David D. Dore, PharmD, PhD

Chief Research Officer

Optum Analytics – Life Sciences

EHR-fueled Trials: A New Approach to Generating Evidence of Drug Safety and Efficacy

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Mission

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• Be the gold-standard solution for EHR-fueled trials

• Transform our EHR platform into a pliable research platform that enables the learning health care system

• Create a fully scaled, end-to-end solution for clinical trial design, patient identification trial execution and analytics

• Employ industry-leading experts and connecting sponsors, care delivery organizations, and research participants to move research closer to clinical practice

Extending our analytics for faster, cheaper, better, more relevant clinical research to drive patient

engagement, improve outcomes, reduce costs, facilitate population health management and speed

medical product development.

3 https://healthpolicy.duke.edu/sites/default/files/atoms/files/rwe_white_paper_2017.09.06.pdf

Why we do randomized trials

Hernan and Robins. Epidemiology • Volume 17, Number 4, July 2006

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Optum’s unique sourcing model enables most comprehensive clinical data

Longitudinal Comprehensive Dataset:

70M+ Lives

Medical groups

Integrated delivery

networks

Staging Area

Hospitals

Multi-specialty practices EMR1

Small group practices EMR2

Physician offices EMR 3

Rx platform

Billing system

Rx platform

Billing system

Rx platform

Billing system

Rx platform

Billing system

EMR1

EMR2

EMR3

• Demographics • Lab results • Provider notes

(NLP) • Procedures

• Diagnosis • Medications • Outpatient visits • Vital signs

• Hospitalizations • Observations

Data & Analytics for Life Sciences

Analytics for Providers

Processing: Validation. Normalization, Standardization. Mapping.

Optum EHR-fueled clinical trial solution

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Optum is building an end-to-end EHR-fueled clinical trial platform to be embedded within the model of care delivery in order to reflect real-world practice and drive patient engagement.

EHR data-informed and

science-driven fit-to-study

design

• Protocol development, including statistical design elements

• Patient recruitment

• Implementation program

Optum-cultivated cadre of

study sites (CDOs)

• Optimized, data-informed choice of site based on trial needs

• Efficient start-up due to previously-built infrastructure and clinician engagement

• Patient identification, recruitment and retention

EHR data-driven

• Platform-enabled data collection, management and cleaning

• Centralized monitoring and QA processes

Real-world data science

• Data analysis

• Analytics to drive in-trial refinements

• Reporting and research translation

• Medical communications

DESIGN SITE SELECTION &

PATIENT RECRUITMENT

DATA OPTIMIZATION

DATA ANALYSIS, REPORTING & TRANSLATION

$$ $$$$ $$$$ $$

Extending on current infrastructure

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• Separate data process at appropriate

stage

(e.g., Stage Environment, CDR)

• Compartmentalized, compliant

hardware

• Separate team of researchers, data

scientists, engineers, and operations

• Handled, with permission, on behalf of

Care Delivery Organization

• Analyzed within Optum, CDO,

sponsor, and/or regulatory agencies

(e.g., FDA)

• Supplementary electronic data

capture

• Tools and automation for centralized

data verification, edit checks

• Command center intervention enabled

by data visualization

to drive

innovative data

and analytics

offerings

Optum analytics

data factory

EHR-fueled trial “Re-mastered Data”

based on Optum

provider client data

New build

Funded

innovation

Moving Research Closer to Practice

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Research Practice Translate

Practice

Research

Translate

Inform

Current Paradigm

Optum pRCT Platform

Where we’re going – subsequent development/launches

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Trial offerings

• Launch single Phase IV trial across multiple CDO sites

• Launch full Phase IV program (i.e., multiple studies, multiple sites)

• Phase III pilot

Program scope

• Protocol development, refinement, and targeting

• Development of cadre of CDOs sites ready to deploy for study needs

• Automation of data capture

• Clinician engagement and training

• Patient engagement

• Integration of remote patient monitoring tools, telemedicine capabilities

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Value opportunities across trial lifecycle

Centralized data auditing

…

Analysis and reporting

Replace primary data

collection, data monitors, and on-site quality

assurance

Passive follow-up of patients

Light-touch research

Track participants lost

to follow-up

Study conduct and data handling

Identification of specific patients and providers

Recruitment on behalf of

sponsor and CDO by Optum

Group-level randomization

Advanced analytics to

choose efficient populations

Patient recruitment

Identification of specific patients and providers

Enable providers to quantify the

value of participating (e.g., # of

patients, cost, financial risk)

Coordination across CDOs

Site initiation

Quantification of patients at site

Advanced analytics to identify best

sites and patients (e.g.,

likely participants)

Sponsor preferred sites

can be on-boarded, driving growth of Optum

One

Site selection

Data-driven design

Eligibility criteria informed by data

for greater efficiency

Pre-trial analytics ensure

that trial will target most appropriate population

Protocol development

Provide match-making service

between sponsors CDOs

Assess suitability of population

Simulating trials before

recruitment (enabled by data

platform)

Opportunity identification

Value

Current Workstreams

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NETWORK EXPANSION – onboarding preferred research sites

NEW DATA PIPELINE – hardware, data engineering, compliance, security

UPSTREAM DATA ACCESS – working with care-delivery organization as healthcare operations analysts

DATA MEASUREMENT PROJECT(S) – comparing classical data collection to EHR-based

Real-world Data

Real-world Information

Real-world Evidence

Current Workstreams

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NETWORK EXPANSION – onboarding preferred research sites

NEW DATA PIPELINE – hardware, data engineering, compliance, security

UPSTREAM DATA ACCESS – working with care-delivery organization as healthcare operations analysts

DATA MEASUREMENT PROJECT(S) – comparing classical data collection to EHR-based

Real-world Data

Real-world Information

Real-world Evidence

DURATION-3 Results (Selected)

HbA1c (%)

Weight (Kg)

Diamant, et al. Lancet Diabetes

Endocrinol. 2014 Jun;2(6):464-73.

DURATION-3 Results (Selected)

HbA1c (%)

Weight (Kg)

Summary

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• Most healthcare entities base policy decisions on clinical trials

– Pros: Clinical trials are carefully done and give accurate answers

– Cons: Answers may apply only to highly selected populations, ideal settings

• Efficacy = Effectiveness?

• For appropriate applications: With advances in data and research methods, we can conduct OBSERVATIONAL real-world effectiveness studies to directly measure patient outcomes.

• For many applications: We will need to conduct RANDOMIZED trials. This journey is just starting. Please join us.

Thank you. david.dore@optum.com

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Confidential property of Optum. Do not distribute or reproduce without

express permission from Optum.

Thank you

david.dore@optum.com