2. Welcome!
ā¢ Thank you for attending!
ā¢ Goals and objectives
ā« Outlining project vision and aims
ā« Meeting SAFTINet collaborators
ā« Starting the process
ā« Clarifying concerns and questions
ā¢ Agenda
ā¢ Meeting materials
ā« Research strategy
ā« SAFTINet commonly used acronyms
3. Agenda
Agenda Item Time Presenter
Welcome and agenda
SAFTINetContext and Overview
10 mins
20 mins
Bethany Kwan
Lisa Schilling
Introductions and RollCall
Project teams and investigators
AAFP
CINA
University of Utah CHPC
DHHA
Cherokee Health Systems
Intermountain Healthcare
CCMCN/CACHIE
10 mins Bethany Kwan
Comparative Effectiveness Research 10 mins Marion Sills
Partner Engagement Community 10 mins Debbie Graham
TechnicalTeam Presentation 10 mins Michael Kahn
Getting started 5 mins Bethany Kwan
Wrap-up and Questions 15 mins Lisa Schilling
4. IOM Roundtable onValue & Science-
Driven Health Care
ā¢ Goal: by the year 2020, 90 percent of clinical
decisions will be supported by accurate, timely, and
up-to-date clinical information, and will reflect the
best available evidence
ā¢ Learning Healthcare System series
5. IOM Roundtable onValue & Science-
Driven Health Care
ā¢ The Learning Healthcare System (2006)
ā¢ Judging the Evidence: Standards for DeterminingClinical Effectiveness (2007)
ā¢ Leadership Commitments to ImproveValue in Healthcare:Toward Common
Ground (2007)
ā¢ Redesigning the Clinical Effectiveness Research Paradigm: Innovation and
Practice-Based Approaches (2007)
ā¢ Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a
PublicGood (2008)
ā¢ Engineering a Learning Healthcare System:A Look to the Future (2008)
ā¢ LearningWhat Works: Infrastructure Required for Learning Which Care Is Best
(2008)
ā¢ Value in Health Care: Accounting for Cost, Quality, Safety, Outcomes and
Innovation (2008)
6. Comparative Effectiveness Milestones
ā¢ 2003 - MMA Section 1013 authorizes AHRQ to conduct and
support research with a focus on āoutcomes, comparative
clinical effectiveness, and appropriateness of health care
items and services (including prescription drugs)ā
ā¢ 2007- IOM Report - LearningWhatWorks Best:The Nationās
Need for Evidence on Comparative Effectiveness In Health
Care
ā¢ 2009 - ARRA provides $1.1 Billion to NIH/HHS/AHRQ
ā¢ 2010 - Patient Protection and Affordable Care Act
7. ā¢ Need for substantially improved understanding of the
comparative clinical effectiveness of healthcare
interventions.
ā¢ Strengths of the randomized controlled trial muted by
constraints in time, cost, and limited applicability.
ā¢ Opportunities presented by the size and expansion of
potentially interoperable administrative and clinical datasets.
ā¢ Opportunities presented by innovative study designs and
statistical tools.
ā¢ Need for innovative approaches leading to a more practical
and reliable clinical research paradigm.
ā¢ Need to build a system in which clinical effectiveness
research is a more natural by-product of the care process.
8. Redesigning the Clinical
Effectiveness Research Paradigm
ā¢ Address current limitations in applicability of research results
ā¢ Counter inefficiencies in timeliness, costs, and volume
ā¢ Define a more strategic use to the clinical experimental model
ā¢ Provide stimulus to new research designs, tools, and analytics
ā¢ Encourage innovation in clinical effectiveness research conduct
ā¢ Promote the notion of effectiveness research as a routine part of practice
ā¢ Improve access and use of clinical data as a knowledge resource
ā¢ Foster the transformational research potential of information technology
ā¢ Engage patients as full partners in the learning culture
ā¢ Build toward continuous learning in all aspects of care
9. ā¢ resulting research paradigm, with randomized
controlled double blind trials at the pinnacle, has
often left important evidence needs unmet when
combined with the costs, complexity, and lack of
generalizability of RCTs.
11. Why distributed?
ā¢ Minimize security risks by allowing the data
repositories of multiple parties to remain separately
owned and controlled.
ā¢ These models also provide an interface to these
stores of highly useful data that allows them to
function as a large combined dataset.
12. ā¢ Patient preferences and perspectives.What
approaches might help
ā¢ to refine practical instruments to determine patient
preferencesā
ā¢ such as NIHās PROMIS (Patient-Reported Outcomes
Measurement
ā¢ Information System)āand apply them as central
elements of outcome
ā¢ measurement?
15. Patient Protection and Affordable Care
Act- Public Law 111-148 :Subtitle D
ā¢ Patient-Centered Outcomes Research
ā« Comparative Clinical Effectiveness Research
ā¢ Defined Comparative Clinical Effectiveness Research
ā« The terms ācomparative clinical effectiveness
researchā and āresearchā mean research evaluating and
comparing health outcomes and the clinical
effectiveness, risks, and benefits of 2 or more medical
treatments, services..ā as describedā¦
16. Patient Protection and Affordable Care
Act- Public Law 111-148 :Subtitle D
ā¢ Medical treatments, services, and items described in
this subparagraph are health care interventions,
protocols for treatment, care management, and
delivery, procedures, medical devices, diagnostic
tools, pharmaceuticals (including drugs and
biologicals), integrative health practices, and any
other strategies or items being used in the
treatment, management, and diagnosis of, or
prevention of illness or injury in, individuals.
17. Patient Protection and Affordable Care
Act- thereās more
ā¢ Established Patient-Centered Outcomes Research
Institute (PCORI), a non-profit corporation with
duties including:
ā« Identifying national research priorities
ā« Establish a research agenda to address these priorities
ā« Carry out the research agenda (systematic reviews,
primary research, funding)
ā« Disseminate
18. SAFTINet Overview
ā¢ AHRQ ARRA OS: Recovery Act 2009: Scalable Distributed
Research Networks forComparative Effectiveness Research
(R01)
ā¢ Goal: enhance the capability and capacity of electronic
health networks designed for distributed research to conduct
prospective, comparative effectiveness research on
outcomes of clinical interventions.
ā¢ These distributed research network projects will:
ļ Build on and expand existing electronic health infrastructure
ļ Broad, scalable and sustainable systems
ļ Enable the collection of longitudinal and comprehensive data across
diverse healthcare delivery settings
ļ Evaluate effectiveness of clinical interventions for a diverse set of
clinical conditions.
19. $1.1 Billion -ARRA Allocations
ā¢ Research
ā¢ Data Infrastructure
ā¢ Dissemination and Adoption
ā¢ Administrative support,
inventory, evaluation
ā¢ $681 M (62%)
ā¢ $268 M (24%)
ā¢ $132 M (12%)
ā¢ $ 19 M ( 2%)
20. Federal Coordinating Council for Comparative
Effectiveness and Research (FCC)
ā¢ FCC-CER IOM ā¢ Data infrastructure ā¢ Dissemination and translation ā¢ Human
and scientific capital ā¢ Real-world settings for subpopulations, priority
conditions and interventions ā¢ 100 top priority CER topics ā 50% focus on
health care delivery systems ā Only three of the topics are narrowly focused on
drug vs. drug ā¢ Enhanced State Data for Analysis andTracking of Comparative
Effectiveness Impact: Improved ClinicalContent and Race-Ethnicity Data ā¢
Registry of Patient Registries
ā¢ Select examples of AHRQ funding ā¢ Electronic Data Methods (EDM) Forum
for Comparative Effectiveness Researchā¢ Enhanced Registries for Quality
Improvement and Comparative Effectiveness Research Select examples of OS
funding
21. AHRQ Support of Actionable Evidence
ā¢ 15 Evidence-based Practice Centers (EPCs),
ā¢ 13 Developing Evidence to Inform Decisions
about Effectiveness (DEcIDE) Network,
ā¢ 14 Centers for Education and Research on
Therapeutics (CERTs),
22. AHRQ Support of CER
ā¢ CER Methodology - 19 funded projects -****
ā¢ Laurer, Collins JAMA 2010:303;2182
23. Why CER
Physicians, health insurers, & patients need information
about the CE and safety of drugs, devices, therapies
and processes of care.
Non-randomized studies using data collected primarily
for care (or billing) can supplement the evidence of
RCT.
Improving the value of CER means improving: data
collection & use, data availability and access, CER
methodology (design, analysis) and reporting.
24. Cart before horse
ā¢ 1904 first radical prostectomy
ā¢ Jan 2010 1st US RCT active survelleince vs RP for
localized prostate Ca
ā¢ 100 years of action without evidence
25. Project Requirements
ā¢ Primary focus
ā« Develop an electronic health network that collects and
links data from multiple and different healthcare delivery
settings
ļ Capability for near-real time data extraction of de-identified
patient-level data, data analysis, and new data collection at
the POC
ā« Demonstrate capabilities for conducting methodologically
rigorous Comparative Effectiveness Research (CER)
ļ Capability for collecting HRQoL measures, other patient-
reported outcomes at the POC
26. Funded Projects
ā¢ Scalable Architecture for FederatedTherapeutic
Inquiries Network (SAFTINet)
ā« Lisa M. Schilling, University of Colorado Denver (R01
HS19908-01)
ā¢ SCANNER: Scalable National Network for Effectiveness
Research
ā« Lucila Ohno-Machado, University of California San Diego
(R01 HS19913-01)
ā¢ Scalable PArtnering Network for CER: Across Lifespan,
Conditions, and Settings
ā« John F. Steiner, Kaiser Foundation Research Institute (R01
HS19912-01)
27. SAFTINet Governance
AHRQProjectOfficer
Lisa Schilling, MD, MSPH
Principal Investigator
David West, PhD
Co-investigator
Project Oversight
Michael Kahn, MD, PhD
Co-investigator
DARTNet/SAFTINet Informatics
Cathy Bryan, RN, MHA
QED Clinical, Inc. d/b/a CINA
Julio Facelli, PhD
Univ of Utah Center for High
Performance Computing
SAFTINetTechnical Team
Wison Pace, MD
Co-investigator
DARTNet/SAFTINet Informatics
Art Davidson, MD, MSPH
Co-investigator
DH Informatics,
Medicaid Relationships
Marion Sills, MD, MPH
Co-investigator
CER, Cohort Development
SAFTINet Comparative
Effectiveness ResearchTeam
DebbieGraham, MSPH
AAFP/NRN
Partner Engagement Community
SAFTINet Partner Engagement
Community Group
Bethany Kwan, PhD, MSPH
Project Manager
28. Research Partnership and
LearningCommunity
ā¢ Specific Aim 1: Establish a broad, safety-net focused,
research partnership and learning community to govern
relationships, establish priorities, provide data quality
oversight, and evaluate the purpose and value of the
communityās effort that leverages the established
governance structure of DARTNet.
ā¢ Overall Goal: Create a trusted, valued multi-state
community of safety net stakeholders and researchers
to lead and participate in a learning community to
address evidence-gaps relevant to the safety net
populations ā with special emphasis upon those
populations served by Medicaid and State Child Health
Insurance Program (SCHIP).
29. Technology Development
ā¢ Specific Aim 2: Extend the DARTNet framework to
build, deploy and assess a safety-net focused
distributed research network which combines
ambulatory and inpatient clinical data and Medicaid
claims and eligibility data for clinical and research
purposes
ā¢ Overall Goal: Build the technology necessary to support
a valued, virtual organization that securely federates
clinical EHR and Medicaid/CHIP+ data, (consistent with
Medicaid agency efforts to develop Medicaid
InformationTechnologyArchitecture plans and systems)
to promote quality care and provide enhanced data for
comparative effectiveness research.
30. Comparative Effectiveness Research
ā¢ The conduct and synthesis of research comparing the
benefits and harms of different interventions and strategies
to prevent, diagnose, treat and monitor health conditions in
āreal worldā settings.
ā« Including delivery system strategies
ā¢ Specific Aim 3: Develop and enhance four sentinel cohort
pairs of patients with asthma (pediatric and adult),
hypertension, and hypercholesterolemia distinguished by
their care delivery characteristics which can support
comparative effectiveness research.
ā« System-level factors
ļ Patient-Centered Medical Home
ļ Integrated Mental Health care
ā« Enhanced data collection at point-of-care
31. Introductions
ā¢ BRIEF organizational descriptions, roles and
personnel
ā¢ Roll Call
ā¢ Investigator bios and full research strategy posted
on SharePoint site
32. Project team investigators
Partner Engagement Community TechnicalTeam Investigators
ā¢ Debbie Graham (AAFP)
ā¢ Jeanne Rozwadowski (DHHA)
ā¢ Lucy Savitz (IMH)
ā¢ Parinda Khatri (CHS)
ā¢ Heather Stocker (CCMCN)
ā¢ Bethany Kwan (UCD)
ā¢ Lisa Schilling (UCD)
ā¢ Michael Kahn (UCD)
ā¢ Wilson Pace (UCD)
ā¢ Julio Facelli (Utah)
ā¢ Cathy Bryan (CINA)
ā¢ Ron Price (Utah)
ā¢ Jim May (CINA)
ā¢ Art Davidson (DHHA)
ā¢ Nathan Hulse (IMH)
ā¢ Lisa Schilling (UCD)
33. Project team investigators
CERTeam Investigators
ā¢ Marion Sills (UCD)
ā¢ Elaine Morrato (UCD)
ā¢ Lisa Schilling (UCD)
ā¢ Karl Hammermeister (UCD)
ā¢ Monica Federico (UCD)
ā¢ Ben Miller (UCD)
ā¢ RobValuck (UCD)
ā¢ Diane Fairclough (UCD)
ā¢ Bethany Kwan (UCD)
ā¢ BarbaraYawn (consultant)
ā¢ Lucy Savitz (IMH)
ā¢ Brian Sauer (Utah)
SAFTINet CERTeam
CER
Methodology
Experts
Health Outcomes
Content Experts
Health Care
Delivery
System and
Process Experts
34. AmericanAcademy of Family Physicians (AAFP)
National Research Network (NRN)
ā¢ Personnel:
ā« DebbieGraham, MSPH, AAFP Site PI
ā« Elias Brandt, Research SystemsAnalyst
ā« Project Manager, to be hired
ā¢ Established in 1999 to conduct, support, promote, and
advocate for primary care research in practice-based
settings.
ā¢ Role in project:
ā« Coordination with CINA activities
ā« Partner EngagementCommunity leadership
35. QED Clinical, Inc. d/b/a CINA
ā¢ Personnel
ā« Cathy Bryan, MHA, BSN, RN, Chief ClinicalOfficer
ā« Jim May, MBA, Chief Executive Officer
ā« Project Manager, to be named
ā¢ CINA provides innovative technology solutions that support quality
focused, evidence-based health care.
ā¢ CINA technology can be used for discrete, validated data extraction
virtually real-time from ambulatory clinical records for research purposes.
ā¢ CINA also provides tools for Point of Care decision support, Population
reporting, and Disease Registries http://cina-us.com/
ā¢ Project Role
ā« Data extraction, standardization, reporting processes (Cherokee)
ā« Data aggregation across sources (Cherokee, Medicaid) and sharing with
SAFTINet , as applicable
ā« Contributing to technological development for scalable, distributed
networking
36. University of Utah
Center for High Performance Computing
(CHPC) and Biomedical Informatics (BMI)
ā¢ Personnel andTechnicalTeam:
ā« Julio Facelli, PhD, CHPC Director, BMIVice Chair, PI of UtahTeam
ā« Ron Price, Sr. Software Engineer/Architect and Project Manager
ā« Derick Huth, Jr. Software Engineer
ā« Jody Smith, DatabaseAdministrator
ā« Walter Scott, DatabaseAdministrator
ā« Steve Harper, SystemAdministrator
ā¢ Project role
ā« Build the Grid
37. Cherokee Health Systems, Inc.
ā¢ Personnel
ā« Parinda Khatri, PhD,CHS Director of Integrated Care, CHS Site PI
ā« Jeff Howard, CPA, CHS Chief FinancialOfficer
ā« Bob Franko, MBA, CHS training and marketing
ā« Monty Bryant, BS, Programmer/Analyst
ā« Jennifer Poling, MBA, DataAnalyst
ā¢ Cherokee Health Systems is a network of 20 clinical sites in 14
counties inTennessee, with strategic emphases on integration of
behavioral health and primary care, outreach to underserved
populations, and safety net preservation
(http://www.cherokeehealth.com )
ā¢ Project role
ā« Collaboration on technical and Partner EngagementCommunity
teams
ā« Supporting participatingCherokee practices for data sharing, point of
care data collection, and data use
38. Intermountain Healthcare
ā¢ Personnel
ā« Lucy Savitz, PhD, MBA, Director of Research and Education, Institute
for Health Care Delivery Research, Intermountain site PI
ā« Nathan Hulse, PhD, Intermountain informaticist
ā« Brian Sauer, PhD,CER methodology expert
ā« AmyWuthrich, MS, Project Manager
ā¢ Non-for-profit integrated health care delivery network of 24
hospitals, more than 130 outpatient clinics, a 1,000 member
employed physician group with 2,000+ affiliated physicians, and
associated care delivery support functions located in Utah and
southeastern Idaho.
ā¢ Project roles
ā« Collaboration on technical, CER, and Partner EngagementCommunity
teams
ā« Supporting participating Intermountain practices for data sharing,
point of care data collection, and data use
39. CCMCN/CACHIE
ā¢ Personnel
ā« JasonGreer, CACHIE Director
ā« DanTuteur, CCMCN Executive Director
ā« Heather Stocker, CCMCN Director of Clinical Programs & Development
ā¢ ColoradoCommunity ManagedCare Network (CCMCN)
ā« A non-profit Network of 15 Federally Qualified Health Centers (FQHCs)
providing primary health care services to the medically underserved
throughout Colorado.
ā¢ ColoradoAssociated Community Health Information Enterprise (CACHIE)
ā« Built and maintains a shared data warehouse on behalf of CCMCN health
centers
ā¢ Project Role
ā« Collaboration on technical and Partner EngagementCommunity teams
ā« Supporting two participatingColoradoCommunity Health Centers for
data sharing, point of care data collection, and data use
40. Denver Health & Hospital Authority
(DHHA)
ā¢ Personnel
ā« Art Davidson, MD, MSPH, SAFTINet Co-Investigator
ā« Jeanne Rozwadowski, MD, DHHA Site Co-investigator
ā« Dean McEwen, MS, Informatics
ā¢ Vertically integrated, public urban safety net health care
system
ā« Eight federally qualified community health centers, twelve
school-based clinics in the Denver public school system
ā¢ Project roles
ā« Collaboration on technical and Partner Engagement
Community teams
ā« Supporting participating DHHA FQHCs for data sharing,
point of care data collection, and data use
41. Project teams
ā¢ Partner Engagement Community
ā¢ Technical team
ā¢ Comparative effectiveness research team
42. Partner Engagement Community
ā¢ Mission
ā¢ Culture of collaboration
ā¢ Community-based participatory research
ā¢ Objectives
ā¢ Vehicle for communications between partners
ā¢ Decision making (e.g., POC data collection)
ā¢ Encouraging members to identify topics, bring value to stakeholders,
prioritize future CER questions
ā¢ LearningCommunity
ā¢ Membership
ā¢ Meet monthly ā 1st Wednesday at 12:00 MT/1:00CT/2:00 ET
ā¢ Listserv
43. TechnicalTeam Presentations
ā¢ Aims and objectives
ā¢ Process
ā« Technical requirements
ā¢ Milestones and timeline
ā« Build the grid
ā« Set up the nodes
ā« End of year 1 goal:Two entities with nodes on the grid
44. Informatics Objectives:
Starting with the End Objectives
ā¢ What we need to accomplish:
ļ A way for local participants to control what data are and are not
available for collaborative projects - what is āon the gridā
ļ A way to control who/what/where/when/why for all data access
ļ A way to ensure patient confidentiality
ļ A way to include patient-reported data
ļ A way to include State Medicaid data
ā¢ Not all of the technical details are completely
determined
ļ Some āgivensā; others open for negotiation
ļ Need to engage the various technical teams
45. EHR
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
ā¢ EHR: Electronic Health Record
46. CER/CDS
DM
Other
EHR
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
In SAFTINet participants, we have:
ā¢CINA CDR
ā¢Local data warehouse
ā¢ EHR: Electronic Health Record
ā¢ Other data sources include: claims, hospital, and third party databases
ā¢ CER/CDS DM: Comparative effectiveness research/Clinical decision
support data mart
48. PE
CER/CDS
DM
Other
EHR
Guideline
protocols
Patient
specific
report
Practice
provider
reports
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
Shared Data
w/ PHI
Shared Data
Encrypted
Developed and Supported by SAFTINet
ā¢ EHR: Electronic Health Record
ā¢ Other data sources include: claims, hospital, and third party databases
ā¢ CER/CDS DM: Comparative effectiveness research/Clinical decision
support data mart
ā¢ PE: Protocol Engine
49. PE
CER/CDS
DM
Other
EHR
Guideline
protocols
Patient
specific
report
Practice
provider
reports
ā¢ EHR: Electronic Health Record
ā¢ Other data sources include: claims, hospital, and third party databases
ā¢ CER/CDS DM: Comparative effectiveness research/Clinical decision
support data mart
ā¢ PE: Protocol Engine
ā¢ TRIAD: Translational Informatics and Data management
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
Shared Data
w/ PHI
Shared Data
Encrypted
Developed and Supported by SAFTINet
Saftinet
Portal
TRIAD
Node
Web Services
Queries and Data Transfers
50. CER/CDS
DM
Other
EHR
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
Shared Data
w/ PHI
Shared Data
Encrypted
Developed and Supported by SAFTINet
Saftinet
Portal
TRIAD
Node
Web Services
Queries and Data Transfers
The Trickier Bitsā¦..
Medicaid
Data
? ?
?
Uni-directional
Or Bi-directional?
51. CER/CDS
DM
Other
EHR
Color code:
ā¢ Blue = A given
ā¢Yellow = Optional
ā¢Red = Still in analysis
Shared Data
w/ PHI
Shared Data
Encrypted
Developed and Supported by SAFTINet
Saftinet
Portal
TRIAD
Node
Web Services
Queries and Data Transfers
The Trickier Bitsā¦..
Medicaid
Data
? ?
?
Patient
Reported
Data
?
?
52. Whatās been happening
ā¢ Creating the use cases
ā« What types of actions do we need to support?
ļ Types of questions to be answered
ļ Types of security and access controls
ā¢ Use cases drives data elements and database
ā« What do we need to extract from each CER/CDS?
ā¢ Pilot implementations ofTRIAD technology
ā« Kicking the technology tires with large data sets to
discover the warts and āgotchasā
53. Whatās next?
ā¢ Engage all of the technical contacts
ā« Share use cases to understand data availability and
gaps
ā« Discuss how best to develop data extracts
ā« Develop data quality procedures
ā« Develop technology deployment and support plans
ļ Including validation, acceptance, and training
55. Comparative Effectiveness Research
ā¢ The conduct and synthesis of research comparing
the benefits and harms of different interventions
and strategies to prevent, diagnose, treat and
monitor health conditions in āreal worldā settings.
ā« Including delivery system strategies
56. CER Aim
ā¢ Specific Aim Related to CER (Aim 3): Develop and
enhance four sentinel cohort pairs of patients with
asthma (pediatric and adult), hypertension, and
hypercholesterolemia distinguished by their care
delivery characteristics which can support
comparative effectiveness research.
57. CER Goals
ā¢ Demonstrate the capability of SAFTINet to collect
and accurately link patient-level data necessary for
CER of delivery systems
ā¢ Lay the groundwork to conduct prospective
observational studies and clinical trials
58. CER Hypothesis
ā¢ Health care delivery system factors, such as the
patient-centered medical homeā¦
PROCESSES OF
CARE
(clinician factors)
+
STRUCTURES OF
CARE
(system factors)
+ PATIENT FACTORS ā
OUTCOMES
(chronic disease
control)
59. CER Hypothesis
ā¢ Health care delivery system factors, such as the
patient-centered medical home,
PROCESSES OF
CARE
(clinician factors)
+
STRUCTURES OF
CARE
(system factors)
+ PATIENT FACTORS ā
OUTCOMES
(chronic disease
control)
outweigh clinician factors, patient factors, and
medication effectivenessā¦
60. CER Hypothesis
ā¢ Health care delivery system factors, such as the
patient-centered medical home,
PROCESSES OF
CARE
(clinician factors)
+
STRUCTURES OF
CARE
(system factors)
+ PATIENT FACTORS ā
OUTCOMES
(chronic disease
control)
outweigh clinician factors, patient factors, and
medication effectiveness in the control of asthma,
high blood pressure and hypercholesterolemia.
61. CER Conceptual Model
Relatively
Mutable
Clinical inertia
Counseling
Drug selection
Dosage selection
Concomitant meds
Follow-up
Decision support
Patient-Centered
Medical Home
Integrated Mental
Health Care
Disease-specific case
management
Access to care
Outcomes feedback
Therapy adherence
Therapy persistence
Mental health status
Health knowledge
Perceived need for
care
Symptoms
Drug side effects
Patient-centered
outcomes
Health-related
quality of life
Clinical outcomes
Process
PROCESSES OF
CARE
(clinician
factors)
+
STRUCTURES OF
CARE
(system factors)
+ PATIENT FACTORS ā OUTCOMES
Relatively
immutable
Appointment time
Patient load
Physical facilities
Practice type
Support personnel
Generalist vs.
specialist
Age
Gender
Race/ethnicity
SES
Marital status
Religious/cultural
beliefs
Comorbidity
63. CER Process:TheTeam
Marion Sills
Co-investigator
SAFTINet Comparative
Effectiveness Research
Team
Measures experts
Cohort experts
Brian Sauer
Diane Fairclough
RobValuck
Elaine Morrato
PCMH:
Lisa Schilling
IMHC:
Ben Miller
Pediatric asthma:
Monica Federico
Adult asthma:
BarbaraYawn
HTN,
Hypercholesterolemia:
Karl Hammermeister
CER methods experts
64. CER Process: Sources of Data
ā¢ Electronic health records
ā¢ Medicaid claims
ā¢ Enhanced point-of-care data collection
ā¢ Organizational or practice-level survey
CER/CDS
DM
Other
EHR
Medicaid
Data
Org.
Survey
Patient
Reported
Data
CER/CDS DM:
Comparative
effectiveness
research/Clinical
decision support
data mart
65. CER Process
ā¢ Establish data dictionary
ā¢ Develop CER-specific technology use cases
ā¢ Review data profiling and quality reports to improve
data quality (ongoing)
ā¢ Analytical plan
66. CER Process
ā¢ Establish data dictionary
ā¢ Develop CER-specific technology use cases
ā¢ Review data profiling and quality reports to improve
data quality (ongoing)
ā¢ Analytical plan
68. CER Process
ā¢ Hypothesis generation
ā¢ Cohort identification
ā« Clinical/demographic parameters
ā« Eligibility
CER/CDS
DM
EHR
Medicaid
Data
69. CER Process
ā¢ Hypothesis generation
ā¢ Cohort identification
ā¢ Measures
ā« Outcome measures
ā« Explanatory measures
ā« Covariates
PROCESSES OF
CARE
(clinician factors)
+
STRUCTURES OF
CARE
(system factors)
+ PATIENT FACTORS ā
OUTCOMES
(chronic disease
control)
CER/CDS
DM
EHR
Medicaid
Data
70. CER Process
ā¢ Hypothesis generation
ā¢ Cohort identification
ā¢ Measures
ā¢ Enhanced data collection
ā« Patient-reported outcomes
ā« Quality of life
ā« PCMH
ā« IMHC
CER/CDS
DM
Org.
Survey
Patient
Reported
Data
71. CER Process
ā¢ Hypothesis generation
ā¢ Cohort identification
ā¢ Measures
ā¢ Enhanced data collection
CER/CDS
DM
Other
EHR
Medicaid
Data
Org.
Survey
Patient
Reported
Data
72. CER Process
ā¢ Establish data dictionary
ā¢ Develop CER-specific technology use cases
ā¢ Review data profiling and quality reports to improve
data quality (ongoing)
ā¢ Analytical plan
74. Getting Started
ā¢ Subcontracts
ā¢ SharePoint
ā« Shared Documents
ā« Calendar
ā« Announcements
ā« Discussions
ā¢ Upcoming events and meetings
ā« Partner Engagement Community (Scheduling in progress)
ā« Technical team (Thursdays @ 2pm MT starting 12/16)
ā« CER team (Mondays @ 1:30pm MT)
ā¢ Quarterly SAFTINet Update meetings
75. Wrap-Up
āKnowing is not enough; we must apply.Willing is not
enough; we must do.ā āGoethe
Questions/Comments --
Editor's Notes
Bethany will bring meeting to order, welcome everyone and summarize the goals and objectives of the meeting, and briefly review the agenda
The series collectively characterizes the key elements of a healthcare system that is designed to
generate and apply the best evidence for healthcare choices of patients and providers. A related purpose of these meetings is the identification and
engagement of barriers to the development of the learning healthcare system and the key opportunities for progress.
Copyright National Academy of Sciences. All rights reserved.
This summary plus thousands more available at http://www.nap.edu
Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches: Workshop Summary
http://books.nap.edu/catalog/12197.html
SUMMARY
The series collectively characterizes the key elements of a healthcare system that is designed to
generate and apply the best evidence for healthcare choices of patients and providers. A related purpose of these meetings is the identification and
engagement of barriers to the development of the learning healthcare system and the key opportunities for progress.
Copyright National Academy of Sciences. All rights reserved.
This summary plus thousands more available at http://www.nap.edu
Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches: Workshop Summary
http://books.nap.edu/catalog/12197.html
SUMMARY
In addition to this: prohibiting health insurers from denying coverage or refusing claims based on pre-existing conditions, expanding Medicaid eligibility, subsidizing insurance premiums, providing incentives for businesses to provide health care benefits, establishing health insurance exchanges, and support for medical research
From the Congressional Record: http://thomas.loc.gov/cgi-bin/query/R?r111:FLD001:S13891
The Public Law: http://www.gao.gov/hcac/pcor_sec_6301.pdf
Definition: Research evaluating and comparing health outcomes and the clinical effectiveness, risks, and benefits of 2 or more medical treatments, services, and items such as health care interventions, protocols for treatment, care management, and delivery, procedures, medical devices, diagnostic tools, pharmaceuticals (including drugs and biologicals), integrative health practices, and any other strategies or items being used in the treatment, management, and diagnosis of, or prevention of illness or injury in, individuals.
PCORI: The purpose of the Institute is to assist patients, clinicians, purchasers, and policy-makers in making informed health decisions by advancing the quality and relevance of evidence concerning the manner in which diseases, disorders, and other health conditions can effectively and appropriately be prevented, diagnosed, treated, monitored, and managed through research and evidence synthesis that considers variations in patient subpopulations, and the dissemination of research findings with respect to the relative health outcomes, clinical effectiveness, and appropriateness
of the medical treatments, services, and items described in subsection (a)(2)(B).
Providing context for SAFTINet
Why was this funded? Who funded it? What were the goals of the RFA?
What are the long-term goals?
What other projects are there?
Project descriptions will be provided here:
The applications must provide adequate details of a governance plan for project oversight, especially on issues related to data linkage, access, and privacy and confidentiality of patient information.Ā In addition, the governance plan should describe provisions for oversight and responsibility concerning operational issues and scientific and technical concerns related to study design, implementation and data analysis.Ā The plan must address potential solutions to barriers raised by organizational, business or other considerations that will impede collaboration and sharing of data between the partner organizations of the network.Ā Provisions to review and manage the conflict of interest of investigators and center personnel on an ongoing and regular basis must be included.Ā The governance plan must also describe provisions for obtaining input and feedback concerning important aspects of the design of data collection infrastructure as well as on the comparative research undertaken.Ā End-user feedback will be important in designing the infrastructure so that the data are efficiently collected and clinical workflow is minimally impacted.Ā The patientās perspective must also be represented in the governance plan.
Specific Aim 2.1 Implement and deploy data Extraction, Transformation and Loading (ETL) processes with terminology standardization; patient data capture tools; data de-identification, and use of the Translational Informatics and Data Management Grid (TRIAD) technology and research portal.
Specific Aim 2.2 Share state Medicaid entitiesā claims and eligibility data with their respective clinical partners via secure File Transfer Protocol (sFTP). Implement local data de-duplication using local Protected Health Information (PHI) data elements
Specific Aim 2.3 Implement TRIAD nodes for Medicare participants. Establish prototype grid-based identity management solution using hashed identifiers. Deploy production SAFTINet federated grid nodes and TRIAD portal
The purpose of CER is to improve health outcomes by developing and disseminating evidence-based information to patients, clinicians, and other decision-makers, responding to their expressed needs, about which interventions are most effective for which patients under specific circumstances.
What is the best way to explain the relationships among these groups? Want people to see the interdependency, the collaborative nature, and the sense of community and mutual goals.
Briefly introduce
Investigator list, role and expertise
Team leaders to introduce themselves
There are additional members of these teams as well, just not āinvestigatorsā.
The American Academy of Family Physicians National Research Network (AAFP NRN) is housed within the Division of Scientific Activities at the American Academy of Family Physicians (AAFP).
The AAFP, founded in 1947, is the largest medical specialty society devoted solely to primary care and the third largest medical professional organization in the United States.
The 94,000 members of the AAFP provide comprehensive, coordinated, and continuing care to all members of the family and serve as the patientās advocate in the changing healthcare system.
Cherokee Health operates the NextGen EHR to which CINA has proven interfaces. Cherokee Health anticipates using the CINA decision support reports for clinical care improvement. Cherokee also participates in their local health information exchange and we will explore how linkages with this system can enhance data from hospitals, emergency departments and other clinical settings.
Intermountain Healthcare is a non-for-profit integrated health care delivery network of 21 hospitals, more than 70 outpatient clinics, a 500 member employed physician group, and associated care delivery support functions (e.g., durable medical equipment, group supply purchasing) located in Utah and southeastern Idaho. Intermountainās facilities range from major adult and pediatric tertiary-level teaching/research facilities, through large urban collector hospitals, to a series of small hospital/clinics that are the only source of care in many rural Utah communities. Intermountain Healthcare uses Electronic Medical Records extensively and collects clinical data into a group of encounter-based, Hospital Information Systems (the HELP Systems), a longitudinal Clinical Data Repository (CDR), and an Enterprise Data Warehouse (EDW). Intermountain has an inpatient clinical information system and an electronic longitudinal medical record, the Clinical Data Repository (CDR).
Intermountain Health (IMH) represents a third approach to participating in SAFTINet. IMH has a well-developed, integrated electronic health record that supports clinical decision support and includes robust reporting systems. Codified clinical data is standardized using a central data dictionary. We will work with the IMH information services group to create an ETL function specific to their system to directly create the CCD-PHI data structure in Figure 2. We will initially crosswalk (where necessary) the required data elements for this project between the IMH data dictionary and the SAFTINet/DARTNet data dictionary. Given that both systems can link to and use established data standards, such as SNOMED CT, we expect that this will prove to be straightforward. The SAFTINet CCD-PHI will be created within the IMH taxonomy server. The creation of the CCD-NoPHI using the one-way hash function could be performed within the IMH system or created within a SAFTINet-provided stand-alone system and then sent to the local TRIAD server node.
CCMCN-CACHIE presents a different approach to participating in SAFTINet. CACHIE has an existing clinical data warehouse with demographic and clinical data elements; laboratory results standardized to LOINC codes; and medications used for most common conditions coded with NDC, AFHS, and HIC3 codes. CCMCN-CACHIE will use the CINA solution to complete the transformation, de-identification and loading activities to a local TRIAD server. The CACHIE data warehouse already contains most required data elements; any new data elements will be added through their current data warehouse vendor (Plurasoft, Inc). Since the CACHIE data warehouse pulls data from multiple EMRs, linking CINA to CACHIE provides a single point for obtaining data from multiple clinical sites with disparate EMRs. In addition, since CACHIE performs harmonization to standards, the effort to create a CCD is markedly reduced.
The Colorado Associated Community Health Information Exchange (CACHIE) is a collaborative project of Colorado Community Health Network (CCHN) and Colorado Community Managed Care Network (CCMCN).Ā With funding from the Health Resources and Services Administration (HRSA), the Agency for Healthcare Research and Quality (AHRQ), The Colorado Health Foundation (TCHF) and member investment dues, Colorado community health centers (CHCs) are joining forces to create a shared information technology system to support quality reporting, analysis, and improvement.
Denver Health (DH) is an integrated, public urban safety net health care system that provides services to 25% of all Denver residents, or approximately 150,000 individuals, including 35% of Denverās children and a large proportion of the indigent, vulnerable and minority populations in the city and county of Denver. DH is the major provider for the uninsured and Medicaid patients in the state of Colorado. Among DH patients, 65% are below 185% of the federal poverty level. Over 50% of DH patients are uninsured, and over 70% of DH patients represent racial and ethnic minorities. In 2007, the D
, H system had over 143,000 patients with over 617,000 outpatient visits.
Ā
The fourth approach to participating in SAFTINet will be developed by DHHA Public Health Informatics and eHealth Services groups. A comprehensive clinical and administrative (e.g., demographic, laboratory, radiologic, medication, diagnostic, charges, utilization, plus others) data warehouse already exists across Denver Health (DH) entities. The specification for the SAFTINet data repository will drive development of a project-specific data mart for this research effort. Data elements will be defined and mapped to specific fields and tables in the DH warehouse. Data will be normalized to the determined vocabulary code set. DH has used SNOMED-CT for other projects. Like IMH, the DHHA-created data mart will contain PHI elements. The application of the one-way hash function to PHI data elements can occur within the DHHA data mart or could be performed by a stand-alone system. The CCD-nonPHI data structure is then sent to the local TRIAD server node.
Development of agenda/topics for discussion
For this proposal, we will implement trial SAFTINet nodes using the grid technologies developed as part of the NCI caBIG (cancer biomedical informatics grid - https://cabig.nci.nih.gov) program, called caGrid. The caGrid software is open source grid software infrastructure aimed at enabling multi-institutional data sharing and analysis. caGrid supports a wide range of use cases in basic, translational, and clinical research. Key innovations in caGrid support large scale, secure, semantically meaningful data sharing and analysis.
The CER definition has already been reviewed by Lisa. The purpose of CER is to improve health outcomes by developing and disseminating evidence-based information to patients, clinicians, and other decision-makers, responding to their expressed needs, about which interventions are most effective for which patients under specific circumstances.
The grantās 3rd specific aim presents the CER-related goals, which include development of four cohorts, and demonstrating the feasibility, within these cohorts, of performing CER on delivery system characteristics.
The hypothesis related to this first goal, performing CER of delivery systems, states that health care deliver system factors, such as the patient-centered medical home
outweigh clinician factors, patient factors, and medication effectiveness
in the control of asthma, high blood pressure and hypercholesterolemia.
The conceptual model underlying this hypothesis is shown here. This delineates some of the Outcome measures, Explanatory measures, and Covariates for the CER analyses.
I will now give an overview of the CER process for SAFTINet.
The CER team includes cohort experts for each of the 4 cohorts, CER methods experts, and measures experts for the delivery systems measures. The team meets weekly on Monday afternoons.
The CER analyses will draw on several sources of data in SAFTINet. These include EHR, Medicaid, data collected at the POC, and organizational or practice-level survey data. Borrowing Michaelās graphics, the data sources are shown at the bottom of this slide, in primary colors.
The general steps involved in the CER process include establishing a data dictionary, developing CER-specific use cases, reviewing the quality of data, and implementing the CER analytic plan
The current focus of CER team activity is establishing a data dictionary.
The process for developing the data dictionary involves first generating the specific hypotheses for each cohort. These hypotheses supplement the primary hypothesis mentioned previously. WRT hypothesis generation, we will also involve the Partner Engagement Community to identify priority hypotheses and research questions amenable to OCER within the 4 cohorts.
For each cohort, the CER team then develops a cohort definition, using variables available in the existing data sources. This includes both clinical and demographic parameters as well as eligibility factors.
For each cohort, the CER team then develops measures necessary for testing each study hypothesis, using variables available in the existing data sources. Data elements necessary for hypothesis testing, including those for cohort identification and measure definition, are being compiled through this process to convey in dictionary format to the Technical Team.
After conveying to the Technical Team the data elements requested from the EHR and claims data, the next phase involves developing methods for enhanced data collection, including patient-level data such as patient-reported outcomes and QOL, and organization-level information, such as PCMH and IMH. This process will involve input from the Partner Engagement Community.
Adding enhanced data to the data elements required for building the cohorts and testing the hypotheses, the CER team will then broaden the data dictionary of necessary elements for the CER analyses.
Future steps involved in the CER process include developing CER-specific use cases, reviewing the quality of data, and implementing the CER analytic plan. As with the current phase, each part of the CER process involves collaboration with the Technical Team and the Partner Engagement Community.