Conducting a Clinical Trial is a complex process, consisting of activities such as protocol preparation, site selection, approval of various authorities, meticulous collection and management of data, analysis and reporting of the data collected Each activity is benefited from the development of point applications which ease the process of data collection, reporting and decision making. The recent advancements in mobile technologies and connectivity has enabled the generation and exchange of a lot more data than previously anticipated. However, the lack of interoperability and proper planning to leverage this data, still acts as a roadblock in allowing organizations truly harness their data assets. This document will help life sciences IT professionals and decision makers understand challenges and opportunities around clinical data integration

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Challenges and Opportunities Around
Integration of Clinical Trials Data
30 November, 2017 | Author: Narayana Subramanian: Healthcare Business Analyst,
Rajat Shukla: Sr. Healthcare Consultant, CitiusTech
CitiusTech Thought
Leadership

2. 2
Objective
 To understand the processes involved in conducting clinical trials and the stakeholders
involved
 To identify the typical IT landscape across organizations that conduct / facilitate clinical trials
 To identify the potential opportunities for integrating data assets (R&D as well as RWD) and
the possible synergies that be offered by the integration
 To illustrate the benefits of moving to a platform-based approach for data integration and the
limitations of point solutions

3. 3
Agenda
 Introduction
 Clinical Trial Overview
 Clinical Trials: Existing IT Landscape and Challenges
 Clinical Systems: Challenges with Integrating Data
 Clinical Systems: Opportunities for a Platform
 Clinical Systems: Author Perspective
 References

4. 4
Introduction
 Operationally, a Clinical Trial can be defined as a study of human subjects that is
designed to answer specific questions related to biomedical or behavioral
interventions (drugs, biologics, treatments, devices, or new ways of using known
drugs, biologics, treatments, or devices)
 Conducting a Clinical Trial is a complex process, consisting of activities such as
protocol preparation, site selection, approval of various authorities, meticulous
collection and management of data, analysis and reporting of the data collected
 Each activity is benefited from the development of point applications which ease the
process of data collection, reporting and decision making
 The recent advancements in mobile technologies and connectivity has enabled the
generation and exchange of a lot more data than previously anticipated. However,
the lack of interoperability and proper planning to leverage this data, still acts as a
roadblock in allowing organizations truly harness their data assets
 This document will help life sciences IT professionals and decision makers
understand challenges and opportunities around clinical data integration.

5. 5
Clinical Trial: Drug Development Lifecycle
Drug Discovery
10,000
Compounds
250
Compounds
Pre-Clinical
IND Submitted
Phase I: 20-100
Phase II: 100-500
Phase III: 1000-5000
NDA Submitted
1 FDA
Approved Drug
5 Compounds Clinical Trial
Volunteers
FDA Review
Large-Scale Manufacturing
Phase IV
5 Years
1.5 Years
6 Years
2 Years
2 Years
A Typical Drug Development Process

6. 6
 The role of the sponsor is to finance the study of a new drug or a device and provide
management of the trial which includes designing the trial, providing materials,
collecting data, monitoring the trial and auditing all procedures and data submitted
to support the application for approval from the government
 The sponsors can be pharmaceutical or biotechnology companies, Government
funded agencies like NIH and NCI, hospitals and universities, NGOs, as well as
individual or group of physicians
Clinical Trial Participants (1/2)
Sponsor
 Clinical Research Associate (CRA)
Acts as an agent of the drug company and monitors how the trial is being conducted
at the study sites
 Medical Research Associate (MRA)
Functions like a CRA and is in-house at the sponsor’s facility
 Medical Monitor
Physician on-call for protocol questions or safety issues
 If a sponsor doesn’t have its own staff to handle the administrative work for the trial,
it may hire
• Contract Research Organization (CRO) – Serves as a broker or administrator
• Site Management Organization (SMO) – Provides managerial services for a
network of sites
Sponsor’s Team

7. 7
 Principal Investigator (PI) is the most important site staff and the person responsible
for conducting the clinical trial at the study site
 Sub-investigators assume the responsibility for patient care assessment
 Clinical Research Coordinator (CRC) or Study Coordinator is the person in charge of
managing the individual study site
 Pharmacist (PHA) dispatches the drugs prescribed by the physician along with the
study drug dosage to be given at that particular visit
 Lab Coordinator (LC) oversees the lab tests as requested by the PI
 Clinical Data Manager (CDM), Clinical Data Programmer (CDP), and the data
management staff ensure that data is entered, tracked and validated with widely used
industry standard software tools and agreed upon database protocols
Clinical Trial Participants (2/2)
Site Staff
 Institutional Review Board (IRB) is a committee designated to review the
participation of subjects in research studies
 An independent Data Safety Monitoring Board (DSMB) may also be included on
important trials. It consists of experts in the field and statisticians who inspect the
particular treatment under study. The DSMB can recommend changes during the
conduct of the trial or may also stop a trial prematurely due to safety concerns.
 The Food and Drug Administration (FDA) provides regulatory oversight for the
pharmaceutical industry and assures drug quality and safety
Regulatory
Bodies
 Subjects are volunteers on whom study is conductedSubject

8. 8
Clinical Trials: Existing IT Landscape and Challenges (1/4)
Some of the major activities / functional areas where IT systems are used in Clinical Trials are:
 Clinical Data Management: For conducting paper-based and electronic trials. The availability of
Electronic Data Capture systems has greatly facilitated the conduct of globally distributed trials
 Trial Management Systems: For planning and tracking trials, deadlines and milestones. This is a
project management system which is extremely useful in determining optimum trial sites and
investigators
 Medical Coding Systems: For coding drug names and medical events / adverse events against
industry standards (MedDRA and WHODrug for instance)
 Drug Safety Systems: For recording and reporting adverse events to regulatory bodies per laid
down regulatory framework. Signal Detection Systems are used for monitoring safety profile of
the marketed medicinal products
 Clinical Data Warehouses: Used to data from all R&D systems and analyzing the same. These
have emerged recently and are becoming increasingly relevant today
These systems present a paradoxical situation today : It is extremely complex to integrate them,
but it’s equally important to achieve this integration.
Role of IT Systems in Clinical Trials

9. 9
Clinical Trials: Existing IT Landscape and Challenges (2/4)
Challenges with Information Integration:
IT systems in Life Sciences have evolved over a period of time to address specific
business needs. These weren’t built to be interoperable, therefore integrating
information isn’t an easy task.
Silos of Information:
Safety and efficacy of a product are the two fundamental parameters used to
evaluate the performance of a medicinal product. However, this information is
distributed across multiple R&D and RWE systems.
Standalone Approaches:
When faced with a clinical question, often the approach is to build a new system
that addresses immediate requirements. RWE is one such example, where the
focus is exclusively on using RWD sources for decision making and not much on
integrating / mapping RWD sources with those from R&D to build better decision
support systems.
Current Challenges faced

10. 10
Clinical Trials: Existing IT Landscape and Challenges (3/4)
Stakeholder Engagement:
 Clinical systems (like EDC,
CTMS), integrated with
patient ePROs, devices and
mobile apps can connect
patients and physicians
together
 Such systems can enhance
trial participation / safety
surveillance, improve RWD
data collection and can
positively influence patient
compliance
Enhanced Decision Support:
 R&D and RWE data, when
comprehensively plotted, can
yield tremendous insights
about comparative
effectiveness, true AE profile,
and can aid in arriving at value-
based / outcomes-based
pricing.
 It can also help in faster
initiation and conduct of trials
by helping in site identification,
and improving the RBM
framework
Interoperability and Reporting:
 A data platform can
support integration and
standardization of data
from multiple sources
 This can shorten the time
needed to prepare
regulatory reports, and
enable seamless
integration with different
partner systems, making
concepts like eSourcing
achievable
Need for Integration of Clinical Data across Sources

11. 11
Clinical Trials: Existing IT Landscape and Challenges (4/4)
Integrating safety and efficacy data across diverse sources can significantly enhance
commercial and clinical decisions; however several challenges prevent this from happening.
Integration of R&D and RWE Sources: Possible Synergies
Safety & Efficacy Data
R&D as well as RWE sources contain this
information about a drug. RWD sources
present more complexity in analysis, but offer
data a much larger subject population.
Value Based Pricing
RWE can bridge the gap between commercial
decision making and R&D. It can generate hard
evidence for product differentiation and
value/outcome based pricing.
Enhance Future Product Research
RWE generates insights into possible, newer
indications for an existing product or patient
population where the drug is more effective.
This allows organizations to repurpose existing
drugs or enable personalized medicine.
Improve Operational Efficiencies
RWE can generate insights into the patient
population distribution and help identify
potential sites, but when mapped with data
present in R&D systems, it can make site
identification far more reliable.

12. 12
Clinical Systems: Challenges with Integrating Data (1/2)
 Evolving IT Landscape
Most of the current systems have evolved as point-in-case solutions for a pressing business
need, and have developed in features and complexities over the years. The advent of wearable
devices, and a need for Patient Reported Outcomes (ePRO) has added a new range of systems
and complexities to existing IT landscape
 Little Interoperability Built into Systems
While the systems address the reporting to regulatory in a defined, standardized format (e.g.,
E2B R2/3 formats for safety reporting), not all support interoperability across solution vendors
 Evolution of Regulatory Standards
The regulatory standards too, have evolved progressively. This makes them a moving target to
adhere to, something which is not supported by all product versions
 Data Model Differences
Due to inherent variability in data models for different applications, building integrations across
systems is a complex and time consuming exercise
 Mergers and Acquisitions in ISV Space
Several large acquisitions (like that of Relsys and Phase Forward by Oracle Corporation) have
led to a sudden obsolescence of some products, forcing users to switch to a new application.
This requires complex system migration exercise which often lasts for a few years

13. 13
Clinical Systems: Challenges with Integrating Data (2/2)
Custom
Integration
Legacy /
Partner
Systems
Integrated
System
Regulatory
Standards
Report Per
Standards
Regulatory
Agencies
Custom Integrations, created to link active and legacy / partner
systems; programmed to report against existing regulatory standards.
Integration Challenges:
 Most organizations adopt a strategy of integrating critical systems, however such integrations
usually break as soon as the product versions are upgraded, when the product vendor is
switched, or when the regulatory standards evolve
 ISVs, on the other hand, build features for regulatory compliance in new product versions. This
presents a compliance challenge
Current IT
System

14. 14
Clinical Systems: Opportunities for a Platform (1/2)
RWE
Platform
Multiple Data
Model Support
Data Security
Big Data
& NLP
Data
Exploration
/ Discovery
Faster Data
Integration
Scalable and
Future Proof
Machine
Learning, AI
Reporting and
Data Analytics

15. 15
Clinical Systems: Opportunities for a Platform (2/2)
 Point solutions for integrating data have a limited utility. They are tied to specific data sources
and formats, and need a lot of effort to keep up with evolving regulatory standards
 On the other hand, a platform with a well defined data model, backed by a metadata based
repository to map different systems, can simplify integration efforts
 A platform, while being nimble to onboard new sources, also offers quick solutions to assess
the relevance and reliability of the data being collected
 These are the same aspects which regulatory authorities like FDA consider before evaluating a
hypothesis which is backed by integrating data from diverse sources
 By its very nature, a platform can be instrumental in letting organizations anonymize their
data and share it amongst themselves by onboarding their systems on the platform
 Initiatives like i2B2 and ICHOM rely on the same philosophy of enriching a common data lake,
which can be a source for better clinical and operational decision making
 Availability of standards like FHIR and SDTM have made it relatively simpler to integrate data
from sources across R&D and RWD. A platform with data modeling and transformation
capabilities can leverage these standards, and facilitate faster integration

16. 16
Clinical Systems: Perspective
 A platform can benefit organizations by
giving it the flexibility to both on-board new
data and to conduct deeper analysis on the
aggregated data
 An ideal step would be to initiate integration
of R&D sources, thereby introducing
efficiencies in internal, clinical processes
(cross trial analysis, improved RBM
framework, faster data transformation)
 Organizations with mature systems and
processes can explore on-boarding newer
data sources (real world / partner) and
extending the platform to newer users. This
can help them explore areas such as
comparative effectiveness and
determination of true AE profile of a drug
 By breaking the data silos, organizations will
be able to optimize their clinical, operational
as well as commercial decision making, and
be in a better position to collaborate with
their partners
R&D and Partner
Systems
RWE Data Sources

17. 17
References
 IDC Health Insights, 2009
 DIA Conference, 2015
 http://www.marketsandmarkets.com
 https://www.openclinica.com
 http://www.clinovo.com/
 http://www.centerwatch.com
 http://www.eclinicalsol.com
 http://edcmarket.appspot.com
 http://www.ppdi.com
 https://thenounproject.com
 https://commons.wikimedia.org
 https://blog.frogslayer.com/building-software-products-vs-platforms/

18. Thank You
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Authors:
Narayana Subramanian
Healthcare Business Analyst
Rajat Shukla
Sr. Healthcare Consultant
thoughtleaders@citiustech.com