2. CCDA vs. QRDA1

3. 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

4. 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?

5. 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 $$

6. 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.

7. 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

8. 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

9. Data Quality Improvement
Identify Gaps in EMR Capability

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

15. 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