This document discusses data quality in electronic health records and its importance for Medicare programs and healthcare quality. It makes three key points: 1) Medicare spending is unsustainable at current rates and data quality is important for value-based programs like MACRA that tie reimbursement to quality and cost measures. 2) MACRA was introduced to replace the flawed Sustainable Growth Rate formula and moves Medicare reimbursement towards value-based payments through programs like MIPS and APMs that require accurate clinical data. 3) High quality clinical data is essential for measuring healthcare quality, costs, and outcomes required by programs like MACRA and value-based payment models. Data profiling of EHRs reveals many quality issues that can

1. DATA QUALITY MATTERS: EHR DATA
QUALITY, MACRA, AND IMPROVING
HEALTHCARE
2017
Michael Hogarth, MD, FACP, FACMI
Professor, Internal Medicine
Professor and Vice Chair, Dept. of Pathology and Laboratory Medicine
http://hogarth.ucdavis.edu
mahogarth@ucdavis.edu

2. Summary
Three Fundamental Questions:
 How is Medicare doing today?
 Why is MACRA here (and what is it exactly)?
 Why does clinical data quality matter?

3. Question 1
How is Medicare doing today?

4. US Healthcare is unsustainable
In 6 years, NHE will be 19.3% of $26T!!
This is a 60% increase in total expenditure
60% increase

5. What is Medicare?

6. Medicare Today -- A Runaway Train…

7. The widening gap between beneficiaries and contributors
2016 Medicare Trustee’s Report

8. 2014: 43% of all healthcare in the US paid directly by government
Govt sponsor Percentage
Medicare 20%
Fed Medicaid 10%
State Medicaid 6%
VA/DOD/CHIP 4%
Public Health 3%

9. And you are loosing money…

10. Medicare Projection of Cost vs. Assets (solvency)
Costs exceed assets
~2022-2028

11. Oh by the way, US healthcare gets a ”D-”

13. Physician actions affect Medicare in many ways

14. Physicians prescribing decisions are far reaching…

15. Question 2
Why is MACRA here and
what is it exactly?

16. SGR
Introduced

17. What was wrong with the SGR?
• Fundamentally flawed
• Attempted to limit
expenditures on physician
services by restraining
payments without
limiting the growth in
volume and complexity
of the services provided
• In 2015, SGR would have
invoked a 24% fee
reduction for Medicare
providers
History of the “doc fixes”

19. What is the scope of MACRA?
The financial footprint
1,048,575 Providers
Physicians, PAs, clinical nurse specialists, anesthetists
The Medicare provider footprint today
~300,000
physicians
(2013)

20. The Importance of MACRA and beyond
 MACRA is the ‘start’ of an evolution towards value
based purchasing
 Value-based reimbursement requires managing
patients across multiple providers -- requires data
exchange between EHR systems
 Value-based reimbursement increases the need for
your organization to know where it stands
 High quality clinical care data
 Health analytics

21. MACRA’s two pathways
 MIPS: “Merit Based Incentive Payment Program”
 ~90% of practices will choose this option
 MIPS is a Modified fee-for-service
 Combines meaningful use with cost, quality, and clinical practice
improvement – A Composite Performance Score (CPS)
 APM: “Alternative Payment Model”
 Models that reduce costs and drive high quality
 Reporting is different than MIPS
 Incentives, NO penalties
https://www.greenwayhealth.com/blog/path-macra-paved-big-decisions/

25. Calculating Provider Payment

26. GPRO Quality Measures

28. Advancing Care Information

29. APM – Alternative Payment Model

30. Its not just Medicare…

31. ACO Lives Covered and Payer Distribution

32. Question 3
Why does data quality
matter?

33. c.2017 – Is data quality important?

34. Clinical Data Quality: What is it?
The 5 Dimensions of Clinical Data Quality
1) Completeness – is the EHR record complete?
2) Correcteness – Is an element in the EHR true?
3) Concordance – Is there agreement between elements in the
EHR, or between the EHR and another data source?
4) Plausability – Does an element make sense in light of other
knowledge at a given point in time?
5) Currency – Is a piece of data a relevant representation of
the patient at a given point in time?
6) Relevance/Fit-for-use – Are the elements needed for a
metric of high quality?
Data Quality
CompletenessCorrectness
Concordance Plausability
Relevance (fit for use) Currency

35. Context – A Key Factor in Data Quality

36. Healthcare -- ‘fit for purpose’ involving a population
context (usually a specific population)

37. Correctness and Completeness in early EHRs

38. “The Tethered Meta-Registry”
cohort inclusion through “tagging” with real-time rule-based algorithms
The UCD Tethered
Meta-Registry
- “Meta-Registry” – All
data for all registries
is in one repository
- “Tethered” – routine,
automated data
extraction from
source systems
- Computable cohorts –
algorithms “tag”
patients as being in
one or more registries
- Automated
dashboards and
reports

39. • “Meta Registry”
• Shared data dimensions / standardized definitions
Sepsis
Registry
Mobility
(ICU)
Registry
Diabetes
Registry
Transfusion
Registry
Source Data
“Tether”
EMR
Reporting
Database
Administrativ
e Data
Laboratory
Information
System
TMR Patient
TMR Encounters
TMR Flowsheets
TMR Procedures/Labs
TMR Medications
• Individual Registries
• Leverage “Meta Registry”
39
The UC Davis Health Tethered Meta Registry (TMR) Architecture and Data
Flow
2.2 Million
25 Million
100 Million
57 Million
16 Million

40. The UCDHS “Diabetes Registry” (4/7/2017)

41. Transfusions
Missing
data

42. Female + Prostate Cancer
UC-ReX: ~14M patient records (UCLA, UCSF, UCSD, UCD, UCI)
UCD: has 41 EHR records with
female gender and prostate cancer
diagnosis

43. Urine pregnancy tests from 2015-2017
17patients?

44. Diabetics and a glucose test
UCD: 17% of diabetics do not have a
glucose test result in their EHR
record

45. Surgery and Coagulation Testing
UCD: 61.1% of patients undergoing
surgery did not have a coagulation
test in their EHR record

46. Pregnancy tests
10pts?

47. Pregnancy tests in males…
24 males with urine
pregnancy testing

49. Distributed Analytics
Sending the analytics procedure to the data
visualization

50. Clarity
The Observational Medical Outcomes
Partnership (OMOP) Common Data
Model
and
Data Profiling (ACHILLES)
ACHILLES

51. What is Data Profiling?
• Systematic and generalizable method of
data quality assessment
• Can you answer the following questions
– Does your organization have a clinical data
repository?
– Does the group that manages this
repository implement data profiling in any
way?
– What kind of skill sets are required for a
group to optimally perform good data
profiling?
Aim of Research

52. Using OMOP and ACHILLES – profiling your data
75% of records
have unknown race?
Nobody is older than 85?
(1) Only have dx for pts. admitted after
1984?
(2) Someone is pre-admitted for 2020....
35 million procedures are “unknown” type?
We have a procedure for someone
To be admitted 12 years from now
Only 659,000 records have a diagnosis!!!

53. Births and Deaths “en masse”
• UCDHS – 2.3M patient records
• Created a histogram of “deceased” across months/years
• 26,000 patients “died” on Jan 1 1980...
– Nobody could remember why this was the case...
UCDHS pScanner data profiling with ACHILLES
Over 300,000 born in 1930?

54. Death/mortality
(UCDHS pScanner database)

55. Data Density
(UCDHS pScanner database)

56. Conditions “Heat Map” (Asthma)

57. Condition map – Breast Neoplasm

58. UC Health Patients Alive and >85
There were only 600,000 Californians
over 85 in 2010!
1.8M non-deceased and over
85 across UC Health

59. Who should own data quality?
https://gcn.com/articles/2016/07/13/data-quality-responsibility.aspx?admgarea=TC_BigData

60. Eastern Sierras
“The Range of Light”