best practices · rethink survival and growth strategies. a growing trend towards the...
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BEST PRACTICES: How Multi-Domain MDM Drives
Business Value Through Innovation
Rajan Chandras
June 20, 2012
Synopsis The healthcare industry is in the throes of a transformation that some consider the most momentous since the introduction of Medicare and Medicaid in the 1960s and HMO’s in the 1970. The inexorable move towards consumer-driven healthcare (CDH), driven by the ever-escalating cost of care combined with increased utilization, is forcing health plans to rethink survival and growth strategies. A growing trend towards the consumerization of health care is ushering in a new era of agility and competitiveness that requires health plans to rapidly evolve and innovate in provider management and member outreach – both of which begin with a “mastery” over provider and member data. At Horizon Healthcare Services (NJ), an upfront partnership with business and unwavering focus on the business value of MDM has led to an innovative yet challenging single-instance, multi-domain MDM implementation – multiple but closely related entities mastered together in a single hub – that provided measurable business benefits even before going live. This case study describes the critical success factors behind the eventful journey of the MDM implementation from concept to reality, including:
• Creating an innovative data/solution architecture that embeds Data Governance principles
• Optimizing the partnering relationship with Systems Integrators and MDM solution vendors
• Reducing project risk and demonstrating business value through proof-of-concept
Company Biography
Founded in 1932, Horizon Healthcare Services, Inc. has a long and proud history of being the premier health insurance choice of residents and businesses in the Garden State. Incorporated as a not-for-profit, Horizon Healthcare Services, Inc. operates as a health insurance business for the benefit of its members. The company, governed by a 15-member board of directors, does not have shareholders. The company is not a state agency and is not the state’s insurer of last resort.
Horizon Healthcare Services, Inc. serves more than 3.6 million members. In 2011, it processed more than 58 million medical claims totaling more than $13.8 billion for its members, who have access to a network of more than 27,000 doctors and health care professionals and 74 hospitals. The company has more than 5,000 employees and is headquartered in Newark, NJ with offices in Harrison, Wall Township, Mt. Laurel, and West Trenton.
Horizon Healthcare Services, Inc. is committed to ensuring its members have access to safe, affordable and effective health care. The company is dedicated to improving the health and well being of its members and New Jersey’s communities and to distinguishing itself through service excellence.
Presenter: Rajan Chandras
– Delivery Lead, Data Strategy & Management, Horizon Healthcare Services, Inc; previously Principal Consultant with CSC
– Over 20 years experience in enterprise data management (data architecture, master data management, data warehousing, business intelligence), from vision/strategy to implementation and beyond
– Experience across multiple industries including Healthcare, Pharmaceutical, Utilities and others
– Columnist for InformationWeek group of publications, with about 200 writings over 10 years
– Academia • M.S. Computer Science (Georgia Institute of Technology) • M.Sc. Physics (University of Pune, India) • PMP, PAHM Certifications
Contents
– Business Drivers for MDM – The DG-MDM Collaborative – Preparing for the Journey – Takeoff – The Light at the end of the Tunnel – Looking Ahead
Business Drivers for MDM
Master Data in Context – Using Business Example
Birthdate Gender
Patient name, address, phone
Insured name, address, phone
DX
CPT
TIN Billing provider
Relationship • Transactions pull together
the different types of data: • Master • Control/Reference • Transactional (values)
CONTROL / REFERENCE DATA Generally controlled through Approved Lists of Values (LOVs)
State
MASTER DATA (Who, What, Where)
TRANSACTIONAL DATA (How many, how fast, how much)
Charges Units
Whe
re
INFERRED through the Master Data Provider Type Provider Specialty
The Information Challenge We Face
Data Governance
?
Customer Service
Consumerism & Health Exchanges
Compliance Sales Marketing
Medicare Advantage
Medical Claims
Provider Data Management
Dental Medicaid
Medicare PPO/HMO Medicaid
Dental
Physicians
Ancillaries
Hospitals
Pended Claims
Incorrect Contact Info No View of
Consumer
Joseph Braverman, M.D. Dr. Joe Braverman Kate Smith
450 Second Street HMO
Katlyn Smith 452 2nd St
Newark, NJ-07105 PPO 20/150/40 Kate Smith
Medicare B
Portals
Synthesize & govern data across silos & sources
The Vision: Centralized Master Data
Data Governance
Customer Service
Consumerism & Health Exchanges
Compliance Sales Marketing
Medicare Advantage
Medical Claims Provider Data Management
Dental Medicaid Portals
Provider Customer Employee Member Product
Enterprise Master Data Hub
Broker … ICD-10
Kate Smith 452 Second
Street, Newark, NJ-07105
Disparate Data Reliably Resolved
3rd Party Organization
Joseph Braverman, M.D. [email protected]
Business Drivers for MDM Industry Trend Drivers
• Escalating Healthcare costs • Need to improve Stakeholder Satisfaction • Emerging changes in delivery models • Increasing role of the educated consumer
Information Technology Drivers • IT computing innovations (Virtualization, Clouds, Web 2.0, Semantic Web, etc.)
• Stakeholder Expectations of Data, Information and Knowledge “On Demand”
• IT Costs & Spending Trends
• eBusiness Changes (Human interaction through Portals, Electronic Data Interchange – EDI, Social Media etc.)
Legal, Compliance & Regulatory Drivers • Healthcare Reform • “Blue” Association Operating Compliance • Regulatory Reporting & Compliance • Industry Standards Bodies/Committees
• On Demand Distribution of Products, Services, Data, Information & Knowledge
• Data & Information Standardization • Converging Business Platforms • Transparency to Stakeholders
Corporate Strategy Drivers • Business Mission, Vision, Objectives, Goals • IT Mission, Vision, Objectives, Goals • Increase Market Share, Revenue • Reduce Expenses
Healthcare Business Value Proposition
Member & Provider Collaboration
Claims Reimbursements
Authorizations Referrals
Customer service Network management
Case Management Care Management
Pricing Portals
Marketing Segmentation CRM Strategy
Service & Billing ACO
Enabling a unified, consistent, accurate, and timely view of Master Data
Member Profile Provider Profile
Contracting Demographics
Networks
Capitation
Provider Types
Physicians Groups
Hospitals Diagnostics & Labs
Durable Medical Dental & Vision
Retail Health Clinics Delegates
Subscribers
Patients Contacts
Preferences
Segmentation
Unified view of Provider and Member Data across enterprise applications and lines of business, improving collaboration and quality of care and reducing costs- creating a patient centric view
Flexible data model
Definable business logic & workflows
Configurable user interfaces
Solves any business problem
Can use the same platform to scale to other business problems/ domains
Pre-defined data model
Pre-defined business logic & workflows
Pre-configured user interfaces
Solves the given business problem
Scaling to other business problems/ domains requires additional applications
Platform MDM Application MDM Entry Point Transactional Applications
An adaptive approach that relies on configuration based on unique requirements
Implications
Why Did We Need an MDM Platform?
Pre-packaged application that solves specific MDM issues (e.g. Provider, Member, Customer, Product, Finance)
Additional Benefits
– Standardize data services • Deliver consistent information to members and providers across channels • Decouple and insulate downstream systems from changes to upstream systems
– Retire obsolete components from infrastructure • Reduce the cost of maintenance • Reduce data inconsistencies across systems and improve business and IT
operational efficiencies • Increase agility with faster time to implement new business functionality
– Enable portals and self-service administration • Implement necessary workflows to authorize changes • Improve operational efficiency, customer satisfaction and regulatory compliance
– Enable reporting and analytics • Unified view of providers and members across all plans, lines of business and
market segments
Role of MDM in EDW/BI Strategy
We need to first fix the data
– Data Warehousing and Business Intelligence/Analytics depends on good quality and consistent/conformed master data
– Big Data Analytics – an appliance by itself does not solve the problem
– Therefore our strategy is first MDM, then Enterprise Data Warehouse
IDS For
HDAP
• Atomic dimensional model at transaction grain
• Aggregated data for roll-up analysis
• Data domains: • Member • Group • Provider • Medical Claims • Rx Claims
• MDM publishing layer
• Upgraded BI platform
• Rationalized reports/cubes
• New reporting portal (replaces InfoInsight)
• Informatics Sandbox
Master Data
• Current view • Limited history • Normalized for
easier updating
• IDS consumers
MDM
Extract
HADW
Transaction Data
Operational Sources
Claims Member Provider
Others
The DG-MDM Collaborative
DG & MDM – Which comes first?
– Business optimization drives the need for Data Governance
• Lack of data quality, accuracy, consistency and uniqueness is a business problem
• Business process optimization cannot succeed without a strong data foundation
• Analytics is unreliable without good data
– Master data management enables effective Data Governance
• Single, consistent view of the important master data entities
• Ability to integrate master data into operational systems
Preparation – preliminary steps to identify key participants, key data elements & sources, and individuals to be interviewed; to socialize operating model with key stakeholders; and to define scope and approach
Discovery – gain understanding of the data and processes in the current environment, introduce Data Governance concepts, best practices and roll-out of Data Governance program
Analysis – pinpoint and clarify key data touch points, data handling processes, issues and work-arounds; bridge from discovery to synthesis , with focus on clearly defining the current environment
Synthesis – generate informed recommendations based on discovery and analysis. Tailor and define the near & future state policies and procedures, including gaps and potential remediations
Data Governance Approach
Preparation Discovery Analysis Synthesis
Communicate With Stakeholders
Collaborative Effort Between Business and IT
Recommendations
Data Profiling Requirements Logs
Requestor (Stakeholder, PM)
Request
Populate: Enterprise Metadata Manager Root Cause Analysis Enterprise Data Issue Log
If needed: Impact Analysis Impact Assessment
Data Stewards Subject Matter Experts IT Custodians Stakeholders Project Teams Solution Architects
Data Steward
DG Core Team
Review and update: Enterprise Data Issue Log Decision Matrix Impact Assessment
Approve Escalate
Stakeholders
Create
Review
Review and update: Enterprise Data Issue Log Decision Matrix Root Cause Analysis
Notify
Approve
Data Stewards Stakeholders Solution Architects
DG Governor
Provide Input
DG Council
Post to Sharepoint
Data Governance Stakeholders & Process
1
2
3
5
6
8
1. Steward or Stakeholder(s) discovers and logs an issue.
2. Steward completes (or delegates) root cause analysis.
3. Steward sends prioritized issue log and root cause analysis to DG Core Team.
4. Stakeholders review data quality issue, gauge potential system impact (if any) and advise Steward of same.
5. DG Core Team acts as escalation point.
6. DG Core Team sends recommended action list to Steward.
7. DG Core Team posts decisions to Sharepoint.
8. Steward communicates recommendations to Stakeholders for implementation.
7
4
• Data Governor chairs the DG Council and provides strategy, vision, prioritized direction and empowerment for Business and IT staff
• The Data Governance Council drives governance Standards, Policies, Procedures, Metrics, etc.
• The Data Governance Council is populated with key leadership that has the authority, accountability and responsibility to get things done
• This is “not” a Consensus Model… Data Trustees make it clear as to what they need and why they need it and all other leaders exist to help clarify vision and how to accomplish the requirement. The only Consensus is in the prioritization of work
• The Data Governance Council partners with the Planning Council to align funding for Initiatives, Programs, Projects, etc.
• The Data Governance Council adjudicates issues that are brought to the Council and ensures clarity through education & training.
Data Governance Council Composition
Data Governor
Data Governance Council (Business, IT, and Administrative)
IT
Administration
Membership & Benefits
Claims
Provider
Auditing Security
Service
Product & Marketing
Healthcare Management
Classification of Points of Pain
BUSINESS CRITICALITY BUSINESS VALUE Level of Effort
Data Governance Maturity Model
Approx Timeframes Year 1 Year 2 Year 3 Year4…..
Risk
High
Low
Low
High
Rew
ard
People, Process, Technology Adoption
No concept of data management as separate discipline
Data management is considered to be part of IT
Value of Data Management is recognized, but capabilities are under-developed and under-funded.
Characterized by tactical, reactive, decentralized, business unit funded approach to data issues
Some collaboration, common data solutions and data standards,
typically informal via cross functional teams
Characterized by proactive quality improvement programs
Formal Data Management Office established involving business and operational stakeholders supported by senior management.
Characterized by:
Executive level - support for short and long term initiatives
Involved - stakeholders from multiple business units and support functions (Technology & Operations)
Well defined Enterprise governance and data stewardship roles and responsibilities
Data management seen as a critical component of the overall business
Data is seen as a corporate asset
Organization’s people, processes, and technology working together organically and autonomically
Stage 1 Basic
Stage 2 Managed
Stage 3 Repeatable
Stage 4 Quantitatively
Managed
Stage 5 Continuous
Improvement
2012 Target
Information Accuracy & Organizational Confidence
Preparing for the Journey
Phasing the Implementation
– Provider Data Governance and Stewardship • Definitions and identifiers • Individuals, groups and organizations, provider types • Member, subscribers, dependents and patients
– MDM Tools Selection • Determine needs and MDM style (Integration, Registry, Hub) • Single-domain versus multi-domain
– MDM Pilot • Focus on how provider data is used in the business • Engage Data Stewards to prototype match/merge and trust rules
– Initial Rollout – Provider Directories, Member and Provider portals and communications • Individuals, groups, organizations • Service and billing locations • Provider and Member email and contacts
– Innovation Center (“MDM Lab”) • Focus on new functionality and product upgrades to be tested in a Lab • Simulate Production like environment before starting development (Adopt Lessons Learned)
Single-Domain Versus Multi-Domain
– Multi-domain solutions versus Single-domain• We chose to leverage and reuse a single tool to master Provider, Member, and
reference data• Eliminates cost of buying multiple tools and creating competencies around
multiple, domain-specific tools /apps – Multiple MDM instances versus Single MDM instance
• We chose to integrate provider and member master using a party model that aligns with the enterprise data model we are designing
– Key value of having a single instance is the ability to identify and manage roles & relationships
PhysiciansPhysicians
PatientsPatients
MembersMembers
CustomersCustomers
PlanPlan
NetworksNetworks
Multi-Domain MDM Data Model
Dependent Member Role
Credentialed Provider Specialty
Individual Provider Type
Member Role
Network
Party QualificationPerson Marital Status
Person Ethnicity
Subscriber Member Role
Specialty
Marital Status
Organization Provider Role
Person GenderProvider Type
Individual Provider RoleParty Role Relationship
Provider Network
Provider Specialty
Person
Party Role Contact Mechanism
Organization
Party Identification
Party
Provider Role
Party Role
Group Organization Provider Role
Institutional Organization Provider Role
plays
MDM Tool Selection Evaluation Criteria
MDM Business Value Categories
Third party data service integration
Data Stewardship support
Workflow and rules
Multi‐domain and Payer data models
Match/merge technology
Training and professional services
Total cost of ownership
Health plans/Blues customer success
MDM Technical Value Categories
Extensible data models
Integration with enterprise applications
Change and release management support
Pre‐canned web services
Supports common MDM styles
Integration with infrastructure services
Integration with BPM tools
MDM Evaluation Activities
Written questions
Demonstrations
Technical deep‐dive sessions
Use case run‐throughs
Total cost of ownership
Third Party Data Selection CriteriaEvaluation Criteria Evaluation Questions
Cost What is the pricing model (Geography, Type, Frequency, etc)? What are the contract lengths and terms? What are the costs for the offered menu of data and/or services?
Data Coverage What is the provider or member match rate? What data elements are offered?
Data Model Fit How well does the vendor’s data model map to the Horizon’s use cases and Data Model?
Data Quality Lift What quality improvements and enhancements have been measured?
Data Sourcing Which data elements are sourced a) by the vendor b) from public sources c) from third parties?
Metadata and trust rules
What metadata is provided for defining trust and usage rules?
Reporting and analytics
What analysis does the vendor provides around the data?
Data Security The vendor must completed a BAA and Horizon’s Vendor Information Security Risk Assessment Survey. Horizon IT Security and Legal approval are required before sample data can be sent to the vendor.
Batch Data Publishing Details around a unidirectional vendor to Horizon data feed for NJ, NY, PA and DE.
Batch Data Matching Details around providing a bidirectional data feed with a data service to match and append Horizon supplied data.
Online Lookup Details around vendor offered portal, web site or application so that Horizon staff can perform ad-hoc lookups.
Real-time Web Service Details around vendor offered real-time web services that can be integrated with Horizon’s MDM solution.
Previewing with MDM Pilot
– MDM Pilot Goals• Integrate Provider data from two or more representative systems and create
a "golden" record• Integrate Member data from two or more representative systems and create
a "golden" record• Integrate email data from two or more sources into Provider and Member
“golden” record.• Build an MDM workflow and execute data stewardship transactions • Stretch Goal: Improve Provider/Member data quality using external
– MDM Pilot Learning• Source data quality• Business rules• Data integration challenges• Match/Merge Rules and Trust Framework
MDM Pilot ResultsParty Match-Merge Results…”Golden Record” count
Counting Noses & Belly Buttons…
<1%<1%
<1%
2.1%Total
Subscribers41%
Providers
13%
TotalDependents
43%
MDM COMPARISONSBefore After
Distinct Addresses 71.27% 48.72%Candidates for Merging 0.06% 24.04%Undeliverable Address 3.60% 2.04%Parties with No Address 0% 11.10%Standardized Address 0.37% 68.69%
Takeoff!
Completed In progress Scheduled
– Validate the capabilities of Informatica Master Data Management (MDM) solution specific to Horizon BCBS Provider and Member domains, data sources and consumer systems
• Data Integration: Integrate Provider and Member data from multiple sources• Data Quality Improvement: Enrichment Data using Third Party data providers• Data Governance: Generated workflows to support data stewardship• Consolidated outputs: Produce data extracts to demonstrate “full lifecycle” MDM value
MDM Implementation Time Lines
Oct 11Q3 FY11
Jun 12May 12Apr 12Q1 FY12 – Q2 FY12
Mar 12Feb 12Jan 12Dec 11Nov 11Activity
Parallel RunsUAT TestingSIT TestingDevelopmentDesignRequirementsData Analysis
Jul 12
Staffing for Success
– Staffed people with experience and depth in:• Information Management Strategy• Enterprise Data Architecture• Provider, Member and Product data• Prior success with the MDM Tool• Prior success with big data integration• Health Care Analytics
– Partnered with SI vendor• Company• Vision & Strategy• Accelerators• Implementation Experience• Resources• Pricing
– Partnered with Product vendor• Validation of approach, architecture and data model• Solution expertise
ETLServer
Provider Data Management
Internal DATA SOURCES
Enclarity
ETL ServerIntegration and
Data QualityServicesCredentialing
Dental
MDM Pre Processing
Batch/Real Time
ODBCBatch/Real
Time
ODBC
SharePoint Site1. Ancillaries2. Hospitals3. Radiology4. Imaging5. Vision
Batch/Real Time
ODBC
External DATA SOURCES
Batch/Real Time
PWX
Provider Portal
CAQH
Master Data
Standardize Master
Master Data Services
Internal Consumers
Other Sources
ApplicationServer
Integrated Workflow Services
Provider Web
Services
External Consumers
Business Partner
Provider Data
NDMProvider
Batch Services
Online Provider Directory
Data Stewards
Provider Data Management
Tasks
Lookups
Resolution
ApprovalsETL
CDC
Cleanse
Data flow
InteractionOutputStaging
Batch/Real Time
Batch/Real Time
MDM Server
FTP Pull / Batch
Batch/Real Time
Transform to Common
Record Format
Maintain ModelConfigure Hub
Generate Web Services
Conflict ResolutionApproval Workflows
Dashboard
Match/MergeSurvivorship
Golden Record
Provider MDM Architecture View
ETLServer
Membership System
Internal DATA SOURCES
Acxiom
ETL ServerIntegration and
Data QualityServices
Claims SystemNew Membership
System
MDM Pre Processing
Batch/Real Time
ODBC
Batch/Real Time
ODBC
External DATA SOURCES
Batch/Real Time
PWX
Master Data
Standardize Master
Master Data Services
Internal Consumers
Active Directory
ApplicationServer
Integrated Workflow Services
Member Web Services
External Consumers
Member Batch
Services
e-Business
Data Stewards
Membership System
Tasks
Lookups
Resolution
ApprovalsETL
CDC
Cleanse
Data flowInteraction
Interface Hub
FTP Pull / Batch/Real Time
Batch/Real Time
Email1.Sales2.Cust Service
Transform to Common
Record Format
MDM Server
Maintain ModelConfigure Hub
Generate Web Services
Conflict ResolutionApproval Workflows
Dashboard
Match/MergeSurvivorship
Golden Record
Member MDM Architecture View
Staying the Course
– Managing risks• MDM was a new tool and discipline at Horizon• Although the tool has been used extensively in Pharma, this is the first
implementation of the tool at a Payer• Data integration (ETL) is always a challenge• Onboarding the right resources at the right time was key
– Project agility• Architectural and data model changes• Project re-planning• Resource and workload balancing
– Communication with stakeholders• DG as key communication channel• Continuous education
The Light at the End of the Tunnel
MDM Implementation Results (in UAT)Match and Merge Statistics
<1%
<1%
<1%
20%Member
Subscribers40%
Providers
5.7%
MemberDependents
33%
MDM Distribution of Providers & Members -IMPLEMENTATION
After Match & Merge Auto Match ManualTotal Prty 8,734,482 56,778 Provider 485,095 16,829 Member 8,215,203 39,846
Provider & member 30,258 103 Member Subscriber 4,167,399 28,815 Member Dependent 3,105,329 3,890
Member Dependent +Subscriber 92,733 7,244
• 275K Providers automatically merged, 0.3% queued for manual merge
• 1.1 Million Members automatically merged, less than 0.1% in manual queue
<1%<1%
<1%
2.1%Total
Subscribers41%
Providers
13%
TotalDependents
43%
MDM Distribution of Providers & Members – PILOT
MDM Key Insights and Success Factors
Key Insights• Overlaps between Providers and Member• Overlaps between Member Subscribers and Member Dependents• 275K Providers automatically merged, 0.3% queued for manual merge• 1.1 Million Members automatically merged, less than 0.1% in manual queue
Success Factors• Multi-domain single-instance MDM Hub• Strong architectural vision• Partnering with Data Governance• Early business adoption of golden records• Clear vision to future – “define/refine business value” continuous loop• Never leave the soap box• Success is spelled “p-e-o-p-l-e”
Looking Ahead
The future’s so bright, I gotta wear shades…Timbuk 3
Future State MDM/IDS Distribution Solution Framework
Acquisition & AuthoringReal-time/Near Batch Change Capture
DistributionSelf-service (pull) interfacePublishing (push) interfaceMessaging-oriented interface
Data QualityValidationAudit, balance & controlQuality Tracking
MetadataMetadata shoppingImpact analysisOperations Monitoring Quality Investigations
Administration and MaintenanceChange managementSecurity managementOperational supportProcess monitoring Performance management
Stewardship Process Distribution RequestNew/changed Master Data RequestQuality Improvement InitiativeOperations&Quality Monitoring
Master Data ManagementHistorical Data ManagementStorage servicesSchema servicesMapping/alignment ServicesHierarchy Management
SuppliersAuthoritative Sources End-user Authoring
Data Governance & Stewardship
–
Acquisition &
Authoring Services
Distribution Services
Metadata Services
Master DataManagement Services
Data Quality Services
AdministrationServices
MaintenanceServices
Workflow Services
Workflow ServicesReview and Correction Approval and Publishing Exception escalation
ConsumersSuppliers
TransactionsInteractions
Member Information Products & Benefits
– Basic– Preferences– Extended– Prospect/Lead– Broker– Health Risk– PHR– Provider, PCP– Alternate Programs (e.g., ACO, etc.)– COB & other Ins.
Data
– Service Requests– Messages– Care Messages– Correspondence– Payment– Member Provider Interactions
Interaction Data
– Product– Benefit– Renewal– Ancillary Product– Wellness & Disease– HSA/FSA/HRA
Data
– Claims– Pre-Cert & Pre Authorizations– Care Management– Appeals & Grievances– Billing– Delegate Partner Data– EOB
Interaction Data
Enabling the Consumer 360°Using Multi Domain MDM
Consumer 3600 View - Data ElementsIntegrating customer related data from internal and external sources is a key enabler with MDM
