© 2014 Healthline Networks Inc. Confidential and Proprietary.

The Power and Promise of Unstructured Patient Data

2

BIG DATA

U.S. Spending on Healthcare

Little Insight

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

1 2 3 42.5

2.6

2.7

2.8

2.9

3.0

3.1

3.2

3.3

3.4

3.5$

Tri

llio

ns

Total U.S. Healthcare expenditures

What curve would look like with savings

from successful use of Big Data

U.S. Spending on Healthcare

2012 2013 2014 2015

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

Clinical Analysis, Data Mining, and Predictive Modeling Top of Mind

Source: SearchHealthIT.com's business intelligence survey

other

none

administrative business intelligence

predictive analysis

data mining

clinical data analysis

0 10 20 30 40 50 60 70 80

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

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

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

IMPROVE PATIENT CARE

BETTER PRIORITIZE AND FOCUS

HEALTHCARE RESOURCES

UNDERSTAND AND REDUCE RISK

Understanding Unstructured Patient Data Can Provide New Insights

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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 Measures2 COPD added to CMS Readmission measures for October 2014

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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

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 ECONOMICSAverage 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

PotentialCost

Savings

12

Important to a Growing Array of Risk-Bearing Entities (RBEs), Especially Providers

Life Science(21%)

Insurance (25%)

Provider(54%)

Physicians(9%)

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

1 2 3 4 5 6 7 80.0

5.0

10.0

15.0

20.0

25.0

Spending on Healthcare Analytics

$ Bi

llion

s

2013 2014 2015 2016 2017 2018 2019 2020

~25% CAGR

~65% from providers

© 2014 Healthline Networks Inc. Confidential and Proprietary.

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