This document discusses how New York City Health & Hospitals Corporation (HHC) is using data analytics and big data to transform healthcare. It describes HHC's goals of improving patient outcomes and reducing unnecessary healthcare utilization. It also outlines some of the data challenges HHC faces with disparate legacy systems and a lack of data consolidation. HHC is creating an enterprise information management team and technical framework to develop business intelligence capabilities that help power strategic decision making and create a single source of truth across the organization using analytics and insights from their large amounts of patient data.

1. Transforming Healthcare
with Big Data, Social and Mobile
Sri Srinivasan - Global Industry Leader, Healthcare & Life Sciences
Paul Contino, Chief Technology Officer, NYC Health & Hospitals Corp.
Phani Konduru – Enterprise Data & Analytics, Humana
© 2014 IBM Corporation

2. 2
New York City
Health & Hospitals
Corporation
Paul Contino
Corporate Chief Technology Officer
New York City Health & Hospitals Corporation

3. HHC Hospitals
Bellevue
(Manhattan)
Coney Island
(Brooklyn)
Elmhurst
(Queens)
Harlem
(Manhattan)
Jacobi
(Bronx)
Kings County
(Brooklyn)
Lincoln
(Bronx)
Metropolitan
(Manhattan)
North-Central Bronx
(Bronx)
Queens
(Queens)
Woodhull
(Brooklyn)

4. About HHC
New York City Health & Hospitals Corporation’s (HHC)
 Largest public healthcare organization in the U.S.
 $6.7 billion integrated delivery system
 38,000 employees; 5,000 physicians
 Serve 1.5 million New Yorkers every year, 475,000 uninsured
Clinic Visits: 4,876,259
ER Visits: 1,190,413
Facilities:
 11 acute care hospitals, 4 skilled nursing facilities, 6 large
diagnostic and treatment centers
 Health and Home Care (in-home services)
 More than 70 community health clinics
 420,000 member health plan (MetroPlus)

5. Goals at HHC
 To better serve the patients of NYC who
have complex medical and behavioral
health needs.
 To improve patient outcomes through
improved care management and care
coordination.
 To reduce inappropriate healthcare
utilization (ED and Inpatient visits) by
coordinating and providing access to
needed services.

6. Data Challenges at HHC
 Complex array of systems, disparate data sources
 Inefficient and ineffective information management
(business siloes)
 No trusted source of fact for corporation.
 More metrics than we can effectively use.
 Little transparency into LOB processes that create this data.
 Legacy EMR – 8 Instances + 8 clinical data warehouses
 Preparing for Epic EMR implementation – data consolidation,
harmonization, migration/conversion/archiving
 No MDM / EMPI strategy

7. Business Intelligence / Analytics
Primary Drivers
Support the enterprise goals and objectives
Make the best use of our information assets
Enhance business processes with timely analytics
Help enterprise with strategic, tactical & operational
decision making
Create a ‘Single Source of Truth’
Transform Data into Information,
Information into Insight
Insight in Action
Focus on “Actionable Data” –
measure what has real business impact

8. The Promise of BI
Created an Enterprise Information Management team
 Rationalizing and streamlining data sources.
 Focus on data quality
 Enhance value of data by enabling cross business-area
analysis.
 Foster collaboration and interactivity between business and IT
to drive continuous improvement.

9. Creating A Comprehensive Information Platform
9

10. Business Intelligence: Technical Framework
10

11. What’s Coming - Big Data In Healthcare
2-5 Terabytes per Patient
Electronic Medical Records
Medical Imaging (CT/PET/MRI)
Personal Health Records
Consumer Health / Home Medical Devices
Omics Data (Proteomics / Genomics)

12. Remote Patient Monitoring &
Personal Health Dashboards

13. Transforming Healthcare
EMREMR OmicsOmics
Population Health Personalized Medicine
Healthcare Consumer Data
“Quantified Self”
AnalyticsAnalytics
Predictive Models
Machine Learning
Pattern Recognition

14. THANK YOU
14