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Safti net kick off 12092010-mrs
This document summarizes a kick-off meeting for the SAFTINet project. The meeting welcomed collaborators and outlined goals of establishing a distributed research network to conduct comparative effectiveness research using electronic health data from multiple healthcare organizations. The agenda included introductions of participating organizations, presentations on comparative effectiveness research and the technical capabilities needed, and discussions around engaging partners and getting started with the work.
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Safti net kick off 12092010-mrs
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- 2. Welcome! • Thank youfor 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
- 3. Agenda Agenda Item TimePresenter 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 • 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
- 5. IOM Roundtable onValue& Science- Driven Health Care • The Learning Healthcare System (2006) • Judging the Evidence: Standards for DeterminingClinical Effectiveness (2007) • Leadership Commitments to ImproveValue 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 PublicGood (2008) • Engineering a Learning Healthcare System:A Look to the Future (2008) • LearningWhat Works: Infrastructure Required for Learning Which Care Is Best (2008) • Value in Health Care: Accounting for Cost, Quality, Safety, Outcomes and Innovation (2008)
- 6. 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 - LearningWhatWorks 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
- 7. • Need forsubstantially 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.
- 8. Redesigning the Clinical EffectivenessResearch 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
- 9. • resulting researchparadigm, 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.
- 10. • Geisinger exampleof the power of proper EHR use • (pg 28 – Redesigning)
- 11. Why distributed? • Minimizesecurity 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.
- 12. • Patient preferencesand 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?
- 13.
- 15. Patient Protection andAffordable 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…
- 16. Patient Protection andAffordable 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.
- 17. Patient Protection andAffordable 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
- 18. SAFTINet Overview • AHRQARRA OS: Recovery Act 2009: Scalable Distributed Research Networks forComparative 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.
- 19. $1.1 Billion -ARRAAllocations • Research • Data Infrastructure • Dissemination and Adoption • Administrative support, inventory, evaluation • $681 M (62%) • $268 M (24%) • $132 M (12%) • $ 19 M ( 2%)
- 20. Federal Coordinating Councilfor 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 andTracking of Comparative Effectiveness Impact: Improved ClinicalContent 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
- 21. AHRQ Support ofActionable 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),
- 22. AHRQ Support ofCER • CER Methodology - 19 funded projects -**** • Laurer, Collins JAMA 2010:303;2182
- 23. Why CER Physicians, healthinsurers, & 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 •1904 first radical prostectomy • Jan 2010 1st US RCT active survelleince vs RP for localized prostate Ca • 100 years of action without evidence
- 25. Project Requirements • Primaryfocus ▫ 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
- 26. Funded Projects • ScalableArchitecture for FederatedTherapeutic 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)
- 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 •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).
- 29. Technology Development • SpecificAim 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 InformationTechnologyArchitecture plans and systems) to promote quality care and provide enhanced data for comparative effectiveness research.
- 30. 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
- 31. Introductions • BRIEF organizationaldescriptions, roles and personnel • Roll Call • Investigator bios and full research strategy posted on SharePoint site
- 32. Project team investigators PartnerEngagement Community TechnicalTeam 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)
- 33. Project team investigators CERTeamInvestigators • Marion Sills (UCD) • Elaine Morrato (UCD) • Lisa Schilling (UCD) • Karl Hammermeister (UCD) • Monica Federico (UCD) • Ben Miller (UCD) • RobValuck (UCD) • Diane Fairclough (UCD) • Bethany Kwan (UCD) • BarbaraYawn (consultant) • Lucy Savitz (IMH) • Brian Sauer (Utah) SAFTINet CERTeam CER Methodology Experts Health Outcomes Content Experts Health Care Delivery System and Process Experts
- 34. AmericanAcademy of FamilyPhysicians (AAFP) National Research Network (NRN) • Personnel: ▫ DebbieGraham, MSPH, AAFP Site PI ▫ Elias Brandt, Research SystemsAnalyst ▫ 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 EngagementCommunity leadership
- 35. QED Clinical, Inc.d/b/a CINA • Personnel ▫ Cathy Bryan, MHA, BSN, RN, Chief ClinicalOfficer ▫ 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
- 36. University of Utah Centerfor High Performance Computing (CHPC) and Biomedical Informatics (BMI) • Personnel andTechnicalTeam: ▫ Julio Facelli, PhD, CHPC Director, BMIVice Chair, PI of UtahTeam ▫ Ron Price, Sr. Software Engineer/Architect and Project Manager ▫ Derick Huth, Jr. Software Engineer ▫ Jody Smith, DatabaseAdministrator ▫ Walter Scott, DatabaseAdministrator ▫ Steve Harper, SystemAdministrator • Project role ▫ Build the Grid
- 37. Cherokee Health Systems,Inc. • Personnel ▫ Parinda Khatri, PhD,CHS Director of Integrated Care, CHS Site PI ▫ Jeff Howard, CPA, CHS Chief FinancialOfficer ▫ Bob Franko, MBA, CHS training and marketing ▫ Monty Bryant, BS, Programmer/Analyst ▫ Jennifer Poling, MBA, DataAnalyst • 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 ) • Project role ▫ Collaboration on technical and Partner EngagementCommunity teams ▫ Supporting participatingCherokee practices for data sharing, point of care data collection, and data use
- 38. 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 ▫ AmyWuthrich, 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 EngagementCommunity teams ▫ Supporting participating Intermountain practices for data sharing, point of care data collection, and data use
- 39. CCMCN/CACHIE • Personnel ▫ JasonGreer,CACHIE Director ▫ DanTuteur, CCMCN Executive Director ▫ Heather Stocker, CCMCN Director of Clinical Programs & Development • ColoradoCommunity ManagedCare Network (CCMCN) ▫ A non-profit Network of 15 Federally Qualified Health Centers (FQHCs) providing primary health care services to the medically underserved throughout Colorado. • ColoradoAssociated 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 EngagementCommunity teams ▫ Supporting two participatingColoradoCommunity Health Centers for data sharing, point of care data collection, and data use
- 40. 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
- 41. Project teams • PartnerEngagement Community • Technical team • Comparative effectiveness research team
- 42. 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 • LearningCommunity • Membership • Meet monthly – 1st Wednesday at 12:00 MT/1:00CT/2:00 ET • Listserv
- 43. TechnicalTeam Presentations • Aimsand 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
- 44. Informatics Objectives: Starting withthe 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
- 45. EHR Color code: • Blue= A given •Yellow = Optional •Red = Still in analysis • EHR: Electronic Health Record
- 46. CER/CDS DM 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
- 47. PE CER/CDS DM Other EHR Guideline protocols 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
- 48. PE CER/CDS DM Other EHR Guideline protocols Patient specific report Practice provider reports Color code: • Blue= A given •Yellow = Optional •Red = Still in analysis Shared Data w/ PHI Shared Data Encrypted 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
- 49. PE CER/CDS DM Other EHR Guideline protocols Patient specific report Practice provider reports • EHR: ElectronicHealth 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 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: • Blue= A given •Yellow = Optional •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 Bits….. Medicaid Data ? ? ? Uni-directional Or Bi-directional?
- 51. CER/CDS DM Other EHR Color code: • Blue= A given •Yellow = Optional •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 Bits….. Medicaid Data ? ? ? Patient Reported Data ? ?
- 52. 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 ofTRIAD technology ▫ Kicking the technology tires with large data sets to discover the warts and “gotchas”
- 53. What’s next? • Engageall 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
- 54. Comparative Effectiveness Research •Aims • Process
- 55. 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
- 56. CER Aim • SpecificAim 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 • Demonstratethe 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
- 58. CER Hypothesis • Healthcare delivery system factors, such as the patient-centered medical home… PROCESSES OF CARE (clinician factors) + STRUCTURES OF CARE (system factors) + PATIENT FACTORS → OUTCOMES (chronic disease control)
- 59. CER Hypothesis • Healthcare delivery system factors, such as the patient-centered medical home, PROCESSES OF CARE (clinician factors) + STRUCTURES OF CARE (system factors) + PATIENT FACTORS → OUTCOMES (chronic disease control) outweigh clinician factors, patient factors, and medication effectiveness…
- 60. CER Hypothesis • Healthcare delivery system factors, such as the patient-centered medical home, PROCESSES OF CARE (clinician factors) + STRUCTURES OF CARE (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.
- 61. CER Conceptual Model Relatively Mutable Clinicalinertia 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 → 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
- 62. Comparative Effectiveness Research •Aims • Process
- 63. CER Process:TheTeam Marion Sills Co-investigator SAFTINetComparative 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: Sourcesof Data • Electronic health records • Medicaid claims • Enhanced point-of-care data collection • 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 • Establishdata dictionary • Develop CER-specific technology use cases • Review data profiling and quality reports to improve data quality (ongoing) • Analytical plan
- 66. CER Process • Establishdata dictionary • Develop CER-specific technology use cases • Review data profiling and quality reports to improve data quality (ongoing) • Analytical plan
- 67. CER Process • Hypothesisgeneration
- 68. CER Process • Hypothesisgeneration • Cohort identification ▫ Clinical/demographic parameters ▫ Eligibility CER/CDS DM EHR Medicaid Data
- 69. CER Process • Hypothesisgeneration • Cohort identification • Measures ▫ Outcome measures ▫ Explanatory measures ▫ Covariates PROCESSES OF CARE (clinician factors) + STRUCTURES OF CARE (system factors) + PATIENT FACTORS → OUTCOMES (chronic disease control) CER/CDS DM EHR Medicaid Data
- 70. CER Process • Hypothesisgeneration • Cohort identification • Measures • Enhanced data collection ▫ Patient-reported outcomes ▫ Quality of life ▫ PCMH ▫ IMHC CER/CDS DM Org. Survey Patient Reported Data
- 71. CER Process • Hypothesisgeneration • Cohort identification • Measures • Enhanced data collection CER/CDS DM Other EHR Medicaid Data Org. Survey Patient Reported Data
- 72. CER Process • Establishdata dictionary • Develop CER-specific technology use cases • Review data profiling and quality reports to improve data quality (ongoing) • Analytical plan
- 73. Next steps… • Gettingstarted • Wrap-up
- 74. 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
- 75. Wrap-Up “Knowing is notenough; we must apply.Willing is not enough; we must do.” —Goethe Questions/Comments --
Editor's Notes
- #3 Bethany will bring meeting to order, welcome everyone and summarize the goals and objectives of the meeting, and briefly review the agenda
- #5 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
- #6 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
- #16 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.
- #18 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).
- #19 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?
- #27 Project descriptions will be provided here:
- #28 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. 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. 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. Provisions to review and manage the conflict of interest of investigators and center personnel on an ongoing and regular basis must be included. 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. End-user feedback will be important in designing the infrastructure so that the data are efficiently collected and clinical workflow is minimally impacted. The patient’s perspective must also be represented in the governance plan.
- #30 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 entities’ 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
- #31 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.
- #32 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.
- #34 Briefly introduce Investigator list, role and expertise Team leaders to introduce themselves There are additional members of these teams as well, just not “investigators”.
- #35 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 patient’s advocate in the changing healthcare system.
- #38 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.
- #39 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. Intermountain’s 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.
- #40 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). 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.
- #41 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 Denver’s 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. 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.
- #43 Development of agenda/topics for discussion
- #44 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.
- #56 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.
- #57 The grant’s 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.
- #59 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
- #60 outweigh clinician factors, patient factors, and medication effectiveness
- #61 in the control of asthma, high blood pressure and hypercholesterolemia.
- #62 The conceptual model underlying this hypothesis is shown here. This delineates some of the Outcome measures, Explanatory measures, and Covariates for the CER analyses.
- #63 I will now give an overview of the CER process for SAFTINet.
- #64 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.
- #65 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 Michael’s graphics, the data sources are shown at the bottom of this slide, in primary colors.
- #66 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
- #67 The current focus of CER team activity is establishing a data dictionary.
- #68 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.
- #69 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.
- #70 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.
- #71 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.
- #72 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.
- #73 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.
- #75 Project management considerations