CCDA vs. QRDA1

Claimed diagnoses, procedures, medications Patient Out of Pocket Take 3 diabetes measures: 1) Appropriate Testing, 2) Control <8, 3) Out of Control >9 Claims Data Claimed diagnoses, procedures, medications Patient Out of Pocket Claims: Medicaid Claims: Commercial 1 Claims: Commercial 2 Claims: Commercial 3 Claims: Commercial 4 Medicare Patient A Patient D Commercial EHR 1 12.1% EHR 2 Public Health Department EHR 3 EHR 4 9% SureScripts EHR 5 7.6% EHR 6 8.5% Independent Pharmacies EHR 7 Federal Source (VA/DoD/IHS) EHR 8 10% EHR 9 8.6% EHR10 8% Patient C 9.8% 10.5% 7% 10% 8% Patient B 6.9% 7.5% 0% NA 33% 0% 100% 66% 50% 100% 33% 33% 100% 0% 100% 50% 0% 50% 0% 100% 100% 50% 50% 0% 100% 100% 0% 0% NA

Take 3 Diabetes Measures: Payers will get multiple scores on the same patient– what do they do with that? Looking at populations, we cannot roll these up . . . Isn’t this what we really want to know?

Pay for Value: Trusted 3rd Party Payer Provider Clinical Data Claims Payer-specific Metrics ER Utilization Admissions Prescription drug use Etc. Value Provider-specific Metrics Clinical outcomes BP mgmt DM performance Etc. MyHealth Analytics: Trusted Third Party $$

Measurement for everyone No More Waiting for Quality Metrics: Real-Time Clinical Quality Calculation 6+, 3-, 3E = 6/9 = 67% 5+, 4-, 3E = 5/9 = 56% 4+, 1-, 2E = 4/5 = 80% 3+, 1-, 1E = ¾ = 75% 2+, 1-, 1E = 2/3 = 67% 4+, 3-, 3E = 4/7 = 57% Provider 2 Employer + E + Provider 1 - + - Specialist - E + E + + Payer 1 + - + + E + - + = patients that count positively to eCQM’s + + - E E - + - E Geographic Region 1 E + - - + E = patients that count negatively to eCQM’s - + + + E - + - - E = patients that are excluded from eCQM’s E E + + eCQM’s calculated in real time based on changes in a patients cross-community data by placing a box around any portion of a population.

Data Quality: Chain of evidence Data entry Data mapping in EHR Data export in CCDA, ADT, ORU, etc. Data Transport Data Extraction from CCDA, ADT, ORU, etc. Data transformation Data load to Central Data Repository Automated Code Normalization Manual Code Normalization Presentation of data Provider/Practice EHR Vendor MyHealth Access Network

Classification Engine Messages Classified By: Source, Format & Type SFTP Raw Data: HL7 CCD CDA EHR TCP WS Classified Message Data Transformation Tool 3rd Party Application SFTP, etc. Measure Reporting Data Access Layer Data Import Layer Reporting Environment (Analytics) Risk Stratification Data Transformation Tool Master Person & Provider Index Population Health Clinical Data Repository HIE Portal

Data Quality Improvement Identify Gaps in EMR Capability

How important is comprehensive data? Clinical Data Claims Data Accurate measures possible here.

CCDA vs. QRDA1 vs. QRDA3 CCDA QRDA1 QRDA3 Frame of Reference Individual Patient, all data Individual patient, data relevant to measure Population of patients, ? attribution Time window CSV- episodic TOC VDT Historical Varies, depending on when pulled: previous calendar year, previous 12 months, time since Jan 1 Same as QRDA1 Data Scope All MU data set requirements and expanding rapidly Only variables relevant for measures Only population level results of measures Potential Uses Quality measure calculation, Point of Care decision-making, Care gap analysis, Transitions of Care, Patient portal, Etc. Measurement Measurement reporting Data Provenance Chain of evidence relatively intact Editorial Decisions made by vendor, impossible to evaluate Validation of measure performance Potential to validate large populations 56 test patients cover ~100 eMeasures Ability to support measure Must be tuned and potentially cleaned. Missing data an issue, benefits from adding claims data Will get an answer for the measure, limited in scope to single source