strictly confidential nhin slipstream project executive briefing meeting – hand-out materials...

Strictly confidential

NHIN Slipstream Project

Executive Briefing Meeting – Hand-out materials

April 9, 2007

This presentation discusses a NHIN Architecture Prototype project made possible by a contract from the Office of the National Coordinator for Health Information Technology (ONC), DHHS. The content is solely the responsibility of the authors and does not necessarily represent the official view of ONC.

Copyright © 2007 Accenture All Rights Reserved. 209 Apr 2007

• Setting the context – – Background – what is the NHIN

– The objectives for the NHIN Slipstream project

• What is the current state and context for today – What did we accomplish with NHIN Slipstream project

– Use case activities –

• Matching patients to trials, Clinical data capture, Drug safety surveillance

– The current environment: The NHIN – RHIO shift

• Advice and recommendations– Process to follow on opportunities

– Opportunities going forward

Meeting Agenda


Background

• 2005 – National Health Information Network (NHIN) contracts awarded by Office for the National Coordinator for Healthcare Information Technology (ONCHIT) to build prototypes.

• 2006 – Slipstream established by Accenture (one of the contract winners) to understand the NHIN capabilities and understand what is needed to fully leverage them from a pharma perspective.

• 2006 – AZ, BMS, Pfizer, Wyeth invest $150K each to participate.

• 2007 – Slipstream Phase 1 completed, NHIN prototype demonstrations conducted, Slipstream use cases made public.


There are local, regional, and national components of the Health Information Exchange landscape – Due to gaps in records of care and lack of standards in local health records, regional and nationally exchanged health records have greater potential to support continuity of care and other critical use cases.

LocalLocal

National

Regional

Local

Illustrative

NHIN

RHIORHIO

LocalLocal

LocalLocal

LocalLocal

LocalLocal

LocalLocal

LocalLocalLocalLocal

Local health record examples – hospital systems, outpatient, physician offices, home care, pharmacy, labs, etc.


The US Federal Healthcare IT Landscape

AHIC

Breakthrough Workgroups &

Use Cases

Guides ONC Activities

Consortia-based NHIN Prototype Contracts

HISPCPrivacy

NHIN HITSPStandards

CCHITCertifications

IBM Northrup Grumman CSC Accenture

NHIN Forum:Public Comment on Requirements and Policy Issues

• Consumer Empowerment• Electronic Health Records• Bio Surveillance


Characteristics of Accenture’s NHIN Prototype

• Characteristics of health care markets:– Rural– Have RHIOs but do not have regional

information infrastructures for sharing health data

– Hospital and provider systems are all different with few systems based on federal health standards

• Accenture Consortium Technical Partners:

– Accenture

– Oracle

– Cisco

– Quovadx

– Apelon

– Orion Health

– Sun Microsystems

Appalachian Region

Tennessee

Kentucky

Virginia

WestVirginia

• CareSpark from the tri-cities region of northeastern Tennessee and southwestern Virginia;

• West Virginia eHealth Initiative;

• Eastern Kentucky Regional Health Information Organization

– CGI-AMS

– Creative Computing Solutions

– eTech Security Pro

– Intellithought

– Lucent Glow

– Oakland Consulting Group


Why are these national efforts important to Pharmaceutical companies?

Able to determine answers to critical questions -

– What are the Pharma-specific use cases that could leverage Clinical Data Exchanges and a Nationwide Health Information Network?

– How can this lead to improvements – reduced costs or improved insights – through-out the development, administration, patient safety and surveillance of drugs and medical products?

– What additional value can be derived through data capture and integration with new sources of data (e.g., genotypic data)?

– What are the obstacles and key enablers to the pharmaceutical industry realizing the benefits of this emerging infrastructure?

• Legal and Policy

• Standards

• Data Ownership

• Financing

• Governance

• Technical


The NHIN Slipstream Project Context

• Why:

– Recognition that the Pharma industry is not fully contributing to activities and opportunities in the Health Information Technology (HIT) arena

– Recognition that Pharma companies can help to define the key HIT use cases for enabling clinical research and monitoring the safety and effectiveness of medicines

– Assessment that the Pharma industry would have greater impact if it were able to speak with a unified voice in national, regional, and local HIT efforts

– Desire to identify opportunities to pilot the use case concepts and help move toward realizing the value offered by HIT

• Who:

– Four pharmaceutical companies: AstraZeneca, Bristol-Myers Squibb, Pfizer, and Wyeth

– Steering committee with working groups comprised of subject matter experts

– Meetings & deliverables facilitated by Accenture

• What:

– Ongoing monitoring of national & regional HIT activities, including the NHIN prototypes

– List of use cases relevant to Pharma, prioritized down to the top three

– Three detailed use cases, including value propositions and proof of concept opportunities

– Internal / external communication planning










Meeting Agenda


Clinical Research Clinical Development Regulatory / Safety

Commercial

1. Genetic Association and Linkage Analysis

2. Clinical Validation – Target, Biomarker, and Diagnostic

3. Clinical Trial Executiona. Connect Patients to Trials

b. Data Collection & Mgmt

c. Investigator Services

d. Compliance

e. Placebo Populations

4. Clinical Trial Simulation

5. New Indication Identification

6. Interim analyses

10. Post-Marketing a. Safety /

Adverse Event Monitoring

b. Pharmaco-vigilance

c. P-Epi & Data Mining

11. Manufacturer’s Recall

12. Pharmaco-economics

13. Marketing Comparative Studies

14. Pharmaceutical/ Disease Management Programs

15. e-Prescribing

The group looked across the Pharmaceutical value chain and determined a set of priority Use Cases

7. Personalized Medicine – Pharmacogenomics

8. Outcomes Studies

9. Disease and Care Management Modeling

Prioritized High-Level Use Cases


Connecting Patients to Trials Use Case

• Scope:– Determine value-added outputs and services that can be provided to patients, physicians, investigator sites, and

clinical trial sponsors based on improved matching of patients to trials via electronic health record information.

• Value-added Services Identified:– Direct to Patient Clinical Trial Matching Service

• Compare a patient’s health record and indication preferences and against pre-screening criteria of all registered clinical trials. Provide report of matching trials to patient with information about how to get screened for the trial.

– Service for Site / Physician to Match Patients to Trials

• Allow investigator sites and physician offices to run a report that will match their patients to clinical trials for which the patients meet the pre-screening criteria based on the patients’ electronic health records.

– Clinical Trial Recruitment Feasibility Analysis Service

• Allow clinical trial sponsors to determine the patient populations that meet the pre-screening criteria of their trials, stratified by location

– Inform Investigator of Qualifying Patients in His/Her Geography

• Allow trial investigator sites to run reports that will identify the physicians in their geographic area that currently treat patients that match the pre-screening criteria of a trial being run at their sites.

• Key Obstacles:– Privacy & Consent: policies regarding patient consent and privacy protections to share health information for

purpose of clinical trial matching. This includes agreement of who can access identified and de-identified patient data.

– Standards: terminology standards necessary to create consistent, computable, interoperable health record data for comparison against structured clinical trial pre-screening criteria

– Data Ownership & Governance: agreements of who owns patient data, how it will be governed, whether it can be aggregated and by whom, and who can use it for what purposes.


Post-Marketing Drug Safety & Surveillance Use Case

• Scope:– Evaluate how electronic health records can be used to improve post-marketing safety and surveillance of medicines,

including receipt, evaluation, and reporting of individual adverse events, signal detection for patterns of drug effects, and longitudinal data mining for hypothesis testing and pharmacoepidemiology.

– This use case focuses only on “spontaneous” reporting, and will not include safety and surveillance of drug reactions occurring during clinical trials.

• Scenarios Identified:– EHR-enabled Adverse Drug Reaction Reporting (ICSR)

• Enable healthcare professionals to more easily report adverse events with higher quality supporting data available in electronic medical record and other electronic systems. Create a central repository & workflow capabilities that can shared by drug manufacturers and regulatory agencies for collection, management, and reporting of adverse events.

– Signal Detection of Drug Reactions• Detection of patterns of drug reactions using signal detection algorithms against comprehensive, longitudinal

electronic patient health records available through health information exchanges.– Epidemiology, Hypothesis Testing, & Longitudinal Data Mining

• Allow researchers to execute data queries to test hypotheses and evaluate patterns of drug effects against one or more repositories of standardized, anonymized patient health information for large numbers of patients across the country.

• Key Obstacles:– AE Reporting: physicians and other healthcare professionals must be given incentive to report adverse events through

EMR systems with high quality supporting data.– Regulatory Agreement: gain agreement from regulators to change current processes for adverse event reporting to a

new model that allows manufacturers and regulators to use one central system for AE collection and reporting.– Data Ownership & Governance: agreements of who owns patient data, how it will be governed, whether it can be

aggregated and by whom, and who can use it for what purposes.– Privacy & Consent: policies regarding patient consent and privacy protections to share health information. This includes

agreement of who can access identified and de-identified patient data.– Standards: terminology standards necessary to create consistent, computable, interoperable health record data.


How does this map to AZ objectives

Slipstream Use Case / POC Opportunity

Clinical Objectives

Connecting Patients to Trials

1. Using the local Strategic development model, deliver US phase IV Studies to time, cost and quality

2. Through the Study Recruitment Center of Excellence, effectively leverage key areas of partnership with External Scientific Affairs (ESA) and Commercial to optimize delivery of Clinical programs

3. Increase Diversity in recruitment of US Clinical Studies by partnering with key stakeholder groups

Safety Surveillance

1. Provide necessary drug safety and Medical Science support for specific US safety issues – IOM, benefit-risk plans

2. Identify needs for ‘ongoing, real-time safety surveillance’ in clinical programs and propose plan to clinical team by end of Q2 to meet these needs

Superior Patient Safety Work stream


Slipstream Use Cases – Communication has been extensive and is on-going

CRIX InternationalDecember 9, 2006

CRIX InternationalDecember 9, 2006

FDA Sentinel Network MeetingMarch 7-8, 2007

Meeting Summary and Outcomeshttp://www.fda.gov/oc/op/sentinel/

Meeting SummaryThe FDA held a two-day public meeting to explore opportunities to collaborate with

other public and private organizations to create a Sentinel Network to monitor the safety of medical products. Andrew von Eschenbach and Janet Woodcock kicked off the meeting and laid out three main components of the network:

Data Collection

• Identifying data source systems, including EMRs and large databases (claims, clinical, lab, etc)

Risk Identification and Analysis• Integrated networks to connect data sources• Tools and methods for data mining for safety signals• Agreement on methodologies used for signal detection and validation• Ability to study subgroups, biomarkers, & genomic markers

Risk Communication• How to get new information into physicians’ workflows (decision

support)

The panelists for the meeting were made up of different FDA departments, CDC, DoD, VA, CMS, ONC, & AHRQ. Participating speakers came from academic medical centers, industry associations, health information exchanges, payers, pharma companies (GSK, J&J, Pfizer), and technology companies to present their ideas on the Sentinel Network.

FDA Sentinel Network MeetingMarch 7-8, 2007

Meeting Summary and Outcomeshttp://www.fda.gov/oc/op/sentinel/

Meeting SummaryThe FDA held a two-day public meeting to explore opportunities to collaborate with

other public and private organizations to create a Sentinel Network to monitor the safety of medical products. Andrew von Eschenbach and Janet Woodcock kicked off the meeting and laid out three main components of the network:

Data Collection

• Identifying data source systems, including EMRs and large databases (claims, clinical, lab, etc)

Risk Identification and Analysis• Integrated networks to connect data sources• Tools and methods for data mining for safety signals• Agreement on methodologies used for signal detection and validation• Ability to study subgroups, biomarkers, & genomic markers

Risk Communication• How to get new information into physicians’ workflows (decision

support)

The panelists for the meeting were made up of different FDA departments, CDC, DoD, VA, CMS, ONC, & AHRQ. Participating speakers came from academic medical centers, industry associations, health information exchanges, payers, pharma companies (GSK, J&J, Pfizer), and technology companies to present their ideas on the Sentinel Network.

Over 35 opportunities to brief stakeholders on Slipstream use cases:

• NIH• AHIC• NCVHS• CRIX• FasterCures• PhRMA• FDA • MHRA• CDC• eClinical Forum• Additional Pharma

companies

• And on-going…


Progress towards stated goals

Goal Name Goal Description Status

Education on NHIN & HIT

Educate participating company representatives on national HIT initiatives in the US. This includes work to develop a Nationwide Health Information Network, standards, privacy/security guidelines, and certification criteria for electronic health record products.

Complete

Identify pharma impact of NHIN

Identify the impact that the NHIN and other HIEs will have on the pharmaceutical industry. Define which areas will be most impacted and how.

Complete

Influence the national HIT agenda

Determine ways that the Slipstream participants can influence the national HIT agenda. Recommend actions and communications with ONC, AHIC, HITSP, and other groups.

Ongoing

Coordinate efforts with pharma industry

Communicate with other pharma companies to create alignment of interests and priorities in HIT.

Complete

Coordinate efforts with broader clinical research industry

Communicate with other clinical research stakeholers, including government research groups, academic medical centers, regulators, and advocacy groups. Build alignment with Slipstream concepts and priorities.

Ongoing

Determine potential pilot projects in HIT

Identify potential proof of concept projects for the prioritized use cases developed through the project. Scope these PoCs and recommend roadmap of evolving projects.

Complete


Update on AHIC’s NHIN Prototype Efforts

• Successfully completed the NHIN prototype effort – – Achieved all objectives in SOW including connecting 15 health care

organizations in three distinct markets and demonstrating that data extraction and normalization are possible.

• Presentation at AHIC National Forum on January 24th-25th, 2007 well received by over 600 attendees

• Presented at HIMSS to International audience

• Over twenty requests from agencies and clients for demonstrations

• ONC Regional Implementation RFPs expected May 2007– Expect 10-14 awards for total of $20M

– Still not a lot of details on RFPs


What We Set Out To Do

Build a secure NHIN prototype that leveraged existing infrastructure and:

Allow patient control of their health informationConnect systems with a wide variety of IT platforms Deal with the critical issues of data normalization Provide enough flexibility to allow local choice in the degree of centralization of dataMeet the requirements of the three use cases

Show we could quickly build out RHIOs


The shift from the NHIN to RHIOs

So now what happens…

• Lots of talk and emerging efforts at regional and state levels– Few $’s

– Governance still an issue

– Business Case less than compelling

• Look to:– State Medicaid programs as nexus for efforts (ability to leverage MMIS matching

federal funds)

– Emergence of PHR information aggregators

– Health IT Bill seeking to make owners of data HIPAA “covered entities”


How should a company “play” during different stages of market maturity?

Stage

Description Strategic Objective Success Measure

Concept/ Incubation Ideas “Get Ahead of the Market with the Idea”

Awareness

Reputation as innovator

Proof of Concept Pilots “Gain Experience on Key Success Factors/Create Credentials”

Innovative credentials on key issues

Clarity on key success factors needed for positioning

Early Adopters <20% market adoption “Be the Logical Choice for Early Adopters”

Invitation to bid on all relevant opportunities

Wins on key early trendsetting projects

Wide Spread Adoption 20%-75% market adoption “Be the One to Beat and Scale/Defend”

High % of Wins

Business as Usual >75% market penetration “Harvest” Market share

Market Maturity: Strategy and Tactics










Meeting Agenda


Advice and recommendations for going forward

• Implement a best practice-based approach for managing Healthcare IT roadmap and investments

– Start with a real business challenge on a real drug project and ask “how could Health IT help solve this”

• Ensure linkage to business goals, objectives, and priorities

• Execute projects that support actual drug development projects

– Establish a governance and portfolio approach for managing proof of concept and/or scale-up projects

• Develop and maintain strong connections with the broader Clinical Research and Health IT communities

• Pursue convergence opportunities wherever possible – CRIX, PhRMA, NIH, Regulators, others


Opportunity =Use CaseTestable

Component

Provider Partner(s)

(or others, e.g., Regulators)

TechnologyVendor(s)+ +

Business Engagement +

Company Priorities

HIT Roadmap

Patient data

source

Compatible with Use

Case

Requirements

Opportunity assessment framework – Critical elements to consider in evaluating an opportunity

Other considerations• Ability to execute• Realistic expectations – where we are and what can be accomplished• Scale and fit with use case• Re-use and growth path• Governance – how to control the effort


Proof of Concept Opportunities

• The group has identified several Proof of Concept project ideas. Some are firm ideas, others are more speculative, and some are prospects. The table below organizes the ideas by use case:

Use Case Firm Opportunities Speculative Opportunities Other Prospects

1. Matching Patients to Trials

1.a CRIX - National expansion of BreastCancerTrials.org (powered by caMATCH)

1.b. Geisinger EPIC

1.c. W. Virginia Med Ctr - EPIC

1.d. Siemens matching technology

• Cleveland Clinic – EPIC

• InterMountain Health – GE

• Kaiser – EPIC

• Stanford U.

• U. Pittsburgh Med. Ctr. – EPIC/ Cerner

2. Drug Safety and Surveillance

2.a. Surface IHE RFD form for Drug AE reporting from within EMR – DONE

2.b. MHRA eYellow Card and Next Generation GPRD

2.c. Geisinger AE Reporting via EMR

2.d. W. Virginia Medical Institute

2.e. Signal detection on longitudinal health record data (Allscripts pilot, MHRA GPRD, Health Dialog data)

• Cleveland Clinic –EPIC


• Kaiser - EPIC

• Stanford U. - EPIC

3. Clinical Trial Data Collection / Mgmt

3.a. NIH CTSA CR NHIN

3.b. Allscripts pilot

3.c. IHE/CDISC – next phase of piloting (Cerner, Siemens)

3.d. EDC & EMR Vendor Pilot

• eClinical Forum EHR project



• Kaiser - EPIC

• Stanford U. - EPIC

4. Appropriate Care

4.a. Geisinger EPIC 2nd -ary Use of Data Pilot

4.b. Allscripts pilot

4.c. Health Dialog pilot

4,d. Kaiser - EPIC



• Stanford U.


Focused opportunities to put Slipstream Use Cases to practice – Projects under active consideration

• Matching Patients to Trials– Establish an “EHR enabled” Matching Patients to Trials capability under CRIX

building upon lessons and capabilities under BreastCancerTrials.org at UCSF

• Drug Safety Surveillance– Design and implement a globally harmonized EHR enabled-AE collection

capability and Signal Detection database capability leveraging the UK MHRA’s e-Yellow Card and next generation GPRD initiatives

• Clinical Data Capture, Management, and Control– Leverage the NHIN Prototype to “integrate” EHR-enabled Clinical Trial

Administration and Data collection across a fragmented NIH GCRC / CTSA sites


• How should Slipstream proceed now?

– Slipstream going forward can provide the following:

• Evolve into an “incubator” for PoC projects – focal point for planning and co-funding of emerging efforts

• Provide PoC planning, progress updates, and discussion forums to disseminate results

• Deliver educational / briefing sessions for participant senior management and other stakeholders

• Continued communication and influence planning and execution

• Marketing of the use cases

– Slipstream would require “thinner” resourcing as most effort and investment would be pushed to PoC projects.

– Incremental budget to support governance/operating model support.

– Merge into CRIX? Merge with PhRMA HIT Forum?

Future Slipstream Operating Model – Proposals for Moving Forward with Slipstream Phase 2


Recommendations

• External– Establish an AZ cross functional team to participate in Slipstream Phase 2

• Identify specific components of the use cases where proof of concept (POC) activities can be tested (2007 – 2008)

• Internal– Align Slipstream with Superior Patient Safety initiative

– Align with the Information Strategy

– CRIX Alignment


Questions?

Strictly confidential

Appendix

Additional Detail on Slipstream Implementation Opportunities



• Matching Patients to Trials– Establish an “EHR enabled” Matching Patients to Trials capability under CRIX

building upon lessons and capabilities under BreastCancerTrials.org at UCSF






ClinicalTrialsMatch.orgThe Vision: Speed drug approvals and new therapies by accelerating accrual to trials

• Providing consumers a platform to engage in clinical trial enrollment

• Nationally trusted non-profit portal for clinical trial information and matching

• Collaboration among stakeholders:– Patients/Physicians– Trial Investigators– Government Agencies– Pharma/Biotech

• Integrated with other healthcare data repositories:– EMR and PHR Data– Trial Registries and Management Systems – Disease Registries

• Conform to National/Industry Healthcare Standards

• Iterative development:

Apply “lessons learned” to implement and evaluate a trial matching service that is extensible to other disease states

BCT_Pilot BCT_Nation CTM.org


CRIX is interested in Matching Patients to Trials as a consortium service

2. BCT returns matches with trialsummaries and contact information

4. Patient Visits Research Site:• Research staff/investigator determine patient eligibility• Patient elects whether or not to enroll

Personal HealthRecords

Matching Rules

Database

Trial Criteria1. Patient self-reports Personal Health Record

3. Patient contacts research site:

• Calls research site

• Sends Personal Health Record via Secure Message Center.

2. BCT returns matches with trialsummaries and contact information2. BCT returns matches with trialsummaries and contact information2. BCT returns matches with trialsummaries and contact information




Matching Rules

Database

Trial Criteria


Matching Rules


Matching Rules

Database

Trial Criteria1. Patient self-reports Personal Health Record1. Patient self-reports

Personal Health Record1. Patient self-reports

Personal Health Record







BreastCancerTrials.org: Overview of current operating model

9Dubman: Clinical Trial Matching as a CRIX Service

Development of a CRIX Clinical Trial Matching Service can be Very Cost Effective

Able to leverage existing solution (bct/caMATCH)– Originally designed for and by patients

– Scenarios driven by domain experts and actual users– Initial implementation by UCSF

– Additional joint development by UCSF and the NCI (caMATCH)– Current UCSF investments towards standards-based solution

• Leveraging existing international standards/ data models (CDISC/ Niland’s work)• Moving towards fully caBIGTM compatible, scalable architecture/ infrastructure

• Provides for future Interoperability with other critical components – The WHO clinical trial registry, other caBIGTM data sets, tools, etc.

Benefit from what was learned in earlier pilot tests

Benefit from other relationships built by UCSF/Quantum Leap

Able to leverage existing CRIX capability: Firebird– There will be some challenges/ enhancements needed for Firebird but these are minor

compared to a brand new development

Bottom line: This is a Win-Win-Win for Researchers, Patients and Industry

The near-term opportunity is to converge effort and build Matching Patients to Trials services under the CRIX service umbrella



• Matching Patients to Trials– Establishing an “EHR enabled” capability under CRIX building upon lessons and

capabilities under BreastCancerTrials.org at UCSF






Overview of the Drug Safety Surveillance component of Slipstream 2

• The concept– Upgrade the MHRA’s capabilities in AE collection and Signal Detection:

• Convert the Yellow Card Scheme from paper-based to electronic – create the eYC

• Upgrade the data collection and processing capabilities that underpin GPRD

– In parallel…implement similar capabilities at an established Health Information Exchange

• Leverage RFD capability to simplify the collection of AEs and collect GPRD-like datasets

– Merge the two efforts to create a global capability

Initial focus will be on the implementation of an Electronic Yellow Card concept

Next steps are to outlines the scope, high level solution, estimates and plans for the development of a production solution.


Schematic of the collection of Safety Data – Currently this is a manual and costly process for both the MHRA and Pharmaceutical companies

UK GeneralPractitioner

EMRFile on

Disk

YellowCards(paper)

Data Entry, Cleaning, etc.


PopulationDatabase

PV Capabilities

AEDatabase

PharmaCompanySponsor


Global check

AE Db

Reporting

CURRENT


Schematic of the collection of Safety Data from within an EMR – Automate e-Yellow Card and the loading of population data for signal detection

UK GeneralPractitioner

EHR

e-YellowCards

Direct dataload

Data investigation

PopulationDatabase

PV Capabilities

AEDatabase

PharmaCompanySponsor

Global check

AE Db

Reporting

US RHIOEHR

Direct dataload

FUTURE

1

2

3

1 e-Yellow Card from EHR

2 Auto-load of EHR data to GPRD

3 US Health Information Exchange extending GPRD



• Matching Patients to Trials– Establishing an “EHR enabled” capability under CRIX building upon lessons and

capabilities under BreastCancerTrials.org at UCSF






Develop a Clinical Research NHIN to converge Health Record and Clinical Research data collection, management, and control

• The problem– Health care data exists in paper or if electronic, it exists in hundreds of disparate legacy

systems.

– Efficiently accessing it to improve clinical research currently is not practical

• Components needed to solve this problem– A functional health information exchange (HIE) that can extract and normalize data from

legacy systems

– Access to a governance body that can influence behavior of disparate organizations

– Access to an organization with a huge need to share data

– Participation by organizations who can help drive a market solution and influence governmental and industry

– Access to people with the skill set and passion to pull this off

– Funding


• The Solution– Identify two NIH CTSA (Clinical and Translational Science Award) consortium and pilot HIE implementations at both

institutions to capture clinical data in a standard way.

– Work with all twelve CTSA institutions, HL7, NLM and CDISC and the Pharma Industry to define requirements. Phase data requirements into prototypes. The 12 institutions forming the initial consortium:

• Columbia University Health Sciences - Irving Institute for Clinical and Translational Research (IICTR)

• Duke University - Clinical Translational Science Institute (CTSI)

• Mayo Clinic College of Medicine - Center for Clinical and Translational Research (CCTR)

• Oregon Health & Science University - Oregon Clinical and Translational Science Institute (OCTSI)

• Rockefeller University - Rockefeller University Center for Clinical and Translational Sciences

• University of California, Davis - Clinical and Translational Science Center (CTSC)

• University of California, San Francisco - Clinical and Translational Science Institute (CTSI)

• University of Pennsylvania - Institute for Translational Medicine and Therapeutics (ITMAT)

• University of Pittsburgh - Clinical and Translational Science Institute (CTSI)

• University of Rochester - University of Rochester Clinical and Translational Science Institute (UR CTSI)

• University of Texas Health Science Center at Houston - Center for Clinical and Translational Sciences (CCTS)

• Yale University - Yale Center for Clinical Investigation (YCCI)

– Depending on funding, consider different architectures for data collection at the sites.

• The Value of this Approach– Engages 12 topic health care institutions, NIH and Pharma Industry around solving a critical, but doable project

– Create learnings and excitement; impact the cost of doing business in the short-run

– Public/Private/Academic involvement is a great model of cooperation

– Tools, knowledge and personnel exist to solve this problem

Develop a Clinical Research NHIN – Participation of NIH and key Academic Medical Center create an incentives driven operating model

strictly confidential nhin slipstream project executive briefing meeting – hand-out materials...

Documents

health datahospital

local health records

exchanged health records

nhin slipstream projectwhat

nhin capabilities

slipstream use cases

records of care

nhin prototype demonstrations