9/30/2014
Minnesota Health Care Claims
Title Xx
Reporting
System
Subtitle Xx
MN APCD Stakeholder Workgroup
Minnesota Department
of Health
Presenter,
Title
09/30/2014
Date
Proprietary and Confidential
11
About Onpoint Health Data
• Formed in 1976 as an independent, nonprofit
• Based in Portland, ME
• Record of innovation
– 1980s. Data organization supporting Dr. J. Wennberg’s smallarea variation studies
– 1995. Built first-of-its-kind, multi-payer claims database for
ME statewide business coalition
– 2002. Developed APCD for State of Maine
– 2006. First to integrate Medicare claims data into APCD
– 2010. 3-state APCD integration for landmark variation study
– 2013. Data organization behind Dartmouth Institute’s
Pediatric Atlas & Total Cost of Care studies
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9/30/2014
About Onpoint Health Data
• Depth of health data integration expertise
– 35 years’ experience working with healthcare data
– 20 years’ experience integrating, analyzing claims data
– Extensive Medicare and Medicaid integration experience
– Dental claims, provider data integration experience
– Extensive data linkage expertise: EHR, clinical registry, public
health registry
– Expert in data enhancements designed to support analytics:
claim consolidation, disease flags, i/p stay identifiers,
diagnostic and procedure groupers, provider attribution, risk
scoring, HEDIS and other performance measures
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About Onpoint Health Data
Solution: Onpoint CDM
• Scalable, efficient, in production for 10+ years
• Continuous enhancement over six statewide APCD
implementations
• Rigorous QA procedures
• More than 200 carriers submitting monthly
• More than 10M lives and 10TB of data
• Data encrypted in motion and at rest, zero data breaches in
more than 30 years and 9 billion records
• Best of breed COTS and open-source technologies
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Dartmouth Pediatric Atlas
Based on Onpoint’s Integration of APCDs from ME, NH, & VT
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Diagnostic Imaging & Prescription Drug Use
From the APCD-based Dartmouth Pediatric Atlas
Map 25. Head CT scans per
1,000 children among hospital
service areas (2007-10)
MN APCD Stakeholder Workgroup 2014
Map 32. Percent of children
filling at least one prescription
for an ADHD medication
among hospital service areas
(2007-10)
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Potentially Avoidable ED Use
From the APCD-based Tri-State Variation Study
Across 67 hospital service areas
in northern New England,
population-based rates varied
8-fold for the commercial
population.
Caribou
(136.3)
Burlington
(16.1)
MN APCD Stakeholder Workgroup 2014
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Medical Home Evaluation
Vermont Blueprint for Health
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Primary Care Practice Profiles
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Dartmouth Total Cost of Care Study
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Understanding MHCCRS
APCDs versus MPCDs
State
MA
MD
ME
MN
NH
RI
VT
CO
KS
UT
CA
WI
WA
TN
Commercial
TPAs/Self-Funded
Medicaid
All-Payer Claims Databases (APCDs)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Multi-Payer Claims Databases (MPCDs)
Y
N
Y
Y
N
Y
Y
Y
N
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Medicare
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
N
N
N
MN APCD Stakeholder Workgroup 2014
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Understanding MHCCRS
Data Submitters
• Payer registration
– All health plans, TPAs, and PBMs covering MN residents
– Annual registration each April 1
– 2014: 241 registered payers
• Submitters
– Paid at least $3M in institutional, professional, and/or
pharmacy claims
– $300K if PBM
– Evaluated annually during registration
– 2014: 90 submitters
MN APCD Stakeholder Workgroup 2014
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Understanding MHCCRS
APCD statistics
• In production for 5+ years
• Data from 2008 onward
• Collection of enrollment data, medical claims, and pharmacy
claims files
• Defined by statutes and finalized through the public rule making
process
• More than 18,600 submissions
• Almost 4.5 billion records submitted
Number of Submitters
Count
Cumulative
Current
Total
Eligibility Data
111
100
Medical Claims Pharmacy Claims
84
77
90
85
66
64
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Our Solution: Onpoint CDM
www.onpointcdm.org
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Data Quality and Validation - Intake
• Onpoint uses over 500 DQ checks
– Data element level
– Within a file, across data elements
– Across files
– Trending
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Data Quality and Validation - Intake
Load Threshold Reporting
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Data Quality and Validation - Intake
Data Quality Reporting
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MN APCD Stakeholder Workgroup 2014
Data Quality and Validation - Intake
Trend Reporting
Dashboard
Membership
Detail
Module
Claims Paid
Detail
Module
Total Claims
Supported
by Eligibility
Module
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Data Quality and Validation - Intake
Percent of required resubmissions - MHCCRS
90%
80%
70%
one submission
60%
resubmissions
50%
40%
30%
20%
10%
0%
2009
2010
2011
2012
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Protecting Patient Privacy
Hashing data
• Per the administrative rule, PII (personally identifiable
information) is required to be de-identified
• Hashing versus encryption
– Hashing: one-way, permanent, and nonreversible
– Encryption: two-way, usually need a key to decrypt
• Hashing using SHA-512 transforms any data element into a 128character string
• Performed by submitters prior to submission to Onpoint
• Validations ensure designated fields are hashed prior to load
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Protecting Patient Privacy
Hashed Data Elements
Eligibility File
Medical Claims
Pharmacy Claims
ME009
Plan-Specific Contract #
MC008 Plan-Specific Contract #
PC008
Plan-Specific Contract #
ME014
Member Date of Birth
MC013 Member Date of Birth
PC013
Member Date of Birth
ME101
Subscriber Last Name
MC101 Subscriber Last Name
PC101
Subscriber Last Name
ME102
Subscriber First Name
MC102 Subscriber First Name
PC102
Subscriber First Name
ME103
Subscriber Middle Initial
MC103 Subscriber Middle Initial
PC103
Subscriber Middle Initial
ME104
Member Last Name
MC104 Member Last Name
PC104
Member Last Name
ME105
Member First Name
MC105 Member First Name
PC105
Member First Name
ME106
Member Middle Initial
MC106 Member Middle Initial
PC106
Member Middle Initial
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Data Standardization and Processing
•
•
•
•
Creating a unique patient ID
Creating a unique provider ID
Standardization
Consolidation of claims
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Data Standardization and Processing
Creating a Unique Patient ID – an example
Method
First Name
Last Name
DOB
Contract #
Unique ID
Direct
Identifiers
John
Smith
3/15/1964
BCBS456
123
Jon
Smith JR
3/15/1964
AETNA123
123
Hashed
Identifiers
A1G584DD
KU5D4S3R
K8G5D3D2
L5B2HJJF
123
TH2SS5F31
H5D3D15W
K8G5D3D2
W5RF2GH7
456
A1G584DD
KU5D4S3R
JT3S1V58
K8G5D3D2
L5B2HJJF
123
TH2SS5F31
H5D3D15W
JT3S1V58
K8G5D3D2
W5RF2GH7
123
Collection
of SSN
SSN
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MN APCD Stakeholder Workgroup 2014
Data Standardization and Processing
Unique Patient ID – Medical enrollment survival analysis
2009
2010
2011
2012
6,000,000
5,000,000
100%
100%
100%
93%
93%
87%
89%
83%
79%
4,000,000
3,000,000
2,000,000
1,000,000
0
2009
2010
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2011
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Data Standardization and Processing
Creating a Unique Provider ID
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Data Standardization and Processing
Standardization
• Data elements are standardized across all data sources for
consistency in analysis and reporting
– All crosswalks are stored in reference tables (e.g., relationship
codes, dependency status, product codes, gender codes, etc.)
NCPDP Standards Relationship
(Pharmacy)
Description
Code
Self
01
Spouse
02
Child
03
Other
04
MN APCD Stakeholder Workgroup 2014
X12 Standards Relationship
(Medical & Eligibility)
Code
Description
18
Self
01
Spouse
19
Child
G8
Other
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Data Standardization and Processing
Consolidation example – simple aggregation
Claim #
Claim Status
Paid Date CPT Code
Charge
Paid
ABC
Primary
02/03/2014
99214
250.00
135.00
ABC
Reversal
03/24/2014
99214
-250.00
-135.00
ABC
Primary
03/24/2014
99214
250.00
155.00
ABC
Primary
03/24/2014
99214
250.00
155.00
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Data Enhancements and Delivery
Extracts
• Value adds
– Claim type
– Inpatient discharge grouping
• Reference tables / referential integrity
• Additional post-consolidation quality assurance
• Groupers
– APCs for outpatient hospital
– DRGs for inpatient hospital
» MS-DRGs
» APR-DRGs
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Challenges and Recommendations
•
•
•
•
•
•
•
Additional patient identifiers (SSN) – Unique Patient ID
Group name – tracking movement of large groups
Primary insurance indicator – coordination of benefits (COB)
Discharge date – hospital stays, readmissions
Present on admission – improved MS-DRG assignment
Claim status (medical)- COB, denials
Pharmacy Location – in/out of state
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Data Quality Reporting
•
•
•
•
Due for completion November 2014
Collaborative effort –MDH & Onpoint
Includes overview of Onpoint’s data quality assurance steps
Includes trending and data quality analysis for enrollment,
medical claims and pharmacy claims
• Analysis based on most current extract
• Will update semi-annually with delivery of extracts
• QA cannot be static
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