Unstructured search capabilities, superior natural language processing, and healthcare ontology capabilities will help distinguish the leading products in the category (information and data-driven decision making).

1. © 2014 Healthline Networks Inc. Confidential and Proprietary.
The Power and Promise of
Unstructured Patient Data

2. 2
BIG DATA
U.S. Spending on Healthcare
Little Insight

3. 3
Data-Driven Solutions Can Improve Outcomes and Bend Cost Curves
Source: JEGI, Gartner, McKinsey, ADA, AHA, HealthPartners Research Foundation, Healthline analysis
McKinsey estimates the U.S. can save $300B-$450B per year from investments in Big Data analytics
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
1 2 3 4
$Trillions
U.S. Spending on Healthcare
2012 2013 2014 2015

4. 4
Driving Data from Descriptive to Prescriptive/Predictive Analytics
Source: Liquid Analytics
Tech investments shifting from collecting data to understanding it to making it actionable at the point of care
Data Latency
Reporting Analytics
What
happened?
What will
happen?
Why did it
happen?
What is
happening?
What should
we do?
What can we
offer?
Data Information Knowledge
Data Freshness

5. 5
Clinical Analysis, Data Mining, and Predictive Modeling Top of Mind
Source: SearchHealthIT.com's business intelligence survey
0 10 20 30 40 50 60 70 80
other
none
administrative business intelligence
predictive analysis
data mining
clinical data analysis
Which advanced analytics tools does your organization plan to you use in the next 2 years?
Results based on 243 responses from CIOs and senior IT executives at medical centers, health systems and physician practices across U.S.

6. 6
Goal: Making Unusable Data Actionable
90% of healthcare data over the next decade will be unstructured
(IDC, Kaiser Family Foundation)
• Healthcare is moving to a value based model
• Providers need to make investments in data-driven technologies to
manage the health of their patient populations more effectively
• A major factor mitigating the power of these analytics solutions is
access to information-rich unstructured data (e.g., physician notes,
family histories, etc.)
• Leveraging data—structured and unstructured—from disparate
sources is key
Leveraging Unstructured Data and Data from Disparate Sources Is Critical

7. 7
Unstructured search capabilities, superior
natural language processing, and
healthcare ontology capabilities will help
distinguish the leading products in the
category (information and data-driven
decision making).
Robust Health Informatics is the Key to Unlocking the Unusable Data
“
“
Source: JEGI HCIT Issues, Trends and M&A Outlook 2014

8. 8
IMPROVE PATIENT CARE
BETTER PRIORITIZE AND FOCUS
HEALTHCARE RESOURCES
UNDERSTAND AND REDUCE RISK
Understanding Unstructured Patient Data Can Provide New Insights

9. 9
For Instance: Risk Assessment for Readmission
Source: CMS, Healthcare Cost Utilization Project, AHA, Healthline analysis
Seven conditions / procedures account for 30
percent of potentially preventable
readmissions:
1. Heart failure (HF) 1
2. Chronic obstructive pulmonary disease (COPD) 2
3. Pneumonia 1
4. Acute myocardial infarction 1
5. Coronary artery bypass graft surgery
6. Percutaneous transluminal coronary angioplasty
7. Other vascular procedures
Heart Failure Readmissions
Average 300-bed hospital at 90% occupancy
• 27,000 stays
• 1,755 HF stays (~6.5%)
• 439 HF readmissions (25%)
• $15,000 average cost of HF readmission
• $6.6M total HF readmission costs
BY THE NUMBERS
Note: Hospitals with high avoidable readmission for highlighted conditions/procedures currently penalized by CMS
1 Currently part of CMS Readmission Measures
2 COPD added to CMS Readmission measures for October 2014

10. 10
UNLOCKING UNSTRUCTURED DATA CAN ENABLE SYSTEMS TO IDENTIFY
WHO IS IN THE HIGHEST RISK CATEGORY BASED ON A VARIETY OF
FACTORS:
1. Medical / Health Factors
2. Psycho-Social Factors
3. Socio-Economic Factors
Understanding who is a highest risk for readmission makes the targeting of
scare resources in terms of interventions and support possible at scale.

11. 11
Risk Assessment for Heart Failure (HF) Readmission
Assumptions: 6.5% HF stays / total hospital stays; 25% HF readmission rate; $15,000 avg cost of HF readmission; 75% of HF readmits theoretically avoidable (CMS)
Source: CMS, Healthcare Cost Utilization Project, AHA, Healthline analysis
HF READMISSION – CUSTOMER ECONOMICS
Average 300 Bed Hospital (90% Occupancy)
27,000 stays 1,755 HF stays
439 HF
readmits
$15,000 per
readmit
$6.6M total
15% reduction
in readmits
~$1M cost
savings
$564 savings
per admit
Patients
Costs
Potential
Cost
Savings

12. 12
Important to a Growing Array of Risk-Bearing Entities (RBEs), Especially Providers
Life
Science
(21%)
Insurance
(25%)
Provider
Physicians
Hospital
(45%)
Source: JEGI, Gartner, McKinsey, Nuance, Healthline Analysis
U.S. HCIT Market ~$72B (2014)
~5% CAGR
“Main driver of HCIT spending in U.S. can be attributed to
hospitals, clinics and private practices implementing health IT
solutions.”
– VP Healthcare Solutions, Nuance
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Spending on Healthcare Analytics
$Billions
2013 2014 2015 2016 2017 2018 2019 2020
~25% CAGR
~65% from providers

13. © 2014 Healthline Networks Inc. Confidential and Proprietary.
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