Breakout Session from "Data Summit 2014" in New York City on May 14, 2014 (Big Data and Healthcare: Key Technologies and Strategies)

1. © 2014 IBM Corporation
C203: Big Data and Healthcare: Key
Technologies and Strategies
Turning Big Data Insights into Action through
Advanced Analytics
DATA SUMMIT 2014
Craig Rhinehart
Director, IBM Smarter Care Strategy and
Market Development

2. © 2014 IBM Corporation 2
C203: Big Data and Healthcare: Key Technologies and Strategies
2:00 pm - 2:45 pm
Turning Big Data Insights into Action through Advanced Analytics
•Healthcare is being disrupted by the collision of a wave of innovation. The
emergence of Big Data, cloud computing, analytics, and social media and
increasing wellness awareness through mobile devices are revolutionizing
patient-centered connected care. This session discusses the new technologies
and approaches that offer promise in terms of better data understanding for
organizations and individuals.
Hashtag: #BigDataNY
Twitter: @CraigRhinehart, @DBTADataSummit
Craig Rhinehart Blog: http://craigrhinehart.com

3. © 2014 IBM Corporation 3
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Therefore, no assurance can be given that an individual user will achieve results
similar to those stated here.

4. © 2014 IBM Corporation 4
• Time Machine: A Decade of Reversal: An Analysis of
146 Contradicted Medical Practices
• Case Study: Seton Healthcare
• Case Study: University of North Carolina School of
Medicine
• Introduction to Smarter Care
• Questions and Discussion
4
Topics

5. © 2014 IBM Corporation 5
Who Do You Trust?
A Decade of Reversal:
An Analysis of 146 Contradicted Medical Practices
“40.2% reversed the original standard
of care … and only 38.0% reaffirmed
the original standard of care”
“Amputations or Analytics …”
Craig Rhinehart Blog

6. © 2014 IBM Corporation 6
(Billion) Wasted on missed
opportunities along with
unnecessary, error-prone and
inefficiently delivered services 3
$585B
US physicians still reliant on
paper based medical
records systems 4
45%
Hours each day an average
family physician spends on
direct patient care 3
8.0
Hours required to meet the
patient care guidelines each
day 3
21.7
1 World Health Statistics 2011 from World Health Organization
2 The World Health Report 2000 – Health Systems: Improving Performance from World Health Organization
3 Best Care at Lower Cost: The Path to Continuously Learning Health Care in America from Institute of Medicine / National Academy of Sciences
4 Physician Adoption of Electronic Health Record Systems: United States, 2011 from National Center for Health Statistics
US rank in Healthcare
spending 1
1st
US rank in quality of care
delivered 2
37th
80%90% of the world’s data is unstructuredof the world’s data was
created in the last two years
An Ocean of Unused
Data
Healthcare Transformation: A Work in Progress

7. © 2014 IBM Corporation 7
Unstructured data is messy but filled with key
medical facts
Medications, diseases, symptoms, non-symptoms,
lab measurements, social history, family history …
and much more

8. © 2014 IBM Corporation 8
20% of people generate
80% of costs
Health care spending
Health status
Healthy
Low Risk
High RiskAt Risk
Active
Disease
Early
Clinical
Symptoms
Disease and cost-of-care progression
Time
Early intervention opportunities identification
Early intervention opportunities identification
70% of US deaths from
chronic diseases

9. © 2014 IBM Corporation 9
Seton Healthcare Family
Reducing CHF readmission to improve care
Business Challenge
Seton Healthcare strives to reduce the occurrence of high
cost Congestive Heart Failure (CHF) readmissions by
proactively identifying patients likely to be readmitted on an
emergent basis.
What’s Smart?
IBM Content and Predictive Analytics for Healthcare
solution will help to better target and understand high-risk
CHF patients for care management programs by:
Smarter Business Outcomes
• Seton will be able to proactively target care management
and reduce re-admission of CHF patients.
• Teaming unstructured content with predictive analytics,
Seton will be able to identify patients likely for re-
admission and introduce early interventions to reduce
cost, mortality rates, and improved patient quality of life.
IBM solution
• IBM Content and
Predictive Analytics
for Healthcare
• IBM Cognos Business
Intelligence
• IBM BAO solution
services
• Utilizing natural language processing to extract key elements
from unstructured History and Physical, Discharge Summaries,
Echocardiogram Reports, and Consult Notes
• Leveraging predictive models that have demonstrated high
positive predictive value against extracted elements of
structured and unstructured data
• Providing an interface through which providers can intuitively
navigate, interpret and take action
“IBM Content and Predictive Analytics for Healthcare uses the same type of natural language processing as IBM Watson,
enabling us to leverage information in new ways not possible before. We can access an integrated view of relevant clinical
and operational information to drive more informed decision making and optimize patient and operational outcomes.”
Charles J. Barnett, FACHE, President/Chief Executive Officer, Seton Healthcare Family
Featured on

10. © 2014 IBM Corporation 10
The Data We Thought Would Be Useful … Wasn’t
• Structured data not available, not accurate enough, without the unstructured data - which was more trustworthy
What We Thought Was Causing 30 Day Readmissions … Wasn’t
• 113 possible candidate predictors expanded and changed after mining the data for hidden insights
New Hidden Indicators Emerged … Readmissions is a Highly Predictive Model
• 18 accurate indicators or predictors (see next slide)
Predictor Analysis % Encounters
Structured Data
% Encounters
Unstructured Data
Ejection Fraction (LVEF) 2% 74%
Smoking Indicator 35%
(65% Accurate)
81%
(95% Accurate)
Living Arrangements <1% 73%
(100% Accurate)
Drug and Alcohol Abuse 16% 81%
Assisted Living 0% 13%
What Really Causes Readmissions at Seton
Key Findings from Seton’s Data
97% at 80th
percentile
49% at 20th
percentile

11. © 2014 IBM Corporation 11
What Really Causes Readmissions at Seton
Top 18 Indicators
1. Jugular Venous Distention Indicator
2. Paid by Medicaid Indicator
3. Immunity Disorder Disease Indicator
4. Cardiac Rehab Admit Diagnosis with CHF Indicator
5. Lack of Emotion Support Indicator
6. Self COPD Moderate Limit Health History Indicator
7. With Genitourinary System and Endocrine Disorders
8. Heart Failure History
9. High BNP Indicator
10. Low Hemoglobin Indicator
11. Low Sodium Level Indicator
12. Assisted Living
13. High Cholesterol History
14. Presence of Blood Diseases in Diagnosis History
15. High Blood Pressure Health History
16. Self Alcohol / Drug Use Indicator
17. Heart Attack History
18. Heart Disease History
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
0 1 2 3 4 5 6
RankingofStrengthofModelVariable
Projected OddsRa o
18 17 16 15 14 13 12 11 10
9 8 7 6 5 4 3 2 1
New Insights Uncovered by Combining Content and Predictive Analytics
• Top indicator JVDI not on the original list of 113 - as well as several others
• Assisted Living and Drug and Alcohol Abuse emerged as key predictors - only found in
unstructured data
• LVEF and Smoking are significant indicators of CHF but not readmissions
• A combination of actionable and non-actionable factors cause readmissions

12. © 2014 IBM Corporation 12
The Impact of Readmissions at Seton
CHF Patient X – What Happened? Admit / Readmission
30-Day Readmission
98% 98% 96% 95% 96% 100%
Apr-18-2009 May-12-2009 May-20-2009 Oct-11-2009 Nov-24-2009 Dec-20-2009
8 days24 days 144 days 44 days 26 days
Individual Patient Data at Each Encounter (Patient X @ Dec 20, 2009)
Patient X was hospitalized 6 times over an 8 month period. The same basic
information was available at each encounter and Patient X’s readmission
prediction score never dropped below 95% (out of possible 100%)
Patient Population Monitoring
Clinical and Operational Data

13. © 2014 IBM Corporation 13
Overview
• Improve collection of Physician Quality Reporting
System (PQRS) measures
• Prepare for meaningful use (Stage 3) and improve test
result follow-up
• Avoid Centers for Medicare and Medicaid Services
(CMS) readmission penalties

14. © 2014 IBM Corporation 14
Physician Quality Reporting System (PQRS)
measures
PQRS is a reporting program that uses a
combination of incentive payments and payment
adjustments to promote reporting of quality
information by eligible professionals (EP)
• Services furnished for Medicare Part B
PQRS measures
• Breast cancer screening (mammograms)
• Colorectal cancer screening
• Pneumovax

15. © 2014 IBM Corporation 15
Problem
Mechanism for capturing procedures
performed at outside institutions is
unreliable:
• Mammogram
• Colo-rectal cancer (CRC) screening
• Pneumovax
PQRS measures are typically captured
using structured Electronic Medical
Record (EMR) data

16. © 2014 IBM Corporation 16
Health maintenance in electronic medical record

17. © 2014 IBM Corporation 17
Free-text note in patient’s EMR
Not captured in
structured data

18. © 2014 IBM Corporation 18
Physician Quality Reporting System (PQRS)
measures
UNC Healthcare utilized IBM Content
Analytics (ICA) to analyze free-text clinical
documents to improve the accuracy of our 2012
PQRS measures
We used ICA to analyze clinical notes for
those patients whose structured EMR data did
not indicate compliance with PQRS measure
requirements
• Mammogram, CRC screening, Pneumovax

19. © 2014 IBM Corporation 19
Results
142
155
236
49
27
247
Mammogram
(297)
CRC screening
(285)
Pneumovax
(271)NLP Capture of PQRS
Measure
0
50
100
150
200
350
300
250
Numberofpatientsoutofcompliance
(structureddata)

20. © 2014 IBM Corporation 20
Results
60
53
55
61
Mammogram CRC screening Pneumovax
Structured data
48
50
52
54
56
64
62
58
Percentofpatientsmeetingrequirements
60
Structured data + NLP
53
63

21. © 2014 IBM Corporation 21
Follow-up of abnormal test results documented in
free-text reports
Mammograms | Colonoscopies | Pap tests | Chest x-rays

22. © 2014 IBM Corporation 22
Breast cancer and mammograms
• Second deadliest cancer in women
• Screening mammograms can
reduce mortality
28%of women requiring short-term follow-up for
abnormal mammography results do not receive
recommended care

23. © 2014 IBM Corporation 23
Mammography reports

24. © 2014 IBM Corporation 24
Performance of NLP for accurately identifying
mammography results
Characteristics Value
Precision 98%
Recall 100%
F Measure 99%
• Precision = positive predictive value
If NLP reports a result, the probability
that the result is correct
• Recall = sensitivity
if there is a result in the mammography
report, the probability that NLP identifies
and accurately classifies the result
• F-measure = summary measure of
NLP performance
Revised NLP model has 100% performance for all characteristics

25. © 2014 IBM Corporation 25
Structured Data is Not Enough
 Unstructured data significantly increases the
richness and accuracy of analysis and decision
making … including paper / faxes
Today’s Care Guidelines Only Get You So Far
 Not granular enough to deliver on the promise of
personalized medicine with data driven insights 1,2
Manual Processes and Traditional Workflow
Approaches Don’t Work
 Process complexity increases with disease
complexity … changing conditions require process
adaptability 3
© 2012 IBM Corporation
Prediction Results of Knowledge-driven Features plus Data-driven
Features
!AUC significantly improves as complementary data driven risk factors are
added into existing knowledge based risk factors.
!A significant AUC increase occurs when we add first 50 data driven features
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Knowledge and
Guidelines
Data Driven
Insights
1. Dijun Luo, Fie Wang, Jimeng Sun, Marianthi Markatou, Jianying Hu,Shahram Ebadollahi, SOR: ScalableOrthogonal Regression for Low-
Redundancy Feature Selection and its Healthcare Applications. SDM’12
2. Jimeng Sun, Jianying Hu, Dijun Luo, Marianthi Markatou, Fei Wang, Shahram Edabollahi, Steven E. Steinhubl, Zahra Daar, Walter F. Stewart.
Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records. Under submission at AMIA’12
3. Blind Surgeon Metaphor Problem - W.M.P. van der Aalst, M. Weske, and D. Grünbauer. Case Handling: A New Paradigm for Business Process
Support. Data and Knowledge Engineering, 53(2):129-162, 2005
What Have We Learned So Far?

26. © 2014 IBM Corporation 26
A Smarter approach to delivering better outcomes
• Build longitudinal “data driven”
evidence based population insights
• Uncover hidden intervention
opportunities
• Proactively deliver accountable and
personalized care in a patient
centered model
• Collaborate across caregivers to focus
on high cost, high need patients
• Prevent at-risk patients from
progressing to high cost, high need
IBM Strategy to Support Care Coordination:

27. © 2014 IBM Corporation 27
The path forward
IBM Smarter Care uncovers valuable insights into lifestyle choices, social determinants,
and clinical factors enabling holistic and individualized care to optimize outcomes and
lower costs
Social
determinants such as where one is born,
grows, lives, works and ages have direct
impact on an individual’s overall health and
well being
Lifestyle
choices have direct impact on an
individual’s mental and physical wellness
Clinical
factors such as specific medical symptoms,
history, medications, diagnoses, etc are
indicators of an individual’s health

28. © 2014 IBM Corporation 28
© 2013 IBM Corporation
28
Find out more about IBM Smarter Care
http://www.ibm.com/smarterplanet/us/en/smarter_care/overview/
Visit my blog or follow me on Twitter
http://craigrhinehart.com
@CraigRhinehart
Craig Rhinehart
Director, IBM Smarter Care Strategy and Market Development
craigrhinehart@us.ibm.com
Thank You