National Workshop to Advance Use of Electronic Data

PATI ENT-C ENTER ED OUTCOMES RESEARCH INST I TU T E
What
Are
We
Looking
For?

Building
a
Na+onal
Infrastructure
for
Conduc+ng
PCOR
July
2,
2012

Joe
Selby,
MD,
MPH
Execu5ve
Director,
PCORI

2

PCORI
Mission
and
Vision

PCORI
Vision

Pa5ents
and
the
public
have
informa5on
they
can
use
to
make
decisions
that

reﬂect
their
desired
health
outcomes.

PCORI
Mission

The
Pa5ent-‐Centered
Outcomes
Research
Ins5tute
(PCORI)
helps
people
make

informed
health
care
decisions,
and
improves
health
care
delivery
and

outcomes
by
producing
and
promo5ng
high
integrity,
evidence-‐based

informa5on
that
comes
from
research
guided
by
pa5ents,
caregivers
and
the

broader
health
care
community.

Addressing
PCORI’s
Strategic
Impera?ves

3

*
Pa5ent-‐Centered
Outcomes
Research

Developing
Infrastructure

PCORI
promotes
and
facilitates
the

development
of
a
sustainable
infrastructure

for
conduc5ng
PCOR*.

Advancing
Use
of
Electronic
Data
Supports
Impera5ve
to
Develop
Infrastructure
to
Conduct
PCOR*

4

Ideal
Data

Infrastructur
e
for
PCOR

Covers
large,

diverse

popula5ons
from

usual
care
seSngs

Allows
for

complete
capture

of
longitudinal

data

Possesses
capacity

for
collec5ng
pa5ent

reported
outcomes,

including
contac5ng

pa5ents
for
study-‐
specific
PROs

Includes
ac5ve

pa5ent
and

clinician

engagement
in

governance
of

data
use

Is
affordable—
efficient
in
terms

of
costs
for
data

acquisi5on,

storage,
analysis

Has
linkages
to

health
systems
for

rapid
dissemina5on

of
findings

Is
capable
of

randomiza5on—
at
individual
and

cluster
levels

Desirable
Characteris?cs
for
Data
Infrastructure

to
Support
PCOR

Funders,
Models,
and
Opportuni?es

Special
Socie5es
Payers
Innovators

and
Entrepreneurs

Industry

•  Meaningful
Use

•  EHR
Cer5ﬁca5on

programs

•  Standards
&

Interoperability

Framework

•  SHARP
Program

•  BEACON

Communi5es

ONC

•  Sen5nel

•  OMOP

FDA

•  DRNs

•  PBRNs

•  Registries

•  SPAN

•  PROSPECT

•  EDM
Forum

AHRQ

•  CTSA

•  Collaboratory

•  CRN,
CVRN

•  ClinicalTrials.gov

•  eMERGE
Network

•  PROMIS/
NIH
-‐
Snomed-‐CT,

LOINC

NIH

•  VistA

•  iEHR
(2017)

VA

2011
Report:
Digital

Infrastructure
for
the

Learning
Health
System:

The
Founda+on
for

Con+nuous
Improvement

in
Health
and
Health
Care

IOM

Where
We
Need
Your
Help

Framework
and

Ac5on
Items
for

PCORI’s
Role
in

Improving
the

Na5onal
Data

Infrastructure

Deﬁning
the

Na5onal
Data

Infrastructure

Needed
for
PCOR

Iden5fying
Meaningful

Opportuni5es
to
Close

Gaps
in
Na5onal
Data

Infrastructure
for

PCOR

Vision

Strategy

In
the
PCORI
Quiver

Funding
Research
in
Priority
Areas

Convening
Relevant
Stakeholder
Groups

Establishing
Standards
for
PCOR

Engagement
of
Pa5ents
and
Other
Stakeholders

Strategic
Investments
and
Partnerships

Challenges
Ahead

Breakout
Groups
to
Address
Large
Areas
for

Improvement
of
the
Electronic
Health
Infrastructure
for
PCOR

Need
Iden?fied
To
Be
Addressed

Governance
Which
models
of
governance
best
address
the
challenges
of
data
ownership
and

availability,
protect
intellectual
property,
and
ac5vely
engage
pa5ents
and

clinicians
in
overseeing
data
use?

Data
Standards
and

Interoperability

What
must
be
done
to
assure
that
data
collected
across
mul5ple
sites
holds

common
defini5on
and
can
be
aggregated
reliably
for
analy5c
purposes?

Architecture
and
Data

Exchange

What
network
design
best
address
desires
for
both
local
control
of
na5ve
data

and
researchers
need
for
cross-‐site
data
access?

How
do
advancements
like

cloud
compu5ng
affect
network
design?

Privacy,
and

Ethical
Issues

What
must
be
done
to
preserve
pa5ent
privacy
while
allowing
data
to
flow

between
pa5ents,
clinicians,

and
researchers
for
the
conduct
of
PCOR?

Methods
What
methods
can
be
used
to
overcome
the
limita5ons
of
imperfect
data?

Incorpora?ng
Pa?ent-‐
Reported
Outcomes

What
must
be
done
to
assure
that
systems
support
the
collec5on
and
analysis
of

data
that
are
most
meaningful
to
pa5ents?

“Unconven?onal”

Approaches

How
can
we
expand
on
innova5ons
such
as
ac5vated
online
pa5ent
communi5es

and
those
from
other
industries
to
increase
the
capacity
to
conduct
PCOR
as
well

How
Will
We
Do
This?

Vision

Defining

our
goal

Discovery

Surveying
the

landscape

Idea?on

Iden5fying

opportuni5es

Priori?za?on

Deciding

where
to
start

Ac?on

Iden5fying

next
steps

July
2

Morning

July
2

AHernoon

July
3

•  Survey
of
the

landscape

•  Lessons
from

the
field

•  Case
Studies

•  Panelist

Responses

•  Breakout

Groups

•  Poster
Sessions

•  Recap
of
Poster

Session

•  Exploring
Top

Ten
Poster

Session

Proposals

•  Reflec5ons

A Vision For A National Patient-Centered Research Network
Francis S. Collins, M.D., Ph.D.
Director, National Institutes of Health
National Workshop to Advance the Use of Electronic Data in
Patient-Centered Outcomes Research
July 2, 2012

Why is it so hard to do effective and
efficient clinical research?
§  Few pre-existing cohorts of substantial size
§  Even fewer with broad disease relevance
§  Absence of longitudinal follow up
§  Paper medical records the norm until very recently
§  Lack of population diversity
§  Vexing consent issues
§  Multiple IRBs
§  Privacy and confidentiality challenges
§  Chronic difficulty achieving enrollment goals
§  Limited data access
§  Heavy costs of start-up and shut-down

Imagine …
A National Patient-Centered Research Network
§  Bringing together 20–30 million covered lives, with
–  Good representation of gender, geographic, ethnic, age,
educational level, and socioeconomic diversity
–  Broad opt-in consents from 80 - 90% of participants
–  Longitudinal follow up over many years
§  Offering a stable research infrastructure
–  Including trained personnel in each of the participating health
services organizations
–  Making it possible to run protocols with low marginal cost

Imagine …
§  Drawing on electronic health records (EHR) for all
patients, with
–  Interoperability across all sites
–  Meaningful use for research purposes
§  An efficient Biobank
§  Promoting data access policies that provide for broad
research use but protect privacy and confidentiality
§  Providing governance with extensive patient participation
in decision making

What Could We Do With a National
Patient-Centered Research Network?
§  Rapidly design and implement observational trials
–  At very low cost
§  Quickly and affordably conduct randomized studies
–  Using individual or cluster design
–  In diverse populations and real-world practice settings
§  Significantly reduce usual expenses associated with
start-up and shut-down of clinical research studies

Examples of Studies That Could Be Facilitated By
mHealth Applications
§  Prevention
–  Monitor obesity management programs
–  Assess sleep apnea at home
–  Support tobacco cessation
§  Chronic disease management
–  Continuous glucose monitoring for diabetes
–  Monitor ambulatory blood pressure in real time
–  Continuous EKG monitoring for arrhythmias
§  National patient-centered research network would ...
–  Provide a real world laboratory for assessing whether mHealth-
based interventions actually improve outcomes

§  Most acute LBP resolves with conservative management
§  But about 20% of LBP becomes chronic
–  Common treatments: medications–physical therapy–chiropractic/
manipulative therapy–acupuncture–surgery
–  Complex fusions for spinal stenosis up 15x in recent decades
§  National patient-centered research network would ...
provide large # of participants; longitudinal follow-up to
–  Determine how to prevent acute LBP from progressing to chronic
–  Compare risks and benefits of common treatments
–  Discern appropriate use of lumbar imaging for evaluation
Low Back Pain (LBP)

Large-Scale Pharmacogenomics
§  Example -- Clopidogrel (Plavix): powerful antiplatelet drug used in
patients at risk for heart attack, stroke
–  CYP2C19 genotype may identify decreased responsiveness
–  FDA added black box warning – but other research has raised
doubts about clinical importance of CYP2CI9 genotype
§  National patient-centered research network would …
facilitate trials to examine conflicting data
–  Large-scale, rapid-fire clinical trial of patients with acute coronary
syndrome, recent stroke, recent placement of drug-eluting stent
•  Randomized trial (individual or cluster)
•  Only short-term (e.g. 6 to 12-month) follow-up needed
–  Model could be applied to other pharmacogenomic questions
By synchronizing with EHR data, one could
do large definitive trials quickly at low cost

What Could Go Wrong?
§  EHRs won’t turn out to be that useful for research (hey,
we’d better solve that one at this meeting!)
§  Business managers of health services organizations will
perceive a conflict between health care delivery and
research
§  Patients (especially underrepresented groups) will be
unwilling to participate
§  The network will be too large to evolve when it needs to,
and will become quickly ossified
§  An entitlement will be created – once a node in the
network is supported, it can never be terminated

Why Now?
§  For the first time in the U.S., health services organizations
with EHRs have reached the point of making this network
feasible on a large scale
§  Scientific opportunities and the urgency of getting answers
to clinical questions have never been greater
§  If we are ever to engage a larger proportion of the
American public in medical research, we need to come to
them – in partnership
§  General feasibility has been demonstrated through modest
prior efforts (e.g. HMORN, eMERGE, etc.)
§  PCORI has arrived on the scene – and successful
establishment of this Network, potentially with NIH and
AHRQ as partners, could be PCORI’s most significant
contribution and enduring legacy

Works Best as a Team Sport
So let’s go for the gold!

Building an Electronic Clinical Data
Infrastructure to Improve Patient
Outcomes

July 2, 2012
PCORI Methodology Committee - Electronic Data Workshop
Erin Holve, PhD, MPH, MPP
The EDM Forum is supported by the Agency for Healthcare Research and Quality (AHRQ) through the American
Recovery & Reinvestment Act of 2009, Grant U13 HS19564-01.

The Electronic Data Methods
(EDM) Forum
à  Advancing the national dialogue on the
use of electronic clinical data (ECD) to
generate evidence that improves patient
outcomes.
– Comparative Effectiveness Research
(CER)
– Patient-Centered Outcomes Research
(PCOR)
– Quality Improvement (QI)

Research Networks in CER and QI
à  Networks include between
11,000 and 7.5 million
patients each; more than
18 million in total
à  38 CER studies are
underway or will be
conducted
–  Address most of AHRQ’s
priority populations &
Conditions
à  Over 300,000 participants
in the CER studies
3

ARRA-CER Funding for
Infrastructure
Electronic Clinical Data Infrastructure
$276 Million (25.1% of ARRA-CER funding)
Clinical and claims
databases, electronic health
records, and data
warehouses
Patient Registries
Distributed and federated
data networks
Informatics platforms,
systems and models to
collect, link and
exchange data
Infrastructure & Methods Development
$417.2 Million (37.9% of ARRA-CER Funding)
Governance Data Methods Training
Total ARRA-CER Funding
$1.1 Billion
Evidence
development and
synthesis
Translation and
dissemination
Infrastructure and
methods
development
Priority Setting
Stakeholder
Engagement

Convening Bodies:
EDM Forum
BEIN
CTSA KFCs
HIT Taskforce (ONC)
RoPR
Implementation
& Application
Clinical &
Community Care
(Delivery)
Research
Discovery
(Cutting Edge)
CER PILOTS
Enhanced Registry – DRN – PROSPECT
SHARPn (ONC)
DARTNeT
REDCap
PACES &
JANUS (FDA)
DEcIDE
(AHRQ)
Sentinel
Network (FDA)
VINCI (VA)
MPCD
HMORN
INFRASTRUCTURE BUILDING
Enhanced Registry – DRN – PROSPECT
HITIDE (VA)
Query Health
(ONC)
Beacon
Communities
High Value
Healthcare
Collaborative
Landscape of Electronic Health Data Initiatives for Research
QI PILOTS
Enhanced Registry
State HIEs
OMOP (FNIH)
eMerge
caBIG
i2b2
iDASH

Clinical Care
Delivery
Healthcare
System
Evidence
Generation
EDM
Forum
Knowledge Management &
Dissemination
Data Flow
Figure adapted from: IOM (Institute of Medicine). 2011. Engineering a learning healthcare system: A look at
the future: Workshop summary. Washington, DC: The National Academies Press.
Generating Evidence to Build a
Learning Health System
Community

Understanding the Landscape
à  Discussions to identify
priorities and challenges
–  Steering Committee
–  Stakeholder Symposium
à  Connections/collaboration
with
–  Relevant e-Health initiatives
–  Stakeholder groups
à  Site Visits (n=6)
à  Stakeholder Interviews
(n=50)
à  Literature Reviews
–  Peer-reviewed Literature
–  Grey Literature
•  Social media
–  Translation and
dissemination opportunities
à  Issue briefs
à  Commissioned papers

Lessons from Experts at the
Frontier
à  24 commissioned and
invited papers on
governance, informatics,
analytic methods, and the
learning healthcare
system
à  > 90 collaborators; >40
institutions
à  First half of these just
published in Medical Care

By Design, Papers Address
Current Gaps in the Literature
à  A review of challenges of traditional research designs and data
that can potentially be addressed using electronic clinical data
(Holve et.al)
à  A framework for comprehensive data quality assessment (Kahn
et.al)
à  Cohort identification strategies for diabetes and asthma (Desai,
et. al.)
à  A review of informatics platforms for research, including i2b2,
RedX, HMORN VDW, INPC, SCOAP, CER Hub (Sittig et.al.)
à  Desirable attributes of common data models (Kahn, et.al)
à  Comparison of data collection methods including paper,
websites, tablet computers (Wilcox et.al.)
à  Privacy-preserving strategies for hard-coded data (Kushida et.
al.)
à  Comparison of processes to facilitate multi-site IRB review
(Marsolo)

Breakouts and Important Areas for
Further Discussion
à  Governance
à  Informatics
à  Methods
and
à  Patient Reported Health Information
à  Innovative Approaches
à  Training
*Dissemination/Incentives to Collaborate

à  Electronically collecting patient-reported
information can
–  Offer a unique, important, and patient-centered
perspective for clinical care, QI, and research
–  Increase the efficiency of information exchange
with potential to make a difference in real-time
à  Known and anticipated challenges for
collecting, using, and implementing patient
report of data and information for PCOR lays
out an extensive research agenda
Patient Reported Health
Information

Innovators & Game Changers
ePatients; Citizen Science
à  Patient Contributed Data,
mHealth, Biomonitoring,
and Crowd-Sourced Data
–  Patients Like Me
–  tuDiabetes
–  www.asthmapolis.com
–  www.quantifiedself.com
–  Google Flu
–  personalexperiments.org
–  Wellvisitplanner.org
à  Portable legal consent

Training (EDM and Beyond)
à  How will social diffusion of new methods
and emerging standards take place?
–  For trainees
–  For those currently in the field
–  Experiential learning opportunities likely key
•  Delivery System Science Fellowship
–  Geisinger, Intermountain, PAMFRI
à  Engaging BIG data requires
–  Data sandboxes & Data playgrounds
–  Teaching governance
–  Design and UI for HIT/mHealth
–  Training observational researchers in experimental
methods

In a Dynamic, Learning System
Dissemination Should Facilitate the Journey,
Not Just Describe the Destination
à  HSR and medical journals
focus on research results. Not
ideally designed for:
–  Process (e.g. Lab/study notes)
–  Novel designs/approaches
–  Quick turnaround
–  Discussion
–  Engaging non-research
audiences
à  Stakeholders increasingly
perceive a need to rapidly
disseminate “street
knowledge” that is:
–  Peer reviewed
–  Open access
eGEMS
- Guidance on the conduct of
research and QI:
Papers;
Visualizations;
Other media (audio/video)
- Contributions evaluated on
Usefulness;
Credibility;
Novelty
* Facilitates discussion and
collaboration
* Encourages transparency
and reproducibility

Transforming the Research Enterprise
“Make the idea bigger”
How to link emerging data and tools in a
marketplace of people and ideas
committed to transforming clinical
research?
Discovery
Implementation
Research
Care

A New Marketplace for PCOR Data and Tools
“The Miracle Mile”
Exchange Interoperability Data Quality
Integration
Platforms/ Data
Warehouses
Middleware
(e.g. Automated
abstraction, NLP,
Interface Adaptors)
Data Models
(e.g. VDW,
OMOP)
Automated
Queries
(e.g., RedX)
Governance:
Security, Privacy, COI, Rules of Engagement
Partnerships for Research (Networks)
Mediated
Queries
(e.g. i2b2+)
Analytic Tools
(e.g., OCEANS)
Flexible and Reusable Access and Use for Research
“Stickiness”
CPR tools
(e.g., WICER tablet
adaptation)

Join the discussion! www.edm-forum.org
Current Features:
à  Medical Care supplement
à  Issue Briefs:
–  Meaningful Engagement
–  Protected Health Information
à  CER Project Profiles
à  eHealth data initiatives for
research & QI
Coming Soon:
à  Webinar registration
à  eGEMs updates (August ’12)
17
Join the Discussion
Sign up at edmforum@academyhealth.org

The
analyses
upon
which
this
publica2on
is
based
were
performed
under
Contract
Number
HHSM-‐500-‐2009-‐00046C
sponsored

by
the
Center
for
Medicare
and
Medicaid
Services,
Department
of
Health
and
Human
Services.

Research
Data
Networks:

Privacy-‐Preserving
Sharing

of
Protected
Health
Informa>on
Lucila Ohno-Machado, MD, PhD
Division of Biomedical Informatics
University of California San Diego
PCORI Workshop 7/2/12

21st Century Healthcare
What
is
the
inﬂuence
of

gene0cs,
environment?

What
therapies
work
best
for

individual
pa0ents?

•  Genome
–  Arrays, sequencing
•  Phenome
–  Personal monitoring
•  Blood pressure, glucose
–  Personal Health Records
–  Behavior monitoring
•  Adherence to medication, exercise
•  Environment
–  Air sensors, food quality
–  Location
Source: DOE

Personalized Medicine
Requirement for Handling Big PHI Data
- Secure Electronic Environment
• Electronic Health Records
• Genetic Data
Prevention, Diagnosis and Therapy
–  Genetic predisposition
–  Biomarkers
–  Pharmacogenomics

Practical Risk Assessment by Clinicians

Hudson KL. N Engl J Med 2011;365:1033-1041.
Examples of Drugs with Genetic Information in Their Labels
Hudson KL. N Engl J Med 2011

This patient has genotype
VKORC1 GG and CYP2C9 *1*1
Start Warfarin at 5 -7 mg
Needed Decision Support for Clinicians

How can we accelerate research?
•  Build infrastructure to access large data
repositories
–  Enhance policy and technological solutions to the
problem of individual and institutional privacy
–  Lower the barriers to share data
•  Share tools to analyze the data
–  Meta-data: data harmonization and annotation
–  Algorithms and computational facilities

Best
Prac>ces
and
Minimal
Standards

Systema0c
Reviews

(3,057
documents)

•  Architectures

•  Data
harmoniza0on

•  Governance

•  Privacy
protec0on

9

commissioned
by

User requests data for
Quality Improvement
or Research
Are the data
available?
• Identity & Trust
Management
• Policy
enforcement
Trusted
Broker(s)
Healthcare Entities
AHRQ R01HS19913 / EDM forum
QI and Clinical Research Data Networks
•  Scalable
networks
for

compara0ve

eﬀec0veness
research

•  Re-‐usable

infrastructures
to

lower
barriers
to
add

–  Policies

–  Studies

–  Ins0tu0ons

Example: UC ReX - Research eXchange
•  Current
plans:
Integra0on
of
Clinical
Data

Warehouses
from
5
Medical
Centers
and

aﬃliated
ins0tu0ons
(>10
million

pa0ents)

–  Aggregate
and
individual-‐level
pa0ent
data

will
be
accessible
according
to
data
use

agreements
and
IRB
approval

•  Future
plans:
Integra0on
with
clinical
trial

management
systems,
biorepositories
Funded by the UC Office of the
President to the CTSAs

Privacy
Protec>on

–  Use
of
clinical,
experimental,
and
gene0c
data
for

research

•  not
primarily
for
clinical
prac0ce
(i.e.,
not
for
health
care)

•  not
primarily
for
quality
improvement
(i.e.,
not
for
IRB

exempt
ac0vi0es
–
regulatory
ethics
commiZee)

–  Data
networks
must
host
and
disseminate
data

according
to

•  Federal
and
state
rules
and
regula0ons

•  Data
owner
(e.g.,
ins0tu0onal)
requirements

•  Consents
from
individuals

13
funded
by
NIH
U54HL108460

User requests data for
Quality Improvement
or Research
Are the data
accessible?
• Identity & Trust
Management
• Policy
enforcement
Trusted
Broker(s)
Security Entity
AHRQ R01HS19913 / EDM forum
QI and Clinical Research Data Networks
Wu Y et al. Grid Binary LOgistic REgression
(GLORE): Building Shared Models Without Sharing
Data. JAMIA 2012
Diverse Healthcare
Entities
in 3 different states
(federal, state, private)

Summary
of
recommenda>ons

•  Data
Harmoniza0on

–  Common
data
model

–  Meta-‐data

•  Privacy

–  Access
controls,
audits

–  Encryp0on

–  Assess
risk
of
re-‐
iden0ﬁca0on

15


–  Distributed

–  Centralized

Models
for
Data
Sharing

• Cloud
Storage:
data
exported
for

computa0on
elsewhere

– Users
download
data
from
the
cloud

• Cloud
Compute
and
Virtualiza0on:

computa0on
goes
to
the
data

– Users
query
data
in
the
cloud

– Users
upload
algorithms
to
the
cloud

16
funded
by
NIH
U54HL108460

Shared
Services
and
Infrastructure

7/2/12

SaaS

PaaS

IaaS

Operators,
Developers, Collaborators
Researchers, Developers
Collaborators
Healthcare professionals,
End-user services
•  So_ware
as
a
Service

•  Pla`orm

•  Infrastructure

•  Security
&
Policies

•  Scalability
&
Reliability

•  Flexibility
&
Extensibility
Frame/Infrastructure
Body/Platform
Business/Service

Research data from several institutions:
Clinical & genomic data hosting in a HIPAA compliant facility
•  315TB
Cloud
and
project

storage
for
100s
of
virtual

servers

•  54TB
high-‐speed
database

and
system
storage;
high-‐
performance
parallel

databases

•  10Gb
redundant
network

environment;
ﬁrewall
and

IDS
to
address
HIPAA

requirements

•  Mul0ple-‐site
encrypted

storage
of
cri0cal
data

Shared
Infrastructure

Summary
of
recommenda>ons

•  Data
Harmoniza0on

–  Common
data
model

–  Meta-‐data

•  Privacy

–  Access
controls,
audits

–  Encryp0on

–  Assess
risk
of
re-‐
iden0ﬁca0on

20


–  Distributed

–  Centralized

•  Governing
body

–  Data
use
agreements

–  Policy
for
IP

–  Consent

–  Include
stakeholders

Informed
Consent
Management
System
Do I wish to
disclose data D
to U?
Information
Exchange
Registry
User U requests Data
D on individual I for
Quality Improvement
or Research
Are the data
available?
Yes
No
Yes
No
Preferences
Inspection
• Identity
Management
• Trust
Management
Home
Trusted
Broker(s)
Patient I
Security Entity
Healthcare Entity
Privacy Registry
I can check who
or which entity
looked (wanted to
look) at the data
for what reasons
AHRQ R01HS19913 / EDM forum NIH U54HL10846
Patient-Centered Data Sharing

Patient-Centered Outcomes Research Institute
Workshop to Advance the Use of
Electronic Data for Conducting
PCOR
Lessons from the Field:
HMO Research Network Virtual
Data Warehouse

2

Contents
§  Origins and Goals
§  HMO Research Network Virtual Data Warehouse at a Glance
§  Accomplishments
§  Expansion and Growth Opportunities
§  Expansion Potential: Facilitators and Barriers
§  The HMO Research Network Virtual Data Warehouse & PCORI
§  Lessons to be Learned

HMO Research Network
Virtual Data Warehouse
(HMORN VDW)
Presented by Eric Larson, MD MPH
Group Health Research Institute
3

Background of the HMORN VDW
The HMORN is a consortium
of 19 health systems with
affiliated research centers
committed to “closing the
loop” between research and
clinical care delivery
§  Reduce resources needed to create high quality data
sets for each new study
§  Promote understanding and valid use of complex real-
world data
4

Founded in 2003, the HMORN VDW was originally created by
one of the HMORN’s consortium projects – the NCI-funded
Cancer Research Network (CRN), in order to:

Background of the HMORN VDW
5

Now governed and supported by the HMORN Board, the
HMORN VDW’s expanded breadth and depth allow the
model to support research on virtually any disease topic
Research activities supported by the HMORN VDW include:
§  Behavioral and mental health
§  Cancer
§  Comparative effectiveness
Complementary and alternative
medicine
§  Communication and health literacy
§  Dissemination and implementation
§  Epidemiology
§  Genomics and genetics
§  Health disparities
§  Health disparities
§  Health informatics
§  Health services and economics
§  Infectious and chronic disease
surveillance
§  Drug and vaccine safety
§  Primary and secondary
prevention
§  Systems change and
organizational behavior

HMORN VDW at a Glance
§  A distributed data model, not a centralized database
§  Applicable for multi-center health services and population health
research (currently, 16.5 million covered lives in total)
§  Facilitates multi-center research while protecting patient
privacy and proprietary health practice information
§  Data remain at each institution until a study-specific need
arises and ethical, contractual and HIPAA requirements are met
§  Data sourced from clinical systems including those used in
pharmacy, lab, pathology, disease registries, radiology, and
modern Electronic Health Records (EHR) in all care settings
§  Clinical data are supplemented by data from health plan
systems (e.g. claims, enrollment, finance/accounting)
6

Participating sites agree on data to make available for research
and standard definitions and formats
Sites map rich and complex data to agreed upon standards
Data model is standardized; the data themselves are not
7

HMORN Governing Board provides overall policy direction about
content, resources and access
VDW Operations Committee (VOC) manages cross-site
development activities, with technical and scientific input
VDW Workgroups for specific data areas define, maintain and
interpret data file specifications, propose specification changes,
perform quality assurance, and aid sites in implementation
VDW Implementation Group (VIG) extract information from local
systems, convert it to standard VDW structures, ratify
specifications and share best practices
VOC staff financed by HMORN operating budget; member sites
contribute workgroup and VIG members
8

Use published data standards (e.g., NDC, ICD-9/10, CPT-4,
DRG, ISO) where available and create our own when necessary
Each site needs hardware and software to store, retrieve,
process, and manage datasets
HMORN VDW data tables are designed and optimized to meet
research needs
Sites contribute to data documentation (e.g., source of variable,
variations) on a password-protected web site
For quality control, periodic checks look at ranges, cross-field
agreement, implausible data patterns, and cross-site comparison
9

Accomplishments
The HMORN VDW is used by major consortia:
10

§  Cancer Research Network (CRN) – NCI
§  Cardiovascular Research Network (CVRN) - NHLBI
§  Mental Health Research Network (MHRN) - NIMH
§  Center for Education & Research on Therapeutics (CERT) -
AHRQ
§  Surveillance, Prevention, & Management of Diabetes Mellitus
(SUPREME-DM) – AHRQ
§  Mini-Sentinel – FDA
§  Medication Exposure in Pregnancy Risk Evaluation Program
(MEPREP) – FDA
The CRN alone has 284 publications

Accomplishments
§  Health plans and care delivery systems increasingly use
the HMORN VDW for internal reporting, analysis, and
disease management (registries)
§  Patient care, clinical guidelines, policy, and quality
metrics are frequently impacted indirectly via research
findings
§  The HMORN VDW has great potential to more directly
impact patient care, guidelines, and policy, but has not yet
established a formal process to receive and carry out such
inquiries

Expansion and Growth Opportunities
The VDW has expanded in terms of…
§  covered population (10 million to now 16.5 million)
§  geographic / institutional diversity (11 to now 19 sites; rural
and urban; HMO and traditional indemnity)
§  breadth of data (e.g. death, laboratory results, vital signs,
social history)
§  depth of data (e.g. additional variables in each area)
§  quality of data (dedicated quality improvement operations)
§  history of data (allows further longitudinal analyses)
§  online query tools (e.g., PopMedNet used by SPAN, PEAL,
and other networks )
12

Expansion and Growth Opportunities
Breadth, depth, quality & tools can continue to be expanded as
resources become available
Patient-reported outcomes (e.g., risk factors, HQ-9, etc) are an
example of available patient-centered data not yet incorporated
into the VDW
The HMORN VDW as a data model is at once broad and deep,
longitudinal and prospective
13

The VDW is a powerful tool for
conducting outcomes research,
but does not yet meet the far
reaching goals of PCOR

Expansion Potential: Facilitators
The VDW model is public and has a strong community of active
developers and users
Successful infrastructure, governance, and collaborative
oversight exist to aid in implementation, quality assurance, and
development of the model
Participating sites typically have strong ties with their health
systems which aids in the development and expansion of content
14

Expansion Potential: Barriers
Underlying data are collected for treatment, payment, and
operations – not specifically for research
Source systems vary substantially within and across sites
It takes time (and resources) to:
15

§  Agree on the need for a new variable or data area
§  Develop clear specifications to guide implementers and
end-users
§  Implement new variables at each site
§  Verify and document the implementations
§  Consult with users throughout

Expansion Potential: Barriers
Health plans continually change their information systems, often
requiring adaptation or re-implementation of the VDW at sites
(e.g., ICD-10)
Limited largely by the availability of funding; VDW Operations
already accounts for > ½ of the HMORN’s annual operating
budget
Project-specific grant funding does not support the level of cross-
site and cross-project upkeep and knowledge sharing that is
needed for a Network-wide resource
Sharing data beyond project collaborators is complicated for
technical, regulatory, and political reasons
16

HMORN VDW and PCORI
The HMORN VDW:
Low degree of patient engagement overall in HMORN research
activities and VDW at the present time
17

§  Covers a large and geographically diverse population
(including pregnant women, children, elderly, under-served)
§  Captures clinical and administrative data over multiple
decades
§  Supports a broad range of research activities, including
feasibility work, surveys, focus groups, chart reviews,
recruitment, individual and cluster randomized trials
§  Has a collaborative governance and data development model
§  Directly links to clinical delivery systems and health plans,
though this is variable
§  Is highly affordable by leveraging data already acquired;
maintenance and development are primary costs

Lessons Learned
Technology is rarely the limiting factor – privacy, regulatory
process, and proprietary interests often the greatest barriers
Function over form – the VDW model focuses on what works for
a wide audience, not on advancing the field of Informatics
Linking HMORN VDW data with clinical text in the EHR and
using Natural Language Processing (NLP) – holds great potential
to improve accuracy and efficiency in research
Patient involvement – challenging to attain when dealing with
large databases, and without incentives from traditional funders
Explicitly endorsed expanded data sharing (e.g., PopMedNet) in
Collaboratory – short of a broad partnership there is little incentive
to do so; some sites may never fully buy in
18

11
Electronic Data for Conducting PCOR
Sentinel Initiative
Patrick Archdeacon, MD
Medical Officer
Office of Medical Policy/CDER/FDA

22
Disclaimer
•  The opinions and conclusions expressed
in this presentation are those of the
presenter and should not be interpreted as
those of the FDA

3
FDA Amendments Act of 2007
Section 905: Active Postmarket Risk Identification and Analysis
•  Establish a postmarket risk identification and
analysis system to link and analyze safety data
from multiple sources, with the goals of including
–  at least 25,000,000 patients by July 1, 2010
–  at least 100,000,000 patients by July 1, 2012
•  Access a variety of sources, including
–  Federal health-related electronic data (such as data from the
Medicare program and the health systems of the Department of
Veterans Affairs)
–  Private sector health-related electronic data (such as
pharmaceutical purchase data and health insurance claims data)

4
Sentinel Initiative
•  Improving FDA’s capability to identify and
investigate safety issues in near real time
•  Enhancing FDA’s ability to evaluate safety
issues not easily investigated with the passive
surveillance systems currently in place
•  Expanding FDA’s access to subgroups and special
populations (e.g., the elderly)
•  Expanding FDA’s access to longer term data
•  Expanding FDA’s access to adverse events occurring
commonly in the general population (e.g., myocardial
infarction, fracture) that tend not to get reported to FDA
through its passive reporting systems
**Will augment, not replace, existing safety monitoring systems

5
Sentinel Initiative: A Collaborative Effort
•  Collaborating Institutions (Academic and Data Partners)
– Private: Mini-Sentinel pilot
– Public: Federal Partners Collaboration
•  Industry
– Observational Medical Outcomes Partnership
•  All Stakeholders
– Brookings Institution cooperative agreement
on topics in active surveillance

66
Mini-Sentinel
www.mini-sentinel.org
Contract awarded Sept 2009 to
Harvard Pilgrim Health Care Institute
•  Develop the scientific operations needed for an
active medical product safety surveillance system
•  Create a coordinating center with continuous
access to automated healthcare data systems,
which would have the following capabilities:
–  Provide a "laboratory" for developing and evaluating
scientific methodologies that might later be used in a
fully-operational Sentinel System.
–  Offer the Agency the opportunity to investigate safety
issues in existing automated healthcare data system(s)
and to learn more about some of the barriers and
challenges, both internal and external.

7
The annotated Mini-Sentinel
•  Supplement to Pharmacoepidemiology and Drug
Safety
•  34 peer reviewed articles; 297 pages
•  Goals, organization, privacy policy, data systems,
systematic reviews, stats/epi methods, chart retrieval/
review, protocols for drug/vaccine studies...

8
Mini-Sentinel goals
q Develop a consortium
q Develop policies and procedures
q Create a distributed data network
q Evaluate/develop methods in safety
science
q Assess FDA-identified topics

9
Governance
q Planning board – principal investigators,
FDA, public representative
q Operations center
q Cores: data, methods, protocols
q Policy committee
q Safety science committee
q Privacy board
q Workgroups

10
Governance principles/policies
q  Public health practice, not research
q  Minimize transfer of protected health information and
proprietary data
q  Public availability of “work product”
•  Tools, methods, protocols, computer programs
•  Findings
q  Data partners participate voluntarily
q  Maximize transparency
q  Confidentiality
q  Conflict of Interest

11
Mini-Sentinel’s Evolving Common Data
Model
q  Administrative data
•  Enrollment
•  Demographics
•  Outpatient pharmacy dispensing
•  Utilization (encounters, diagnoses, procedures)
q  EHR data
•  Height, weight, blood pressure, temperature
•  Laboratory test results (selected tests)
q  Registries
•  Immunization
•  Mortality (death and cause of death)

12
The Mini-Sentinel Distributed Database
q  Quality-checked data held by 17 partner
organizations
q  Populations with well-defined person-time for
which medically-attended events are known
q  126 million individuals*
•  345 million person-years of observation time
(2000-2011)
•  44 million individuals currently enrolled, accumulating
new data
•  27 million individuals have over 3 years of data
*As
of
12
December
2011.
The
poten6al
for
double-‐coun6ng
exists
if
individuals
moved
between
data
partner
health
plans.

13
Mini-Sentinel Partner Organizations
Ins$tute
for
Health

14
Why a Distributed Database?
•  Avoids many concerns about inappropriate use
of confidential personal data
•  Data Partners maintain physical control of their
data
•  Data Partners understand their data best
–  Valid use / interpretation requires their input
•  Eliminates the need to create, secure, maintain,
and manage access to a complex, central data
warehouse

15
1-‐
User
creates
and

submits
query

(a
computer
program)

2-‐
Data
partners
retrieve

query

3-‐
Data
partners
review

and
run
query
against

their
local
data

4-‐
Data
partners
review

results

5-‐
Data
partners

return

summary
results
via

secure
network/portal

6
Results
are
aggregated

Mini-Sentinel Distributed Analysis

16
Distributed Querying Approach
Three ways to query data:
1) Pre-tabulated summary tables
2) Reusable, modular SAS programs that
run against person level Mini-Sentinel
Distributed Database
3) Custom SAS programs for in-depth
analysis
Results of all queries performed publically posted once activity complete

17
Current Modular Programs
1. Drug exposure for a specific period
–  Incident and prevalent use combined
2. Drug exposure with a specific condition
–  Condition can precede and/or follow
3. Outcomes following first drug exposure
–  May restrict to people with pre-existing diagnoses
–  Outcomes defined by diagnoses and/or procedures
4. Concomitant exposure to multiple drugs
–  May restrict to people with pre-existing conditions

18
New Modular Program
Capabilities On the Horizon…
•  Modular Programs capable of perform
sequential monitoring using different
epidemiology designs and analysis
methods to adjust for confounding:
– Cohort study design using score-based
matching (propensity score and/or disease
risk score) adjustments
– Cohort study design using regression
techniques
– Self-Controlled Cohort study design

19
In Progress / Future Mini-Sentinel Activities
•  Expand MSDD/CDM (e.g., add additional
laboratory and vital sign data)
•  Continue methods development and HOI
validation
•  Semi-automated or automated confounding
control using propensity and disease risk scores
•  Evaluation of emerging safety issues and conduct
of routine surveillance with NMEs
•  Evaluation of emerging safety issues with drugs
on market > 2 yrs

20
Coordinating
Center(s)†
Quality of Care
Sponsors*
*Sponsors initiate and pay for
queries and may include government
agencies, medical product
manufacturers, data and analytic
partners, and academic institutions.
†Coordinating Centers are
responsible for the following:
operations policies and procedures,
developing protocols, distributing
queries, and receiving and
aggregating results.
Public Health Surveillance
Sponsors*
Coordinating
Center(s)†
Medical Product Safety
Sponsors*
Coordinating
Center(s)†
Sponsors*
Biomedical Research
Coordinating
Center(s)†
Comparative Effectiveness Research
Sponsors*
Coordinating
Center(s)†
Results
Queries
Results
Queries
Results
Providers
•  Hospitals
•  Physicians
•  Integrated Systems
Payers
•  Public
•  Private
Registries
•  Disease-specific
•  Product-specific
Common
Data Model
Distributed Data and
Analytic Partner Network

21
Barriers and Lessons Learned
Barriers
Ø  Study methodologies and
statistical approaches
require further
optimization
Ø  Policies and governance
appropriate for PHS
activities may not
translate to CER
Ø  Limited resources and
funding
Lessons
Ø  Some competition is
healthy, but collaboration
is critical to success
Ø  Establishing effective
governance and policies
is time-intensive – start
early!!
Ø  Technical barriers
(methods, statistics, data)
exist but do not represent
the biggest challenges

Distributed
Research
Networks:

Opportuni7es
for
PCORI

1

Jeﬀrey
Brown,
PhD

Richard
Pla5,
MD,
MS

Department
of
Popula=on
Medicine

Harvard
Pilgrim
Health
Care
Ins=tute/
Harvard
Medical
School

Mul&ple
Networks
Sharing
Infrastructure

2

FDA
Mini-‐Sen&nel

Health

Plan
2

Health

Plan
1

Health

Plan
5

Health

Plan
4

Health

Plan
7

Hospital
1

Health

Plan
3

Health

Plan
6

Health

Plan
8

Hospital
3

Health

Plan
9

Hospital
2

Hospital
4

Hospital
6

Hospital
5

Outpa&ent

clinic
1

Outpa&ent

clinic
3

Outpa&ent

clinic
2

Outpa&ent

clinic
4

Outpa&ent

clinic
6

Outpa&ent

clinic
5

PCORI
NIH
AHRQ

Mul&ple
Networks
Sharing
Infrastructure

3

FDA
Mini-‐Sen&nel

Health

Plan
2

Health

Plan
1

Health

Plan
5

Health

Plan
4

Health

Plan
7

Hospital
1

Health

Plan
3

Health

Plan
6

Health

Plan
8

Hospital
3

Health

Plan
9

Hospital
2

Hospital
4

Hospital
6

Hospital
5

Outpa&ent

clinic
1

Outpa&ent

clinic
3

Outpa&ent

clinic
2

Outpa&ent

clinic
4

Outpa&ent

clinic
6

Outpa&ent

clinic
5

PCORI
NIH
AHRQ

•  Each
organiza&on
can
choose
to
par&cipate
in
mul&ple

networks

•  Each
network
controls
its
governance
and
coordina&on

•  Networks
share
infrastructure,
data
cura7on,

analy7cs,
lessons,
security,
so?ware
development

PCORI
Distributed
Research
Network

SPAN
PEAL
MDPHnet

Data
Partners
can
par&cipate
in
speciﬁc

PCORI
studies
if
they
choose
to.

•  SPAN:
Scalable
PArtnering
Network
for
CER
(AHRQ
HMORN)

–  ADHD
and
Obesity
cohorts

•  PEAL:
Popula&on-‐Based
Eﬀec&veness
in
Asthma
and
Lung

Diseases
Network
(AHRQ
HMORN+)

–  Asthma
cohort

•  Mini-‐Sen7nel
(FDA)

–  U&liza&on
/
enrollment
data
for
126
million
covered
lives

–  Extensible
data
model
includes
selected
laboratory
tests,

linkage
to
state
registries

•  MDPHnet
(ONC):
MA
Department
of
Public
Health

–  EHR
data
from
group
prac&ces,
currently
>1
million
pts

–  Current
focus
on
diabetes
and
inﬂuenza-‐like
illness

Extant
Linkable
Distributed
Networks

5

•  PCORI
can
beneﬁt
from
leveraging
exis&ng

distributed
networks

•  Several
exis&ng
networks
use
the
same
distributed

approach
and
soaware
–
PopMedNet
–
enabling
any

of
them
to
par&cipate
in
another’s
ac&vity

•  Adding
data
sources
to
networks
is
feasible

–  Pa&ent-‐reported
outcomes

–  Reuse
of
stand-‐alone
prospec&ve
datasets

•  Using
exis&ng
networks
and
soaware
allows
sharing

of
infrastructure
and
development
costs

–  Open-‐source
model
of
network
development

Take
home
messages

6

Addi&onal
informa&on

7

PopMedNet
Overview

•  Open
source
soaware
that
facilitates
crea&on
and

opera&on
of
distributed
networks

•  Used
in
several
networks
and
planned
for
others

•  Na&onal
Standard:
PMN
is
a
key
component
of
the

ONC’s
QueryHealth
Ini&a&ve:

–  Endorsed
by
the
ONC
community
as
a
distributed

querying

plaform
for
policy
and
governance

–  Included
in
each
QueryHealth
Pilot
project

–  PMN
design
mee&ngs
na&onal
standards
for
distributed

querying

•  Standards
&
Interoperability
(S&I)
Framework:

hip://wiki.siframework.org/Home

•  Technical
work
group:

hip://wiki.siframework.org/Query+Health+Technical+Approach

8

Enhancing

Exis&ng
Resources
(1)

Add
pa7ent
reported
outcomes
to
exis7ng
data

resources

Mini-‐Sen&nel
Data

Partner
1

Enrollment

Diagnoses

Procedures

Dispensings

Demograph.

Encounters

PCORI
variables
at
Data

Partner
1

Pain
scale

SF-‐6

Health
U7lity

Index

HRQoL
Scale

Diabetes
QoL

COPD
QoL

PCORI
Data
Resource
at
Data
Partner
1

Pain
scale

SF-‐6

Health
U7lity

Index

HRQoL
Scale

Diabetes
QoL

COPD
QoL

Enhancing

Exis&ng
Resources
(1)

Add
pa7ent
reported
outcomes
to
exis7ng
data

resources

Enrollment

Diagnoses

Procedures

Dispensings

Demograph.

Encounters

Mini-‐Sen&nel
Data

Partner
1

Enhancing

Exis&ng
Resources
(2)

Enrollment

Diagnoses

Procedures

Dispensings

Demograph.

Encounters

Add
data
to
exis7ng
data
resources
(within
a
table)

• Dispense
date

• NDC

• PATID

• Days
supplied

• Amount
dispensed

Dispensing

(Mini-‐Sen7nel)

• Dispense
date

• NDC

• PATID

• Days
supplied

• Amount
dispensed

• Formulary
status

• Prescribing
physician

• Indica7on

• Copayment

• Plan
payment

• Tier

• Beneﬁt
package

Dispensing

(PCORI)

Enhancing

Exis&ng
Resources
(3)

•  Add
new
partners
to
network

•  Create
addi&onal
sub-‐networks
of
unique
resources

•  Enable
reuse
of
project-‐speciﬁc
data
collec&on

eﬀorts

–  No
more
“one
and
done”
datasets

Electronic Data for Conducting
PCOR
DARTNet
David R. West, PhD
Colorado Health Outcomes Program
School of Medicine
University of Colorado

Thanks and acknowledgements to:
§  Wilson D. Pace, MD
CEO, DARTNet Institute
§  Lisa Schilling, MD
PI, SAFTINet
University of Colorado
§  Michael Kahn, MD, PhD
Director, Biomedical Informatics Core, Colorado
Clinical Translation Science Institute

DISCLOSURE STATEMENT
§  I have no financial investments in and
receive no funding from any of the
companies mentioned in this presentation.
§  No off label medication use will be
discussed.
§  I have made many mistakes in my
professional career, and expect to
continue doing so.

Distributed Ambulatory
Research in Therapeutics
Network (DARTNet)

Why DARTNet?
§  Concept developed by Wilson Pace at the University of
Colorado, as a mechanism to leverage commercially
available clinical decision support technology to meet the
needs of primary care clinicians and researchers
§  An outgrowth of the Primary Care Practice-Based Research
Movement - to link physician practices together to provide
them with the tools for improving quality and performance,
independent of integrated healthcare systems or third
party payers
§  To create linked clinical data to provide an improved/
enriched data source for Comparative Effectiveness
Research (both observational and prospective)

What is DARTNet?
§  A Federated Network – Launched with support from AHRQ
as a prototype to extract and capture, link, codify, and
standardize electronic health record (EHR) data from
multiple organizations and practices
§  Now a Research Institute (a not-for-profit corporation)
that “houses” a Public/private partnership including:
9 research networks,12 academic partners,
American Academy of Family Physicians, QED
Clinical, Inc., and ABC – Crimson Care Registry
§  A Learning Community

eNQUIRENet
CCRN
CCPC
FREENet
MSAFPRN
SAFTINet*
STARNet
UNYNet
WPRN
DARTNet
Institute
*Technical Partner

DARTNet Governance
Legal
•  A not-for-profit
corporation
§  Participant model rather
than membership model
§  Ability to independently
contract and secure
grants
§  Ability to charge
indirects to cover
infrastructure needs
Practical
—  BOD with Committee
structure for decision-
making
—  Speed boat rather than oil
tanker
—  Customer service driven
—  Learning/Translation focus
—  Centralized Expertise/
Support: BA, DUA, LDS, PHI
protection, IRB, HIPAA,
Security, Intellectual Property,
Master Collaborative
Agreements

DARTNet Scope and Scale
Organizations ~ 85
Practices = >400
Clinicians > 3000
Patients ~ 5 million
•  EHR’s = 15
•  States = 25
•  Male 42%
•  Female 58%
•  0-17 12%
•  18-24 7%
•  25-64 63%
•  65-older 18%

How does DARTNet work?
Step 3
Comparative Effectiveness
Research
Step 2
Clinical Quality Improvement
Step 1
Federated EHR Data

Data management overview
§  Data stays locally
§  Standardized locally with retention of
original format for both:
o Quality checks
o Recoding in future
§  Each organization retains control of
patient level data
§  Local processing allows expansion and
scale up

Technical overview
§  EHR independent
§  Data standardization middle layer
tied to clinical decision support at
most sites
§  Exploring alternative data collection
approaches
§  Adding multiple data sources

Single Practice Perspective
i
CDR
GRID DB
DARTNet
Webservices
Claims
Rx
Quality improvement
Reports
Disease registries
Clinical tools
Translation interface
EHRLab
Hospital
Queries
and Data
Transfers!

Technical Advancement :
SAFTINet
AHRQ R01 HS019908-01 (Lisa Schilling- PI)
§  New Grid Services
o Based on TRIAD
o Underlying database extension of OMOP
o Provider, visit, claims extensions
§  Data moving to OMOP terminology
§  Adding clear text and privacy protected record
linkages for 3rd party data
§  Incorporation of Patient Reported Outcomes
§  Focus upon the underserved

Introducing ROSITA
Reusable OMOP and
SAFTINet Interface Adaptor
..and ROSITA it the only
bilingual Muppet

Why ROSITA?
Converts/Translates EHR data into
research limited data set
1.  Replaces local codes with standardized
codes
2.  Replaces direct identifiers with random
identifiers
3.  Supports clear-text and encrypted
record linkage
4.  Provides data quality metrics
5.  Pushes data sets to grid node for
distributed queries

Key Achievements
§  Successful completion of pragmatic trails
§  Successful completion of observational
studies
§  Numerous publications and monographs
§  Successful funding record from AHRQ,
NIH, others…Spawned SAFTINet (ROSITA)
§  Practices achieved significant performance
improvement (with tangible returns via
PQRS, MOC IV, and Meaningful Use)

Opportunities/Gaps/Needs
§  Unlimited scale-up potential
§  GRID Computing Technology is not yet
mature – but holds tremendous promise
§  Enhancing Technology and Culture to
collect Patient Reported Outcomes: A
research terms that encompasses so
much
§  Testing, using, sharing ROSITA – an
important contribution
§  Sorting out linkage to Medicaid data

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
1

7/2/12

Lessons from the Field: SCANNER
Michele Day, PhD
Program Manager
University of California, San Diego

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
2

7/2/12

Background
Scalable Distributed Research Network
SCANNER = SCAlable National Network for Effectiveness Research
Principal Investigator
Lucila Ohno-Machado, MD, PhD
Project Dates
Sept. 30, 2010 – Sept. 29, 2013
Overall Goal
Develop a scalable, flexible, secure, distributed network infrastructure to
enable near real-time comparative effectiveness research (CER) among
multiple sites

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
3

7/2/12

§  Compare risk of bleeding from medications prescribed for
cardiovascular conditions
§  Sharing summary data
AnDplatelets

AnDcoagulants

clopidogrel

(old
drug)

warfarin

(old
drug)

prasugrel

(new
drug)

dabigatran

(new
drug)

vs.

vs.

Acute
Coronary
Syndrome
(ACS)
with
Drug

EluDng
Stents
(DES)

Atrial
FibrillaDon
(AF)
or

Venous
Thromboembolism
(VTE)

Condi&ons
Comparisons

USE
CASES

Medication Surveillance

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
4

7/2/12

Medication Therapy Management
§  Compare care management of patients with diabetes or hypertension
§  Sharing limited data
Physician

only

Physician

only

Physician

+

Pharmacist

Physician

+

Pharmacist

vs.

vs.

Diabetes

Hypertension

Condi&ons
Comparisons

USE
CASES

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
5

7/2/12

§  Low-income groups
§  Minority groups
›  Hispanic/Mexican American or Latino
›  American Indian/Alaska Native
›  Asian
›  Native Hawaiian or other Pacific Islander
›  Black or African American
§  Women
§  Elderly
§  Individuals with special health care needs
›  Those with disabilities
›  Those who need chronic care
›  Those who live in inner-city areas
›  Those who live in rural areas
AHRQ Priority Populations

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
6

7/2/12

SCANNER at a Glance
Data
Set
Library
Analysis
Policy Enforcement
SCANNER
Portal
Site 1
Data
Set
Library
Analysis
Policy Enforcement
Site n
Protocols
…
CER researcher
Analysis/Aggregation
Policy Enforcement
Results Dissemination
SCANNER core
Authentication
Analysis Request

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
7

7/2/12

How SCANNER Works
Data
Set
Library
Analysis
Policy Enforcement
Site 1
Data
Set
Library
Analysis
Policy Enforcement
Site n
Protocols
…
Policy Enforcement
Protocols
SCANNER core
Authentication
Analysis Request
Protocols
Results
Results

Results

Query

Login

CER researcher

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
8

7/2/12

§  Using CDM from the Foundation for the NIH
›  Observational Medical Outcomes Partnership (OMOP)
§  Collaborated with SAFTINet researchers and OMOP staff to recommend
changes
Common Data Model (CDM)
Note: Tables are modified or new as
compared to OMOP CDM v2.

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
9

7/2/12

§  Data Network Architecture
›  Design for overall network is a challenge
§  Data Standards and Interoperability
›  Selection of the CDM is important
›  Distributed sites must maintain complete consistency
§  Governance
›  Policy features must address federal, state, and institutional
requirements
›  Detailed requirements planning supports the operationalization of
appropriate policies
Lessons Learned

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
10

7/2/12

SCANNER and PCORI
Data
Set
Library
Analysis
Policy Enforcement
SCANNER
Portal
Site 1
Data
Set
Library
Analysis
Policy Enforcement
Site n
Protocols
CER researcher
Policy Enforcement
SCANNER core
Authentication
Analysis Request
Data
Set
Library
Analysis
Policy Enforcement
New Site
Clinic
Patient-Centered Policy Enforcement

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
11

7/2/12

Partners
Brigham and Women’s Hospital (BWH)
Charles Drew University of Medicine and Science
RAND Corporation
Resilient Network Systems
San Francisco State University (SFSU)
Vanderbilt University Medical Center & TVHS Veterans
Administration Hospital (TVHS VA)
UC Irvine
UC San Diego

Supported
by
the
Agency
for
Healthcare
Research
and
Quality
(AHRQ)
Grant
R01
HS19913-‐01
12

7/2/12

Thank you! Questions?
!
!
!
Data
Set
Library
Analysis
Policy Enforcement
SCANNER
Portal
Site 1
Data
Set
Library
Analysis
Policy Enforcement
Site n
…
CER researcher
Policy Enforcement
SCANNER core
Authentication
Analysis Request
http://scanner.ucsd.edu/

Peter
Margolis,
MD,
PhD

James
M
Anderson
Center
for
Health
Systems
Excellence

Cincinna9
Children’s
Hospital
Medical
Center

Supported
by

NIH
NIDDK
R01DK085719

AHRQ
R01HS020024

AHRQ
U18HS016957

Learning
Health
Systems

•  Pa9ents
and
providers
work
together
to
choose
care

based
on
best
evidence

•  Drive
discovery
as
natural
outgrowth
of
pa9ent
care

•  Ensure
innova9on,
quality,
safety
and
value

•  All
in
real-‐9me

Ins9tute
of
Medicine

Yochai
Benkler,
“The
Wealth
of
Networks”

Network-‐Based
Produc9on

A
C3N
is

a
network-‐based

produc9on
system

for
health
improvement

Percent
of
Pa9ents
in
Remission

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jul-2007N=338
Aug-2007N=396
Sep-2007N=428
Oct-2007N=479
Nov-2007N=508
Dec-2007N=531
Jan-2008N=570
Feb-2008N=607
Mar-2008N=643
Apr-2008N=654
May-2008N=667
Jun-2008N=671
Jul-2008N=686
Aug-2008N=731
Sep-2008N=754
Oct-2008N=801
Nov-2008N=832
Dec-2008N=901
Jan-2009N=973
Feb-2009N=995
Mar-2009N=1021
Apr-2009N=1070
May-2009N=1112
Jun-2009N=1194
Jul-2009N=1240
Aug-2009N=1277
Sep-2009N=1314
Oct-2009N=1344
Nov-2009N=1366
Dec-2009N=1400
Jan-2010N=1421
Feb-2010N=1410
Mar-2010N=1440
Apr-2010N=1455
May-2010N=1461
Jun-2010N=1471
Jul-2010N=1489
Aug-2010N=1518
Sep-2010N=1547
Oct-2010N=1576
Nov-2010N=1985
Dec-2010N=2032
Jan-2011N=2043
Feb-2011N=2065
Mar-2011N=2124
Apr-2011N=2191
May-2011N=2206
Jun-2011N=2272
Jul-2011N=2301
Aug-2011N=2335
Percent
of
Pa8ents

Month

Percent
of
IBD
Pa8ents
in
Remission
(PGA)

Crandall,
Margolis,
Colle]
et
al

Pediatrics
2012;129:1030

Remission
rate:

55%
to
75%

36
Care
Sites

310
physicians

>10,000
pa8ents

Standardized
care

How
do
you
create
a
network–based
produc8on

system
for
health
and
health
care?

1.  Build
Community
–
Social
Opera9ng

System

2.  Develop
Technical
Opera9ng
System

3.  Enable
Learning,
Innova9on
and

Discovery
–
Scien9ﬁc
Opera9ng
System

Building
Community

•  Compelling
purpose

•  Core
leadership
–
pa9ents,
clinicians,
researchers

•  Sharing
stories

•  Many
ways
to
contribute

Building
community

•  Sharing
stories

•  Pa9ent
and
parent
advisory
councils

•  Parents
on
QI
teams

•  Pa9ents
on
staﬀ

•  Parents
and
pa9ents
at
network
mee9ngs

•  Lots
of
places
to
communicate
(care
centers,

educa9on
days,

integrated
website,

newslegers,
social
media)

Jill
Plevinsky

Eden
D’Ambrosio

Lisa
Vaughn
etc
.

Evalua9ng
Leadership
Behavior
During
Design
Phase

June 2010 August 2010
October 2010December 2010
Create
Core

Develop
Prototype

Teams

Peter
Gloor,
PhD.

MIT
Center
for

Collec9ve
Intelligence

Reducing
Transac8onal
Costs

Technical
Opera8ng
System

Example:
Data
Collec9on

13

Courtesy

Richard
Colle],
MD

Keith
Marsolo,
PhD

“Enhanced”
Registry

John
Hugon,
MD;
Keith
Marsolo,
PhD;
Charles
Bailey,
MD;
Christopher

Forrest,
MD,
PhD;
Marshall
Joﬀe,
MD,
PhD;
Wallace
Crandall,
MD;
Mike

Kappleman,
MD,
MPH;
Eileen
King,
PhD

•  CER
using
distributed
registry
(>10,000
pa9ents)

•  Chronic
care
processes

•  QI
reports

•  Data
Quality

•  Support
for
experiments

Tes9ng
Mul9ple
Interven9ons
Simultaneously

23
Full
Factorial
Design
with
3
Replica9ons

Treatment
Combination
Pre-visit
Planning
Population
Management
Self-
Management
Support
Site 1 - - -
Site 2 + - -
Site 3 - + -
Site 4 - - +
Site 5 + - +
Site 6 - + +
Site 7 + + -
Site 8 + + +

Molly’s Story
Heather
Kaplan,
MD,
MSc

Jeremy
Adler,
MD,
MPH

Ian
Eslick,
MS

Reducing
Burden
of
Data
Collec9on

Anmol
Madan,
PhD

Ginger.io

How
can
PCORI
build
on
the
C3N
model?

•  Expand
to
all
care
centers
and
all
children
with

IBD
(50-‐75,000)

•  Build
addi9onal
communi9es
to
work

together
to
co-‐create
learning
health
systems

•  Support
research
at
whole
system
level

– Support
design
and
prototype
to
see
how
to
ﬁt

pieces
together

•  Data
sharing
linked
to
ac9on

hgp://www.c3nproject.org

Collabora9ve
Learning
System
for
Pa9ents,
Clinicians

and
Researchers

Ac8ve/Passive

Surveillance

Understand

Health
Status

and

Causes
of

Varia9on

Reduce

Varia8on

Eliminate

varia9on

Formal

Experiments

Iden9fy
what

works
best

Increased
Conﬁdence
in
Finding
the
Right
Treatment

Improved
Outcomes

Increased
Knowledge
of
Disease

Increasing
Evidence

Initial Collaborators

•  ImproveCareNow

–  36 care centers

–  >10,000 patients

•  Patients

•  Lybba Design and
Communications

•  Associates in Process
Improvement

•  U of Chicago Booth School of
Business

•  Creative Commons

•  MIT Media Lab

•  MIT Center for Collective
Intelligence

•  UCLA Center for Healthier
Families and Children

2
•  Background: Definition, Ideal Registry for PCOR, Existing
Registries and Suitability for PCOR,
•  Accomplishments: Key Achievements with respect to PCORI
goals
•  Expansion and Growth Potential: Characteristics Suitable
for Expansion, Expansion Example, How PCORI might Use/
Extend Existing Registries
•  Barriers: What PCORI can do to Extend the Model Broadly
•  Additional
•  Registry Standards (Draft)
•  Registry of Patient Registries
Overview

3
A patient registry is an organized
system that uses observational study
methods to collect uniform data
(clinical and other) to evaluate
specified outcomes for a population
defined by a particular disease,
condition, or exposure, and that
serves a predetermined scientific,
clinical, or policy purpose(s).
Definition of Patient Registry
Gliklich RE, Dreyer NA: Registries for Evaluating Patient
Registries: A User’s Guide: AHRQ publication No. 07-EHC001.
Rockville, MD. April 2007

The Ideal
Registry for
PCOR
• Collects uniform, clinically rich data including
risk factors, treatments and outcomes at key
points for a particular disease or procedure
• From multiple sources (doctors, patients,
hospitals) and across care settings
(practices, hospitals, home)
• Leverages HIT systems through
interoperability and data sets from other
sources through linkage
• Uses standardized methods to assure
representative patient sample, data quality
(accuracy, validity, meaning, completeness)
and comparability (risk adjustment)
• Provides rapid or real-time feedback/
reports at patient and population levels to
facilitate care delivery, coordination,
quality improvement, and quality reporting
(to third parties)
• Can change in response to changing
information or needs or addition of new
studies
• Maintains high levels of participation by
providers and patients and a sustainable
business model
• Can be randomized at the site or patient
level for certain sub-studies
Ongoing
treatments,

intermediate
outcomes

Enrollment,
Demography,

Risk
factors,
Ini;al
Evalua;on

Outcomes,
Final
disposi;on

Pa;ents

+/-sampling
Quality
Assurance

Reports

Timeline (T)

Registries that have higher likelihood to constitute long-term infrastructure
are those with at least one purpose being QI. They also have additional
benefits in terms of communicating and disseminating PCOR findings.
Inputs: Obtaining data
•  Identify/enroll representative
patients (e.g. sampling)
•  Collect data from multiple
sources and settings
(providers, patients, labs,
pharmacies) at key points
•  Use uniform data elements
and definitions (risk factors,
treatments and outcomes)
•  Check and correct data
(validity, coding, etc.)
•  Link data from different
sources at patient level
(manage patient identifiers)
•  Maintain security and privacy
(e.g. access control, audit trail)
Outputs: Care Delivery
and Coordination
•  Provide real-time feedback
with decision support
(evidence/guidelines)
•  Generate patient level reports
and reminders(longitudinal
reports, care gaps, summary
lists/plans, health status)
•  Send relevant notifications to
providers and patients (care
gaps, prevention support, self
management)
•  Share information with patients
and other providers
•  List patients/subgroups for
proactive care
•  Link to relevant patient
education
Outputs: Population
Measurement and QI
•  Provide population level
reports
•  real-time/rapid cycle
•  risk adjusted
•  include standardized measures
•  include benchmarks
•  enable different reports for
different levels of users
•  Enable ad-hoc reports for
exploration
•  Provide utilities to manage
populations or subgroups
•  Generate dashboards that
facilitate action
•  Facilitate 3rd party quality
reporting (transmission)

Registries today vary by organization, condition and type.
They exhibit different strengths and limitations.
They are more prevalent and sustained in certain conditions.
Types of Organizations Condition Registry Type Example Strength Example Limitation
Professional society
Heart failure
Surgical care
Hospitalization
Procedure &
Hospitalization
High participation
Strong quality assurance
methods including audits
Limited follow-up
Cannot obtain data across
settings
Patient advocacy
organization
Cystic fibrosis Disease High participation
Not interoperable with HIT
systems
Integrated delivery
system
Diabetes Disease
Extensive care delivery
and care coordination
functionalities
Accessible population too
limited for PCOR
Individual hospital Orthopedics Procedure
Collects nationally
standardized data
elements
Non-representative
sampling methods
Regional/
Community
Arthritis
Orthopedics
Disease
Data from doctors and
patients
Representative sampling
Limited quality assurance
Very low participation
Government entity
Stroke
Cancer
Hospitalization
Disease Mandated participation
No risk adjustment
No outcomes data
Manufacturer
Acute coronary
syndrome
Liposome
storage
diseases
Drug
Disease
Strong methods
High follow-up rates
Use of PROs
May not be sustained
Potential conflicts of
interest for PCOR

7
Key Achievements
Example relevant achievements and ability to meet core
electronic data model requirements for PCOR
Achievements
Patient Care
•  AHA GWTG registries reduce healthcare
disparities.
Research
•  STS, ACC NCDR and AHA GWTG have
produced hundreds of peer reviewed
publications
Clinical Guidelines
•  NCCN registry assesses and reports on
guidelines
Policy
•  ACC NCDR ICD registry has been utilized for
Coverage under Evidence Development
New Quality Measures
•  STS registry, ACS NSQIP and AHA GWTG
have all developed nationally recognized
measures
Ability to meet core requirements for EDM
Large, diverse populations from usual care
•  Available from most national society and
patient organization driven registries
Complete capture longitudinal data
•  CFF registry captures longitudinal data at set
intervals
Patient reported outcomes (PROs)
•  PROs routinely captured in RIGOR, ASPS
TOPS, and CFF registry
Patient and clinician engagement
•  Patients and clinicians represented in CFF
and ACS registries governance
Linkage to health systems for dissemination
and automation
•  AHA GWTG and ACS NSQIP provide real-
time feedback to health systems; ASPS uses
retrieve form for data capture (RFD) to
integrate registry with EMRs
Capable of randomization
•  AHA registries have incorporated
randomization for sub-studies
A
American Academy of Ophthalmology Ophthalmic Database, RIGOR (www.aao.org)
Agency for Healthcare Research and Quality RIGOR (www.ahrq.gov)
American Heart Association Get With the Guidelines (www.heart.org)
American College of Cardiology NCDR®, PINNACLE (www.cardiosource.org)
American Collgeof Gastroenterology GiQuic (www.gi.org)
American College of Surgeons NSQIP, NCD, Bariatric (www.facs.org)
American Society of Plastic Surgeons TOPS (www.plasticsurgery.org)
Cystic Fibrosis Foundation (www.cff.org)
National Comprehensive Cancer Network (www.nccn.org)
Society of Thoracic Surgeons (Database www.sts.org)

Registries with strong geographic reach, high participation,
modifiable data collection systems (including PRO and
randomization) and sustainable business models are best options.
These attributes vary significantly by condition and by specific registry.
Types of
Organizations
Conditions Can Model address PCORI’s goals? Barriers
Professional society various
Large, diverse populations from usual
care settings, PRO capacity, Patient
and clinician engagement, affordable,
linkage to health systems, capable of
randomization
Many societies in early stages of
developing programs, only some are of
sufficient infrastructure to scale and
those are in a limited number of disease
areas. Vary in quality
Patient advocacy
organization and
communities
various
PRO capacity, patient and clinician
engagement, affordable, linkage to
health systems possible, capable of
randomization
Limited number of groups have active
registries in place today. Those that do
vary in quality and extensibility of
architecture
Integrated delivery
system
various
Complete capture of longitudinal data,
PRO capacity, patient and clinician
engagement, linkage to health
systems
Would need to be linked to other IDNs
using common data standards in
federated networks to meet goals
Regional/
Community
various
Large, diverse populations from usual
care settings, PRO capacity, patient
and clinician engagement, linkage to
health systems, capable of
randomization
Limited number of community efforts and
participation within communities typically
varies
Government entity various Large, diverse populations from usual
care settings, PRO capacity
Most programs are funded for limited
duration and may not be sustainable

9
Expansion Potential: Example
AHRQ RIGOR
(CER)
Ophthalmic
Patient
Outcomes
Database
(Quality)
FDA
Intraocular
Lens
Registry
(Safety)

How PCORI might use/extend existing registries
Registry Examples Large, diverse
popoulations
from usual
care settings
Complete
capture of
longitudinal
data
Ability to
contact patients
for study
specific PROs
Patient and
clinician
engagement
in data
governance
Linkage
to health
systems
Capable of
randomizat
ion
American Heart Association
(Get With the Guidelines
Stroke, Heart Failure,
Resuscitation)
Yes No
Extend with
linkage
Not routine
Has been used
in substudies,
ePRO capable
Yes Yes Yes
American College of
Cardiology (NCDR,
PINNACLE)
Yes Mixed
Extend with
linkage
Yes
Cystic Fibrosis Foundation
Registry
Yes Yes Yes Yes Yes Yes
American Society of Plastic
Surgeons (TOPS)
Yes Longitudinal,
focused
Yes, ePRO Yes Yes
AHRQ (RIGOR) with AAO,
Quintiles Outcome
Yes Longitudinal,
focused
Yes, ePRO Mixed Yes,
practices
Yes
American College of
Surgeons (NSQIP, Bariatric,
NCD)
Yes Mixed
Extend with
linkage
Mixed Mixed Yes Yes
American College of
Gastroenterology (GIQuic)
-- No
Extend with
Linkage
Not routine,
systems capable
Yes Yes
National registry examples in a range of conditions and procedures

11
• Promote core data set development for PCOR through
multi-stakeholder collaboratives
Data elements and definitions
not standard for most
conditions
• Advance patient identity management solutions (e.g.
secure anonymized patient ID linkages)
Data is not easily collected
across care settings or long-
term
• Leverage interoperability solutions (e.g. HITSP TP-50) for
registries and EHRs as part of meaningful use
HIT systems not yet
interoperable with registries
• Specify acceptable methods and quality assurance
requirements for use of data for PCOR*
Lack standardized methods
for sampling, data quality and
risk adjustment
• Promote standardized approaches for linkage
• Seek clarification of linkage issues under HIPAA from HHS,
address access issues such as to death indices
Linkage of data from different
sources limited by
inconsistent methods and
HIPAA concerns
• Leverage registries with high participation rates.
• Work with HHS (HIPAA and Common Rule) with respect to
increasing efficiency of IRB and consent requirements for
core registry and PCOR within existing registries
Participation is highly variable
and related to incentives and
interpretation of rules
• Focus on registries with sustainable models
Not all registries have
sustainable business models
What can PCORI do to extend
the model more broadly?

13
Standards for Data Registries
From PCORI Draft Methodology Report
• Develop a Formal Study Protocol
• Measure Outcomes that People in
the Population of Interest Notice
and Care About
• Describe Data Linkage Plans, if
Applicable
• Plan Follow-up Based on Registry
Objective(s)
• Describe Data Safety and Security
• Take Appropriate Steps to Ensure
Data Quality
• Document and Explain Any
Modifications to the Protocol
• Collect Data Consistently
• Enroll and Follow Patients
Systematically
• Monitor and Take Actions to Keep
Loss to Follow-up to an Acceptable
Minimum
• Use Appropriate Statistical
Techniques to Address Confounding

14
•  Registry of Patient Registries (RoPR)
>  AHRQ, Outcome DEcIDE in collaboration with NLM
Where to Find Registries?
14

1
July 3, 2012
Charting the Course – Exploring Top Proposals
from Poster Sessions

2
Opportunity Identification and
Prioritization
Breakout Groups
Recommendation
Development
Voting Process
Ranking Process
• All participants were assigned to seven breakout groups focused on:
1. Governance
2. Data Standards & Interoperability
3. Architecture & Data Exchange
4. Privacy & Ethical Issues
5. Methods
6. Unconventional Approaches
7. Incorporating Patient Reported Outcomes into Electronic Data
• Each group was tasked with generating 3-4 actionable recommendations that
support PCORI’s mission. Recommendations included the following dimensions:
1. Time Horizon
2. Cost
3. Feasibility
4. Criticality of PCORI’s Role
5. Efficiency of Resource Usage
• Each group generated a “poster” showcasing its recommendations. The posters
were displayed and all participants, using a controlled number of positive and
negative votes, supported or opposed recommendations
• This morning, we will discuss the top recommendations along with any
recommendations which appeared to be polarizing

3
Top 10 Recommendations
Rank Recommendation Name
Green
Votes
Red Votes
10
Define mechanism to authorize use of data for PCOR purposes:
a) Policies to vet and approve use of network resources and b)
define expectations of data holder and networks
23 4
9
Sponsor and advocate for refinement and curation of clinical
information models and associated value sets, common data
elements that merge clinical and research requirements
25 2
8
Sponsor and advocate for development of data standards about
the care environment in order to facilitate the analysis of care
options
27 1
7
Identify and address barriers and incentives for developing and
using PROs in healthcare systems and PHRs
28 4
6
Develop methods to develop an “n=1” research environment to
investigate impact on patient experiences using diverse eData
29 0

4
Top 10 Recommendations (cont’d)
Rank Recommendation Name
Green
Votes
Red Votes
5
Ask patients what they think are the most important
research questions and create a transparent, dynamic
list of PCORI research priorities, with explanations that
incorporate patient and expert input
34 4
4
Architecture and Exchange: Develop 360o
Patient-
centered longitudinal view, Identity Mgt, Data Curation
36 0
3
Improve outcomes and advance knowledge for patients,
clinicians and researchers with Rapid Learning Networks
44 3
2
Be the national leader to ensure meaningful and
representative patient engagement in research
networks’ governance (ex. ID people, train people,
advise, etc.)
44 0
1
Establish PCORI criteria for governance for focus on: a)
meaningful and representative patient engagement, b)
data stewardship, c) dissemination of information, and
d) sustainability
46 0

5
Lowest Ranking Recommendations
Rank Recommendation Name Green Votes Red Votes
1 Seek to broadly understand patient benefit 1 0
2
Understand which groups engage and why to ensure
inclusiveness
3 0
3
Conduct survey of initiatives for implementation of PROs
in healthcare systems & PHRs
4 1
4 Explore IRB models that facilitate patient engagement 5 0
5
Support methods to develop a portfolio of studies to
balance the eData trade-off and developing methods to
assess level of control of confounding in the data
7 0
5
Develop a manual for EHR based research reporting
standards
7 7

6
Governance
Establish PCORI criteria for governance
a)meaningful/representative pt engagement
b)data stewardship
c)dissemination of information
d)sustainability

7
Governance
Be national leader to ensure
meaningful and representative patient
engagement in research networks’
governance
(e.g., ID people, train people, advise,
etc.)

8
Unconventional Approaches
1.The National Patient Network
2.Rapid Learning Networks to Improve
Outcomes and Advance Knowledge

9
Data Standards & Interoperability
and Architecture and Exchange
Patient-Centered Longitudinal View
Sponsor Development of Data
Standards About the Care Environment
to Facilitate Analysis of Care Options

10
Sponsor and Advocate For:
– Development of Data Standards About
the Care Environment In Order to
Facilitate the Analysis of Care Options

11
1. Sponsor and Advocate For:
– Sponsor and advocate for refinement
and curation of clinical information
models and associated value sets,
common data elements that merge
clinical and research requirements

12
Architecture and Exchange
–Patient-Centered Longitudinal View
–Identity Management
–Data Curation

13
Incorporating Patient Reported
Outcomes into Electronic Data
Identify and address barriers and
incentives for developing and
using PROs in healthcare systems
and PHRs

14
Methods
Methods to develop an n=1
research environment to
investigate impact on patient
experiences using diverse eData.

15
Thank you for your participation!

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