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

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

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.

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.

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.

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

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

The eCQI Ecosystem

eCQM Review Process

Tools for Electronic Clinical Quality Measurement MAT Measure authoring https://www.emeasuretool.cms.gov/ VSAC Value set authoring and management https://vsac.nlm.nih.gov/ Bonnie Measure testing https://bonnie.healthit.gov/ Cypress EHR certification http://projectcypress.org/ PopHealth Measure calculation and reporting http://projectpophealth.org/

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

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

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

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

BONNIE Patient Builder

BONNIE Patient Bank

BONNIE Complexity Analysis: Measure Comparisons

BONNIE Complexity Analysis: Measure Comparisons

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

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

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

Existing Standards for Clinical Quality Improvement

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

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

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

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.

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

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.

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

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

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

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

Pilots Topic Current Contributors Point of Contact Cardiology: Appropriate Use Criteria in Ischemic Heart Disease Evaluation American College of Cardiology Jimmy Tcheng, james.tcheng@duke.edu 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, Johanna.GoderreJones@hhs.gov Immunization Decision Support HLN Consulting, LLC Daryl Chertcoff, daryl@hln.com Ischemic Vascular Disease: Use of Aspirin or Another Antithrombotic Motive Medical Intelligence Cognitive Medical Systems Julie Scherer, jscherer@motivemi.com Oncology Decision Support Evinance Elekta Mosaic Chad Armstrong, chad.armstrong@evinance.com Radiology Appropriateness of Use National Decision Support Company American College of Radiology Epic TransformativeMed (Cerner integ.) IHE (for Profile) Tom Conti, tconti@acrselect.org

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

Standards development and direction-setting How to Contribute Standards development and direction-setting Call coordinates at www.cqframework.info 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 www.cqframework.info Ken Kawamoto: kensaku.kawamoto@utah.edu Peggy Tsai: mtsai@acr.org