© 2014 Healthline Networks Inc. Confidential and Proprietary.

The Power and Promise of Unstructured Patient Data

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BIG DATA

U.S. Spending on Healthcare

Little Insight

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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

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$ Tr

illio

ns

What curve would look like with savings

from successful use of Big Data

U.S. Spending on Healthcare

2012 2013 2014 2015

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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

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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.

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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

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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  

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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  U7liza7on  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

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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.

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Risk Assessment for Heart Failure (HF) Readmission

Assump7ons:  6.5%  HF  stays  /  total  hospital  stays;  25%  HF  readmission  rate;  $15,000  avg  cost  of  HF  readmission;  75%  of  HF  readmits  theore7cally  avoidable  (CMS)  

Source:  CMS,  Healthcare  Cost  U7liza7on  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

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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

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Spending on Healthcare Analytics

$  Billion

s  

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~25% CAGR

~65% from providers

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