1. Health IT Enabled Quality Improvement:. Current and Future State
Health IT Enabled Quality Improvement: Current and Future State Standards

2. Meaningful Use is a Building Block
Use information to transform
Use technology to gather information
Improve access to information
Enhanced access and continuity
Data utilized to improve delivery and outcomes
Data utilized to improve delivery and outcomes
Patient self management
Patient engaged, community resources
Care coordination
Care coordination
Patient centered care coordination
Patient engaged
Evidenced based medicine
Team based care, case management
The main goals of health IT adoption are to achieve improved health and health care quality, safety, and communication among all members of the care team while decreasing costs and increasing value. These goals reflect the HHS National Quality Strategy (NQS), which describes HHS’ strategy and implementation plans to achieve better care, healthy people and communities and affordable care.
As we progress through the MU 2 and look towards MU 3, we see not only that expectations increase, but the focus moves to improved outcomes and Quality Improvement with ever-larger roles for interoperable, population-level data to enable care transformation. We envision a transformed health care system that creates a Learning Healthcare System, enabled by robust, interoperable health IT.
Basic EHR functionality, structured data
Connect to Public Health
Registries for disease management
Registries to manage patient populations
Privacy & security protections
Privacy & security protections
Privacy & security protections
Privacy & security protections
Structured data utilized for Quality Improvement
Connect to Public Health
Connect to Public Health
Connect to Public Health
PCMHs
3-Part Aim
ACOs
Stage 3 MU
Stage 1 MU
Stage 2 MU

3. The Learning Healthcare System
Collaborate to foster knowledge translation
Leverage analytics to extract actionable knowledge
Focus on “Making it Easy to do the Right Thing”
Build evidence out of practice
Set standards based on clinical goals, and evidence-based practice
Leverage EHR to optimize workflow and support clinical decision making
Develop reports to monitor the practice change
Measure the impact of the change through outcomes analysis and research
In a Learning Healthcare System, collecting, sharing and using data for QI activities will provide the right information to the right individuals when they need it. Individuals, patients, providers, researchers, payers and policy makers will benefit from this data accessibility and transparency, using new analytical tools and decision support resources to inform decision making to better manage health and health care.
A core precept of the LHS is “Capture once and reuse:” By encouraging the development of user-centered health IT systems, providers will naturally capture all necessary data in structured format during the normal course of providing care. These data can be used to not only track individual patients’ progress and needs, but also be extracted, collected and examined to drive decision support, quality measurement and reporting, population health management, public health and outcomes research. These efficiencies will also decrease costs for stakeholders.

4. ONC’s Vision for Health IT-Enabled Quality
ONC has worked closely with Federal partners to develop and publish a vision for health IT enabled quality improvement. It outlines how we could to approach building a future where we go beyond meaningful use, to improving the quality, efficiency, and cost of health care for all Americans. We envision an electronically enabled QI ecosystem that promotes better health and care, improved communication and transparency, rapid translation of knowledge for all stakeholders and reduction in the burden of data collection and reporting for providers, using data collected during the normal course of care to inform individual care decisions, population health management, research, and improvement across the spectrum, from research to practice and back again.

5. Health IT-Enabled Quality Improvement Ecosystem
In the traditional QI cycle:
Research studies are the basis of guidelines
Guidelines are translated into clinical decision support which allows for high quality care to be applied to one patient to population
Guidelines are also translated into clinical quality measures – or eCQMs – that measure implementation of the guidelines at the point of care
Feedback on health outcomes via these measures loop back to research to provide insights for the direction of new studies
However, in a health IT-enabled quality improvement ecosystem, the same data can be reused to drive CDS to improve care, and eCQMs to not only measure that improvement but also to inform population health and quality surveillance (via reporting), but also to drive new research, improving guidelines and CDS.

6. Clinical Quality Ecosystem Support via CMS/ONC
The Tacoma Project:
Working across agencies to harmonize standards, drive deployment and testing, and align measurement for eCQMs and CDS
Manage the certification of health IT for quality measures for Federal programs:
Provide technical assistance for measure development and measure policy
Provides tools for validation, test-driven development, and knowledge-sharing
Support reference implementation software:
Cypress tool for eCQM testing
PopHealth tool for eCQM reporting 6

7. TACOMA: IMPROVING THE DEVELOPMENT PROCESS, TOOLS, AND STANDARDS FOR eCQMs and CDS

8. The eCQI Ecosystem

9. eCQM Review Process

10. Tools for Electronic Clinical Quality Measurement
MAT
Measure authoring
VSAC
Value set authoring and management
Bonnie
Measure testing
Cypress
EHR certification
PopHealth
Measure calculation and reporting

11. Measure Authoring Tool https://www.emeasuretool.cms.gov/

12. Measure Authoring Tool https://www.emeasuretool.cms.gov/

13. Value Set Authority Center http://vsac.nlm.nih.gov/

14. BONNIE Testing Tool https://bonnie.healthit.gov/

15. BONNIE Patient Builder

16. BONNIE Patient Bank

17. BONNIE Complexity Analysis: Measure Comparisons

18. BONNIE Complexity Analysis: Measure Comparisons

19. Jira Issue Tracking for eCQMs http://jira.oncprojectracking.org/

20. Jira Issue Tracking for eCQMs http://jira.oncprojectracking.org/

21. Future HHS/ONC Improvements to eCQM Development
Start with standardized system data elements whenever possible
Reduce mapping of data at each transition point
Map workflow and test the impact of measurement prior to release
Allow providers themselves to use federal tools to make their own quality metrics
Pair clinical decision support and registry data with quality measurement across all settings
Engage patient data, other care team members and other settings

22. Existing Standards for Clinical Quality Improvement

23. Standards for Electronic Clinical Quality Measurement
Measure Definition Standards
Quality Data Model (QDM)
Health Quality Measure Format (HQMF)
Measure Reporting Standards
Quality Reporting Data Architecture (QRDA)
QRDA Category I for patient level data
QRDA Category III for aggregate data

24. Standards for Clinical Decision Support
HL7 standards from Health eDecisions (HeD)
CDS Knowledge Artifact Specification (KAS)
Decision Support Service (DSS) and Implementation Guide
Other relevant standards
vMR: CCD, CCDA, QRDA, Infobutton
KAS: Order Set DSTU, GELLO, Arden, CDS Consortium
DSS: Infobutton, IHE Request for Clinical Guidance, REST
From HeD:
Virtual Medical Record (vMR) data model and templates
CDS Knowledge Artifact Specification (KAS)
Rules, order sets, documentation templates
Decision Support Service (DSS) and Implementation Guide
Others:
vMR: CCD, CCDA, QRDA, Infobutton
KAS: Order Set DSTU, GELLO, Arden, CDS Consortium
DSS: Infobutton, IHE Request for Clinical Guidance, REST

25. Computable Expression Logic
Summary
The standards used for the electronic representation of CDS and eCQM were not developed in consideration of each other, and use different approaches to patient data and computable expression logic.
Adhering to different standards places an additional implementation burden on vendors and providers with homegrown systems.
It is currently difficult to share logic between eCQMs and CDS rules.
Patient Data
Computable Expression Logic
Clinical Decision Support 
Virtual Medical Record (for both physical and logical models)
CDS Knowledge Artifact Implementation Guide
Electronic Clinical Quality Measurement (eCQM)
Quality Reporting Data Architecture (for physical model)
Quality Data Model (for logical model)
Health Quality Measure Format (for physical model)
Quality Data Model (for logical model) ,
Draft project charter

26. The Challenge
A CDS rule authors cannot easily re-use the work of an eCQM developer or vice versa.
Harmonization of the CDS and eCQM standards is required to reduce implementation burdens, promote integration between these two domains, and facilitate care quality improvement.

27. The Clinical Quality Framework: An S&I INITIATIVE
Harmonizing Clinical Decision Support and Clinical Quality Measurement Standards to Enable Interoperable Quality Improvement
The Clinical Quality Framework: An S&I INITIATIVE

28. Public-Private Partnership
ONC and CMS
Initiative Coordination and Support: Ken Kawamoto, Sarah Ryan, Bridget Blake
Initiative SMEs: Aziz Boxwala, Bryn Rhodes, Chris Moesel, Claude Nanjo, Jason Mathews, Lloyd McKenzie, Mark Kramer, Marc Hadley
Community Contributors: various private and public sector contributors, including EHR vendors, knowledge vendors, professional associations, providers, technologists, and many others.

29. Goals Expected Benefits CQF Charter
To harmonize standards for CDS and eCQM
To refine the standards via pilot implementations
Expected Benefits
Reduced implementer burden
Increased re-use of eCQM artifacts in CDS and vice versa
Improved standards quality through unification of community effort

30. CQM Specific Standards CDS Specific Standards
Goal: Shared Standards Clinical Quality Measurement and Clinical Decision Support
Common Metadata Standard
CQM Specific Standards
HQMF
QRDA Category-1
QRDA Category-3
QDM
CDS Specific Standards
HeD
vMR
Common Data Model Standard (QUICK)*
Common Expression Logic Standard (CQL)**
* Quality Improvement and Clinical Knowledge
** Clinical Quality Language

31. QUICK is a logical model derived from Quality FHIR profiles
QUICK on FHIR
QUICK is a logical model derived from Quality FHIR profiles
Adds extensions to base FHIR resources for additional data required for CQI purposes
Developing QUICK in parallel with FHIR development
Also working on alignment with FHIR profiles being developed by other teams

32. Standards Status CQL QUICK Quality FHIR Profile
Requirements balloted in Jan 2014
Comment-only ballot in Sept 2014, DSTU ballot in Jan 2015
QUICK
Requirements (QIDAM) balloted in Jan and May 2014
Comment-only ballot in Sept 2014, DSTU ballot in May 2015
Quality FHIR Profile
Comment-only ballot in Jan 2015, DSTU ballot in May 2015
HQMF R2.1 published in Aug 2014
CQF-based HQMF IG comment-only ballot in Jan 2015
HeD KA R1.2 published in July 2014
KA R1.3 update being planned

33. Pilots Topic Current Contributors Point of Contact
Cardiology: Appropriate Use Criteria in Ischemic Heart Disease Evaluation
American College of Cardiology
Jimmy Tcheng,
Chlamydia Screening
HHS Office of Population Affairs
CDC Divisions of Reproductive Health and STD Prevention
Avhana Health
Meliorix
RAND Corporation
Veracity Solutions
Johanna Goderre Jones,
Immunization Decision Support
HLN Consulting, LLC
Daryl Chertcoff,
Ischemic Vascular Disease: Use of Aspirin or Another Antithrombotic
Motive Medical Intelligence
Cognitive Medical Systems
Julie Scherer,
Oncology Decision Support
Evinance
Elekta Mosaic
Chad Armstrong,
Radiology Appropriateness of Use
National Decision Support Company
American College of Radiology
Epic
TransformativeMed (Cerner integ.)
IHE (for Profile)
Tom Conti,

34. Tacoma Standards Timeline (Conceptual)
Mar 2014
May 2014
Jul 2014 …
Sep 2015
Nov 2015
Use Case
Kick-off
Kick-off (3/21)
Use Case Consensus
Initiative End
Pilot Kick-off
Pre-Discovery, Call for Participation
Discovery
Charter, Use Cases, Functional Requirements
Implementation
Standards Development and Review
Pilot
Evaluation
Use Case Pilots
Technology Evaluations 34

35. Standards development and direction-setting
How to Contribute
Standards development and direction-setting
Call coordinates at
All-Hands Meeting Thur 11am-12:30pm ET
Logical Expression Meeting Wed 11am-12pm ET
Data Model Meeting Wed 1-2pm ET
Piloting and standards refinement
Contact information at
Ken Kawamoto:
Peggy Tsai: