SCDM 2017ANNUAL CONFERENCE

September 24-27 I Orlando

Emerging Trends in Clinical Data Capture and

Clinical Research Technologiesv

SCDM 2017

Session Date: 26 Sep 2017: 08.30am-10.00am EST

The Ritz Carlton Ballroom III & IV

Session Chair: Arshad Mohammed

Technology Buzz

EMR

Claims

Pharmacy & Prescription

Labs

eConsent

eSource

Virtual Trials

Wearables

Connected DevicesMachine Learning

Artificial Intelligence

Analytics

Integration

Protocol Optimization

Session Flow

Planning

Protocol Design

Trial Planning

Study Feasibility

Sites Selection

Patient Recruitment

eConsent

Patient Centricity

Execution

eSource

EMR EDC connects

Other data: Labs

ePRO / eCOA

Wearables

Connected devices

Virtual Trials

Drivers

Data Integration

Analytics

Machine Learning / AI

Reporting / Submissions

eTMF

Regulatory

Discussion Panel Members

Angela L Lee

• 17+ years experience in clinical trials particularly Clinical Data Management.

• CDM at CROs as the Lead Project Data Manager as well as a Manager of DMs.

• Currently at Otsuka Pharmaceuticals (New Jersey)

• Associate Director of Data Management.

• Focus: Innovative Clinical Trial Model (ICTM)

Appalla Venkataprabhakar

• 20+ years experience in Pharma & CROs

• Started career as Statistician, moved to Clinical Data Management

• Currently at Novartis (Hyderabad, India)

• Head of India Database Development Operations

• Publications in Data Basics, Presentations at SCDM conferences, Co-Chair India

Conference and India Committee

Discussion Panel Members

Gene Vinson

• 17+ years experience in clinical research and information technology.

• Roles with increasing responsibilities in Clinical Programming, Biostatistics, Data

Management & Technology

• Currently at INC Research/ inVentiv Health (RTP, NC + Florida)

• Senior Director Global Data Technologies Biometrics and Data Operations

• Current focus: eSource, EMR/EDC integration, Clinical Data Lake

Hugh Levaux

• 20+ years experience including United BioSource Corporation (UBC), Product

Strategy at Medidata Solutions; CEO of Ninaza; and SVP at Quintiles

• Growing technology and services organizations with focus on clinical research and

technology.

• Currently at Protocol First - Founder & CEO (San Francisco)

• Presented & chaired multiple panel presentations, hosted conferences like DIA,

ISPOR, CBI, etc.

Discussion Panel Members

Jeff Beeler

• 20+ years experience in clinical research and information technology

• Roles with increasing responsibilities in CDM, Programming, Technology

• Currently at IBM Watson Health (RTP, NC)

• Vice President of Product Innovation.

• Focus: Oversee entire product life cycle, from strategic planning to tactical

activities of eClinical platforms.

Arshad Mohammed (Session Chair)

• 20+ years experience in Pharma, IT & CROs

• Physician, Clinical Research, EDC, IT consulting & product management, CDM

• Currently at QuintilesIMS (RTP, NC)

• Senior Director, Data Sciences

• SCDM Board of Trustees, Presentations/Session Chair - SCDM conferences,

Liaison for India Committee – enabling growth in India

v

Technology Perspective on EMR / EHR Data Acquisition

Failed Approaches to EMR Data

Acquisition:

PUSH data from EMR into EDC

PULL from EHR system using HL7

messages also proved non-scalable

(need for extensive mapping) and

presented real security risks

A Successful Approach to EMR Data

Acquisition:

PULL data from EMR into EDC

FHIR provides security and authentication

Solution allows PULL of data from EHR/EMR

all the way to SDTM without any manual data

transformation

FHIR (Fast Healthcare Interoperability Resources)

v

Operational Perspective on EMR/EHR Data Acquisition

State of FHIR

• FHIR-enabled EHR available worldwide now

• Production rollouts are happening now; completing sometime 2019 (US)

• Almost all EDC systems are not FHIR-enabled; though have a generic EHR

API interface

Sites

• EDC integration with FHIR needs to meet the Site’s security risk standards

• Every Site has a different process to gain approval for FHIR access

Sponsors and CROs need to:

• Adopt nimble monitoring practices when much of the data don’t need SDV

• Adopt nimble data management practices to validate data on the back end

In short, technology and standards are ready…time now to update processes!

v

Innovative Clinical Trial Model (ICTM)*

*ICTM: Otsuka proprietary

v

Data Flow in to Big Data Platform

Big Data Platform

v

eSource

Electronic source data are data initially recorded in electronic format… (1)

Use of eSource in clinical studies will help to (1)

• Eliminate unnecessary duplication of data

• Reduce possibility for transcription errors

• Encourage entering source data during subject’s visit, where appropriate

• Eliminate transcription of source data prior to entry into an eCRF

• Facilitate remote monitoring of data

• Promote real-time access for data review

• Facilitate the collection of accurate and complete data

Difference between eSource and EDC data

• eSource is captured First in Source System, not transcribed

• Considered Source Data - so no SDV

• Data Flow & work flow differ from EDC systems

• Not all data captured in eSource System is necessarily source

‒ Can Site Choose method and does this preserve ALCOA (FDA)

‒ Can the site create a complete; consistent; enduring and available, when

needed.

‒ eSource integrated with EDC / eSource in place of EHR

• Benefits of eSource

• eSource impact on timelines and DB Lock

(1)

fda.gov/downloads/Drugs/Gu

idanceComplianceRegulatory

Information/Guidances/UCM

328691.pdf

v

EHR Integrations and eSource

Creating data system integration between EDC/eSource and EMR or receiving a data file generated

directly from eSource

What are the difference between integrating with eSource and EDC data

• eSource: Original point of capture but system may contain transcribed data

• EDC: Transcribed from other systems and documents not considered source

• Direct Data from EMR systems may be integrated with eSource or EDC systems

Points to Consider

• Data Flow and work flow, they may differ with each system

• Impact of Integrations on Source Document verification

• Not all data captured in EHR is source

• Systems, Query's can be made to differentiate between data sources

• Adds complexity

• Multiple work flows

Integrations with Site EHRs

• You must sell it! YMMV

• Can PI influence the use of integrations

• Will you have access to site IT and EHR Administrators

• Will Research Administrators be willing to participate in EHR Integration

EHR Integration Methods

• Multiple Vendor Systems

• eSource

• SMART on FHIR

v

Cognitive computing understands, reasons and learns to help

deliver the insights that drive transformation.

Extracts and derives meaning from structured and unstructured content–at scale

Can read millions of clinical and scientific reports in minutes

Provides analyses across an array of criteria to transform decision making

Understand research from clinical, academic, commercial and proprietary sources

Dynamically updates hypotheses based on variable chains of evidence

Leverage the power of research to create targeted therapies

Harnesses entire bodies of knowledge

Access and build upon the latest research and clinical trials

v

Transformation in clinical trials

means moving away from linear

processes and incremental changes.

Current

Clinical development value chain

Marketplace assessment

and molecule development (preclinical)

Clinical trial protocol

development

Site selection and patient recruitment

Study conduct,

data collection and operations

Study analysis, clinical study report and submission

Postmarketing and real-world

evidence studies (postmarket

requirements and commitments)

Safety and pharmaceutical

vigilance

Research CommercializationDevelopment

Cognitive technology

Data assets

Emerging technology and the Internet of

Things (IoT)

Cloud

mHealth

Protocol

optimization

Patient

safety

Improving patient

and site selection

Transformed

Data analytics and aggregation

Patient-centric

v

Virtual Trials

16

80% of clinical trials delayed due to enrollment

48% sites fail to meet

enrollment goals

87% of patients

somewhat willing or willing to

participate.

70% of patients live more than 2 hours from clinical site.

Reference : https://lehub.sanofi.com/en/innovation-en/sanofi-launches-digital-clinical-trials-to-improve-recruitment-and-reduce-

trial-times/

$8 million is lost revenue/day due to

enrollment delays.

REMOTE

First Virtual Study By Pfizer in 2011.

Only 18 pts enrolled for the study.

VERKKO

Virtual Study By Sanofi in 2015.

Satisfaction Survey Score of

4.62/5.0.

66% pts felt Virtual trials were more

efficient than non-virtual trials.

BenefitsMaximize Patient Eligibility | Patient Enrollment | Patient Retention | Patient

Engagement | Reduce Risk in Drug Development Process | Improve Patient Safety

v

Artificial Intelligence / Machine Learning

17

“By 2021, the global use of artificial intelligence (AI) in healthcare is expected to achieve a CAGR of 42%”Ref : https://www.gep.com/mind/blog/artificial-intelligence-step-forward-clinical-trials

Patients & Sites

IdentificationImproving Drug

Compliance

Epidemic Outbreak Prediction

Identifying Discrepant

/ Fraud/ Anomaly

data

Intelligent Decision Making

Predicting Patients

Hospitalization

Identifying Drug

Candidates

Improve

Efficiency

Mining of Data for Better &

Quicker Treatment/

Diagnosis

Big Data & ML in Pharma &

Medicine could generate

value up to $100B annually

based on better decision

making.

By 2025, AI systems are

expected to be implemented

in 90% of the U.S. and 60%

of global hospitals and

insurance companies.

Recent study has estimated

that ML could reduce cost of

drug discovery by a whopping

70%.

Business Use Only 18

Doctor – Hospital Visit + mobile Technology = mHealth

Real Time Data Collection

Improves Patient Engagement

Improves Treatment Compliance

Enhanced Data Quality

Reduce cost by eliminating

expensive site visits.

Only 37% of the companies involved in

clinical trials are utilizing mHealth

technologies.

Concerns & Challenges : Data Security is primary concern (32%) | Difficult in

incorporation (29%) | Resistance from patients & physicians (23%)

165,000 mobile health apps available in

market.

4 million pts expected to use remote

monitoring technologies by 2020.

290,000 infants saved through

information campaign sent via SMS

(Bangladesh)

50,000 cases of TB cured through SMS

treatment compliance (Russia)

40,000 nights in hospital saved by

treating certain patients remotely

(Hungary)

2.4 B Euros saved by remotely

monitoring elderly patients (Sweden)mHealth market worth $23billion in 2017 &

estimated to grow at CAGR of 35% over next 3 yrs

v

Emerging Trends in Clinical Data Capture and

Clinical Research Technologiesv

SCDM 2017

Session Date: 26 Sep 2017: 08.30am-10.00am EST

The Ritz Carlton Ballroom III & IV

Session Chair: Arshad Mohammed

Thank you