Architecting a Next Generation Data...
Transcript of Architecting a Next Generation Data...
Architecting a Next Generation Data Platform: Quest Diagnostics Information and Analytics Blueprint Presented by: Jason O’Meara, Director Analytics & Data Architecture
HIMSS Clinical & Business Intelligence Community Feb 2016
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Quest Diagnostics statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at Quest Diagnostics sole discretion.
Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract.
The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
Please note
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Quest Diagnostics: Unmatched Size and Scale
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Laboratory diagnostics provide great value for healthcare organizations
70% of healthcare decisions
of healthcare costs … 3%
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Largest Clinical Data Repository
Over 1.5 billion patient encounters
Expansive Test Menu
>3,500 tests
Provider Insights
50% of US providers
Patient Insights
70 million unique
patients per year
Clinical Workflow Integration
Programs leverage
Quest Diagnostics Touchpoints
Data Insights Action
Quest Diagnostics produces clinical insights that drive action through clinical workflow integration
We leverage our data, expertise and reach to drive action with solutions that integrate with the clinical workflow
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Big Data Lake and Self Service Analytics Next Generation Data Platform
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What is a Data Lake? A central repository of original data of any size and format loaded or streamed into a repository (the lake) without prior schema definition, data transformation, or requirements definition.
Captured
Detected
Inferred
Use structured data & unstructured content
Descriptive Analytics Prescriptive Analytics Predictive Analytics
Make it consumable and accessible to everyone, optimised for their specific purpose, at the point of impact, to deliver better decisions and actions.
Forecasting What if these
trends continue?
Stochastic Optimisation How can we achieve the best
outcome and address variability?
What happened?
What exactly is the problem?
How many, how often, where?
What actions are needed?
Simulation What could
happen?
Optimisation How can we achieve the best
outcome?
Predictive Modelling
What will happen next if?
Analytics Sophistication
Slide courtesy of IBM
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Merging Traditional And Big Data Approaches
Traditional Approach Structured & Repeatable Analysis
Agile Big Data Approach Iterative & Exploratory Analysis
Business Users Determine what Question to ask
IT Structures the data to answer that question
Monthly sales report Profitability analysis Quality reporting Activity monitoring
Market sizing and penetration Product strategy Maximum asset utilization Six Sigma & Operations Science
IT Delivers a platform to enable creative discovery
Business Users Explores data for insights on pressing questions
Slide courtesy of IBM
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Detailed System of Record Data
Data Delta Detailed System of Record Data
Detailed Data Aggregates
Summary Data Aggregates
Dimensional Data
Landing Area
Self Provisioning Data
Hadoop Data store
RDBMS Data Store
Legend
Hybrid Data Store (Hadoop / RDBMS)
Integrated Warehouse & Marts Zone
Deep Data Zone
Data Exploration
Exploration Zone (Discovery Area) Data Sandbox Areas
Landing Area Zone
Data Prediction
Data Flows across the Big Data Reference Architecture
Slide courtesy of IBM
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Raw Data Detailed somewhat Modelled Data
Aggregate Data Detailed Modelled Data
Hadoop Data store
RDBMS Data Store
Legend
Hybrid Data Store
Calculated Data
Patterns discovered by the Data Scientists guide the design of the overall data flow to the subject data users.
User Guided & Advanced Analytics
Detailed System of Record Data
Data Delta Detailed System of Record Data
Detailed Data Aggregates
Summary Data Aggregates
Dimensional Data Landing Area
Self Provisioning Data
Data Exploration Data Sandbox Areas
Data Prediction Subject Data Users
Data Scientists
Analytical or Predictive Models
Visualization, Data Mining & Exploration
User Reports & Dashboards
Slide courtesy of IBM
Data Flows across the Big Data Reference Architecture
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Data Delta
Detailed Attribution & Dimensional Orientation
Summary Aggregates
For-Purpose Dimensional Data Marts
Clinical, Operational an
Financial Metrics
Analytics Appliance & BI Platform
SPARK
Curate & Standardize
(Spark + Scala)
Historical SORs (DWHs)
M/R
Big Data Lake + Advanced Analytics Data Flow
Unstructured Ingest
(Sqoop/Flume)
Streaming Ingest (JMS)
Integrated Subject Areas
(HBase)
Subject-Area Historical
(Hive) Structured
Ingest (Sqoop)
NLP & Facet
Unified Dimensional
HC Data Model
(Parquet & MPP)
MDM & Ref Data (Hive)
Source Batch Files
ESB
Historical SORs (DWHs)
MDM Systems
Normalized & Linked
(Hive)
Orders
Client/ HCP
Receivbls Remit.
Logistics Facility
Bio-Informatic
Diag/ Proc
Revenue/ Costs
Results
Patient
Clinical – Patient & Attribution
Clinical – Disease Pathway
Clinical – Gaps & Util
Billing – Revenue Cycle
Billing - Payer Performance
Comm- Opportunity & Leads
Revenue, Cost, and EPM
Operational - Performance
Operational - Quality
…
Advanced Analytics (Statistical Models, Recommendation, Classification, etc)
Summary Aggregates
Hadoop
SOLR
HDFS
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ABA
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BAB
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Health Plan FQHC Hospital ACO
Data Source Data Source Data Source
Data Source Data Source
Data Lake
Calculations Engine
Health Care Data Model
Quest Analytics Platform
On Demand
Quality Support
Tools Utilization Insights
On Demand
Quality Support
Tools
Utilization Insights
On Demand
Utilization Insights
On Demand
Pop Health
Pop Health
Driving Insights for our Customers through Big Data Analytics Conceptual Architecture
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Foundational Capabilities & Data Governance Orchestrating People, Process, Policy and Technology Investments
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Big Data Lake or Swamp?
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Foundational Enterprise Information Management Capabilities
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• Data Structure Classifications
• CRUD Processes
• Enabling Technologies
Data has one Version of the
truth
Master Data Management
• Traceability • Business to
technical terms linkage
• Enabling Technologies
Metadata Management
Data has Lineage
• Completeness • Correctness • Referential
Integrity • IMR
Data Quality
Data has Quality
• Data Encryption • Data Masking • Network Security • Authentication • De-identification
Data Security
Data is Secure
• Conformity Enterprise
• Standardization • Interoperability
Data Standardization
Data has a Consistent
set of definitions
• Data Integration • Data Model • Data Access • Big Data • Form for Purpose
Data Architecture
Data is Accessible
• Organizational Constituencies
• Policies/ Processes
• Enabling Technologies
Data is Governed as a Strategic
Asset
Governance
To establish data as a strategic enterprise information asset and to provide the foundation for the enabling capabilities, the Master Data and Analytics blueprint focuses on the following foundational capabilities necessary to ensure that data is governed, has high integrity, reliability, usability and accessibility.
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Data Governance roles interactions & processes
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Big Data Context NLP and Master Data Management Capabilities
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Connecting the Dots in Healthcare
The true value of Big Data is in context
Raw data
Feature extraction metadata
Domain and master data linkages
Full contextual analytics
Location risk
Occupational risk
Dietary risk
Family history
Actuarial data
Government statistics Epidemic data
Allergy, Medications
Personal financial situation
Social relationships
Travel history
Weather history
. . .
. . .
Patient records
Slide courtesy of IBM
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Connecting the dots across the healthcare ecosystems
For healthcare companies, the customer is fragmented into differing sets of those who receive, those who order, and those who pay for services
Care Managers Physicians Insurance Companies
Employers
Patients Clients Payers
Providers Contacts
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Sales
Logistics
Lab
Etc.
Billing
• Account-Centric Legacy – ACCOUNTS combine information about
organizations and how Quest Diagnostics serves them.
– PROVIDERS and CONTACTS are established only within the context of an account, and are heavily duplicated across accounts.
– Lab, Billing, and Sales systems have overlapping, differing, and unique attributes about accounts and related providers and contacts
• Patient & Client-Centric Future – Patients receive healthcare services and are
accountable for their well-being
– ORGANIZATIONS include healthcare and non-healthcare clients and prospects
– Healthcare Professionals include providers and contacts, whether affiliated with clients or prospects
– ACCOUNTS focus on the relationships between Quest Diagnostics and clients
– RELATIONSHIPS link organizations to organizations, individuals, patients, and accounts
MDM provides Quest Diagnostics the framework to differentiate and understand the entities with whom we do business.
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Service Item
Members(#)
Enrollment
Register/ Schedule
Patients(#)
Encounter
Observ- ation
Premium Groups
Medication
Diagnosis
Route
Lab Image
Bill/Claim 835 COB
GL A/R A/P
Providers Specimen
Staff Reference Taxonomy
Case
Plans
Payers
Suppliers
Refer
Benefit/ Coverage
Protocol
Contract
Outcome
Facility
Payer Claim 837
External Research
Analytics Inputs/Outputs
Enterprise Data Model eases some of the big data integration challenges
Data Architecture Enterprise Data Model & Business Glossary
Dimensional Warehouse Model
Enterprise Data Model
Business Terms
Supporting Mappings
Business Data Model
Atomic Warehouse Model
Business Solution Templates
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Largest Clinical Data Repository
Over 1.5 billion patient encounters
Expansive Test Menu
3,000 tests
Provider Insights
50% of US providers
Patient Insights
70 million patients
per year
Clinical Workflow Integration
Point of Care & Care
Coordination Touchpoints
Data Insights Action
Driving Meaningful Improvements in the Healthcare
Focus on the Outcome: Driving Action from Insights
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Questions?
Jason O’Meara Director, Enterprise Information Management Email/LinkedIn: [email protected]
Thank You