Safti net kick off 12092010-mrs
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Transcript of Safti net kick off 12092010-mrs
SAFTINet Kick-OffFriday, December 10, 2010
Welcome!•Thank you for attending!•Goals and objectives▫Outlining project vision and aims▫Meeting SAFTINet collaborators▫Starting the process▫Clarifying concerns and questions
•Agenda•Meeting materials▫Research strategy▫SAFTINet commonly used acronyms
Agenda Agenda Item Time Presenter
Welcome and agendaSAFTINet Context and Overview
10 mins20 mins
Bethany KwanLisa Schilling
Introductions and Roll CallProject teams and investigatorsAAFPCINAUniversity of Utah CHPCDHHACherokee Health SystemsIntermountain HealthcareCCMCN/CACHIE
10 mins Bethany Kwan
Comparative Effectiveness Research
10 mins Marion Sills
Partner Engagement Community 10 mins Debbie GrahamTechnical Team Presentation 10 mins Michael KahnGetting started 5 mins Bethany KwanWrap-up and Questions 15 mins Lisa Schilling
IOM Roundtable on Value & Science-Driven Health Care•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
•Learning Healthcare System series
IOM Roundtable on Value & Science-Driven Health Care• The Learning Healthcare System (2006)• Judging the Evidence: Standards for Determining Clinical
Effectiveness (2007)• Leadership Commitments to Improve Value in Healthcare: Toward
Common Ground (2007)• Redesigning the Clinical Effectiveness Research Paradigm:
Innovation and Practice-Based Approaches (2007)• Clinical Data as the Basic Staple of Health Learning: Creating and
Protecting a Public Good (2008)• Engineering a Learning Healthcare System: A Look to the Future
(2008)• Learning What Works: Infrastructure Required for Learning Which
Care Is Best (2008)• Value in Health Care: Accounting for Cost, Quality, Safety,
Outcomes and Innovation (2008)
Comparative Effectiveness Milestones• 2003 - MMA Section 1013 authorizes AHRQ to
conduct and support research with a focus on “outcomes, comparative clinical effectiveness, and appropriateness of health care items and services (including prescription drugs)”
• 2007- IOM Report - Learning What Works Best: The Nation’s Need for Evidence on Comparative Effectiveness In Health Care
• 2009 - ARRA provides $1.1 Billion to NIH/HHS/AHRQ
• 2010 - Patient Protection and Affordable Care Act
• Need for substantially improved understanding of the comparative clinical effectiveness of healthcare interventions.
• Strengths of the randomized controlled trial muted by constraints in time, cost, and limited applicability.
• Opportunities presented by the size and expansion of potentially interoperable administrative and clinical datasets.
• Opportunities presented by innovative study designs and statistical tools.
• Need for innovative approaches leading to a more practical and reliable clinical research paradigm.
• Need to build a system in which clinical effectiveness research is a more natural by-product of the care process.
Redesigning the Clinical Effectiveness Research Paradigm• Address current limitations in applicability of research
results• Counter inefficiencies in timeliness, costs, and volume• Define a more strategic use to the clinical experimental
model• Provide stimulus to new research designs, tools, and
analytics• Encourage innovation in clinical effectiveness research
conduct• Promote the notion of effectiveness research as a routine
part of practice• Improve access and use of clinical data as a knowledge
resource• Foster the transformational research potential of
information technology• Engage patients as full partners in the learning culture• Build toward continuous learning in all aspects of care
• 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.
•Geisinger example of the power of proper EHR use
• (pg 28 – Redesigning)
Why distributed?•Minimize security risks by allowing the
data repositories of multiple parties to remain separately owned and controlled.
•These models also provide an interface to these stores of highly useful data that allows them to function as a large combined dataset.
•Patient preferences and perspectives. What approaches might help
• to refine practical instruments to determine patient preferences—
•such as NIH’s PROMIS (Patient-Reported Outcomes Measurement
• Information System)—and apply them as central elements of outcome
•measurement?
Evidence gaps
Patient Protection and Affordable Care Act- Public Law 111-148 :Subtitle D•Patient-Centered Outcomes Research▫Comparative Clinical Effectiveness Research
•Defined Comparative Clinical Effectiveness Research▫The terms ‘comparative clinical
effectiveness research’ and ‘research’ mean research evaluating and comparing health outcomes and the clinical effectiveness, risks, and benefits of 2 or more medical treatments, services..” as described…
Patient Protection and Affordable Care Act- Public Law 111-148 :Subtitle D•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.
Patient Protection and Affordable Care Act- there’s more•Established Patient-Centered Outcomes
Research Institute (PCORI), a non-profit corporation with duties including:▫Identifying national research priorities▫Establish a research agenda to address
these priorities▫Carry out the research agenda (systematic
reviews, primary research, funding)▫Disseminate
SAFTINet Overview• AHRQ ARRA OS: Recovery Act 2009: Scalable
Distributed Research Networks for Comparative Effectiveness Research (R01)
• 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.
• These distributed research network projects will: Build on and expand existing electronic health
infrastructure Broad, scalable and sustainable systems Enable the collection of longitudinal and comprehensive
data across diverse healthcare delivery settings Evaluate effectiveness of clinical interventions for a
diverse set of clinical conditions.
$1.1 Billion -ARRA Allocations• Research • Data Infrastructure• Dissemination and
Adoption• Administrative support,
inventory, evaluation
• $681 M (62%)• $268 M (24%)• $132 M (12%)• $ 19 M ( 2%)
Federal Coordinating Council for Comparative Effectiveness and Research (FCC)• FCC-CER IOM • Data infrastructure • Dissemination and
translation • Human and scientific capital • Real-world settings for subpopulations, priority conditions and interventions • 100 top priority CER topics – 50% focus on health care delivery systems – Only three of the topics are narrowly focused on drug vs. drug • Enhanced State Data for Analysis and Tracking of Comparative Effectiveness Impact: Improved Clinical Content and Race-Ethnicity Data • Registry of Patient Registries
• Select examples of AHRQ funding • Electronic Data Methods (EDM) Forum for Comparative Effectiveness Research• Enhanced Registries for Quality Improvement and Comparative Effectiveness Research Select examples of OS funding
AHRQ Support of Actionable Evidence•15 Evidence-based Practice Centers
(EPCs), •13 Developing Evidence to Inform
Decisions about Effectiveness (DEcIDE) Network,
•14 Centers for Education and Research on Therapeutics (CERTs),
AHRQ Support of CER•CER Methodology - 19 funded projects -
****•Laurer, Collins JAMA 2010:303;2182
Why CERPhysicians, 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.
Cart before horse•1904 first radical prostectomy• Jan 2010 1st US RCT active survelleince vs
RP for localized prostate Ca•100 years of action without evidence
Project Requirements• Primary focus▫Develop an electronic health network that collects
and links data from multiple and different healthcare delivery settings Capability for near-real time data extraction of de-
identified patient-level data, data analysis, and new data collection at the POC
▫Demonstrate capabilities for conducting methodologically rigorous Comparative Effectiveness Research (CER) Capability for collecting HRQoL measures, other
patient-reported outcomes at the POC
Funded Projects•Scalable Architecture for Federated
Therapeutic Inquiries Network (SAFTINet)▫Lisa M. Schilling, University of Colorado Denver
(R01 HS19908-01)•SCANNER: Scalable National Network for
Effectiveness Research▫Lucila Ohno-Machado, University of California
San Diego (R01 HS19913-01)•Scalable PArtnering Network for CER: Across
Lifespan, Conditions, and Settings▫ John F. Steiner, Kaiser Foundation Research
Institute (R01 HS19912-01)
SAFTINet GovernanceAHRQ Project O
fficer
Lisa Schilling, MD, MSPH
Principal Investigator
David West, PhDCo-investigator
Project Oversight
Michael Kahn, MD, PhDCo-investigator
DARTNet/SAFTINet Informatics
Cathy Bryan, RN, MHAQED Clinical, Inc. d/b/a CINA
Julio Facelli, PhDUniv of Utah Center for High
Performance Computing
SAFTINet Technical TeamWison Pace, MDCo-investigator
DARTNet/SAFTINet Informatics
Art Davidson, MD, MSPHCo-investigatorDH Informatics,
Medicaid Relationships
Marion Sills, MD, MPHCo-investigator
CER, Cohort Development
SAFTINet Comparative Effectiveness Research
Team
Debbie Graham, MSPHAAFP/NRN
Partner Engagement Community
SAFTINet Partner Engagement Community
Group
Bethany Kwan, PhD, MSPH Project Manager
Research Partnership and Learning Community•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 community’s effort that leverages the established governance structure of DARTNet.
•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 – with special emphasis upon those populations served by Medicaid and State Child Health Insurance Program (SCHIP).
Technology Development•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
•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 Information Technology Architecture plans and systems) to promote quality care and provide enhanced data for comparative effectiveness research.
Comparative Effectiveness Research• 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 “real world” settings. ▫ Including delivery system strategies
• 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. ▫ System-level factors
Patient-Centered Medical Home Integrated Mental Health care
▫ Enhanced data collection at point-of-care
Introductions•BRIEF organizational descriptions, roles
and personnel•Roll Call• Investigator bios and full research strategy
posted on SharePoint site
Project team investigatorsPartner Engagement Community
Technical Team Investigators
• Debbie Graham (AAFP)• Jeanne Rozwadowski
(DHHA)• Lucy Savitz (IMH)• Parinda Khatri (CHS)• Heather Stocker (CCMCN)• Bethany Kwan (UCD)• Lisa Schilling (UCD)
• Michael Kahn (UCD)• Wilson Pace (UCD)• Julio Facelli (Utah)• Cathy Bryan (CINA)• Ron Price (Utah)• Jim May (CINA)• Art Davidson (DHHA)• Nathan Hulse (IMH)• Lisa Schilling (UCD)
Project team investigatorsCER Team Investigators
• Marion Sills (UCD)• Elaine Morrato (UCD)• Lisa Schilling (UCD)• Karl Hammermeister (UCD)• Monica Federico (UCD)• Ben Miller (UCD)• Rob Valuck (UCD)• Diane Fairclough (UCD)• Bethany Kwan (UCD)• Barbara Yawn (consultant)• Lucy Savitz (IMH)• Brian Sauer (Utah)
SAFTINet CER Team
CER Methodology Experts
Health Outcomes Content Experts
Health Care Delivery
System and Process Experts
American Academy of Family Physicians (AAFP) National Research Network (NRN)•Personnel:▫Debbie Graham, MSPH, AAFP Site PI▫Elias Brandt, Research Systems Analyst▫Project Manager, to be hired
•Established in 1999 to conduct, support, promote, and advocate for primary care research in practice-based settings.
•Role in project:▫Coordination with CINA activities▫Partner Engagement Community leadership
QED Clinical, Inc. d/b/a CINA• Personnel▫ Cathy Bryan, MHA, BSN, RN, Chief Clinical Officer ▫ Jim May, MBA, Chief Executive Officer ▫ Project Manager, to be named
• CINA provides innovative technology solutions that support quality focused, evidence-based health care.
• CINA technology can be used for discrete, validated data extraction virtually real-time from ambulatory clinical records for research purposes.
• CINA also provides tools for Point of Care decision support, Population reporting, and Disease Registries http://cina-us.com/
• Project Role▫ Data extraction, standardization, reporting processes (Cherokee)▫ Data aggregation across sources (Cherokee, Medicaid) and sharing with SAFTINet
, as applicable▫ Contributing to technological development for scalable, distributed networking
University of Utah Center for High Performance Computing (CHPC) and Biomedical Informatics (BMI)•Personnel and Technical Team:▫ Julio Facelli, PhD, CHPC Director, BMI Vice Chair, PI of
Utah Team▫Ron Price, Sr. Software Engineer/Architect and Project
Manager▫Derick Huth, Jr. Software Engineer ▫ Jody Smith, Database Administrator▫Walter Scott, Database Administrator▫Steve Harper, System Administrator
•Project role▫Build the Grid
Cherokee Health Systems, Inc.• Personnel▫ Parinda Khatri, PhD, CHS Director of Integrated Care, CHS Site PI▫ Jeff Howard, CPA, CHS Chief Financial Officer▫ Bob Franko, MBA, CHS training and marketing▫ Monty Bryant, BS, Programmer/Analyst▫ Jennifer Poling, MBA, Data Analyst
• Cherokee Health Systems is a network of 20 clinical sites in 14 counties in Tennessee, with strategic emphases on integration of behavioral health and primary care, outreach to underserved populations, and safety net preservation (http://www.cherokeehealth.com )
• Project role▫ Collaboration on technical and Partner Engagement Community
teams▫ Supporting participating Cherokee practices for data sharing, point
of care data collection, and data use
Intermountain Healthcare• Personnel▫ Lucy Savitz, PhD, MBA, Director of Research and Education,
Institute for Health Care Delivery Research, Intermountain site PI▫ Nathan Hulse, PhD, Intermountain informaticist▫ Brian Sauer, PhD, CER methodology expert▫ Amy Wuthrich, MS, Project Manager
• 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.
• Project roles▫ Collaboration on technical, CER, and Partner Engagement
Community teams▫ Supporting participating Intermountain practices for data
sharing, point of care data collection, and data use
CCMCN/CACHIE• Personnel▫ Jason Greer, CACHIE Director▫ Dan Tuteur, CCMCN Executive Director▫ Heather Stocker, CCMCN Director of Clinical Programs &
Development• Colorado Community Managed Care Network (CCMCN)▫ A non-profit Network of 15 Federally Qualified Health
Centers (FQHCs) providing primary health care services to the medically underserved throughout Colorado.
• Colorado Associated Community Health Information Enterprise (CACHIE)▫ Built and maintains a shared data warehouse on behalf of
CCMCN health centers• Project Role▫ Collaboration on technical and Partner Engagement
Community teams▫ Supporting two participating Colorado Community Health
Centers for data sharing, point of care data collection, and data use
Denver Health & Hospital Authority (DHHA)• Personnel▫Art Davidson, MD, MSPH, SAFTINet Co-Investigator▫ Jeanne Rozwadowski, MD, DHHA Site Co-investigator▫Dean McEwen, MS, Informatics
•Vertically integrated, public urban safety net health care system▫Eight federally qualified community health centers, twelve
school-based clinics in the Denver public school system• Project roles ▫Collaboration on technical and Partner Engagement
Community teams▫Supporting participating DHHA FQHCs for data sharing,
point of care data collection, and data use
Project teams•Partner Engagement Community•Technical team•Comparative effectiveness research team
Partner Engagement Community• Mission
• Culture of collaboration • Community-based participatory research
• Objectives• Vehicle for communications between partners• Decision making (e.g., POC data collection)• Encouraging members to identify topics, bring value to
stakeholders, prioritize future CER questions• Learning Community
• Membership• Meet monthly – 1st Wednesday at 12:00 MT/1:00 CT/2:00 ET• Listserv
Technical Team Presentations•Aims and objectives•Process▫Technical requirements
•Milestones and timeline▫Build the grid▫Set up the nodes▫End of year 1 goal: Two entities with nodes
on the grid
Informatics Objectives: Starting with the End Objectives•What we need to accomplish:
A way for local participants to control what data are and are not available for collaborative projects - what is “on the grid”
A way to control who/what/where/when/why for all data access
A way to ensure patient confidentiality A way to include patient-reported data A way to include State Medicaid data
•Not all of the technical details are completely determined
Some “givens”; others open for negotiation Need to engage the various technical teams
EHR
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
• EHR: Electronic Health Record
CER/CDSDM
Other
EHR
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
In SAFTINet participants, we have:• CINA CDR• Local data warehouse
• EHR: Electronic Health Record• Other data sources include: claims, hospital, and third party databases• CER/CDS DM: Comparative effectiveness research/Clinical decision support data mart
PE
CER/CDSDM
Other
EHR
Guidelineprotocols
Patient specific report
Practice provider reports
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
• EHR: Electronic Health Record• Other data sources include: claims, hospital, and third party databases• CER/CDS DM: Comparative effectiveness research/Clinical decision support data mart • PE: Protocol Engine
PE
CER/CDSDM
Other
EHR
Guidelineprotocols
Patient specific report
Practice provider reports
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
Shared Dataw/ PHI
Shared DataEncrypted
Developed and Supported by SAFTINet
• EHR: Electronic Health Record• Other data sources include: claims, hospital, and third party databases• CER/CDS DM: Comparative effectiveness research/Clinical decision support data mart • PE: Protocol Engine
PE
CER/CDSDM
Other
EHR
Guidelineprotocols
Patient specific report
Practice provider reports
• EHR: Electronic Health Record• Other data sources include: claims, hospital, and third party databases• CER/CDS DM: Comparative effectiveness research/Clinical decision support data mart • PE: Protocol Engine• TRIAD: Translational Informatics and Data management
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
Shared Dataw/ PHI
Shared DataEncrypted
Developed and Supported by SAFTINet
SaftinetPortal
TRIADNode
Web ServicesQueries and Data Transfers
CER/CDSDM
Other
EHR
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
Shared Dataw/ PHI
Shared DataEncrypted
Developed and Supported by SAFTINet
SaftinetPortal
TRIADNode
Web ServicesQueries and Data Transfers
The Trickier Bits…..
MedicaidData
? ?
?
Uni-directional Or Bi-directional?
CER/CDSDM
Other
EHR
Color code:• Blue = A given• Yellow = Optional• Red = Still in analysis
Shared Dataw/ PHI
Shared DataEncrypted
Developed and Supported by SAFTINet
SaftinetPortal
TRIADNode
Web ServicesQueries and Data Transfers
The Trickier Bits…..
MedicaidData
? ?
?
Patient Reported
Data
??
What’s been happening•Creating the use cases▫What types of actions do we need to
support? Types of questions to be answered Types of security and access controls
•Use cases drives data elements and database▫What do we need to extract from each
CER/CDS?•Pilot implementations of TRIAD technology▫Kicking the technology tires with large data
sets to discover the warts and “gotchas”
What’s next?•Engage all of the technical contacts▫Share use cases to understand data
availability and gaps▫Discuss how best to develop data extracts▫Develop data quality procedures▫Develop technology deployment and
support plans Including validation, acceptance, and training
Comparative Effectiveness Research•Aims•Process
Comparative Effectiveness Research•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 “real world” settings. ▫ Including delivery system strategies
CER Aim•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.
CER Goals•Demonstrate the capability of SAFTINet to
collect and accurately link patient-level data necessary for CER of delivery systems
•Lay the groundwork to conduct prospective observational studies and clinical trials
CER Hypothesis•Health care delivery system factors, such as the
patient-centered medical home…
PROCESSES OF CARE
(clinician factors)+
STRUCTURES OFCARE
(system factors)+ PATIENT FACTORS →
OUTCOMES(chronic disease
control)
CER Hypothesis•Health care delivery system factors, such as the
patient-centered medical home,
PROCESSES OF CARE
(clinician factors)+
STRUCTURES OFCARE
(system factors)+ PATIENT FACTORS →
OUTCOMES(chronic disease
control)
outweigh clinician factors, patient factors, and medication effectiveness…
CER Hypothesis•Health care delivery system factors, such as the
patient-centered medical home,
PROCESSES OF CARE
(clinician factors)+
STRUCTURES OFCARE
(system factors)+ PATIENT FACTORS →
OUTCOMES(chronic disease
control)
outweigh clinician factors, patient factors, and medication effectiveness in the control of asthma, high blood pressure and hypercholesterolemia.
CER Conceptual Model
Relatively Mutable
Clinical inertiaCounselingDrug selectionDosage selectionConcomitant medsFollow-upDecision support
Patient-Centered Medical Home
Integrated Mental Health Care
Disease-specific case management
Access to careOutcomes feedback
Therapy adherenceTherapy persistenceMental health statusHealth knowledgePerceived need for
careSymptomsDrug side effects
Patient-centered outcomes
Health-related quality of life
Clinical outcomesProcess
PROCESSES OF CARE
(clinician factors)
+STRUCTURES OF
CARE(system factors)
+ PATIENT FACTORS → OUTCOMES
Relatively immutable
Appointment timePatient loadPhysical facilitiesPractice typeSupport personnelGeneralist vs.
specialist
AgeGenderRace/ethnicitySESMarital statusReligious/cultural
beliefsComorbidity
Comparative Effectiveness Research•Aims•Process
CER Process: The Team
Marion SillsCo-investigator
SAFTINet Comparative Effectiveness Research Team
Measures experts
Cohort experts
Brian Sauer
Diane Fairclough
Rob Valuck
Elaine Morrato
PCMH: Lisa Schilling
IMHC: Ben Miller
Pediatric asthma: Monica Federico
Adult asthma: Barbara Yawn
HTN, Hypercholesterolemia: Karl Hammermeister
CER methods experts
CER Process: Sources of Data•Electronic health records•Medicaid claims•Enhanced point-of-care data collection•Organizational or practice-level survey
CER/CDSDMOther
EHR MedicaidData
Org. Survey
Patient Reported
Data
CER/CDS DM: Comparative effectiveness research/Clinical decision support data mart
CER Process•Establish data dictionary•Develop CER-specific technology use
cases•Review data profiling and quality reports
to improve data quality (ongoing) •Analytical plan
CER Process•Establish data dictionary•Develop CER-specific technology use
cases•Review data profiling and quality reports
to improve data quality (ongoing) •Analytical plan
CER Process•Hypothesis generation
CER Process•Hypothesis generation•Cohort identification▫Clinical/demographic parameters▫Eligibility
CER/CDSDM
EHR MedicaidData
CER Process•Hypothesis generation•Cohort identification•Measures▫Outcome measures▫Explanatory measures▫Covariates
PROCESSES OF CARE
(clinician factors)+
STRUCTURES OFCARE
(system factors)+ PATIENT FACTORS →
OUTCOMES(chronic disease
control)
CER/CDSDM
EHR MedicaidData
CER Process•Hypothesis generation•Cohort identification•Measures•Enhanced data collection▫Patient-reported outcomes▫Quality of life▫PCMH▫IMHC
CER/CDSDM
Org. Survey
Patient Reported
Data
CER Process•Hypothesis generation•Cohort identification•Measures•Enhanced data collection
CER/CDSDMOther
EHR MedicaidData
Org. Survey
Patient Reported
Data
CER Process•Establish data dictionary•Develop CER-specific technology use
cases•Review data profiling and quality reports
to improve data quality (ongoing) •Analytical plan
Next steps…•Getting started•Wrap-up
Getting Started•Subcontracts•SharePoint▫Shared Documents▫Calendar▫Announcements▫Discussions
•Upcoming events and meetings▫Partner Engagement Community (Scheduling in
progress)▫Technical team (Thursdays @ 2pm MT starting 12/16)▫CER team (Mondays @ 1:30pm MT)
•Quarterly SAFTINet Update meetings
Wrap-Up“Knowing is not enough; we must apply.
Willing is not enough; we must do.” —Goethe
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