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.
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Strictly confidential NHIN Slipstream Project Executive Briefing Meeting – Hand-out materials April 9, 2007 This presentation discusses a NHIN Architecture.
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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.
• 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.
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?
– 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
• 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.
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
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:
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.
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
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
• 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
• InterMountain Health – GE
• 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)
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
4. Patient Visits Research Site:• Research staff/investigator determine patient eligibility• Patient elects whether or not to enroll
4. Patient Visits Research Site:• Research staff/investigator determine patient eligibility• Patient elects whether or not to enroll
Personal HealthRecords
Matching Rules
Database
Trial Criteria
Personal HealthRecords
Matching Rules
Personal HealthRecords
Matching Rules
Database
Trial Criteria1. Patient self-reports Personal Health Record1. Patient self-reports
Personal Health Record1. Patient self-reports
Personal Health Record
3. Patient contacts research site:
• Calls research site
• Sends Personal Health Record via Secure Message Center.
3. Patient contacts research site:
• Calls research site
• Sends Personal Health Record via Secure Message Center.
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
• 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