Arbor, Michigan, October 2014: Annual Meeting
http://lanyrd.com/2014/transmart
16
130

17. Bio IT World, Boston, April 2015
http://bit.ly/1R2N6uz
17
TranSMART wins all the prizes: Best Show Award, Best Practices Award, Best Poster Award

18. Amsterdam, October 2015: Annual Meeting
http://lanyrd.com/2015/transmart-foundation-annual-meeting/
18
160

19. TranSMART: working with clinical data

20. cBioPortal for Cancer Genomics
20
current community a.o.

21. Focus: intuitive exploratory analysis of the data

22. 2.
POPULATION HEALTH DATA
22

23. What Ewan Birney has to say
about it …
(GA4GH Leiden 2015)
23

24. Time To Market: 11 – 18 years
6
NegotiationforReimbursement
27memberStates
EMAFiling
Pre-ClinicalResearch
Closed&OpenInnovation
Clinical Trials
EMAApprovalforSale
HTAApproval
Phase 1 Phase 2 Phase 3
5,000
10,000
Compounds
250
Compounds
3 – 6 Years 6 – 7 Years
5
Therapies
1
Therapy
2 – 5 Years
Number of Patients/Subjects
20-100 100-500 1000-5000
Regulatory
Review
Drug
Discovery
Pre Clinical
Testing
PhV
Monitoring
Total Cost: $2 - $4 Billion USD
Sources: Drug Discovery and Development: Understanding the R&D Process, www.innovation.org;
CBO, Research and Development in the Pharmaceutical Industry, 2006;
Forbes, Matthew Herper, “The Truly Staggering Cost Of Inventing New Drugs”, February 10, 2012
Current EU “Patient Journey” is expensive and slow
New therapies
don’t reach
patients until here
Phase 4 : $0.6BDrug Development : $2.6B

25. Secondary use of health data to enrich research
25
The value of healthcare data for secondary uses in clinical research and development — Gary K. Mallow, Merck, HIMSS 2012
1 2 3 4 5 6 7 8 9
1,000
10,000
100,000
1 million
Years
#PatientExperiences/Records
The “burning platform” for life sciences
Pharma-owned highly controlled clinical trials
data
Clinical practice, patients, payers and providers
own the data
Product
Launch
R&D
Phase IV
Challenge
Today, Pharma doesn’t have ready access to this
data, yet insights for safety, CER and other areas are
within this clinical domain, which includes medical
records, pharmacy, labs, claims, radiology etc.

26. 26
Clinical Trials vs Observational Studies
Clinical Trials Observational Studies
Study Design Controlled (hypothesis driven) “Real world” data
Sample Size Small (10.000 is large) Large (millions of people)
Endpoints Efficacy, safety Effectiveness, economic value
Statistics Descriptive statistics (e.g. ANOVA) Epidemiological modeling
Cost Expensive Not so expensive
Perspective Study population Society in general

27. To become the trusted
European hub for health
care data intelligence,
enabling new insights into
diseases and treatments
EMIF vision
27
Discover
Assess
Reuse

28. Data available through EMIF consortium
§ Large variety in “types” of data
§ Data is available from more than 53 million subjects from seven
EU countries, including
Primary care data
sets
Hospital data Administrative data Regional record-
linkage systems
Registries and
cohorts (broad and
disease specific)
Biobanks
>25,000
subjects in
AD cohorts
>90,000
subjects in
metabolic cohorts

29. >40million
MAAS
SDR
EGCUT
PEDIANET
SCTS
IMASIS
HSD
AUH
IPCI
ARS
SIDIAP
PHARMO
THIN
100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000
Approximatetotal(cumulative)numberofsubjects
Available data sources in EMIF
29
EMIF-Platform
EMIF-Available Data Sources; EXAMPLES
1K
2K
52K
400K
475K
2.8M
2.3M
10M
Status Jan 2016
3.6M
1.6M
1M
12M
6M

30. 30
OMOP & OHDSI - Overview
u OMOP: Common Data Model for observational healthcare data:
persons, drugs, procedures, devices, conditions etc.
u OHDSI: Large-scale analytics tools for observational data
An open source community, a.o. developing:
u Tools to support the ETL / mapping process into OMOP (White Rabbit etc.)
u Tools to perform analytics: e.g. Achilles for data profiling, Calypso for
feasibility assessment
www.omop.org
www.ohdsi.org

31. 31
OMOP Common Data Model v5.0
v OMOP =
Observational
Medical
Outcomes
Partnership
v CDM = Common
Data Model
v SQL Tables

32. 32
OHDSI: Example Query Result
Slide from P. Ryan, Janssen

33. Re-use of healthcare data
33
Prof. Johan van der Lei
Erasmus MC University Medical Center
“We need to learn from experience and find ways
to unite the large volumes of data in Europe. At
the end of the day, we are in this for better health
care.”
Co-coordinator EMIF-Platform
EMIF-Platform

34. 3.
PERSONAL HEALTH DATA
34

35. 35
Challenges in managing chronic diseases
u Health assessments via physician visits are time
limited and subjective, often not representative
for everyday life of the patient
u The disease state can change a lot in between
visits, and important events are not visible to the
physician
But… it’s 2016: it’s now possible to objectively, remotely, and continuously
measure aspects of patient physiology, behavior and symptoms

36. 36
RADAR-CNS: Focus areas
from diagnose & treat à predict & pre-empt
u Epilepsy
u Monitoring and predicting epileptic seizures
u Multiple Sclerosis
u Monitoring exacerbations and disease state
u Depression
u Monitoring for possible relapses, plan timely interventions
u Predict bipolar state transitions

37. 37
Continuous Patient Assessment

38. 38
Potential digital biomarkers

39. 39
Preliminary Technology Stack
Analytics