Hearing on Ensuring the Quality of Quality Data Friday, November 30, 2012 Report to the Health IT Policy Committee David Lansky, Chair, Quality Measures Workgroup Larry Wolf, Co-Chair, Certification & Adoption Workgroup

The Hearing Two Workgroups – Certification and Adoption Workgroup – Quality Measures Workgroup Two Panels – Current State of EHR-Generated Data Quality for Clinical Quality Measurement – Addressing Barriers to EHR-Generated Data Quality Leadership from ONC – Jesse James, MD – Kevin Larson, MD Three Jam Packed Hours

Certification and Adoption Workgroup Marc Probst, Co-Chair, Intermountain Healthcare Larry Wolf, Co-Chair, Kindred Healthcare Joan Ash, Oregon Health & Science University Carl Dvorak, Epic Paul Egerman, Businessman/Entrepreneur Joseph Heyman, Whittier IPA George Hripcsak, Columbia University Elizabeth Johnson, Tenet Healthcare Corporation Charles Kennedy, Aetna Donald Rucker, Siemens Corp. Latanya Sweeney, Harvard University Paul Tang, Palo Alto Medical Foundation Micky Tripathi, MA eHealth Collaborative Scott White, 1199 SEIU Training & Employment Fund

Quality Measures Workgroup David Lansky, Chair, Pacific Business Group on Health Christopher Boone, American Heart Association Tripp Bradd, Skyline Family Practice, VA Russ Branzell, Poudre Valley Critical Access Hospital, CO Helen Burstin, National Quality Forum Neil Calman, The Institute for Family Health Cheryl Damberg, Rand Corp. Timothy Ferris, Partners Healthcare Patrick Gordon, Colorado Beacon Consortium David Kendrick, Greater Tulsa Health Access Network, OK Charles Kennedy, Aetna Karen Kmetik, American Medical Association Robert Kocher, McKinsey & Co Saul Kravitz, MITRE Norma Lang, University of Wisconsin J. Marc Overhage, Siemens Healthcare Laura Petersen, Veterans Admin/Baylor University Eva Powell, National Partnership for Women & Families Sarah Scholle, NCQA Cary Sennett, MedAssurant Jesse Singer, NYC Department of Health Paul Tang, Palo Alto Medical Foundation Kalahn Taylor-Clark, Brookings Institution James Walker, Geisinger Health System Paul Wallace, Kaiser Permanente Mark Weiner, Perelman School of Medicine, University of Pennsylvania

Panel One: Current State of EHR-Generated Data Quality for Clinical Quality Measurement Richard Cramer, Informatica Andrew Mellin, McKesson Howard Bregman, Epic Prashila Dullabh, NORC Ruth Jenkins, Medical University of South Carolina Walter Sujansky, California Joint Replacement Registry Michael Ross, Eastern Maine Medical Center Francis Campion, DiagnosisOne

Panel Two: Addressing Barriers to EHR-Generated Data Quality Puneet Batra, Kyruus Janice Nicholson, i2i Systems Chris Queram, Wisconsin Collaborative for Healthcare Quality Jonathan Keller, Central Utah Informatics Mark Massing, Carolinas Center for Medical Excellence Landen Bain, CDISC Jackie Mulhall, SMC Partners Alan Silver, IPRO Kate Goodrich, CMS

Barriers to Collecting Data Needed for Quality Measures Extra work for users, especially physicians May not be of immediate value to the clinician (and therefore not done consistently) No good feedback loop to the clinicians (and therefore difficult to improve outcomes) Different EHR vendors code the data differently resulting in different calculations of the CQMs Different implementations of the same EHR product code the data differently, resulting in inconsistent reporting Multiple ways to document something with different coding (or no coding), undermining the value of extracted data Inconsistent use of data fields within an EHR Data extraction is difficult – may require special staff, may require add-on software, sometimes can only be done by the EHR vendor

Quality Measure Life Cycle Quality Measure Selection and Specification High Value Uses EHR Product Capabilities Clinical Workflow Other Related Cycles Standards Development Product Development Clinical Process Improvement What can we do to improve the cycle? What policy levers are appropriate?

Quality Measure Life Cycle: Products Product capabilities: – Reduce quality problems through the EHR products – Use certification and standards to influence products: Require data validation checks Standardize query and extraction tools Certify accuracy of CQM calculation Standardize where QM data fields are stored (e.g., the smoking status field) – Reduce use of free text for QM data fields – Move away from “check the box” implementation of measures (get the data from the underlying clinical processes and documentation) – Improve user interfaces and product design, the “extra click” problem – Involve vendors in the selection and refinement of measures before they are published, to identify possible implementation problems early

Quality Measure Life Cycle: Measures Quality measure selection and specification: – Measures that have recognized value to the providers of care (EP, EH, their staff) - may be more commonly process measures than outcome measures – Increase intrinsic motivation to accurately collect data that they care about – For all measures, better specify code sets, value sets, mappings across codes and systems to reduce errors and non-equivalencies, including creating national library or standards bodies, accelerating uniform adoption of new code sets. – Develop measure of “data quality” that helps users determine ability to generate reliable QMs

Quality Measure Life Cycle: Workflow Clinical workflow: – Poorly designed workflow/EHR-flow likely to result in incomplete or error-prone data collection – Address who collects the information and when/where during the care process. – Allow time to providers to design workflow so that the data collection is least burdensome – Immediate re-use of data improves its quality by providing feedback through routine activity – Reconciliation processes (medications, problem list, allergy, patient preferences) will enhance data quality – Patients can be effective participants in getting the data right (see NORC study)

Quality Measure Life Cycle: High Value High value uses: – If clinicians feel Quality Measures are valuable for understanding and improving clinical processes, they will be more thorough and precise in capturing it, correcting it, etc. – If Quality Measures are used for payment, everyone will be motivated to manage data quality more carefully. – We also heard counter-argument that EHR data today is not good enough for payment (and this falls outside of MU purview, anyway, but perhaps speaks to pace of CMS value purchasing shift to e-measures).

Emerging Quality Measures Vision Users understand high quality data is essential for care, quality improvement, population health and payment Quality measures integral to the care process whether or not analyzed in external systems Data collected as part of the care process without any extra clicks Data available from the EHR without extra programming Data aligned with standard vocabulary without extensive mapping tables Data available for aggregate analysis and benchmark development without needing custom transport

Possible Actions to Improve Data Quality Redesign Measure Development to include all stakeholders (clinical, quality, vendor, Federal, …) – learn from agile software methodologies Build an ecosystem for quality reporting and population health, with EHRs as components (enable third-parties to provide the analytics and benchmarks) Refine requirements for CDA/CCD EHR Summaries as the standard data extract (the EHRs do the data mapping once) Explore Natural Language Processing for text to structured/coded data (good enough for population analysis) Engage a wider audience in the SDO process, with standards development as a fluid, dynamic process (as has been done with the S&I Framework) Evaluate certification and standards to anticipate payment requirements of ACOs, episodes, Patient-Centered Medical Homes and other new models Explore the multiple data streams (process & data redundancies) to improve data quality

Next Steps A Data Intermediaries Tiger Team to describe new “ecosystem” and policy actions needed to get there Certification Criteria and Testing Methodologies to increase consistency of data capture, coding, and extraction, with an initial focus on likely Stage 3 CQMs Greater focus on standardizing the data that underlies the quality measures Analysis of the current state of the data (for example, what is contained in the CCDs being exchanged today) to improve utility of standard data extraction records and tools Focused review with CMS and private payers to map data pipeline that supports emerging value-based payment models and ensure EHR functionality