Join Jeff Kelly, Pivotal’s Big Data Strategist and Chris Roche, Aridhia’s CEO, to learn how Big Data and data science are being applied to clinical research. Learn… • Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS • How improving collaboration and productivity accelerates the discovery of insights and increases competitiveness • Why robust data security is critical to modernizing engagement between academia, industry and healthcare • How to reduce research costs while improving commercialization opportunities • Why enabling transparent analysis and reproducibility of research are key to scientific progress • Best practices to get started on your digital transformation and Big Data journey

1. Data Challenges in Clinical Research
Chris Roche, CEO, Aridhia

2. Chris Roche, CEO, Aridhia
Today’s speaker
Chris joined Aridhia in 2014 to provide strategic commercial leadership
as Aridhia embraced new 3rd generation technologies to support their
new cloud based business model and collaborative data science
platform. Chris combines a background in computing, specialising in
artificial intelligence and business transformation. He spent 14 years at
EMC, where he held a number of senior director positions including
serving as Country Manager for EMC in Ireland, Regional Director for
Greenplum, EMC’s Big Data business and as EMEA CTO. He holds a
1st Class Degree in Computing and Information Technology (BScHons),
is a member of the British Computer Society (MBCS) and is qualified as
a Chartered Engineer (CEng) and Chartered Information Technology
Practitioner (CITP).

3. Data Challenges in Clinical Research
Today’s agenda
o Introduction to Clinical Research & Data
o Market Challenges and the Aridhia Business Model
o Data in Clinical Research Use Cases
o Aridhia Business Transformation Journey
o Data, Technology and Roadmap
o Q&A

4. 4

6. Increasing productivity in the collaborative research workflow
Data
discovery
Data capture
Data load
Data quality
Analysis
Visualisation
Data analyst / Data owner /
Biostatistician / Researcher
Data owner
Data analyst /
Biostatistician / CRO
Data steward and analyst
/ Biostatistician / Pharma
End user
(clinician, manager, MDT,
patient, etc.)
Statistician / Data analyst /
Biostatistician and domain
expert etc.
Distribute output

7. Integrating precision medicine into the workflow
Data
discovery
Data capture
Data load
Data quality
Analysis
Visualisation
Data analyst / Data owner /
Biostatistician / Researcher
Data owner
Data analyst /
Biostatistician / CRO
Data steward and analyst
/ Biostatistician / Pharma
End user
(clinician, manager, MDT,
patient, etc.)
Statistician / Data analyst /
Biostatistician and domain
expert etc.
Distribute output

8. Operational healthcare
Multidisciplinary team meeting Risk prediction Population health

9. Collaborative R&D
projects
Pre-competitive
consortia
Research institutions/
ecosystems Diagnostics
Use cases

10. INDUSTRIALISATION GOVERNANCE REPRODUCIBILITY SERVICERAPID PROTOTYPING
What slows the process?

11. The status quo is not an option:
current data capability is unable to cope

12. A new approach is required

13. Change the way research and PM interacts with their data

14. ACRIS – Advanced Clinical Research Information Systems
ACRIS:
• a complex constellation of capabilities
that can rapidly assemble data assets
for clinical research questions
Provides:
• data mining and research process
support to meet the needs of clinical
and translational research
• related biostatistics and biocomputation
Includes:
• electronic health record (EHR) access
• open-source components
• an approach for big data needs

15. International hubs
NetherlandsUK

16. Team
DevOps and
Service Delivery
Enablement
Innovation
Business Operations
Glasgow / Edinburgh

17. EPAD – Alzheimer's Dementia adaptive clinical trial

18. • Traumatic Brain Injury (TBI) is a leading worldwide problem; the annual incidence of
hospitalisation following TBI ranges from 108-332 new cases per 100,000 inhabitants
• 72 million time points of data per patient stay (mean stay 2 days)
• 1.6Gb of data per patient per day, approximately 1.5Mb of data per minute per patient
• Near real-time integration of high-frequency ICU data, research & implement physiological
models & delivery of results back to the bedside

19. • Developing tools that can predict disease activity over a 12-month horizon for adults
recently diagnosed with relapsing-onset multiple sclerosis (MS)
• Patient reported outcomes, clinical, genomic analysis and quantitative neuroimaging data
will be evaluated to determine the optimum predictive tool, enabling NHS clinicians to
map a personalised trajectory of disease and predict those patients with a high likelihood
of transitioning to secondary progressive MS
• Will enable early intervention and result in focused delivery of the finite disease modifying
treatment drug budget to patients with the most compelling basis to receive treatment
• Data capture to deliver real-time NHS data integration across 4 NHS boards

21. “Radboud university medical center is a leading academic center for patient care, education and
research, with the mission ‘to have a significant impact on healthcare’.
Our activities help to improve healthcare and consequently the health of individuals and of society.
We believe we can achieve that by providing excellent quality, participatory and personalized
healthcare, operational excellence and by working together in sustainable networks.”

22. ‘From bricks to clicks’
Education
Digital Learning
Environment (DLE)
Care
Electronic Medical
Record (EMR)
Research
Digital Research
Environment (DRE)

23. Business transformation
Revenue Enablement DevOps

24. Secure FTP
Flat files, XML,
unstructured Virtual DesktopWeb Access
Security and Identity Management
App User / Member Data Scientist Administrator
CollaborativeWorkspaceUserInterface
Secure Messaging
HL, FHIR, other
payload
Active
Database agent
Direct EHR
connection
Active Web
service agent
FHIR, Web Services
HealthcareLandingZone
Services
Apps
Collaborative Services
Audit
Analytical Tools
Data and Compute Fabric
Admin
N3
Connection
Repository
e.g. Genomic store
Hadoop
Workspace DB
Health Analytics
Schema (option)
Workspace DB
MGRID HL7 RIM
Schema
(roadmap)
Workspace DB
Schema-agnostic
data lake
Web Access
SQL, R, R Shiny,
MADLIB
Virtual Desktop
Bring your
own tools
Collaboration
Tagging,
messaging,
notification,
alerts
Audit
Access, usage,
authentication,
provisioning,
upload
Apps
User-built analytical apps and output
or Aridhia-populated apps
e.g. LaTEX publishing, Health Analytic
Schema, data quality…
De-identification Service
Table Mapping
Dataset Library
Health Analytics Schema
Ontology Service
Direct to File Store
Data Quality Reports Data Linkage Reports Data Selection API
Data Staging Network Security App Hosting
Healthcare Landing Zone
Precision Medicine PaaS
Multi-site LIMS
Sample
Management
CTM
System
DICOM
End-Point
BIX Workflow
Data Asset
Repository
Federated Analysis API
Life-Cycle
Container
Secure file share
Large unstructured

25. Precision Medicine
Workspace
Small Clinical Trials
Workspace
Healthcare Landing Zone Workspaces
Key Management
Database
De-identification
Service
Data Preparation
Airlock
CDR
Workspace
Data Export
Participant Privacy
Management
Core Services
Client-specific Services
Available for Subsequent Research Use
Dynamic Consent
Dataset Library
Service
Ontology Service
Health Analytics
Schema
App Hosting Area
(Analytics application/
local applications)
Data Modelling and
Compliance
Financial
& Admin
Research &
Non-clinical
Data
Research
External
Database
Care Domain
(Client)
PACS
(if
required)
EPRS &
Clinical
EPRS Direct
PACS Direct
Custom Reports
Shared Care
Portal
Archiving
Healthcare Landing Zone

26. (* = roadmap 2016)
Benefits - impact on common research tasks
Research task Tools provided Time to Value
Sourcing data Connectivity with NHS sites Weeks
Castor GCP-level EDC (partner service) Days
De-identification De-identification Service <24h
Upload SFTP, tabular data mapping Minutes
Data Quality Data quality report, Ontology Service, Dataset Library Service <3h
Linkage Consistent study IDs, data model support, omics templates* Hours
Visualisation Quick visualisation tools On demand
Exploratory Analytics R, SQL + MADLIB On demand
Feature extraction Bioinformatics, image analysis toolkits On demand
Training models MADLIB, Python, Spark*, parallel computing Hours
Common analysis R packages (e.g. Survival), 3rd party tools Hours
Publication Publication quality PDF, PowerPoint <6h
App prototyping Shiny mini-apps 1-2 days

27. Roadmap – integrated vision for clinical informatics
Collaboration
Participant
Privacy &
Consent
Precision
Medicine
Workflows
Federated
Analysis
AnalyticsHealthcare
Landing
Zone
Hospital Integrations Transactions per patientResearch & Innovation Projects
Ontology
Service
Specialty
Apps
Data
Catalogue
Electronic
Data
Capture

28. 28
Questions?

29. http://pivotal.io/resources
https://blog.pivotal.io/
29
Resources
https://springoneplatform.io/

30. 30
Resources
http://www.aridhia.com/services/analytixagility-project-edition/
http://www.aridhia.com/services/analytixagility-ecosystem-edition/
http://www.aridhia.com/resources/library/