Hearing onEnsuring the Quality of Quality Data

Friday, November 30, 2012

Report to the Health IT Policy CommitteeDavid 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

HighValueUses

EHRProduct

Capabilities

ClinicalWorkflow

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