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Breakout Session from "Data Summit 2014" in New York City on May 14, 2014 (Big Data and Healthcare: Key Technologies and Strategies)
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© 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
© 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
© 2014 IBM Corporation 3
Please note
IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion.
Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.
© 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
© 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
© 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 Organization2 The World Health Report 2000 – Health Systems: Improving Performance from World Health Organization3 Best Care at Lower Cost: The Path to Continuously Learning Health Care in America from Institute of Medicine / National Academy of Sciences4 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
© 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
© 2014 IBM Corporation 8
20% of people generate80% of costs
Health care spending
Health status
Healthy Low Risk
High RiskAt RiskActive
Disease
EarlyClinical
Symptoms
Disease and cost-of-care progression
Time
Early intervention opportunities identification
Early intervention opportunities identification
70% of US deaths from chronic diseases
© 2014 IBM Corporation 9
Seton Healthcare FamilyReducing CHF readmission to improve care
Business ChallengeSeton 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
© 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 % EncountersStructured 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
© 2014 IBM Corporation 11
What Really Causes Readmissions at Seton
Top 18 Indicators
1. Jugular Venous Distention Indicator
2. Paid by Medicaid Indicator3. Immunity Disorder Disease Indicator4. Cardiac Rehab Admit Diagnosis with CHF Indicator5. Lack of Emotion Support Indicator6. Self COPD Moderate Limit Health History Indicator7. With Genitourinary System and Endocrine Disorders8. Heart Failure History9. High BNP Indicator10. Low Hemoglobin Indicator11. Low Sodium Level Indicator12. Assisted Living13. High Cholesterol History14. Presence of Blood Diseases in Diagnosis History15. High Blood Pressure Health History16. Self Alcohol / Drug Use Indicator17. Heart Attack History18. Heart Disease History
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
© 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
© 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
© 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
© 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
© 2014 IBM Corporation 16
Health maintenance in electronic medical record
© 2014 IBM Corporation 17
Free-text note in patient’s EMR
Not captured in structured data
© 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
© 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
Nu
mb
er
of
pa
tie
nts
ou
t o
f c
om
pli
an
ce
(s
tru
ctu
red
da
ta)
© 2014 IBM Corporation 20
Results
60
53
55
61
Mammogram CRC screening Pneumovax
Structured data
48
50
52
54
56
64
62
58
Pe
rce
nt
of
pa
tie
nts
me
eti
ng
re
qu
ire
me
nts
60
Structured data + NLP
53
63
© 2014 IBM Corporation 21
Follow-up of abnormal test results documented in free-text reports
Mammograms | Colonoscopies | Pap tests | Chest x-rays
© 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
© 2014 IBM Corporation 23
Mammography reports
© 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 = sensitivityif 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
© 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
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
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?
© 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
• 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:
© 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
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
© 2014 IBM Corporation 28© 2013 IBM Corporation28
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 RhinehartDirector, IBM Smarter Care Strategy and Market [email protected]
Thank You