Enterprise AnalyticsServing Big Data Projects for

Health Care

Data 360 Healthcare

Andrew Rosenberg MD

Chief Medical Information Officer

March 5, 2015

Increase in Global Data

2

Rise In Unstructured Data (2009-2014)

Changing Nature of Data

New non-relational databases for unstructured data are becoming increasingly

popular.

Unstructured Data Storage

Does not have a pre-defined data model

• Photos

• Videos

• Social Media (Text Mining)

3

Data Manipulation and Storage

The Rise of Non-Relational Databases

Non-relational databases do not rely on a traditional table/key model and require the use of

new data manipulation techniques and data storage methods.

4

Increase in Data Production (2000-2011)

Rapid Expansion of Digital Data

Projected Increase Worldwide

1993 = 3 Exabytes

2007 = 230 Exabytes

2015 = 7988 Exabytes

2020 ~ 35000 Exabytes

Projected Increase in Health Care

2013 = 153 Exabytes

2020 ~ 2,300 Exabytes

Data production and storage are increasing rapidly across industries.

The health care market is expected to see a 660% increase by 2020.

5

Case in Point: Critical Care

Processing Big Data: Volume + Variety + Velocity + Voracity

6

Monitoring in the ICU: Filtering noise from real and meaningful data

7

Logical Use Case Diagram: Acute Hemodynamic Instability

Enterprise Data

Warehouse

8

Using a Cohesive Platform To Link Multiple Projects

9

PROMIS

Epic

Chronic

Dis.

QMP

Qual

Datamart

PACE

Qual

Datamart

CA

Registry

MCIRCC

HSDW

PCORI

Projects

IHPI

Projects

Registries

Bio-

Repository

FFI or

MiCHR

RDW

Transmart

Pharmacok

inetics

MyOnco

Seq

Personalized Health‏Initiatives

Biologic‏

Domain 2:

Big Data – Real

Time Decision

Support

Domain 1:

Data Assets

& Reuse

Domain 3:

Open

Eco-systems

UMHS

Analytics

P1

P1

AHI

P1

P1

P1

P1

NEPTUNE

GWAS

For Big Data Projects To Succeed At UMHS…

10

We need an Enterprise Analytics plan

A roadmap to advance our abilities to support clinical, research

and education programs and priorities currently in place or

planned.

&

Use-Case Driven Future Vision

11

Shared Design Principles

Analytics architecture is based on

actual user requirements and real-

world practice

Analytics capabilities and architecture

will be aligned with federated

governance practices

Common toolsets supported by pre-

packaged analytics and standards

minimize time to value

Enterprise Analytics Planning Framework

12

Roadmap Progression

9 Use Cases

3 Domains

54 User-Informed Scenarios

Functional Requirements

Federated Analytics

Architecture

Federated Enterprise

Data Governance

Enabling Pillars

“How we will get there” “How we will manage”“Where we are going”

Lab

Vital

Demographics

Encounter

Problem List

Diagnosis

Allergy

Bed AssignmentScheduled

Appointment

Pathology

Patient Monitoring

SystemImaging

ECG

EEG

Cardio Vascular

(ECHO)

Implantable Devices

(ICD)

ED

Outpatient Visit/

Service

Inpatient

AdmissionImmunization

Account Transactions Payment Charge Adjustment

Meds

Surgery

Procedure

Smoking

Flowsheet

Clinic Notes

Radiation

Oncology

Claim Rx

Claim DRG

PayerPlan

Claim Line Payment

Lab Order

Survey

Consent

Party

ProviderFaculty Staff

Facilities/Locations

Charge

Study

mRNA

Bio-Assay

Biomaterial

Tissue Sample

Adverse Event

Event

Findings

Bio- DataSet

SNP

NGS

Survival Status Collaborative Staging

Recurrence Metastasis Biomarkers

Cancer

Clinical Terminologies

Learning Objectives

Learning UnitAcademic Rule

Learning Unit

Instance

Learning Object

Learning Result

Academic

Calendar

Student

Staff

Learning Plan

Faculty

Buildings Departments

Locations Facility

RxNorm SNOMED Others

Encounter/Medical

ServicesRevenue Cycle Claim

Master Data Clinical

Operations

Patient History

Patient

Sample Data

Research Data

Research Registries

(Cancer)Education

Representative Subject areas

Organizational

Data

Program

Area of Study

Course

Experiential

Learning

Project Based

Learning

Subject

Animal

Standards

Party BoldServices i.e. Care Delivery,

Research, Education

Care Delivery Research Education

Telemedicine

Consult

Calendar

Total Cost of

Ownership

Over 50

Enterprise Analytics

Recommended

Projects

Domain-Specific Technical Architecture

13

The High-Level Technical Architecture represents the composite view of the

three domain architectures. Domain 1:

Federated Information Management

Domain 2:

Big Data and Real-Time Decision

Support

Domain 3:

Open Analytics Ecosystem

High-Level Architecture

Ex. M-CIRCC

Ex. Digital Health

Engine

Ex. Quality Analytics

Domain 2: Big Data and Real-Time Decision Support

14

High-level technical architecture for big data and real time decision support

Analytics Use Case: Clinical Research

15

UMHS is using the enterprise analytics strategy to support clinical research

through the Early Detection of Hemodynamic Decompensation Pilot (M-CIRCC).

Analytics Use Case: Clinical Research

16

Physical components for the M-CIRCC pilot support the aggregation and analysis

of data from numerous sources

17

Developing a Foundational Analytic Infrastructure

18

● Assess all Analytic tools,‏databases‏and‏data‏sources‏and…● Catalog!

● Reduce duplication, maximize reuse

● Implement Enterprise Data Management● Streamline use of master data elements‏:‏patient,‏provider,‏subject….

● Implement staging platform, standard tools and terminologies : reuse, access and quality

● Enterprise Service Bus● Data sharing and data virtualization

● Provide Enterprise Analytic Services● Data concierge

● Data Governance● Start with existing committees where possible

● Develop enterprise data management team

● Provide Innovative Platforms● Big data, Streaming services, Semantic services, Natural language processing

Data Governance: Federated Model

19

Health System Executive Sponsorship GroupStrategic

Vision &

Final Authority

Enterprise Data Governance Committee

Enterprise Analytics Governance Structure

Ma

ste

r Da

ta M

an

ag

em

en

t

s‏p

an

s a

ll leve

ls

Strategic

Execution &

Decision

Making

Execution &

Compliance

Monitoring

Representation,

Solution Design

& Data

Managers

Data Managers

MDM Leads, Analysts, Architects

Data Stewards

(Subject Area

(Managers‏

Business Users

(Analytics Users)

IT Services

Service Desk

Data

Concierge

Production

Support

Analytics &

Reporting

Services

Support Services‏

Data Governance Working Groups

--------- Functional Working Groups ------

-------- Cross Departmental Data/Analytic Initiatives -----

------ Institutes/Centers -----

|Enterprise

Data

Management | ||Care Delivery AdministrationEducationResearch

Enterprise

“Hub” Function

Business Unit

“Spoke” Function

Business

Executives Role

IT Driven

Role

Operational Data

Management Roles

Business User

Roles‏

In the federated model the Data Governance Committee facilitates the monitoring

and management of data with assistance from key resources at every level.

Challenges in Big Data Adoption

Reducing Common Challenges for Big Data Projects

20Gartner, Survey Analysis: Big Data Adoption: Sept. 2013

For Additional Information

Key Contacts:

Andrew Rosenberg MD

arosen@med.umich.edu

(734) 936-7241

Resources:

Website: http://analytics.medicine.umich.edu

Mailbox: umhs-analytics@med.umich.edu

Mary Hill, MS

maryhill@med.umich.edu

(734)763-6751

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