DATA QUALITY
MANAGEMENT
THE FUNDAMENTALS
FOR TODAY’S
SUCCESSFUL HEALTHCARE ENTERPRISE
WVHIMSS FALL CONFERENCE
NOVEMBER 14, 2014
AGENDA
• DATA 101
• DATA 911
• DATA QUALITY EXPLAINED
• UAB HEALTH SYSTEM – A CASE STUDY
• LESSONS LEARNED AND CALL TO ACTION
• DISCUSSION
DATA 101
• HOW MUCH NEW DATA DOES YOUR ORGANIZATION
CREATE IN A DAY? FROM WHICH SYSTEMS?
• WHERE DOES YOUR BUSINESS-CRITICAL DATA RESIDE
ACROSS THE ENTERPRISE? WITH WHOM IS IT SHARED?
• DO YOU COLLECT AND STORE THE RIGHT
INFORMATION TO SUPPORT THE ORGANIZATION?
• IS THE RIGHT DATA AVAILABLE TO SUPPORT TIMELY
DECISION-MAKING?
• IS THE DATA CONSISTENT WITHIN AND ACROSS THE
ENTERPRISE?
• CAN YOU GENERATE PERFORMANCE IMPROVEMENTS
THROUGH DATA INSIGHTS?
DATA 911:
WHY HEALTHCARE DATA IS ESPECIALLY COMPLEX AND
DIFFICULT TO MANAGE
• LOCATION:
• DEFINITIONS:
• FORMAT:
• COMPLEXITY:
• STRUCTURE:
• REGULATORY REQUIREMENTS:
DATA 911:
COMMON CULPRITS OF BAD DATA QUALITY
• ARCHITECTURE AND APPLICATION COMPLEXITY:
• LACK OF OWNERSHIP AND RESPONSIBILITY FOR DATA QUALITY:
• REPETITIVE OR AMBIGUOUS BUSINESS PROCESSES:
• UNCLEAR AND MULTIPLE DEFINITIONS OF DATA ELEMENTS:
• NO CLEARLY DEFINED DATA QUALITY ESCALATION PROCESS:
WHY DOES DATA QUALITY MATTER?
GOOD DATA IS KEY TO:
•
EFFECTIVE DECISION-MAKING AT
TO THE BOARDROOM
EVERY LEVEL: FROM THE BEDSIDE
• STRATEGIC PLANNING
•
CLINICAL QUALITY
•
HEALTHCARE OPERATIONS AND FINANCIAL MANAGEMENT
• PUBLIC POLICY
• PUBLIC AWARENESS ABOUT FACTORS THAT AFFECT HEALTH
DATA QUALITY FRAMEWORK
DATA QUALITY IS ONE
Assess
COMPONENT OF A
DATA MANAGEMENT
Remediate
PROGRAM
Monitor
•Discovery
•Profiling
•Analysis
•Cleansing
•Enhancement
•Consolidation
•Measure and
Compare
ASSESSING DATA QUALITY
• DATA QUALITY IS A MULTI-DIMENSIONAL CONCEPT
• MEASURE. USABLE METRICS ARE REQUIRED TO DEFINE AND ASSESS
QUALITY.
• DATA CAN BE ASSESSED BOTH OBJECTIVELY AND SUBJECTIVELY
• CONTEXT IS OFTEN IMPORTANT WHEN ASSESSING QUALITY
• QUALITY IS DEFINED BY THE INSTITUTION AND DOES NOT
NECESSARILY MEAN PERFECTION
DIMENSIONS OF DATA QUALITY
QUANTITATIVE DIMENSIONS
QUALITATIVE DIMENSIONS
OTHERS COMMON DIMENSIONS
• ACCURACY
• COMPREHENSION
• ACCESS
• CONSISTENCY
• RELEVANCE
• UNIQUENESS
• TRUST
• LINEAGE
• OBJECTIVITY
• VOLUME
• SECURITY
• INTERPRETABILITY
• REPUTATION
• COMPLETENESS*
• EASE OF MANIPULATION
• TIMELINESS*
• MAINTAINABILITY
• RATE OF DECAY
UAB HEALTH SYSTEM
A CASE STUDY IN DATA QUALITY
PROVIDER DATA QUALITY
PROVIDING MULTIPLE CHALLENGES
TO THE ORGANIZATION.
• DUPLICATE DATA
• MISSING
DATA
• INACCURATE
DATA
UAB PROFILE
• LARGE ACADEMIC HEALTH SYSTEM IN CENTRAL
ALABAMA.
• HEALTH SYSTEM COMPRISED OF MULTIPLE LEGAL
AND OPERATIONAL ENTITIES
• TWO PRIMARY IT ORGANIZATIONS +
DEPARTMENTAL
• HEALTH SYSTEM INFORMATION SERVICES (HSIS)
• MANAGEMENT SERVICES ORGANIZATION (MSO)
WHAT IS “PROVIDER” DATA?
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DISCOVERY
WHERE IS THE DATA?
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DISCOVERY
WHERE DOES THE DATA COME
FROM? WHERE DOES IT GO?
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DISCOVERY
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WHAT IS DATA PROFILING?
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PROFILING
•
DATA PROFILING
DATASET STATISTICS
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PROFILING A DATA ELEMENT
EXAMPLE
DATA PROFILE:
WHY DO WE CARE?
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WHAT WOULD WE EXPECT TO SEE?
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DATA PROFILING EXAMPLE (COLUMN)
NATIONAL PROVIDER IDENTIFIER
Data Type: STRING
The NPI is a numerical value. Identified as a String?
Basic Counts
Should there be Nulls?
Type
Count
Null
Non-Null
Duplicates
Distinct
Non-Unique
Unique
Why are there duplicates?
COUNTS
Null,
19.31%
%
4,576
19.31%
19,118
80.69%
69
.29%
19,049
80.40%
69
.29%
18,980
80.10%
Duplicates
, 0.29%
Distinct,
80.40%
DATA PROFILING EXAMPLE
NATIONAL PROVIDER IDENTIFIER
Pattern Frequency
Value
NULL
Additional Counts
Count
%
Type
Value
Frequency
4,576
19.31
Min Value
1000004138
1
DDDDDDDDDD
17,117
72.24
Median Value
1558551127
1
LDDDDDDDDD
2,000
8.44
Max Value
T000199999
1
1
0.00
DDDDDDDDD
NPI is a 10 digit identifier. Inconsistent findings
Type
Value
Min Length
9
Median Length
10
Average Length
10
Maximum Length
10
DATA PROFILING EXAMPLE
NATIONAL PROVIDER IDENTIFIER
WHAT DID WE FIND?
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DATA PROFILING EXAMPLE
COMMON PROFILING ANALYSIS
Average Value
Mean Value
Median Value
Range Analysis
Sparseness
Cardinality
Uniqueness
Value Distribution
Value Absence
Minimum Value
Maximum Value
Standard
Deviation
Type
Determination
Format
Identification
Overloading
Semantic
Variance Analysis
Key Analysis
Dependency
Analysis
Orphaned
Records
Redundancy
Analysis
Integrity Analysis
WHAT WAS LEARNED?
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ANALYSIS
WHAT WAS LEARNED?
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ANALYSIS
WHAT WAS LEARNED?
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ANALYSIS
ONGOING REMEDIATION
• IT STEWARDSHIP INITIATIVES:
• ACQUISITION OF NEXTGATE MDM SOLUTION
• DATA CLEANUP
• DE-DUPLICATION
• NORMALIZATION
• DEPARTMENTAL INITIATIVES (IT LED)
• WORKFLOW OPTIMIZATION
• INSTITUTE DATA GOVERNANCE (IT INFLUENCED)
• DEFINE OWNERSHIP
• DEFINE STEWARDSHIP
• DEFINE ENTERPRISE SEMANTICS
• SET GOALS, MEASURE, REVIEW, AND REACT
DATA
MANAGEMENT
IS AN
ORGANIZATIONAL
ISSUE
BEST PRACTICES IN DATA QUALITY MANAGEMENT
•
•
BUSINESS OBJECTIVE
DATA “TOXICITY”
• TAXONOMY/DATA DICTIONARY
•
CLEANSE AND ENRICH DATA
• SOFTWARE PLATFORM OR REPOSITORY
• GOVERNANCE AND STEWARDSHIP
HOW TO DELIVER DATA QUALITY
• CREATE THE APPROPRIATE
•
GOVERNANCE
RISK-BASED APPROACH – FOCUS
ON THE AREAS THAT POSE THE GREATEST
RISK TO THE BUSINESS
• MAINTAIN BUSINESS
BENEFIT – PERFECT
DATA IS NOT THE AIM, ONLY DATA FIT FOR
ITS PURPOSE
• PROACTIVE – ADDRESS KNOWN ISSUES THAT
DERIVE FROM POOR DQ, INVESTIGATE AND
ASSESS IMPACT ON BUSINESS STRATEGY
• ITERATIVE
• INTEGRATED
• SUSTAINABLE
• FLEXIBLE
WHERE TO START
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•
QUESTIONS?
Dan Rounds
President
Immersive
darounds@immersivellc.com
888.869.0984
Immersivellc.com
3411 High Cliff Road, Panama City, FL 32409
Brian Bishop
Operations and Interface Manager
UAB Health System Information Services
bebishop@uabmc.edu