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
Call Girls Faridabad Just Call 9907093804 Top Class Call Girl Service Available
Data Quality Matters: EHR Data Quality, MACRA, and Improving Healthcare
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?
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%
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”
18.
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/
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
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
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
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?
The TMR layer represents both raw and derived data. The derived data is the big value add. For example, each encounter is classified as a result of several raw data points. After this derivation, each patient record can be summarized with the number of classified events per time window.