Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015

1. Enterprise Analytics
Serving Big Data Projects for
Health Care
Data 360 Healthcare
Andrew Rosenberg MD
Chief Medical Information Officer
March 5, 2015

2. Increase in Global Data
2

3. 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

4. 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

5. 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

6. Case in Point: Critical Care
Processing Big Data: Volume + Variety + Velocity + Voracity
6

7. Monitoring in the ICU: Filtering noise from real and meaningful data
7

8. Logical Use Case Diagram: Acute Hemodynamic Instability
Enterprise Data
Warehouse
8

9. 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 Health
Initiatives
‫‏‬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

10. 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.
&

11. 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

12. 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 Assignment
Scheduled
Appointment
Pathology
Patient Monitoring
System
Imaging
ECG
EEG
Cardio Vascular
(ECHO)
Implantable Devices
(ICD)
ED
Outpatient Visit/
Service
Inpatient
Admission
Immunization
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 Unit
Academic Rule
Learning Unit
Instance
Learning Object
Learning Result
Academic
Calendar
Student
Staff
Learning Plan
Faculty
Buildings Departments
Locations Facility
RxNorm SNOMED Others
Encounter/Medical
Services
Revenue 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 Bold
Services i.e. Care Delivery,
Research, Education
Care Delivery Research Education
Telemedicine
Consult
Calendar
Total Cost of
Ownership
Over 50
Enterprise Analytics
Recommended
Projects

13. 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

14. Domain 2: Big Data and Real-Time Decision Support
14
High-level technical architecture for big data and real time decision support

15. 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).

16. Analytics Use Case: Clinical Research
16
Physical components for the M-CIRCC pilot support the aggregation and analysis
of data from numerous sources

17. 17

18. 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

19. Data Governance: Federated Model
19
Health System Executive Sponsorship GroupStrategic
Vision &
Final Authority
Enterprise Data Governance Committee
Enterprise Analytics Governance Structure
MasterDataManagement
‫‏‬spansalllevels
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.

20. Challenges in Big Data Adoption
Reducing Common Challenges for Big Data Projects
20Gartner, Survey Analysis: Big Data Adoption: Sept. 2013

21. 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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