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Nov 16, 2017
The global healthcare Natural Language Processing (NLP) market is expected to grow from $1.10 billion in 2015 to $2.67 billion by 2020, according to a report from Markets and Markets. This growth is driven by the need to better understand and manage the health and wellness of patient populations in a changing healthcare landscape. But with 80% of healthcare data in unstructured Big Data, so much insight is trapped in text. How are payers and providers to extract these insights and apply them in statistical and machine learning models?
This Cloudera webinar with Linguamatics explores NLP techniques for data discovery and information extraction to enhance predictive risk models, improve population stratification and automate capture of quality measures.
3 things to learn:
Enhance predictive risk models for healthcare payers and plan providers
Improve population stratification
Automate capturing of quality issues for plan providers
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Use Cases for NLP in Healthcare
with Cloudera certified partner Linguamatics
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Simon Beaulah, Senior Director, Healthcare,
Shawn Dolley, Industry Leader, Health & Life
Use cases for NLP in Healthcare
Implementing NLP in Healthcare
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Cloudera customers in health & life science
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One data platform for many health use cases
Batch or Real- time
● Single place for all data
● Easy in, easy out
● Highly governed
● Shared data experience
● Data discovery
● Democratize NLP
● Real-time processing
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NLP is not a use case
● Natural language processing is an approach to parsing clinical text
● Clinical text is how our clinicians add, engage, and interpret data
● It’s how our patients, administrators, researchers add, engage, and interpret
● Text is the data source, or ‘rides along’ with every data source
● NLP is not a use case, it is every use case’s associate, friend….or BFF
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Top use cases for big data and NLP in Healthcare
Identify financial risk
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What is your top use case for NLP? (pick one)
1. Patient stratification
2. Clinical document improvement
3. Identify financial opportunity
4. Predict outcomes
5. Quality measures
6. All of the above
7. None of the above
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Key challenges in clinical text
Lack of clinical
Understand context, family
history vs problem list
Multiple sources of
Limited availability of NLP
Deal with variability of
language & format, needs
to be data driven
Support retrospective and
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Why NLP - a total view of the patient
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Linguamatics I2E Transforms Text into Structured
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Top 5 Health Plan needed to improve population
stratification to better care for members. Wanted to
integrate unstructured data into analytics
infrastructure to access insights trapped in notes
• Powerful NLP capabilities for multiple applications
• Agile, robust solution
• Enterprise integration with cloudera data lake
Production system deployed based on Linguamatics
I2E to risk stratify CHF and End of Life populations:
• Source data 1.5TB of unstructured data stored in
Cloudera data lake
• C-CDA HL7, OCR patient documents, call
• Information extracted: Smoking status, family
history, problem list, labs and medications
Results in faster model development and more detailed
population stratification models
Use Case #1 Population Stratification Using NLP
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● People with chronic diseases can not be
cared for effectively if they are not properly
● NLP identifies undocumented CHF,
COPD, Obesity, Diabetes, HIV, Hep C
patients for improved care
● NLP extracts ejection fraction, BMI, FEV1,
A1C values to identify gaps in care
● Results in better characterization and
clinical documentation of chronic disease
for improved care
● Additional revenue from Medicare
Use Case #2 Clinical Document Improvement Using
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Large health system wanted to improve early
diagnosis of lung cancer
● Improve outcomes and reduce potential
$1m per missed patient litigation risk
I2E used to nightly screen radiology reports
from routine ER visits for incidental findings
● 1212 cases flagged in 14 months
● 64 biopsies ordered
● 37 malignant cases identified
● 70% lung cancer, 30% other
Resulted in early treatment and improved
Use Case #3 Identify Financial Risk Using NLP
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Large IDN needed to accelerate predictive model
development for patient outcomes. Include insights
into Social Determinants of Health into clinical
models for 30-day Readmission
Need to monitor for predictive factors of success or
failure for clinical outcomes
● Social determinants, ambulatory status and living
I2E used to explore 700,000 discharge summaries
to extract attributes for statistical and Machine
Learning modelling. Resulted in:
● Well characterized, consistent and well populated
data for ML without huge manual curation effort
● Faster identification of patient cohorts and predictive
Use Case #4 Predict Patient Outcomes with NLP
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Linguamatics Data-Driven NLP
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Use Case #5 Quality Measures Extraction Using NLP
Quality measures reporting can
often be a multi-million dollar manual
process of extracting
Linguamatics I2E extracts quality
measures from clinical notes in
cloudera data lake
• Examples include Diabetes
Population Quality Measures
Completed internal and external audits
• Improved extraction of data
compared to manual methods
Results in significant reduction in manual
processes and constant insight into
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Quality Measures Extracted in I2E
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Integrated cloudera and Linguamatics Architecture
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Linguamatics Key Capabilities
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Multiple high value applications from the same big
• cloudera and Linguamatics infrastructure
supports risk stratification, quality measures and
risk adjusted diagnosis
Transforming unstructured data
• Linguamatics I2E is advancing risk stratification
to a much more detailed level of insights
• Supports future areas such as behavioral health
Flexibility and fast time to value
• Powerful NLP engine for data-driven
development and extraction
• Drag and drop NLP system for non-experts
opens up use
Cloudera and Linguamatics Business Impact
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What describes you best? (pick one):
1. Have never used NLP but have a use case in mind
2. Have tried DIY NLP and failed :(
3. Have tried NLP with packaged software
4. Using NLP successfully today but room for improvement
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Congestive Heart Failure Dashboard
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CHF Social Determinants and Raw Data
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Cancer Pathology Extraction
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Cancer Biomarkers and Stage
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Shawn Dolley firstname.lastname@example.org
Healthcare Website: https://www.cloudera.com/solutions/healthcare.html
Simon Beaulah email@example.com
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