The value of healthcare data for secondary uses in clinical research and development

Gary K. Mallow, Ph.D.Director, Healthcare IT

DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

Conflict of Interest DisclosureGary K. Mallow, Ph.D.

• Salary: Merck & Co., Inc.

• Royalty:

• Receipt of Intellectual Property Rights/Patent Holder:

• Consulting Fees (e.g., advisory boards):

• Fees for Non-CME Services Received Directly from a Commercial Interest or

their Agents (e.g., speakers’ bureau):

• Contracted Research:

• Ownership Interest (stocks, stock options or other ownership interest excluding

diversified mutual funds):

• Other:

© 2012 HIMSS

Learning Objectives

• Recognize key secondary uses of health

care data in clinical research and

development.

• Associate secondary uses with business

outcomes.

• Understand key challenges and

opportunities for secondary use of

electronic health records (EHR) in clinical research and development in the life

sciences.

Hard trends in healthcare

• Pay for performance

• Cost pressure

• Shifting demographics

• Expiry

• Access to real-world data flows

• Variation in clinical practice

• Inefficient use of information

• Fragmented healthcare integrated care

• Inefficiency, defensive medicine, & waste

• Protracted adoption of innovation

Graphic from http://www.ted.com/talks/daniel_kraft_medicine_s_future.html

View on healthcare ecosystem trends

Pressure to demonstrate value

Clinical data valuable but no natural ownership

HIT investment is experimental across

areas - placing small bets in consumer genomics and compliance solutions

Testing social media to engage

patients but little proven success

Life Sciences

Heads down on EHR and MU

Need solutions to identify best practices and measure performance (for P4P)

Large IDNs and AMCs ahead of the curve, accumulating longitudinal EHR, some coupled with genetic info

Providers

Investing in HIT to enable shifting from

volume play to value play

Gain access to clinical data and analytics through acquisition

Leverage claims data for CER and develop commercial data services

PBMs investing in CER and

pharmacogenomics

Payers / PBMs

“Play or Pay” – required to offer

“minimum essential coverage” or pay penalties

Promote health and wellness

Encourage adoption of PHR

Leverage HIT for benefit design

Employers

EHR dominates HIT market share; disruptive solutions emerging

Non-traditional players entering the HIT space through acquisition or partnership

Data integration and analytics in high demand (due to the anticipated needs from ACOs) and active for M&A

Consumer/ patient engagement platforms + consumer genomics gaining attention, need to demonstrate commercial viability

HIE holds the promise for future but models are still in infancy, with few having demonstrated economic viability

Technology Providers / Enablers

Desire wellness and self care but lack

effective tools and incentives

More open to share health info online, but value remains to be seen

Still low penetration of PHR with little utilization

Patients / Caregivers / Consumers

Developing primary care capabilities and

sharing medical records

Building eRx capabilities and encouraging widespread Provider adoption

Trying to increase medication compliance

Pharmacies

Health information trends

Administrative Services Clinical Behavioral Environmental Scientific

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rials

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phic

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es/M

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eactio

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bserv

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ily-S

ocia

l-

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al

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n

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uality

(I/E)

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xposure

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ate

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ring

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hain

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al

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s

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l

Media

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ing

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t/Nutritio

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ise/F

itness

•Bio

-sensor

Readin

gs

•Adhere

nce to

Pla

n

10

100

1,000

10,000

100,000

Circa 2000

Circa 2010

• Digitization of health records and depth of health care data (quality)

• Connectedness of data sources (inter-operability)

• Larger population stores (quantity)• Exchange of information driven by meaningful

use

000 Lives

This presents the healthcare ecosystem with challenges and opportunities

The “burning platform” for Life Sciences

Importance of secondary use

Pace of change has accelerated exponentially

Focus on patients and their healthcare team

Requirement to demonstrate product value / fee for service fee for performance

Clinical data valuable but no natural ownership

HIT investment is experimental across areas - placing small bets in consumer genomics

and compliance solutions, clinical trial ops

Testing social media to engage patients but still experimental

Pharma

1.0

Pharma

2.0

Pharma

3.0

Developing drugs,

vertically integrated,

blockbuster model

Developing drugs, More

interdependent ecosystem,

diversified portfolios, V&B,

Licensing, Partnering,

Outsourcing

Delivery of healthy outcomes,

comparative effectiveness

research (CER), entrance of non-

traditional companies, business

models, collaborations

2009 “Zeros”Birth EO 20th Century

Improving decision

support

Examples of some key

clinical R&D outcomes desired

• Improved ability to execute EHR-driven Comparative Effectiveness Research

• New insights into disease prevalence and incidence in populations

• Longitudinal patient views to better understand population/ sub-population trends

CER, Disease & Demographic Models

• Comparator models to provide best in class guidance

• Improved compound selection when analyzing pharmacologic data for early stage trials

Comparator Modeling

• Improved predictability of patient/ dose response

• Better models through enhanced understanding of disease courses within patients

• Better identification of patients who will not benefit/ respond to therapy

Disease Progression & Therapy Treatment Models

• Improved identification of patients who qualify for a study and their subsequent enrollment

• Improved site identification and assessment of country feasibility

• Improved protocol refinement process

• Increased patient adherence

• Improved data quality

• Identification of linkages between active / post-trial data

Predictable Trial Execution and Cycle Time Reduction

• Better clinical risk mitigation through medical literacy directed capabilities

• Patient access to Merck owned medical records

• Improved credentialing and tracking of investigators through digital signatures enabling secure access

Effectiveness and Outcomes

• Today:– Life Science companies each own the transactional infrastructure – Systems are not well integrated across organizational boundaries

– Variations in requirements can lead to investigator frustration, decreased productivity, etc.

– Economic basis for sharing data for secondary use is not sustainable

– Effort to ensure data completeness and quality of intended use is high

• Aspiration:– Investigators leverage the same technology for clinical research as

is used in daily patient care– Infrastructure will be externalized– Variations largely hidden from users– Primary and secondary uses deliver much more value across the

pipeline– External EHR/PHR support MRL research needs in Discovery and

Preclinical Sciences, Development, Regulatory Affairs, and Informatics

“As is” “To be”

for secondary use of healthcare data

A virtuous cycle will be created between primary and secondary use of healthcare data

Patient PCPElectronic

clinical dataImproved and informed

cl inical care

Modeling

Patient ID &

Enrollment

Amendments

Improved modeling through

use of electronic clinical

data (e.g., epidemiology,

observational, interventional

s tudies, etc.)

More rapid,

comprehensive data -

driven patient ID &

enrollment

Fewer amendments through

improved data - driven

protocol design

• Improved understanding and

awareness of:

– Patient populations

– Disease progression and

natural history

– Therapeutics

– Upcoming and ongoing

clinical trials

– Comparative

effectiveness

– Safety and adherence to

medical guidelines

Doctor - patient

interaction

Record cl inical data

in medical record

Primary Use of Data

Secondary Use of Data

Feedback into

clinical care

Diagram from Booz & Company, prepared for PACeR Institute

Current: Culture of oneone

information flows

Many sources with varying curation and little standardization, and overhead to manage all links as “one offs”

Independent interactions i.e. for trial ops and franchise initiatives e.g. translational research

ProviderA

ProviderB

ProviderC Provider

G

ProviderH

ProviderI

ProviderD

ProviderE

ProviderF

ProviderK

Provider...”N”

ProviderJ

Aspirational “To be”Healthcare

Data Aggregator

1

Provider1-1

Provider1-2

Provider1-3..N

Provider2-2

Provider2-3..N

ProviderN-1

ProviderN-2

ProviderN-3..N

ProviderA

ProviderB

Healthcare Data

Aggregator“N”

Healthcare Data

Aggregator2

ProviderC..Z

Services e.g. curate, ensure standards, broker, links back to providers, etc.Support broad/deep data for secondary uses across franchises and

therapeutic areas

Some of the challenges facing growth of secondary use

• Accelerated data generation / EHR adoption fueled by ARRA/HITECH

– A national resource of EHR for secondary will depend on sustainable business models that can support EHR aggregation

• Data may be “miles wide,” but data are often shallow in various areas / don’t cover what is “fit for purpose” for some/all secondary uses

• Diverse data types and formats = integration hurdles for IT

• Multiple organizational silos: data & people

• Increasing number of external collaborations and partnerships

plethora of interchange points

• Limited collaborative data management support

Conclusion: Smart Identification of potential sources, evaluation, and gaining access to EHR is a critical capability for the foreseeable future.

Obtaining EHR for secondary use

• Everything we do in the healthcare ecosystem is for the people.

• Global EHR can be a resource for humanity more valuable than any other

• The healthcare community, including life sciences all have needs for information, and partnering and sharing are the keys to our joint success

• To obtain:1. Be clear what is needed and the value it will provide patients

/ consumers – by therapeutic area, targeted therapy, and its R&D plan

2. Seek3. Find, Refine, Filter4. Evaluate5. Obtain access “fit for purpose”6. Steward as befits information central to human well being and

happiness, assuring privacy and informed consent7. Use responsibly 8. Document value by sharing results as appropriate

A virtuous cycle for

healthcare data

17

Value

Intended

Use

Human Healthcare Ecosystem

Evaluate /

Choose

Obtain,

Steward, Use

Find

Refine

Filter

Where should one start?

0

1

2

3

4

5

6

7

8

9

Need

See

k

Find,

Ref

ine,

Filter

Eva

luate

Obt

ain

Ste

war

d

Use

resp

onsibly

Docu

men

t valu

e

Importance

Implementation Cost

Mean

1. Clarity of need

2. Robust Evaluation

2 case studies• Signature Discovery

– Merck and H. Lee Moffitt Cancer Center & Research Institute Collaboration

– Science requires rich, longitudinal data set which includes de-identified

patient healthcare information and OMICS data e.g. RNA expression appropriate to oncology research

– Information pipeline recognized with the 2008 Bio-IT World Best

Practices Award in Translational and Personalized Medicine

• Clinical trial protocol refinement

– Global Trial Optimization (GTO) team receives protocol draft

– ID draft inclusion and exclusion criteria and required values, and any clinical data collection capabilities that are essential for the trial e.g.

for COPD, quantitative spirometry test data

– Iteratively refine parameters

– Query EMR identifying patients that meet inclusion and exclusion criteria

– Refine query parameters to reduce failure rate for communication back to protocol authors.

Intended use: Protocol Refinement

20

Patient

Patient history &

demographics

History of Present

Illness /

Differential

Diagnosis

Physical

Diagnosis

Therapy / Meds

Outcomes

Intended use: signature discovery and

development

21

Patient

Patient history &

demographics

History of

Present Illness /

Differential

Diagnosis

Physical

Diagnosis

Therapy / Meds

Outcomes

Pathology

Detailed

therapeutic area

data

OMICS Data

Data evaluation...the hard way

• “I can’t be sure of the quality / completeness etc. of the data I have unless I can look at it in a way that is meaningful to me.”

• “These data are not really what I need.”

• The next most common complaint is, “It is too hard to explore the data to ensure it is what I need.”

SDD

Programming

Clinical Data Biosamples

Clinical DataRepository

MolecularData Files

Data Request

Data Results

Days\Weeks

Scientists

Data Managers

So...Improve data exploration tools

23

Data Managers: Accuracy,

Completeness,

Time-related

dimensions,

Consistency,

Accessibility,

IntegrityResearchers query data

directly, presented in

ways that are intuitive

Scientists

Concluding thoughts• National health reform efforts are moving towards outcome-based

payments, followed by Health IT investments to enable transparency through the capture and exchange of real-world data.

• Outcomes research based on real-world health information is becoming faster, cheaper, and therefore more common. It is essential to improving healthcare.

• Today, life sciences companies have limited access to real-world patient health information, but insights for safety, comparative effectiveness, efficient trial operations, and other areas are within this domain.

• In the face of uncertainty (e.g., equivalent of the dot-com era), too much is lost by waiting.

• Stakeholders across the healthcare ecosystem can proactively partner to ensure stakeholder communities have the differentiated access to real-world health information flows they need to perform their role.

Contact information http://www.merck.com

Gary K. Mallow, Ph.D. MRL IT Director, Healthcare Information Technology Merck & Co., Inc.126 E. Lincoln AvenueRY34-A112Rahway, New Jersey 07065

Office Phone: 732 594-2355 gary.k.mallow@merck.com