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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1010

1010

1010

1010

1010

1010

ABA

BA

BA

BA

BA

BAB

AB

AB

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: Jason.X.OMeara@QuestDiagnostics.com

Thank You