keynote address by Onur Torusoglu. Discussing Healthcare Industry trends, challenges and opportunities that can be addressed with data and analytics

1. 1
Transforming Healthcare
through Data & Analytics
Onur Torusoglu
Chief Data & Analytics Officer,
Ochsner Health System, New Orleans, LA

2. 2
We’ll talk about 4 things..
• Provider Industry
Dynamics
• Provider Business
Priorities
• Healthcare Data
State of Union
• Top Trends
• Key Technologies
• Trends in Acute Care,
Aging
Trends
• Challenges
• Opportunities
• Implications for
Acute Care, Aging
What it Means
• Some Thoughts
Call to Action
1 2 3 4

3. 3
Healthcare Provider Industry Dynamics
The healthcare industry
continues to transform and
innovate to deliver higher-value
healthcare, reduce costs, and
engage patients amidst a
changing regulatory landscape
and shifting ecosystem
StateofUnion
Consolidation and disruption across the HCLS ecosystem
Changing policy landscape and healthcare reform
Value based care and changing reimbursement models
Increasing empowerment of consumers and patients
Sources:
Deloitte, 2017 Global Healthcare Outlook
Frost & Sullivan, 2017 Global Healthcare Industry Outlook
IBM
Everest Group, Healthcare & Life Sciences Digital Adoption Trends, April 2016

4. 4
Healthcare Provider Business Priorities
The idea of the “triple aim” in
healthcare – improving health
outcomes, lower costs, and
improving the patient
experience – continues to be
the focus of healthcare
organizations undergoing the
business model transformation
necessary to achieve it Business and operating model transformation
Continued need to lower costs of healthcare
StateofUnion
Transition to value-based care
Patient and consumer engagement
Sources:
Deloitte, 2017 Global Healthcare Outlook
Frost & Sullivan, 2017 Global Healthcare Industry Outlook
Everest Group, Healthcare & Life Sciences Digital Adoption Trends, April 2016
IBM

5. 5
Big Data Explosion in Healthcare
StateofUnion
Source:
IBM

6. 6
Sources of Big Data in Healthcare
Big Data comes from multiple sources
StateofUnion

7. 7
Key Characteristics
Volume
Volume of health data
estimated at over 300
exabytes in 2015, globally
Velocity
Volume of Health Data
estimated to increase 40-
50% each year
With increasing digitization,
data velocity will increase
exponentially
Veracity
Quality and accuracy of
health data a critical
precursor for improving
health outcomes Variety
Data is generated by
EMRs, medical devices,
sensors, wearables, mobile
apps, genomic research,
and even non health
related sources Value
McKinsey estimates value
pathways developed using
Healthcare Big Data could
deliver annual savings
worth $300-450 bn in the
US alone
Key Characteristics of Healthcare Big Data, Global, 2016
StateofUnion
Sources:
Frost & Sullivan, 2017 Report on Big Data in Healthcare
Icons by : Flaticon:

8. 8
We’ll talk about 4 things..
• Provider Industry
Dynamics
• Provider Business
Priorities
• Healthcare Data
State of Union
• Top Trends
• Key Technologies
• Trends in Acute Care,
Aging
Trends
• Challenges
• Opportunities
• Implications for
Acute Care, Aging
What it Means
• Some Thoughts
Call to Action
1 2 3 4

9. 9
Top Trends for Big Data in Healthcare
Most of the trends for Big Data
in Healthcare indicate progress
towards meaningful use of data
to improve outcomes and
reduce the cost of care
Healthcare Internet of Things (IoT)
Value Based Patient Centric Care
Sources:
http://www.itproportal.com/features/five-big-data-trends-in-healthcare/
Frost & Sullivan, 2017 Global Healthcare Industry Outlook
IBM
Trends
Cognitive Computing & Artificial Intelligence gaining
acceptance
Predictive Analytics to Improve Outcomes
Reducing Fraud, Waste and Abuse

10. 10
Key Technologies
Trends
Big Data Technology Stack - 2017
Source:
https://blogs.informatica.com/2017/04/05/big-data-moving-from-technology-to-
business-value-delivery/#fbid=3pgsYm4p4fb

11. 11
A few case studies… (details in slide notes)

12. 12
We’ll talk about 4 things..
• Provider Industry
Dynamics
• Provider Business
Priorities
• Healthcare Data
State of Union
• Top Trends
• Key Technologies
• Trends in Acute Care,
Aging
Trends
• Challenges
• Opportunities
• Implications for
Acute Care, Aging
What it Means
• Some Thoughts
Call to Action
1 2 3 4

13. 13
Current Challenges for Big Data Adoption In Healthcare
Sources:
IBM, McKinsey & Co.
Frost & Sullivan, 2017 Report on Big Data in Healthcare
Icons by : Flaticon:
WhatitMeans
Lack of integration and interoperability
Around 80% of the data is unstructured
In spite of the recognized
benefits and potential of Big
Data in Healthcare, industry
action in this direction has
lagged behind other verticals.
The data exists, but the
actionable insights derived are
very limited
Data security is an overarching concern
Resistance to change – ‘clinical judgement’ vs. protocols
Underinvested due to uncertain ROI – lack of infrastructure and
talent

14. 14
Opportunities for Big Data in Healthcare
The healthcare industry will
continue to aggregate big data
and leverage to gain insights
that improve outcomes, both
clinical as well as financial
Sources:
Frost & Sullivan, 2017 Report on Big Data in Healthcare
Icons by : Flaticon:
WhatitMeans
Population Health Management
Clinical Decision Support Systems
Real World Data Integration
Shift from reactive to proactive care through prescriptive
analytics
Cognitive Computing & Artificial Intelligence gaining
acceptance

15. 15
Implications for Acute Care, Aging
Sources:
https://med.stanford.edu/content/dam/sm/sm-news/documents/StanfordMedicineHealthTrendsWhitePaper2017.pdf
https://www.ncbi.nlm.nih.gov/pubmed/25787904
https://bismart.com/en/10-aging-population-statistics-big-data/
WhatitMeans
Acute Care:
• Mining Data: New ways to mine data analytics
will enable new avenues of research,
identifying new patients prior to acute episodes
and improving efficiency
• Artificial Intelligence : The advent of machine
learning and artificial intelligence is allowing for
the development of more detailed risk profiles,
easier detection of emerging health concerns,
and more personalized treatments for acute
conditions.
• Impact Areas: Top Areas for use of analytics
in acute care will be triage, predicting disease
events and also for population health based on
acute disease incidence
Aging:
• Root Causes: By studying and analyzing trends
and patterns that are happening in aged
populations, social services and other care
providers can pinpoint the real problems, and come
up with solutions to address them before they spiral
into crises.
• Aging in Place: A system of "aging in place" has
been implemented in some high-income countries
in order to better provide coordinated and cost-
effective health services for the elderly. This
approach reduces institutional care while
supporting home- or community-based care and
other services
• Transition: Use of machine learning to manage
patients in transition from hospital and home and
support “aging in place”

16. 16
We’ll talk about 4 things..
• Provider Industry
Dynamics
• Provider Business
Priorities
• Healthcare Data
State of Union
• Top Trends
• Key Technologies
• Trends in Acute Care,
Aging
Trends
• Challenges
• Opportunities
• Implications for
Acute Care, Aging
What it Means
• Some Thoughts
Call to Action
1 2 3 4

17. 17
Some Thoughts
Critical Success Factors for Big Data & Analytics in Provider Organizations
Driving Adoption of Big Data
and Analytics
Culture change and enablement
across levels in the provider
organization will be the game
changer in Big Data adoption
Action : Maintain executive
involvement, Communicate the
vision at all levels
The Journey from Data to
Actionable Insights
Data is necessary, but ROI will be
derived only if actionable insights
are obtained – possible only
through predictive, prescriptive
and cognitive analytics
Action: Invest in specialist
human talent, right tools and
technology
Every stakeholder in the
ecosystem benefits from big
data & analytics
The true power of big data and
analytics can be leveraged when
we can provide these abilities to
an end-user, backed by powerful
tools
Action: Raise the Data &
Analytics IQ of the organization
CalltoAction

18. Thank you!

19. Appendix

20. 20
Evolution of Data in Healthcare, Global, 1980 - 2020