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: HEALTHCARE DATA TENDS

TO BE CREATED AND RESIDE IN MULTIPLE PLACES – DIFFERENT SOURCE SYSTEMS, DIFFERENT DEPARTMENTS, ON INDIVIDUAL DEVICES

• FORMAT: TEXT, NUMERIC, PAPER, DIGITAL, IMAGES, MULTIMEDIA, VIDEO…AND THE SAME DATA CAN EXIST IN DIFFERENT SYSTEMS IN DIFFERENT FORMATS

• STRUCTURE: STRUCTURED VS UNSTRUCTURED DESPITE BEST EFFORTS TO LEVERAGE THE EMR AS A PLATFORM FOR CONSISTENT DATA CAPTURE

• DEFINITIONS: INCONSISTENT, VARIABLE AND SUBJECTIVE DEFINITIONS BASED ON THE SOURCE…AND NEW KNOWLEDGE KEEPS THIS TARGET MOVING

• COMPLEXITY: CLAIMS DATA, CLINICAL DATA, MYRIAD VARIABLES RELATED TO AN AMALGAM OF SYSTEMS, SHIFTING BUSINESS RULES AND DEFINITIONS

• REGULATORY REQUIREMENTS: CMS REPORTING AND HIPAA ARE JUST THE BEGINNING AS THE SHIFT TO VALUE-BASED PURCHASING MODELS WILL LIKELY ADD TO THE REPORTING BURDEN

DATA 911:COMMON CULPRITS OF BAD DATA QUALITY• ARCHITECTURE AND APPLICATION COMPLEXITY: DIFFICULT TO TRACK HOW

CHANGES TO A SYSTEM FILTERS THROUGH AND AFFECTS OTHERS. MULTIPLE, OVERLAPPING APPLICATIONS, OFTEN WITH NON-INTEGRATED DATA SOURCES, CAN CREATE A DATA ENVIRONMENT HIGHLY SUSCEPTIBLE TO BAD DATA QUALITY.

• LACK OF OWNERSHIP AND RESPONSIBILITY FOR DATA QUALITY: WITHOUT OWNERSHIP OF DATA QUALITY AT EACH INTEGRATION POINT, THERE IS A POTENTIAL FOR ASSUMING GOOD DATA QUALITY WHEN IN REALITY IT IS VERY POOR.

• REPETITIVE OR AMBIGUOUS BUSINESS PROCESSES: CREATE REDUNDANCY IN HOW DATA QUALITY IS ADDRESSED, OFTEN RESULTING IN DATA QUALITY CONFLICTS.

• UNCLEAR AND MULTIPLE DEFINITIONS OF DATA ELEMENTS: MISMATCHED SYNTAX, FORMATS, AND STRUCTURE FROM DISPARATE DATA SOURCES REQUIRE CONSISTENT DEFINITIONS OF DATA ELEMENTS TO ENSURE CLEAR MAPPINGS ACROSS THE ENTERPRISE

• NO CLEARLY DEFINED DATA QUALITY ESCALATION PROCESS: WITHOUT CLEARLY DEFINED DATA QUALITY ESCALATION PROCESSES, KNOWN DATA QUALITY ISSUES MAY NEVER BE ADDRESSED.

WHY DOES DATA QUALITY MATTER?

GOOD DATA IS KEY TO:

• EFFECTIVE DECISION-MAKING AT EVERY LEVEL: FROM THE BEDSIDE TO THE BOARDROOM

• STRATEGIC PLANNING

• CLINICAL QUALITY

• HEALTHCARE OPERATIONS AND FINANCIAL MANAGEMENT

• PUBLIC POLICY

• PUBLIC AWARENESS ABOUT FACTORS THAT AFFECT HEALTH

DATA QUALITY FRAMEWORK

• Discovery• Profiling• Analysis

Assess

• Cleansing• Enhancement• Consolidation

Remediate

• Measure and CompareMonitor

DATA QUALITY IS ONE COMPONENT OF A DATA MANAGEMENT PROGRAM

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

• ACCURACY

• CONSISTENCY

• UNIQUENESS

• LINEAGE

• VOLUME

• COMPLETENESS*

• TIMELINESS*

QUALITATIVE DIMENSIONS

• COMPREHENSION

• RELEVANCE

• TRUST

• OBJECTIVITY

OTHERS COMMON DIMENSIONS

• ACCESS

• SECURITY

• INTERPRETABILITY

• REPUTATION

• EASE OF MANIPULATION

• MAINTAINABILITY

• RATE OF DECAY

UAB HEALTH SYSTEMA 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)

DISCOVERY

• DEFINE SCOPE OF DATA

• ORGANIZATIONS & INDIVIDUALS

• PHYSICIANS & MID-LEVEL PROVIDERS

• CREDENTIALED

• REFERRING

• RESIDENTS

• AMBASSADORS

WHAT IS “PROVIDER” DATA?

DISCOVERY

• HSIS – CERNER, HEALTHQUEST, LMS, CUSTOM DATABASES

• MSO – CENTRICITY

• MDSO – HEALTHQUEST, CUSTOM

• MARKETING - CUSTOM

• PHYSICIAN SERVICES – HEALTHQUEST, CUSTOM

• GME, CUSTOM

• HIM – TRANSCRIPTION

• OTHERS…

WHERE IS THE DATA?

DISCOVERY

WHERE DOES THE DATA COME FROM? WHERE DOES IT GO?

• PROVIDERS ARE PROVISIONED DEPARTMENTALLY

• ADDITIONS AND MODIFICATIONS ARE MANUALLY ENTERED INTO ELECTRONIC SYSTEMS AND DISTRIBUTED VIA EMAIL TO INDIVIDUALS OR GROUPS

• PROVISIONED: MSO, MDSO, GME, LAB, RAD, HIM, MARKETING, AMBULATORY CLINICS, HSIS, …

PROFILING

• AN ALGORITHMIC ASSESSMENT OF DATA ELEMENTS, DATA RELATIONSHIPS, AND COMPLIANCE TO BUSINESS RULES.

• MEASURE AND SLICE AND DICE DATA TO IDENTIFY ANOMALIES

WHAT IS DATA PROFILING?

DATA PROFILINGDATASET STATISTICS

• 28K ACTIVE PROVIDER RECORDS

• 2.5K DUPLICATE PROVIDER RECORDS IDENTIFIED

• 3.5K RECORDS TO DEACTIVATE

• ~80% RECORDS WITH 1+ QUALITY ISSUES

PROFILING A DATA ELEMENTEXAMPLE

DATA PROFILE: NATIONAL PROVIDER IDENTIFIER (NPI)

WHY DO WE CARE?

• SYSTEMS INTEGRATION

• BILLING IMPACT

WHAT WOULD WE EXPECT TO SEE?

• TYPICALLY A UNIQUE, 10 BYTE NUMERIC VALUE

DATA PROFILING EXAMPLE (COLUMN)NATIONAL PROVIDER IDENTIFIER

Type Count %

Null 4,576 19.31%

Non-Null 19,118 80.69%

Duplicates 69 .29%

Distinct 19,049

80.40%

Non-Unique 69 .29%

Unique 18,980

80.10%

Null; 19.31%

Duplicates; 0.29%

Distinct; 80.40%

CountsBasic Counts

Data Type: STRING

Why are there duplicates?

Should there be Nulls?

The NPI is a numerical value. Identified as a String?

DATA PROFILING EXAMPLENATIONAL PROVIDER IDENTIFIER

Value Count %

NULL 4,576 19.31

DDDDDDDDDD

17,117 72.24

LDDDDDDDDD 2,000 8.44

DDDDDDDDD 1 0.00

Pattern Frequency

NPI is a 10 digit identifier. Inconsistent findings

Type Value Frequency

Min Value 1000004138 1

Median Value 1558551127 1

Max Value T000199999 1

Type Value

Min Length 9

Median Length 10

Average Length 10

Maximum Length 10

Additional Counts

DATA PROFILING EXAMPLENATIONAL PROVIDER IDENTIFIER

WHAT DID WE FIND?• DISCOVERED UNKNOWN TEMPORARY “SPARSE PROVIDER”

PROCESS RESULTING (AND IDENTIFIED BY “T” PREFIX ON NPI NUMBER.

• 1 INVALID VALUE

• NULLS EXPLAINED AND EXPECTED TO BE REMEDIATED OVER TIME.

DATA PROFILING EXAMPLECOMMON 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

ANALYSIS

WHAT WAS LEARNED?

• MULTIPLE SOURCES OF DATA PROVISIONING

• VARIED PROCESSES SUPPORT PROVISIONING

• LACK OF AUTOMATION AND USE OF EMAIL AS WORKFLOW

• ONGOING INDEPENDENT DATA QUALITY INITIATIVES

ANALYSIS

WHAT WAS LEARNED?

• ABSENCE OF DATA GOVERNANCE

• UNDEFINED DATA OWNERSHIP AND STEWARDSHIP

• INCONSISTENT DATA SEMANTICS

• INCONSISTENT POLICIES

• UNDOCUMENTED/UNKNOWN PROCEDURES

• BETTER COMPLIANCE AWARENESS NECESSARY

• NO MEASUREMENTS OR MONITORING

ANALYSIS

WHAT WAS LEARNED?

• DEMOGRAPHICS ACCURACY OPPORTUNITIES

• INCONSISTENCIES IN DATA BETWEEN SYSTEMS

• TRUST ISSUES

• INADEQUATE SYSTEM & PROCESS INTEGRATION

• LACK OF ENTERPRISE DATA SEMANTICS

• LEGACY REQUIREMENTS RESULTING IN DATA HOARDING

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 ORGANIZATIONA

L ISSUE

BEST PRACTICES IN DATA QUALITY MANAGEMENT

• UNDERSTANDING OF OVERALL BUSINESS OBJECTIVE – CLINICAL, OPERATIONAL, FINANCIAL. USE EXISTING OR UPCOMING INITIATIVE TO INCUBATE PROGRAM.

• ANALYSIS OF DATA “TOXICITY” – PRECISELY WHY IS YOUR DATA DIRTY?

• TAXONOMY/DATA DICTIONARY FRAMEWORK TO FACILITATE CREATION AND MAINTENANCE OF CLEAN DATA

• CONCERTED EFFORT TO CLEANSE AND ENRICH DATA ENTERPRISE WIDE

• SOFTWARE PLATFORM OR REPOSITORY TO HOUSE CLEAN DATA

• GOVERNANCE AND STEWARDSHIP AROUND DATA MAINTENANCE

HOW TO DELIVER DATA QUALITY

• CREATE THE APPROPRIATE GOVERNANCE – DATA IS YOUR BIGGEST ASSET AND DESERVES TOP SPONSORSHIP

• TAKE A 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 – FOCUS ON QUICK WINS, PROVE CONCEPT, FINE TUNE & REPLICATE

• INTEGRATED – PEOPLE, PROCESS AND TECHNOLOGY AND GOVERNANCE ALIGNED TO A COMMON GOAL

• SUSTAINABLE – ESTABLISH A LONG-TERM MODEL THAT CAN BE REPLICATED

• FLEXIBLE – FOCUS ON THE MOST PRESSING NEED

WHERE TO START

• BE CAUTIOUS OF BIG DATA HYPE

• DO SOMETHING! PROFILE SOME DATA

• BE RESTLESSLY FIRM ON SCOPE

• CRAWL, WALK, RUN

• WITHOUT GOVERNANCE, IT’S JUST A CLEANUP

QUESTIONS?

Dan RoundsPresidentImmersivedarounds@immersivellc.com

888.869.0984Immersivellc.com3411 High Cliff Road, Panama City, FL 32409

Brian BishopOperations and Interface ManagerUAB Health System Information Servicesbebishop@uabmc.edu